First results on ProtoDUNE-SP liquid argon time projection chamber performance from a beam test at the CERN Neutrino Platform
DUNE Collaboration, B. Abi, A. Abed Abud, R. Acciarri, M. A. Acero, G. Adamov, M. Adamowski, D. Adams, P. Adrien, M. Adinolfi, Z. Ahmad, J. Ahmed, T. Alion, S. Alonso Monsalve, C. Alt, J. Anderson, C. Andreopoulos, M. P. Andrews, F. Andrianala, S. Andringa, A. Ankowski, M. Antonova, S. Antusch, A. Aranda-Fernandez, A. Ariga, L. O. Arnold, M. A. Arroyave, J. Asaadi, A. Aurisano, V. Aushev, D. Autiero, F. Azfar, H. Back, J. J. Back, C. Backhouse, P. Baesso, L. Bagby, R. Bajou, S. Balasubramanian, P. Baldi, B. Baller, B. Bambah, F. Barao, G. Barenboim, G. J. Barker, W. Barkhouse, C. Barnes, G. Barr, J. Barranco Monarca, N. Barros, J. L. Barrow, A. Bashyal, V. Basque, F. Bay, J. L. Bazo Alba, J. F. Beacom, E. Bechetoille, B. Behera, L. Bellantoni, G. Bellettini, V. Bellini, O. Beltramello, D. Belver, N. Benekos, F. Bento Neves, J. Berger, S. Berkman, P. Bernardini, R. M. Berner, H. Berns, S. Bertolucci, M. Betancourt, Y. Bezawada, M. Bhattacharjee, B. Bhuyan, S. Biagi, J. Bian, M. Biassoni, K. Biery, B. Bilki, M. Bishai, A. Bitadze, A. Blake, B. Blanco Siffert, F. D. M. Blaszczyk, G. C. Blazey, E. Blucher, J. Boissevain, S. Bolognesi, T. Bolton, M. Bonesini, M. Bongrand, F. Bonini, A. Booth, C. Booth, S. Bordoni, A. Borkum, T. Boschi, N. Bostan, P. Bour, et al. (895 additional authors not shown)
FFERMILAB-PUB-20-059-AD-ESH-LBNF-ND-SCDCERN-EP-2020-125
First results on ProtoDUNE-SP liquid argon timeprojection chamber performance from a beam test at theCERN Neutrino Platform
The DUNE Collaboration
B. Abi,
A. Abed Abud, , R. Acciarri, M. A. Acero, G. Adamov, M. Adamowski, D. Adams, P. Adrien, M. Adinolfi, Z. Ahmad,
J. Ahmed,
T. Alion,
S. AlonsoMonsalve, C. Alt, J. Anderson, C. Andreopoulos, , M. P. Andrews, F. Andrianala, S. Andringa,
A. Ankowski,
M. Antonova, S. Antusch, A. Aranda-Fernandez, A. Ariga, L. O. Arnold, M. A. Arroyave, J. Asaadi,
A. Aurisano, V. Aushev,
D. Autiero, F. Azfar,
H. Back,
J. J. Back,
C. Backhouse,
P. Baesso, L. Bagby, R. Bajou,
S. Balasubramanian,
P. Baldi, B. Bambah, F. Barao, , G. Barenboim, G. J. Barker,
W. Barkhouse,
C. Barnes,
G. Barr,
J. Barranco Monarca, N. Barros, , J. L. Barrow, , A. Bashyal,
V. Basque,
F. Bay,
J. L. Bazo Alba,
J. F. Beacom,
E. Bechetoille, B. Behera, L. Bellantoni, G. Bellettini,
V. Bellini, , O. Beltramello, D. Belver, N. Benekos, F. Bento Neves,
J. Berger,
S. Berkman, P. Bernardini, , R. M. Berner, H. Berns, S. Bertolucci, , M. Betancourt, Y. Bezawada, M. Bhattacharjee, B. Bhuyan, S. Biagi, J. Bian, M. Biassoni, K. Biery, B. Bilki, , M. Bishai, A. Bitadze,
A. Blake,
B. Blanco Siffert, F. D. M. Blaszczyk, G. C. Blazey,
E. Blucher, J. Boissevain,
S. Bolognesi, T. Bolton,
M. Bonesini, , M. Bongrand,
F. Bonini, A. Booth,
C. Booth,
S. Bordoni, A. Borkum,
T. Boschi, N. Bostan,
P. Bour, S. B. Boyd,
D. Boyden,
J. Bracinik, D. Braga, D. Brailsford,
A. Brandt,
J. Bremer, C. Brew,
E. Brianne,
S. J. Brice, C. Brizzolari, , C. Bromberg,
G. Brooijmans, J. Brooke, A. Bross, G. Brunetti, N. Buchanan, H. Budd,
D. Caiulo, P. Calafiura,
J. Calcutt,
M. Calin, S. Calvez, E. Calvo, L. Camilleri, A. Caminata, M. Campanelli,
D. Caratelli, G. Carini, B. Carlus, P. Carniti, I. Caro Terrazas, H. Carranza,
A. Castillo,
C. Castromonte, C. Cattadori, F. Cavalier,
F. Cavanna, S. Centro,
G. Cerati, A. Cervelli, A. Cervera Villanueva, M. Chalifour, C. Chang, E. Chardonnet,
N. Charitonidis, A. Chatterjee,
S. Chattopadhyay,
P. Chatzidaki, J. Chaves,
H. Chen, M. Chen, Y. Chen, D. Cherdack, C. Chi, S. Childress, A. Chiriacescu, K. Cho,
S. Choubey, A. Christensen, D. Christian, G. Christodoulou, E. Church,
P. Clarke, T. E. Coan,
A. G. Cocco, J. A. B. Coelho,
E. Conley, J. M. Conrad,
M. Convery,
L. Corwin,
P. Cotte, L. Cremaldi,
L. Cremonesi,
J. I. Crespo-Anadón, E. Cristaldo, R. Cross, a r X i v : . [ phy s i c s . i n s - d e t ] J u l . Cuesta, Y. Cui, D. Cussans, M. Dabrowski, H. da Motta, L. Da Silva Peres, C. David, , Q. David, G. S. Davies,
S. Davini, J. Dawson,
K. De,
R. M. DeAlmeida, P. Debbins,
I. De Bonis, M. P. Decowski, , A. de Gouvêa,
P. C. DeHolanda, I. L. De Icaza Astiz,
A. Deisting,
P. De Jong, , A. Delbart, D. Delepine, M. Delgado, A. Dell’Acqua, P. De Lurgio, J. R. T. de Mello Neto, D. M. DeMuth,
S. Dennis, C. Densham,
G. Deptuch, A. De Roeck, V. De Romeri, J. J. De Vries, R. Dharmapalan, M. Dias,
F. Diaz,
J. S. Díaz, S. Di Domizio, , L. Di Giulio, P. Ding, L. Di Noto, , C. Distefano, R. Diurba,
M. Diwan, Z. Djurcic, N. Dokania,
M. J. Dolinski, L. Domine,
D. Douglas,
F. Drielsma,
D. Duchesneau, K. Duffy, P. Dunne, T. Durkin,
H. Duyang,
O. Dvornikov, D. A. Dwyer,
A. S. Dyshkant,
M. Eads,
D. Edmunds,
J. Eisch,
S. Emery, A. Ereditato, C. O. Escobar, L. Escudero Sanchez, J. J. Evans,
E. Ewart, A. C. Ezeribe,
C. Fabre, K. Fahey, A. Falcone, , C. Farnese,
Y. Farzan, J. Felix, E. Fernandez-Martinez,
P. Fernandez Menendez, F. Ferraro, , L. Fields, A. Filkins,
F. Filthaut, , R. S. Fitzpatrick,
W. Flanagan, B. Fleming,
R. Flight,
J. Fowler, W. Fox, J. Franc, K. Francis,
D. Franco,
J. Freeman, J. Freestone,
J. Fried, A. Friedland,
S. Fuess, I. Furic, A. P. Furmanski,
A. Gago,
H. Gallagher,
A. Gallego-Ros, N. Gallice, , V. Galymov, E. Gamberini, T. Gamble,
R. Gandhi, R. Gandrajula,
S. Gao, D. Garcia-Gamez, M. Á. García-Peris, S. Gardiner, D. Gastler, G. Ge, B. Gelli, A. Gendotti, S. Gent,
Z. Ghorbani-Moghaddam, D. Gibin,
I. Gil-Botella, C. Girerd, A. K. Giri, D. Gnani,
O. Gogota,
M. Gold,
S. Gollapinni,
K. Gollwitzer, R. A. Gomes, L. V. Gomez Bermeo,
L. S. GomezFajardo,
F. Gonnella, J. A. Gonzalez-Cuevas, M. C. Goodman, O. Goodwin,
S. Goswami,
C. Gotti, E. Goudzovski, C. Grace,
M. Graham,
E. Gramellini,
R. Gran,
E. Granados, A. Grant, C. Grant, D. Gratieri, P. Green,
S. Green, L. Greenler,
M. Greenwood,
J. Greer, W. C. Griffith,
M. Groh, J. Grudzinski, K. Grzelak,
W. Gu, V. Guarino, R. Guenette, A. Guglielmi, B. Guo,
K. K. Guthikonda,
R. Gutierrez, P. Guzowski,
M. M. Guzzo, S. Gwon, K. Haaf, A. Habig,
A. Hackenburg,
H. Hadavand,
R. Haenni, A. Hahn, J. Haigh,
J. Haiston,
T. Hamernik, P. Hamilton, J. Han,
K. Harder,
D. A. Harris, , J. Hartnell,
T. Hasegawa,
R. Hatcher, E. Hazen, A. Heavey, K. M. Heeger,
J. Heise,
K. Hennessy,
S. Henry,
M. A. Hernandez Morquecho, K. Herner, L. Hertel, A. S. Hesam, J. Hewes, A. Higuera, T. Hill, S. J. Hillier, A. Himmel, J. Hoff, C. Hohl, A. Holin,
E. Hoppe,
G. A. Horton-Smith,
M. Hostert, A. Hourlier,
B. Howard, R. Howell,
J. Hrivnak, J. Huang,
J. Huang, J. Hugon,
G. Iles, N. Ilic,
A. M. Iliescu, R. Illingworth, A. Ioannisian,
R. Itay,
A. Izmaylov, E. James, B. Jargowsky, F. Jediny, C. Jesùs-Valls, X. Ji, L. Jiang,
S. Jiménez, A. Jipa, A. Joglekar, C. Johnson, R. Johnson, B. Jones,
S. Jones,
C. K. Jung,
T. Junk, Y. Jwa, M. Kabirnezhad,
A. Kaboth,
I. Kadenko,
F. Kamiya, G. Karagiorgi, A. Karcher,
M. Karolak, Y. Karyotakis, S. Kasai,
S. P. Kasetti,
L. Kashur, N. Kazaryan,
E. Kearns, P. Keener,
K. J. Kelly, E. Kemp, C. Kendziora, W. Ketchum, S. H. Kettell, M. Khabibullin, A. Khotjantsev, A. Khvedelidze, D. Kim, B. King, B. Kirby, M. Kirby, J. Klein,
K. Koehler, . W. Koerner, S. Kohn, , P. P. Koller, M. Kordosky,
T. Kosc, U. Kose, V. A. Kostelecký, K. Kothekar, F. Krennrich,
I. Kreslo, Y. Kudenko, V. A. Kudryavtsev,
S. Kulagin, J. Kumar, R. Kumar,
C. Kuruppu,
V. Kus, T. Kutter,
B. Lacarelle, A. Lambert,
K. Lande,
C. E. Lane, K. Lang,
T. Langford,
P. Lasorak,
D. Last,
C. Lastoria, A. Laundrie,
A. Lawrence,
I. Lazanu, R. LaZur, T. Le,
J. Learned, P. LeBrun, G. Lehmann Miotto, R. Lehnert, M. A. Leigui de Oliveira, M. Leitner,
M. Leyton, L. Li, S. Li, S. W. Li,
T. Li, Y. Li, H. Liao,
C. S. Lin,
S. Lin,
A. Lister,
B. R. Littlejohn, J. Liu, T. Liu, S. Lockwitz, T. Loew,
M. Lokajicek, I. Lomidze, K. Long, K. Loo,
D. Lorca, T. Lord,
J. M. LoSecco,
W. C. Louis,
K.B. Luk, , X. Luo, N. Lurkin, T. Lux, V. P. Luzio, D. MacFarland,
A. A. Machado, P. Machado, C. T. Macias, J. R. Macier, A. Maddalena, P. Madigan, , S. Magill, K. Mahn,
A. Maio, , J. A. Maloney, G. Mandrioli, J. Maneira, , L. Manenti,
S. Manly,
A. Mann,
K. Manolopoulos,
M. Manrique Plata, A. Marchionni, W. Marciano, D. Marfatia, C. Mariani,
J. Maricic, F. Marinho, A. D. Marino, M. Marshak,
C. Marshall,
J. Marshall,
J. Marteau, J. Martin-Albo, N. Martinez,
D.A. Martinez Caicedo ,
S. Martynenko,
K. Mason,
A. Mastbaum,
M. Masud, S. Matsuno, J. Matthews,
C. Mauger,
N. Mauri, , K. Mavrokoridis,
R. Mazza, A. Mazzacane, E. Mazzucato, E. McCluskey, N. McConkey,
K. S. McFarland,
C. McGrew,
A. McNab,
A. Mefodiev, P. Mehta,
P. Melas, M. Mellinato, , O. Mena, S. Menary,
H. Mendez,
A. Menegolli, , G. Meng, M. D. Messier, W. Metcalf,
M. Mewes, H. Meyer,
T. Miao, G. Michna,
T. Miedema, , J. Migenda,
R. Milincic, W. Miller,
J. Mills,
C. Milne, O. Mineev, O. G. Miranda, S. Miryala, C. S. Mishra, S. R. Mishra,
A. Mislivec,
D. Mladenov, I. Mocioiu,
K. Moffat, N. Moggi, , R. Mohanta, T. A. Mohayai, N. Mokhov, J. Molina, L. Molina Bueno, A. Montanari, C. Montanari, , D. Montanari, L. M. Montano Zetina, J. Moon,
M. Mooney, A. Moor, D. Moreno, B. Morgan,
C. Morris, C. Mossey, E. Motuk,
C. A. Moura, J. Mousseau,
W. Mu, L. Mualem, J. Mueller, M. Muether,
S. Mufson, F. Muheim, A. Muir, M. Mulhearn, H. Muramatsu,
S. Murphy, J. Musser, J. Nachtman,
S. Nagu,
M. Nalbandyan,
R. Nandakumar,
D. Naples,
S. Narita,
D. Navas-Nicolás, N. Nayak, M. Nebot-Guinot, L. Necib, K. Negishi,
J. K. Nelson,
J. Nesbit,
M. Nessi, D. Newbold,
M. Newcomer,
D. Newhart, R. Nichol,
E. Niner, K. Nishimura, A. Norman, A. Norrick, R. Northrop, P. Novella, J. A. Nowak,
M. Oberling, A. Olivares Del Campo, A. Olivier,
Y. Onel,
Y. Onishchuk,
J. Ott, L. Pagani, S. Pakvasa, O. Palamara, S. Palestini, J. M. Paley, M. Pallavicini, , C. Palomares, E. Pantic, V. Paolone,
V. Papadimitriou, R. Papaleo, A. Papanestis,
S. Paramesvaran, S. Parke, Z. Parsa, M. Parvu, S. Pascoli, L. Pasqualini, , J. Pasternak, J. Pater,
C. Patrick,
L. Patrizii, R. B. Patterson, S. J. Patton,
T. Patzak,
A. Paudel,
B. Paulos,
L. Paulucci, Z. Pavlovic, G. Pawloski,
D. Payne,
V. Pec,
S. J. M. Peeters,
Y. Penichot, E. Pennacchio, A. Penzo,
O. L. G. Peres, J. Perry, D. Pershey, G. Pessina, G. Petrillo,
C. Petta, , R. Petti,
F. Piastra, L. Pickering,
F. Pietropaolo, , J. Pillow,
J. Pinzino,
R. Plunkett, R. Poling, . Pons, N. Poonthottathil,
S. Pordes, M. Potekhin, R. Potenza, , B. V. K. S. Potukuchi,
J. Pozimski, M. Pozzato, , S. Prakash, T. Prakash,
S. Prince, G. Prior,
D. Pugnere, K. Qi,
X. Qian, J. L. Raaf, R. Raboanary, V. Radeka, J. Rademacker, B. Radics, A. Rafique, E. Raguzin, M. Rai,
M. Rajaoalisoa, I. Rakhno, H. T. Rakotondramanana, L. Rakotondravohitra, Y. A. Ramachers,
R. Rameika, M. A. Ramirez Delgado, B. Ramson, A. Rappoldi, , G. Raselli, , P. Ratoff,
S. Ravat, H. Razafinime, J. S. Real, B. Rebel, , D. Redondo, M. Reggiani-Guzzo, T. Rehak, J. Reichenbacher,
S. D. Reitzner, A. Renshaw, S. Rescia, F. Resnati, A. Reynolds,
G. Riccobene, L. C. J. Rice,
K. Rielage,
A. Rigamonti, Y. Rigaut, D. Rivera,
L. Rochester,
M. Roda,
P. Rodrigues,
M. J. Rodriguez Alonso, J. Rodriguez Rondon,
A. J. Roeth, H. Rogers, S. Rosauro-Alcaraz,
M. Rosenthal, M. Rossella, , J. Rout,
S. Roy, A. Rubbia, C. Rubbia, B. Russell,
J. Russell,
D. Ruterbories,
R. Saakyan,
S. Sacerdoti,
T. Safford,
N. Sahu, P. Sala, , G. Salukvadze, N. Samios, M. C. Sanchez,
D. A. Sanders,
D. Sankey,
S. Santana,
M. Santos-Maldonado,
N. Saoulidou, P. Sapienza, C. Sarasty, I. Sarcevic, G. Savage, V. Savinov,
A. Scaramelli, A. Scarff,
A. Scarpelli, H. Schellman, , P. Schlabach, D. Schmitz, K. Scholberg, A. Schukraft, E. Segreto, E. Seltskaya, J. Sensenig,
I. Seong, A. Sergi, F. Sergiampietri,
D. Sgalaberna, M. H. Shaevitz, S. Shafaq,
M. Shamma, H. R. Sharma,
R. Sharma, T. Shaw, C. Shepherd-Themistocleous,
S. Shin,
D. Shooltz,
R. Shrock,
L. Simard,
N. Simos, J. Sinclair, G. Sinev, J. Singh,
J. Singh,
V. Singh, , R. Sipos, F. W. Sippach, G. Sirri, A. Sitraka,
K. Siyeon, D. Smargianaki,
A. Smith, A. Smith, E. Smith, P. Smith, J. Smolik, M. Smy, P. Snopok, M. Soares Nunes, H. Sobel, M. Soderberg,
C. J. Solano Salinas, S. Söldner-Rembold,
N. Solomey,
V. Solovov,
W. E. Sondheim,
M. Sorel, J. Soto-Oton, A. Sousa, K. Soustruznik, F. Spagliardi,
M. Spanu, J. Spitz,
N. J. C. Spooner,
K. Spurgeon,
R. Staley, M. Stancari, L. Stanco, D. Stefan, H. M. Steiner,
J. Stewart, B. Stillwell, J. Stock,
F. Stocker, T. Stokes,
M. Strait,
T. Strauss, S. Striganov, A. Stuart, R. Sulej, , D. Summers,
A. Surdo, V. Susic, L. Suter, C. M. Sutera, , R. Svoboda, B. Szczerbinska,
A. M. Szelc,
R. Talaga, H.A. Tanaka,
B. Tapia Oregui,
A. Tapper, S. Tariq, E. Tatar, R. Tayloe, A. M. Teklu,
M. Tenti, K. Terao,
C. A. Ternes, F. Terranova, , G. Testera, A. Thea,
J. L. Thompson,
C. Thorn, S. C. Timm, A. Tonazzo,
M. Torti, , M. Tortola, F. Tortorici, , D. Totani, M. Toups, C. Touramanis,
J. Trevor, W. H. Trzaska,
Y. T. Tsai,
Z. Tsamalaidze, K. V. Tsang,
N. Tsverava, S. Tufanli, C. Tull,
E. Tyley,
M. Tzanov,
M. A. Uchida, J. Urheim, T. Usher,
M. R. Vagins,
P. Vahle,
G. A. Valdiviesso, E. Valencia,
Z. Vallari, J. W. F. Valle, S. Vallecorsa, R. Van Berg,
R. G. Van de Water,
D. Vanegas Forero, F. Varanini, D. Vargas, G. Varner, J. Vasel, G. Vasseur, K. Vaziri, S. Ventura, A. Verdugo, S. Vergani, M. A. Vermeulen,
M. Verzocchi, H. Vieira de Souza, C. Vignoli, C. Vilela,
B. Viren, T. Vrba, T. Wachala,
A. V. Waldron, M. Wallbank, H. Wang, J. Wang, Y. Wang, Y. Wang,
K. Warburton,
D. Warner, M. Wascko, D. Waters,
A. Watson, P. Weatherly, A. Weber, , M. Weber, H. Wei, A. Weinstein,
D. Wenman, . Wetstein,
M. R. While,
A. White,
L. H. Whitehead, D. Whittington,
M. J. Wilking,
C. Wilkinson, Z. Williams,
F. Wilson,
R. J. Wilson, J. Wolcott,
T. Wongjirad,
K. Wood,
L. Wood,
E. Worcester, M. Worcester, C. Wret,
W. Wu, W. Wu, Y. Xiao, G. Yang,
T. Yang, N. Yershov, K. Yonehara, T. Young,
B. Yu, H. W. Yu, H. Z. Yu,
J. Yu,
Z. Y. Yu, R. Zaki,
J. Zalesak, L. Zambelli, B. Zamorano, A. Zani, , L. Zazueta,
G. P. Zeller, J. Zennamo, K. Zeug,
C. Zhang, M. Zhao, E. Zhivun, G. Zhu,
E. D. Zimmerman, M. Zito, S. Zucchelli, , J. Zuklin, V. Zutshi, and R. Zwaska University of Amsterdam, NL-1098 XG Amsterdam, The Netherlands University of Antananarivo, Antananarivo 101, Madagascar Universidad Antonio Nariño, Bogotá, Colombia Argonne National Laboratory, Argonne, IL 60439, USA University of Arizona, Tucson, AZ 85721, USA Universidad Nacional de Asunción, San Lorenzo, Paraguay University of Athens, Zografou GR 157 84, Greece Universidad del Atlántico, Atlántico, Colombia Banaras Hindu University, Varanasi - 221 005, India University of Basel, CH-4056 Basel, Switzerland University of Bern, CH-3012 Bern, Switzerland Beykent University, Istanbul, Turkey University of Birmingham, Birmingham B15 2TT, United Kingdom Università del Bologna, 40127 Bologna, Italy Boston University, Boston, MA 02215, USA University of Bristol, Bristol BS8 1TL, United Kingdom Brookhaven National Laboratory, Upton, NY 11973, USA University of Bucharest, Bucharest, Romania Centro Brasileiro de Pesquisas Físicas, Rio de Janeiro, RJ 22290-180, Brazil CEA/Saclay, IRFU Institut de Recherche sur les Lois Fondamentales de l’Univers, F-91191 Gif-sur-YvetteCEDEX, France CERN, The European Organization for Nuclear Research, 1211 Meyrin, Switzerland CIEMAT, Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas, E-28040 Madrid,Spain Central University of South Bihar, Gaya – 824236, India University of California Berkeley, Berkeley, CA 94720, USA University of California Davis, Davis, CA 95616, USA University of California Irvine, Irvine, CA 92697, USA University of California Los Angeles, Los Angeles, CA 90095, USA University of California Riverside, Riverside CA 92521, USA University of California Santa Barbara, Santa Barbara, California 93106 USA California Institute of Technology, Pasadena, CA 91125, USA University of Cambridge, Cambridge CB3 0HE, United Kingdom Universidade Estadual de Campinas, Campinas - SP, 13083-970, Brazil Università di Catania, 2 - 95131 Catania, Italy Institute of Particle and Nuclear Physics of the Faculty of Mathematics and Physics of the Charles University,180 00 Prague 8, Czech Republic University of Chicago, Chicago, IL 60637, USA Chung-Ang University, Seoul 06974, South Korea University of Cincinnati, Cincinnati, OH 45221, USA Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (Cinvestav), MexicoCity, Mexico Universidad de Colima, Colima, Mexico University of Colorado Boulder, Boulder, CO 80309, USA Colorado State University, Fort Collins, CO 80523, USA Columbia University, New York, NY 10027, USA Institute of Physics, Czech Academy of Sciences, 182 00 Prague 8, Czech Republic Czech Technical University, 115 19 Prague 1, Czech Republic Dakota State University, Madison, SD 57042, USA University of Dallas, Irving, TX 75062-4736, USA Laboratoire d’Annecy-le-Vieux de Physique des Particules, CNRS/IN2P3 and Université Savoie MontBlanc, 74941 Annecy-le-Vieux, France Daresbury Laboratory, Cheshire WA4 4AD, United Kingdom Drexel University, Philadelphia, PA 19104, USA Duke University, Durham, NC 27708, USA Durham University, Durham DH1 3LE, United Kingdom Universidad EIA, Antioquia, Colombia ETH Zurich, Zurich, Switzerland University of Edinburgh, Edinburgh EH8 9YL, United Kingdom Faculdade de Ciências da Universidade de Lisboa - FCUL, 1749-016 Lisboa, Portugal Universidade Federal de Alfenas, Poços de Caldas - MG, 37715-400, Brazil Universidade Federal de Goias, Goiania, GO 74690-900, Brazil Universidade Federal de São Carlos, Araras - SP, 13604-900, Brazil Universidade Federal do ABC, Santo André - SP, 09210-580 Brazil Universidade Federal do Rio de Janeiro, Rio de Janeiro - RJ, 21941-901, Brazil Fermi National Accelerator Laboratory, Batavia, IL 60510, USA University of Florida, Gainesville, FL 32611-8440, USA Fluminense Federal University, 9 Icaraí Niterói - RJ, 24220-900, Brazil Università degli Studi di Genova, Genova, Italy Georgian Technical University, Tbilisi, Georgia Gran Sasso Science Institute, L’Aquila, Italy Laboratori Nazionali del Gran Sasso, L’Aquila AQ, Italy University of Granada & CAFPE, 18002 Granada, Spain University Grenoble Alpes, CNRS, Grenoble INP, LPSC-IN2P3, 38000 Grenoble, France Universidad de Guanajuato, Guanajuato, C.P. 37000, Mexico Harish-Chandra Research Institute, Jhunsi, Allahabad 211 019, India Harvard University, Cambridge, MA 02138, USA University of Hawaii, Honolulu, HI 96822, USA University of Houston, Houston, TX 77204, USA University of Hyderabad, Gachibowli, Hyderabad - 500 046, India Institut de Fìsica d’Altes Energies, Barcelona, Spain Instituto de Fisica Corpuscular, 46980 Paterna, Valencia, Spain Institute of High Energy Physics, 100049 Beijing, China Istituto Nazionale di Fisica Nucleare Sezione di Bologna, 40127 Bologna BO, Italy Istituto Nazionale di Fisica Nucleare Sezione di Catania, I-95123 Catania, Italy Istituto Nazionale di Fisica Nucleare Sezione di Genova, 16146 Genova GE, Italy Istituto Nazionale di Fisica Nucleare Sezione di Lecce, 73100 - Lecce, Italy Istituto Nazionale di Fisica Nucleare Sezione di Milano Bicocca, 3 - I-20126 Milano, Italy Istituto Nazionale di Fisica Nucleare Sezione di Milano, 20133 Milano, Italy Istituto Nazionale di Fisica Nucleare Sezione di Napoli, I-80126 Napoli, Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova, 35131 Padova, Italy Istituto Nazionale di Fisica Nucleare Sezione di Pavia, I-27100 Pavia, Italy Istituto Nazionale di Fisica Nucleare Laboratori Nazionali del Sud, 95123 Catania, Italy Institute for Nuclear Research of the Russian Academy of Sciences, Moscow 117312, Russia Institut de Physique des 2 Infinis de Lyon, 69622 Villeurbanne, France Institute for Research in Fundamental Sciences, Tehran, Iran Instituto Superior Técnico - IST, Universidade de Lisboa, Portugal Idaho State University, Pocatello, ID 83209, USA Illinois Institute of Technology, Chicago, IL 60616, USA Imperial College of Science Technology and Medicine, London SW7 2BZ, United Kingdom Indian Institute of Technology Guwahati, Guwahati, 781 039, India Indian Institute of Technology Hyderabad, Hyderabad, 502285, India Indiana University, Bloomington, IN 47405, USA Universidad Nacional de Ingeniería, Lima 25, Perú
University of Iowa, Iowa City, IA 52242, USA
Iowa State University, Ames, Iowa 50011, USA
Iwate University, Morioka, Iwate 020-8551, Japan
University of Jammu, Jammu-180006, India
Jawaharlal Nehru University, New Delhi 110067, India
Jeonbuk National University, Jeonrabuk-do 54896, South Korea
University of Jyvaskyla, FI-40014, Finland
High Energy Accelerator Research Organization (KEK), Ibaraki, 305-0801, Japan
Korea Institute of Science and Technology Information, Daejeon, 34141, South Korea
K L University, Vaddeswaram, Andhra Pradesh 522502, India
Kansas State University, Manhattan, KS 66506, USA
Kavli Institute for the Physics and Mathematics of the Universe, Kashiwa, Chiba 277-8583, Japan
National Institute of Technology, Kure College, Hiroshima, 737-8506, Japan
Kyiv National University, 01601 Kyiv, Ukraine
Laboratório de Instrumentação e Física Experimental de Partículas, 1649-003 Lisboa and 3004-516Coimbra, Portugal
Laboratoire de l’Accélérateur Linéaire, 91440 Orsay, France
Lancaster University, Lancaster LA1 4YB, United Kingdom
Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA University of Liverpool, L69 7ZE, Liverpool, United Kingdom
Los Alamos National Laboratory, Los Alamos, NM 87545, USA
Louisiana State University, Baton Rouge, LA 70803, USA
University of Lucknow, Uttar Pradesh 226007, India
Madrid Autonoma University and IFT UAM/CSIC, 28049 Madrid, Spain
University of Manchester, Manchester M13 9PL, United Kingdom
Massachusetts Institute of Technology, Cambridge, MA 02139, USA
University of Michigan, Ann Arbor, MI 48109, USA
Michigan State University, East Lansing, MI 48824, USA
Università del Milano-Bicocca, 20126 Milano, Italy
Università degli Studi di Milano, I-20133 Milano, Italy
University of Minnesota Duluth, Duluth, MN 55812, USA
University of Minnesota Twin Cities, Minneapolis, MN 55455, USA
University of Mississippi, University, MS 38677 USA
National Centre for Nuclear Research, A. Soltana 7, 05 400 Otwock, Poland
University of New Mexico, Albuquerque, NM 87131, USA
H. Niewodniczański Institute of Nuclear Physics, Polish Academy of Sciences, Cracow, Poland
Nikhef National Institute of Subatomic Physics, 1098 XG Amsterdam, Netherlands
University of North Dakota, Grand Forks, ND 58202-8357, USA
Northern Illinois University, DeKalb, Illinois 60115, USA
Northwestern University, Evanston, Il 60208, USA
University of Notre Dame, Notre Dame, IN 46556, USA
Ohio State University, Columbus, OH 43210, USA
Oregon State University, Corvallis, OR 97331, USA
University of Oxford, Oxford, OX1 3RH, United Kingdom
Pacific Northwest National Laboratory, Richland, WA 99352, USA
Universtà degli Studi di Padova, I-35131 Padova, Italy
Université de Paris, CNRS, Astroparticule et Cosmologie, F-75006, Paris, France
Università degli Studi di Pavia, 27100 Pavia PV, Italy
University of Pennsylvania, Philadelphia, PA 19104, USA
Pennsylvania State University, University Park, PA 16802, USA
Physical Research Laboratory, Ahmedabad 380 009, India
Università di Pisa, I-56127 Pisa, Italy
University of Pittsburgh, Pittsburgh, PA 15260, USA
Pontificia Universidad Católica del Perú, Lima, Perú
University of Puerto Rico, Mayaguez 00681, Puerto Rico, USA
Punjab Agricultural University, Ludhiana 141004, India
Radboud University, NL-6525 AJ Nijmegen, Netherlands
University of Rochester, Rochester, NY 14627, USA
Royal Holloway College London, TW20 0EX, United Kingdom
Rutgers University, Piscataway, NJ, 08854, USA
STFC Rutherford Appleton Laboratory, Didcot OX11 0QX, United Kingdom
SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
Sanford Underground Research Facility, Lead, SD, 57754, USA Università del Salento, 73100 Lecce, Italy
Universidad Sergio Arboleda, 11022 Bogotá, Colombia
University of Sheffield, Sheffield S3 7RH, United Kingdom
South Dakota School of Mines and Technology, Rapid City, SD 57701, USA
South Dakota State University, Brookings, SD 57007, USA
University of South Carolina, Columbia, SC 29208, USA
Southern Methodist University, Dallas, TX 75275, USA
Stony Brook University, SUNY, Stony Brook, New York 11794, USA
Sun Yat-Sen University, 510275 Guangzhou, China
University of Sussex, Brighton, BN1 9RH, United Kingdom
Syracuse University, Syracuse, NY 13244, USA
University of Tennessee at Knoxville, TN, 37996, USA
Texas A&M University - Corpus Christi, Corpus Christi, TX 78412, USA
University of Texas at Arlington, Arlington, TX 76019, USA
University of Texas at Austin, Austin, TX 78712, USA
University of Toronto, Toronto, Ontario M5S 1A1, Canada
Tufts University, Medford, MA 02155, USA
Universidade Federal de São Paulo, 09913-030, São Paulo, Brazil
University College London, London, WC1E 6BT, United Kingdom
Valley City State University, Valley City, ND 58072, USA
Variable Energy Cyclotron Centre, 700 064 West Bengal, India
Virginia Tech, Blacksburg, VA 24060, USA
University of Warsaw, 00-927 Warsaw, Poland
University of Warwick, Coventry CV4 7AL, United Kingdom
Wichita State University, Wichita, KS 67260, USA
William and Mary, Williamsburg, VA 23187, USA
University of Wisconsin Madison, Madison, WI 53706, USA
Yale University, New Haven, CT 06520, USA
Yerevan Institute for Theoretical Physics and Modeling, Yerevan 0036, Armenia
York University, Toronto M3J 1P3, Canada
E-mail:
F. Cavanna ([email protected]) , T. Junk ([email protected]) , T. Yang([email protected]) bstract: The ProtoDUNE-SP detector is a single-phase liquid argon time projection chamberwith an active volume of 7 . × . × . . It is installed at the CERN Neutrino Platform in aspecially-constructed beam that delivers charged pions, kaons, protons, muons and electrons withmomenta in the range 0.3 GeV / c to 7 GeV/ c . Beam line instrumentation provides accurate mo-mentum measurements and particle identification. The ProtoDUNE-SP detector is a prototype forthe first far detector module of the Deep Underground Neutrino Experiment, and it incorporatesfull-size components as designed for that module. This paper describes the beam line, the timeprojection chamber, the photon detectors, the cosmic-ray tagger, the signal processing and particlereconstruction. It presents the first results on ProtoDUNE-SP’s performance, including noise andgain measurements, dE / dx calibration for muons, protons, pions and electrons, drift electron life-time measurements, and photon detector noise, signal sensitivity and time resolution measurements.The measured values meet or exceed the specifications for the DUNE far detector, in several casesby large margins. ProtoDUNE-SP’s successful operation starting in 2018 and its production of largesamples of high-quality data demonstrate the effectiveness of the single-phase far detector design.Keywords: Noble liquid detectors (scintillation, ionization, single-phase), Time projection cham-bers, Large detector systems for particle and astroparticle physicsArXiv ePrint: 1234.56789 ontents c beam pionsand muons 766.4.2 Identification and calorimetric energy reconstruction of 1 GeV/ c beam protons 796.4.3 dE / dx for 1 GeV/ c electrons 806.4.4 Particle Identification: Protons and Muons 82 The neutrino detectors at the far site of the Deep Underground Neutrino Experiment (DUNE) [1]are planned to be built inside four cryostats, each of which will contain 17.5 kt of liquid argon (LAr).The first detector to be constructed is planned to be a single-phase time projection chamber (TPC),similar to, but a factor of 25 more massive than the pioneering T600 detector built by the ICARUScollaboration [2]. The ProtoDUNE single-phase apparatus (ProtoDUNE-SP) [3], assembled andtested at the CERN Neutrino Platform (the NP04 experiment at CERN) [4], is designed to act asa test bed and full-scale prototype for the elements of the first far detector module of DUNE [5].ProtoDUNE-SP contains 770 tonnes of LAr, 420 of which are in the active volume of the TPC.– 1 –t is currently the largest liquid argon time projection chamber (LArTPC) ever constructed. It isdesigned to meet the challenges of mechanics, electronics, high voltage, cryogenics, LAr purity,data acquisition, data storage, event reconstruction and analysis. Installation of the ProtoDUNE-SPdetector was completed in early July 2018, the filling of the cryostat with argon was completed bymid-September 2018, and data-taking with the full apparatus started in October 2018.In addition to its role as a demonstration prototype and engineering test bed, the ProtoDUNE-SP TPC was exposed to a tagged and momentum-analyzed particle beam with momentum settingsranging from 0.3 GeV/ c to 7 GeV/ c . This beam enabled the acquisition of large samples of dataon the behavior of charged pions, kaons, protons, muons and positive electrons (positrons) in LAr.The beam was set to deliver only positively-charged particles for the data samples used in thispaper, although future runs will also include negatively-charged particle beams. These data serve astemplates for understanding how these particles will appear when produced in neutrino interactionsin DUNE, and they will be an important reference in the analysis of interactions in DUNE. Thesedata also provide a real-world test bed for the development of algorithms for pattern recognition,event reconstruction and analysis, and they will be used to measure the cross sections of interactionsof charged particles in LAr.The ProtoDUNE-SP apparatus is designed to satisfy the stringent new requirements and achievethe improved levels of performance required by DUNE [6]. The membrane cryostat and itsassociated cryogenic system are the largest LAr systems ever constructed. The argon purificationsystem is the largest constructed to date. As compared to previous devices, such as ICARUS [2],ArgoNeuT [7], LongBo [8], MicroBooNE [9], and the 35-ton prototype [10] which had shortermaximum drift distances, the 3.6 m drift distance in ProtoDUNE-SP makes higher demands onargon purity. The long drift distance also requires higher voltages in the HV system used to providethe drift field, and the stored energy which may be released in a discharge is also higher than inprevious devices. To allow for higher voltages and to reduce the chance of discharges, ProtoDUNE-SP incorporates specially-chosen materials for the cathode and the field cage structure, and newshapes for the field rings. The sense-wire assemblies, known as Anode Plane Assemblies (APAs),contain three planes of readout wires on both faces and are of a novel design and construction. Toimprove the signal-to-noise ratio, the sense wire readout amplifiers and analog-to-digital converters(ADCs) are placed inside the LAr close to the wires. Furthermore, the data acquisition systemaccommodates a higher data rate and larger event sizes than previous LArTPC systems.ProtoDUNE-SP includes a novel photon-detector design which embeds the photon detectorswithin the APAs in order to collect scintillation light from ionized LAr. Due to the small availablearea, the photon detectors are required to be highly efficient for detecting single photons. Theperformance of the photon detectors in ProtoDUNE-SP is a primary topic of this paper.Cosmic-ray interactions with the detector cause a buildup of positive ions that drift very slowlytowards the cathode. The accumulated space charge is proportional to the rate of incident cosmicrays and it depends strongly on the drift distance. The space charge alters the electric field inthe detector, changing both its strength and its direction, causing distortions in both the measuredpositions of particles traversing the detector and their apparent ionization densities. However, theeffects of space charge buildup are expected to be largely absent in the DUNE Far Detector due toits low cosmic-ray rate, a consequence of its deep underground location. In the analyses presentedin this paper, corrections for the effects of space charge are applied where appropriate in order for– 2 –he results of these studies to be generally applicable.The ProtoDUNE-SP Technical Design Report [3] contains a detailed description of the de-sign. A description of the apparatus as built, plus a description of the installation, testing andcommissioning is given in [11].This paper is organized as follows. Section 2 describes different components of the ProtoDUNE-SP detector. Section 3 describes details of the CERN beam line instrumentation. Sections 4-7summarize results on TPC characterization, photon detector characterization, TPC response, andphoton detector response. Section 8 concludes the paper. The ProtoDUNE-SP apparatus, shown in figure 2, is described by a right-handed coordinate systemin which the y axis is vertical (positive pointing up) and the z axis is horizontal and pointsapproximately along the beam direction. The x axis is also horizontal and points along the nominalelectric field direction and is perpendicular to the wire planes . The TPC is installed in a membrane cryostat [12] with internal dimensions of 8.5 m in both the x and z directions, and 7.9 m in y . The cryostat is filled to a height of about 7.3 m and its pressure ismaintained to 1050 mbar (absolute). The TPC is suspended by the detector support system, whichis a network of steel beams held in place by nine penetrations in the roof of the cryostat.A detailed description of the design, construction, leak-checking, testing and validation ofthe cryostat is given in [11]. The cryogenics control system is also described there. We give asummary here of the argon purification system system that has played a crucial role in the detectorperformance achieved.The argon received from the supplier has contaminants of water, oxygen and nitrogen at theparts per million level each. Water and oxygen will capture drifting electrons and the concentrationof these contaminants needs to be reduced by a factor of at least 10 and maintained at this levelto allow operation of the TPC. Purification of argon in the liquid phase, as required for the massof argon involved here, is reported in [13]. The present system builds on purification systemsdeveloped for ICARUS and most recently at Fermilab [14] and [9], including the use of the samefilter materials. The main features of the system are indicated in figure 1. There are three circulationloops. In one, liquid leaves the cryostat via a penetration in the side. It is pumped as liquid througha set of filters, and it is reintroduced to the cryostat at the bottom. The pump can drive about 7 t/hrgiving a volume turnover time of about 4.5 days. In the second loop, argon gas from the purge pipeswith which each signal penetration is equipped is purified directly while warm and it is recondensedto join the liquid flow out of the cryostat. In the third loop, the main boil-off from the argon isrecondensed directly and it then joins the liquid flow out of the cryostat. When the argon is firstcirculated, the contamination level falls following a perfect mixing model with a time constant of Throughout this paper, the charge and energy deposited per unit track length are conventionally referred to as dQ / dx and dE / dx respectively. The dx in these expressions is not oriented along the detector coordinate x but rather it is adifferential step along the track path. – 3 –he turnover time until a steady state is reached in which the rate of contamination from leaks andoutgassing from impurities balances the clean-up rate. In the NP04 cryostat, thanks to the rate ofrecirculation and the avoidance of leaks, this state is equivalent to an oxygen contamination [15]of a few parts per trillion resulting in essentially full-strength signals from the furthest parts of theTPC. Figure 1 : A schematic of the argon purification system at NP04.Instrumentation for monitoring the state of the argon is distributed outside the TPC near theinner walls of the cryostat. Three purity monitors, formerly used with the ICARUS T600 detector [2],were refurbished with new gold photocathodes and quartz fibers. They are deployed in ProtoDUNE-SP, each at a different height. They monitor and give fast feedback on the drift electron lifetimein the liquid argon. Two vertical columns of resistance temperature detectors (RTDs) measurethe temperature gradient of the liquid argon. Computational fluid dynamics (CFD) calculationshave been performed that predict the temperature distribution and the internal flow pattern of theargon [16]. The temperature is predicted to vary by 15 mK total over the height of the liquid. TheRTDs have been cross calibrated in situ to better than 2 mK and their measurements agree withthe predictions within ± . .2 Time projection chamber Figure 2 shows a view of the TPC with its major components labeled and a photo of one of thetwo drift volumes. The active region of the TPC encloses a volume 6.0 m high, 6.9 m along the z direction, and 7.2 m in the x direction. The TPC is divided into two separate half-volumes with asolid, planar cathode at x = y z plane, with three APAs 3.6 m from the cathode on eitherside.The cathode plane of the TPC is formed from six cathode plane assemblies (CPAs). Each CPAis 1.15 m wide and 6.1 m high, and consists of three vertically stacked cathode panels. The storedelectrical energy in the TPC when fully charged presents a challenge. If the cathode were electricallyconducting, an electrical breakdown can discharge it rapidly, endangering the front-end electronics.Instead, the cathode is constructed out of resistive materials which give it a very long discharge timeconstant, reducing the risk. The CPA panels are constructed from FR4, a fire-retardant fiberglass-epoxy composite material. These panels are laminated on both sides with a commercial Kaptonfilm with a resistivity of ∼ Ω /sq. The cathode plane is biased at -180 kV to provide a 500 V/cmdrift field. A field cage with 60 voltage steps on each side of the cathode ensures the uniformityof the nominal drift field between the cathode plane and the sense planes. The electric field differsfrom the nominal prediction due to space-charge effects, which are described in section 6.1.A sketch of an APA is shown in figure 3. Each APA has a rectangular stainless steel frame6.1 m high, 2.3 m wide, and 76 mm thick. There are four layers (planes) of wires bonded on eachside of the frame. The wire planes and their wire orientations are (from outside in) the Grid (G)layer (vertical), the U layer (+35.7 ◦ from vertical), the V layer (-35.7 ◦ from vertical), and the X layer(vertical). A bronze wire mesh with 85% optical transparency is bonded directly over each side ofthe APA frame to provide a grounded shield plane for the four wire planes mentioned above. Eachsuccessive wire plane is built 4.75 mm above the previous layer, including the wire mesh. The wiresare terminated on wire boards which are stacked on the short ends the APA. The G and X layershave the same wire pitch of 4.79 mm, but are staggered by half a wire pitch in relative position. TheU and V wires have a pitch of 4.67 mm. Wires on the two induction planes are helically wrappedaround the frame from the head, to the sides, and then to the foot. Wires are held in place with FR-4boards with teeth cut in them as they wrap around the sides. The wire angle is chosen such that thewires do not wrap more than one revolution to avoid creating ambiguities in track reconstruction.Four wire support combs made out of 0.5 mm-thick G10 (a fiberglass-epoxy composite material)are installed on each side of each APA uniformly spaced along the y direction, in order to holdthe long wires in place, helping to counteract gravitational, electrostatic, and fluid-flow forces thatwould otherwise cause portions of the wires to be displaced from their nominal positions. Eachwire plane is electrically biased at a different potential such that the primary ionization electronscreated in the drift volume pass through the G, U and V planes without being captured, and finallyare collected on the X wires. Therefore, the X plane wires are also referred to as the collectionwires, and the U, V plane wires as the first and second induction plane wires. The grid plane wiresserve as an electrostatic discharge (ESD) protective shield and are not read out. The nominal wire-plane bias voltages, to ensure electron collection only on collection-plane wires, are V G = −
665 V, V U = −
370 V, V V = V X = +
820 V.The G plane on the lowest- z APA on the x < igure 2 : Top: a view of the TPC with its major components labeled; bottom: a photo of one ofthe two drift volumes, where three APAs are on the left side and the cathode is on the right side.connected to its voltage supply. The break in connectivity was determined to be inside the cryostat– 6 – igure 3 : Sketch of a ProtoDUNE-SP APA, showing portions of the U (green), V (magenta), theinduction layers; and X (blue), the collection layer, to accentuate their angular relationships to theframe and to each other. The induction layers are connected electrically across both sides of theAPA. The grid layer (G) wires (not shown), run vertically, parallel to the X layer wires. Separatesets of G and X wires are strung on the two sides of the APA. From ref. [3].and it could not be repaired for the duration of the run. Groups of four G plane wires are connectedto a 3.9 nF capacitor with the other terminal grounded. Without a voltage supply, drifting ionizationelectrons will charge up the G-plane from its initial state to a potential that repels electrons andprevents further charge collection. This charging process takes approximately 100 hours [5], andthe average charge measured by this APA is reduced during the charge-up time.Electron diverters are installed in the two vertical gaps between the APAs on the negative- x side of the cathode, but not between the APAs on the positive- x side. These diverters consist oftwo vertical electrode strips, an inner electrode and an outer electrode, mounted on an insulatingboard that protrudes approximately 25 mm into the drift volume beyond the G plane wires. Voltagesapplied to the diverter electrodes modify the local drift field so that electrons drift away from the gapsbetween the APAs and into the active area. A diagram showing the field lines and equipotentialsin the vicinity of the electron diverters when they are working as designed is given in ref. [3].– 7 –igh currents were drawn from the electron diverters’ power supplies when they were energized,due to one or more electrical shorts in the cold volume. The electron diverters were therefore leftunpowered. A resistive path to ground on each one ensured that the actual voltage on the outerelectrode was close to zero, which was not the intended voltage. The grounded diverter electrodescollected charge near the gaps, and also distorted nearby drift paths. The test beam enters the detector at mid-height and about 30 cm away from the cathode, on thenegative x side. It points down 11 ◦ from the horizontal, and towards the APA on the negative x side,10 ◦ to the right of the z direction. In order to minimize the energy loss of beam particles prior totheir entry in the TPC due to the materials in the cryostat, the 40 cm of inactive liquid argon in frontof the TPC, and the field cage, a “beam plug” [3] is installed on the low- z , negative- x side of theend-wall field cage, as shown in figure 4. This beam plug is constructed from a series of alternatingfiberglass and stainless steel rings to form a cylinder, and capped at entrance and exit ends with lowmass fiberglass plates. The stainless steel rings are connected to three sets of resistors to regulatethe voltage from the field cage to the grounded cryostat membrane. The beam plug extends throughan opening in the field cage about 5 cm inside the field cage boundary. The inside face of the beamplug is covered with a mini field cage made from 0.8 mm thick printed circuit board to reduce thedrift field distortion introduced by this opening. The beam plug is filled with nitrogen at a nominalpressure of 1.3 bar (absolute pressure) to balance the hydrostatic pressure of the liquid argon at thisheight and also to maintain high dielectric strength to avoid HV breakdown. Besides, the cryostatwarm structure and the insulation are also modified to reduce the beam interaction with passivematerials. ~50cm Nitrogen Line &Electrical GroundGlass-EpoxyRing SectionSecondary Beam Plug Support HV ConnectionProfile B e a m Figure 4 : Drawing of the beam plug (left) and an image of the beam plug installed inside thecryostat (right). – 8 – .4 Cold electronics
The U, V and X wire planes on both sides of an APA are read out by 20 front-end motherboards(FEMBs) installed close to the wire boards on top of each APA. The FEMBs amplify, shape, digitize,and transmit all 15,360 TPC channels’ signals to the warm interface electronics through cold datacables, which are up to 7 m in length. Each FEMB contains one analog motherboard, which isassembled with eight 16-channel analog front-end (FE) ASICs [17], to provide amplification andpulse shaping, and eight 16-channel Analog to Digital Converter (ADC) ASICs for a total of 128channels readout per FEMB.Each FE ASIC channel has a dual-stage charge amplifier circuit with a programmable gainselectable from 4.7, 7.8, 14 and 25 mV/fC, and a 5th-order anti-aliasing shaper with a programmabletime constant with peaking times of 0.5, 1, 2, and 3 µ s. The FE ASIC also has an option to enableAC coupling and a baseline adjustment for operation at either 200 mV for the unipolar pulses onthe collection wires or 900 mV for the bipolar pulses on the induction wires. Under normal runningconditions the ASIC gain is set at 14 mV/fC and the peaking time is set at 2 µ s for all channels.On October 11, 2018, the internal ASIC baseline was changed to 900 mV for both induction andcollection channels in order to mitigate ASIC saturation with large input charge. Each FE ASIC alsohas an adjustable pre-amplifier leakage current selectable from 100, 500, 1000, and 5000 pA. Thedefault leakage current is 500 pA. The estimated power dissipation of a FE ASIC is about 5.5 mWper channel at 1.8 V. Each FE ASIC contains a programmable pulse generator with a 6-bit DAC forelectronics calibration, which is connected to each channel individually via an injection capacitor.The ADC ASIC has 16 independent 12-bit digitizers performing at speeds up to 2 megasamplesper second (MS/s).A commercial Altera Cyclone IV FPGA, assembled on a mezzanine card that is attached tothe analog motherboard, provides clock and control signals to the FE and ADC ASICs. The FPGAalso serializes the 16 data streams from the ADCs into four 1.25 Gbps links for transmission to thewarm interface electronics over the cold data cables. The FPGA can also provide a calibration pulseto each FE ASIC channel via the same injection capacitor used for the internal FE ASIC DAC, asa cross-check for the electronics calibration. The production, commissioning and performance ofthe cold electronics components are described in [18].The number of TPC channels that do not respond to charge signals from cosmic-ray muonsevolved over the course of the data-taking period. Twenty-nine channels never showed any sensitivityto signals, from September 2018 to January 2020. An additional seven became solidly unresponsiveduring the run, making the total unresponsive channel count 36 in January 2020. Approximately30 additional channels were found to be intermittently unresponsive during the run. During initialcold-box testing before installation, 34 channels were identified as non-responsive; this includes the29 initially dead channels and five intermittent ones that happened to be non-responsive during thetest. Liquid argon is a prolific emitter of scintillation light. Approximately 2.4 × vacuum ultraviolet(VUV) photons are created per MeV of energy deposited by ionization in LAr at the nominal electric– 9 –eld of 500 V/cm. Photon detectors are installed in ProtoDUNE-SP in order to detect a fractionof these photons to measure interaction times and to get an independent measurement of depositedenergy. These photon detectors, however, cannot be placed outside of the field cage because itblocks the scintillation light, and so the photon detectors are integrated in the APAs, occupying thespace between the two mesh planes. Ten bar-shaped photon detectors with dimensions of 8.6 cm(height) 2.2 m (length) and 0.6 cm (thickness) are embedded at equally spaced heights within eachAPA. A number of different designs of photon-detector technologies are implemented within thissize constraint. In each design, silicon photomultipliers [19] are used to convert the light to electricalsignals, which are brought out of the cryostat on copper cables. Most of the photon detectors sensethe light that reaches the ends of the bars – the exception is the so-called ARAPUCA design, whichcollects light at several positions along the bar. More details on the photon detector system areprovided in section 5. The CRT is a system of scintillation counters that covers almost the entire upstream and downstreamfaces of the TPC. It was installed in order to provide triggers to read out the detector for a set ofcosmic-ray muons that pass through with known timing and direction, parallel to the TPC readoutplanes. Since the ProtoDUNE-SP detector is on the surface, it is exposed to 20 kHz of cosmic-raymuons. Most of these muons are not tagged before entry into the TPC. Both untagged muonsand muons tagged by the CRT are exploited to provide important calibration data and performanceindicators.The CRT uses scintillation counters recycled from the outer veto of the Double Chooz exper-iment [20]. It is constructed in four large assemblies, two mounted upstream and two mounteddownstream of the cryostat. Each assembly covers an area approximately 6.8 m high and 3.65 mwide. The CRT uses 32 modules containing 64 scintillating strips each. The strips are 5 cm wideand 365 cm long. The strips in each module are parallel to each other, and thus a module provides aone-dimensional spatial measurement for each track at a given position along z . In order to enabletwo-dimensional sensitivity in x and y , four modules are placed together into eight assemblies withtwo modules being rotated by 90 degrees to create an assembly of 3.65 m by 3.65 m in size, asshown in figure 5. Four of these units are placed to cover the upstream (front) face of the detectorand the other four placed against the downstream (back) face. Hamamatsu M64 multi-anode pho-tomultiplier tubes detect the scintillation light and the resulting electrical pulses are digitized byADCs and recorded by the data acquisition system along with timestamps with 20 ns resolution.A digitized pulse and its timestamp are called a “one-dimensional hit”. Two-dimensional hits arereconstructed when two one-dimensional hits are recorded in overlapping CRT modules within acoincidence window of 80 ns. A cosmic-ray muon track is reconstructed in the CRT by drawing aline from hits in the front modules to hits in the back modules within a coincidence window dictatedby the estimated time of flight to travel from the front modules to the back modules.Half of the 32 upstream CRT modules cover the upstream face of the detector and the other halfof the CRT modules cover the downstream face of the detector as seen in figure 6. The upstreamCRT modules are offset due to the beam pipe, which enters the cryostat at an angle. Because of this,eight CRT modules cover the area near the cathode along the x direction, but 9.5 m upstream from– 10 – igure 5 : Drawing of CRT modules overlaid (left) and an image of CRT modules installeddownstream (right). Two CRT modules measure the x coordinate and two CRT modules are rotatedto measure the y coordinate.the front face of the TPC. The other eight upstream CRT modules sit to the left of the beam pipewith an offset of 2.5 m upstream from the from face of the TPC. The downstream CRT modules arecentered with respect to the center of the TPC in x and sit 10 m downstream from the front face ofthe TPC. The ProtoDUNE-SP data acquisition system (DAQ) is responsible for reading the data from theTPC, the photon detector and the CRT. It also reduces the data volume using online triggering andcompression techniques and formats the data into trigger records for storage and offline processing.The TPC has two candidate readout solutions under test in ProtoDUNE-SP: RCE (ATCA-based) [21]and FELIX (PCIe-based) [22]. Both of these systems ran simultaneously. For the beam runs, fiveout of the six APAs were read out using RCEs and one APA was read out using FELIX. After thebeam runs, four APAs were converted from RCE readout to FELIX readout. Fermilab’s artDAQ [23]is used as the data-flow software.The ProtoDUNE-SP timing system provides a 50 MHz clock multiplexed on an 8b10b encodeddata stream that is broadcast to all endpoints. The timing system interfaces to the CERN SPS beampresence signals and can be used to switch modes for data taking with and without beam. Thetiming system data stream also provides the trigger distribution. The timing system is partitionable,a feature that allows parts of the experiment to run independently. A clock synchronized to the The word “event” is customarily used for a triggered detector readout in many high-energy physics experiments. Dueto the need to refer to interactions as events and the presence of multiple interactions per detector readout, we standardizeon “trigger record” as the name of a unit of data produced by the DAQ. – 11 –
RT Module Upstream Right Beam side CRT ModulesDownstreamCRT Module Upstream Left
Beam Pipe
CRT Module Upstream Right Beam side CRT Module Downstream LeftCRT ModuleDownstream Right
BeamPipe
CRT Module Upstream Left
Figure 6 : Installation of the Cosmic Ray Tagger (CRT): (top) 3D view with staggered upstreammodules visible in front of the cryostat, (bottom-left) side view, (bottom-right) top view, withpositions of the staggered upstream modules and downstream parallel modules indicated by labels.Global Positioning System provides 64-bit timestamps that are used to mark the trigger and datatimes irrespective of file name, run, or trigger record numbers.A hardware triggering system was designed in order to perform event selection in ProtoDUNE-SP. The core element of this system is the Central Trigger Board (CTB) which is a custom printedcircuit board (PCB) in charge of processing the status of the auxiliary detectors to aid in makingprompt readout decisions. The readout decisions are ultimately made by the timing system whichcommunicates with the CTB through various commands. The CTB hosts a MicroZed, which is acommercial PCB with an onboard System-On-a-Chip (SoC). The SoC contains both ProgrammableLogic (PL) and a Processing System (PS) and serves as an interface between the auxiliary detectors(photon detectors, beam instrumentation, and CRT) and the DAQ through the timing system. TheCTB has 32 individual CRT pixel inputs (a pixel being a unit of two overlapping panels), 24 opticalinputs for the photon detection system, and seven inputs for beam instrumentation signals, all ofwhich are translated into digital pulses and forwarded to the PL for further processing.The CTB triggering firmware operating in the PL is organized into a two-level hierarchy oflow-level and high-level triggers (LLTs and HLTs) which are configurable at run-time by the DAQsystem. LLTs are defined for inputs from a single subsystem while HLTs can be defined usingthe various LLTs and can therefore span any or a combination of the subsystems. Several triggerconditions can be set up; each one is uniquely identified by a bitmask and is embedded into a trigger– 12 –ord issued to the DAQ. An overview of the CTB trigger scheme and its interface with the DAQ isdepicted in figure 7.
Figure 7 : CTB trigger hierarchy.Additionally, multiple trigger conditions can be satisfied during a single triggered detectorreadout. To distinguish between these, the CTB timestamps all LLTs and HLTs generated with the50 MHz system clock. This (64-bit) timestamp is also included in the trigger word along with thebitmask.In the HLT, trigger conditions can be configured to require coincidences or anti-coincidencesbetween the various LLTs. Only when all the required conditions for an HLT are satisfied is atrigger command passed to the timing system. The timing system is then responsible for validatingor vetoing the issued trigger. If accepted, the timing system forwards the readout decision tothe DAQ software and to the individual readout systems (i.e. TPC, photon detectors, and CRT).However, for accountability , the CTB sends a data word directly to the DAQ to be stored regardlessof whether or not the trigger is validated by the timing system.All HLTs can be classified as beam-on or beam-off triggers. The former relies mostly onthe beam instrumentation inputs and requires the conditions to be satisfied during a beam spillwhile the latter requires that the conditions are satisfied outside the beam spill. The most commonexamples of beam-on triggers include those aimed at tagging electron, proton and kaon events.By requiring different signal combinations from the beam instrumentation inputs, one can identifyspecific particles for a relevant energy range, which will be discussed in section 3. In case a trigger has already been issued by the timing system. If beam pile-up occurs, the timing system vetoes any additional beam triggers if it has issued one in the last 10 ms.However, the CTB still reports multiple beam triggers in this case. – 13 –he most common examples of beam-off triggers are those arising from cosmic-ray activity.Several of these triggers are in place to select events with specific topologies, requiring CRT pixelsfrom specific regions to register hits in coincidence with pixels from another region. For example,by requiring that at least one upstream CRT pixel is hit in coincidence with a downstream CRTpixel, one can select throughgoing muon candidates. Another trigger is set up for cathode-crossingmuon candidates, which is achieved by requiring coincidence hits on CRT pixels on opposing driftvolumes and sides of the cryostat. In addition to the logic-specific triggers, an aperiodic randomtrigger is provided to read out the detector without regard to trigger conditions.For each triggered readout of the detector, the TPC data consists of 6000 consecutive samplesof each ADC, which are digitized at a rate of 2 MHz, for a total of 3 ms of time. Each time periodof 500 ns between ADC samples is called a “tick." The data readout starts 250 µ s (500 ticks) beforethe trigger time in order to collect charge deposited by particles that arrive earlier than the triggerbut cause charge to arrive at the anodes during time periods that overlap those of triggered events.Corresponding data from the photon detectors and the CRT are saved in the output data stream foranalysis. Compressed raw data trigger records have a typical size of 60 MB, and trigger rates of40 Hz were reliably sustained by the data acquisition system. A typical physics run lasts severalhours. The ProtoDUNE-SP TPC is located in the CERN North Area in a tertiary extension branch ofthe H4 beam line. The 400 GeV/ c primary proton beam is extracted from the CERN SuperProton Synchrotron (SPS) and is directed towards a beryllium target, producing a mixed hadronbeam with a momentum of 80 GeV/ c . This secondary beam is then transported to impinge on asecondary target, producing a tertiary, very low energy (VLE) beam in the 0.3 - 7 GeV/ c momentumrange. The H4-VLE beam line then accepts, momentum-selects and transports these particles tothe ProtoDUNE-SP detector. The secondary target material can be changed between copper andtungsten. The latter is chosen for momenta below 4 GeV/ c in order to increase the hadron contentof the beam. However, the copper target was unintentionally used for the 2 GeV/ c run instead ofthe tungsten target. The H4-VLE beam line is instrumented with three types of detectors that provide particle identifi-cation and a trigger for the TPC. There are eight profile monitors (“XBPF”), three trigger counters(“XBTF”) and two threshold Cherenkov counters (“XCET”). There are also three bending magnetsthat direct the beam toward the ProtoDUNE-SP detector. The second of these magnets is also usedas part of a momentum spectrometer. The relative positions of each of these features can be seen infigure 8. A description of the beam line design has been reported elsewhere [24], while an in-depthdiscussion of the instrumentation can be found in [25].The XBPFs, described in detail in [26], are scintillating fiber detectors, each containing192 square fibers, approximately 1 mm thick. The fibers are arranged in a planar configurationand cover an area of approximately 20 ×
20 cm . Each device contains a single plane of fibers– 14 – r o t o DUN E - SP Time of FlightMomentum Spectrometer Beam line Trigger28.575 m
XBTF XBPF XCETXBPF XBTF XBTF XBPF
Figure 8 : A schematic diagram showing the relative positions of the trigger counters (XBTFs),bending magnets (triangles), profile montiors (XBPFs) and Cherenkov detectors (XCETs) in theH4-VLE beam line. Combining data from different pieces of instrumentation can be used fortriggering, reconstructing momentum and measuring time of flight.and therefore measures one spatial coordinate. Pairs of these detectors, rotated by 90° with respectto each other, are placed at several points along the beam line. This arrangement allows thebeam position to be tracked on a particle-by-particle basis. The XBPF data is also used in thereconstruction of a particle’s momentum, discussed in section 3.3.1. Hits in the last two sets ofXBPF devices are used to measure the trajectories of the beam particles that are then extrapolatedto the face of the ProtoDUNE-SP TPC. The XBTFs are designed in a similar way. However, insteadof each fiber being read out separately, they are gathered into two bundles and therefore offer noposition resolution. Instead, the signals from upstream and downstream planes, which are separatedby 28.575 m, are connected to a time-to-digital converter (FMC-TDC [27]). The TDC signals fromthese two planes provide a particle’s time of flight (TOF). The resolution of this measurement hasbeen measured to be approximately 900 ps [25].Coincident signals from the middle and downstream XBTFs act as a “general trigger.” Thesegeneral triggers are sent to the CTB serving as conditions for HLTs as described in section 2.7.During data taking across the momentum regime of interest, the measured efficiencies of the XBPFswith respect to these triggers are greater than 95% for all chambers [25].The two Cherenkov counters used in the H4-VLE are of similar design [28, 29], although oneis able to sustain a higher radiator gas pressure. The internal pressures of the two devices weretuned to tag different particle species at various momenta. A combination of the TOF and thetwo Cherenkov signals (high and low pressure), offers particle identification for analysis across thewhole momentum spectrum of interest. During the beam run, signals from these devices were sentto the CTB to form HLTs tagged as various beam particle species.
The beam transported in the H4-VLE beam line is produced by the collision of the secondarymixed hadron beam of 80 GeV/ c with the secondary fixed target. To limit the contributions fromthe decays of unstable low-energy hadrons such as pions and kaons, a beam line length of lessthan 50 m is required. Low-energy beam particles need to be sufficiently separated from the high-– 15 –nergy background in this distance, and enough space for the beam line instrumentation is required.Detailed simulation studies were carried out in order to meet these specifications.The performance of the initial layout was calculated with the beam optics code Transport [30]and refined by a comprehensive
MAD-X [31] (and
MAD-X-PTC [32]) simulation [33]. The Monte Carlosimulations use two frameworks,
G4beamline [34] and
FLUKA [35, 36]. Different target lengthsand materials were investigated to satisfy the experimental needs of rate and beam composition.The target choice (either copper or tungsten) and the different field strengths of the beam line’sdipoles and quadrupoles are incorporated into the
G4beamline and
FLUKA models. Based on thesestudies, estimates of the beam rates, compositions and background rates at the experiment locationare obtained. The background suppression was improved by optimizing the shielding using the
FLUKA simulation [25].
Information from the three types of beam line instruments (discussed in section 3.1) is combinedin order to perform particle identification on an event-by-event basis. A search window in time of500 ns is defined around each general trigger; timestamps associated with data packets from eachdevice are then matched within this interval.
The three XBPF detectors surrounding the middle bending magnet provide a measurement of eachparticle’s momentum. This is illustrated in figure 9 [28]. The lateral position of the particle at eachXBPF detector ( χ , χ , χ ) is provided by the index of the activated fibers in the profile monitors.These measurements, along with the known distances between the monitors ( L , L , L ) and themeasured magnetic field are used with equations 3.1 and 3.2 to determine a particle’s bending angle θ and momentum p . cos θ = M [ ∆ L tan θ + ∆ χ cos θ ] + L ∆ L (cid:113) [ M + L ][( ∆ L tan θ + ∆ χ cos θ ) + ∆ L ] (3.1) p = . θ × ∫ L mag ( Bdl ) (3.2)Here, M ≡ α + χ , α = χ L − χ L L − L cos θ , ∆ L ≡ L − L , and ∆ χ ≡ χ − χ . θ is the nominalbending angle of the beam and is equal to 120.003 mrad [25]. The beam line is designed to provide particle identification (PID) for the various particle types ( p , µ , π , e , K ) comprising the beam. Depending on the beam momentum settings, different conditionsare applied to the data from the beam line instrumentation to extract the particle types. Theseconditions are listed in table 1. This technique is demonstrated for selected runs at various beammomenta in figures 10(a) – 10(d). Figure 11 shows the measured momentum and TOF distributionthroughout the selected runs. The red curves show expected TOF for several particle types ( e , µ , π ,– 16 – - 18 - Figure 18: Layout of the H2-VLE (and similarly H4-VLE) momentum spectrometer around the last dipole.
To validate the performance of the spectrometer, we used the high statistics simulation, which includes all the material in the line, the gas in the Cherenkov detectors at the right pressures per momentum, as well as the expected special resolution of the profile monitors. For each particle, we compute its momentum from the above equation, and therefore the measured Δp/p of the line. Assuming no material in the beam line for a central momentum of 12 GeV/c and position resolutions of 0.2 mm, 0.5 mm and 0.8 mm we obtain a Δp/p of 1.1%, 2.5% and 3.9% accordingly, as shown on Figure 19. When the material along the beam is included, the reconstructed momentum resolution
Δp/p deteriorates , because of the multiple scattering, with the effect becoming more significant in lower energies, as shown on Figures 20, 21 and 22.
For the 2 GeV beam, the reconstructed momentum resolution with all material included and with
Figure 9 : A schematic diagram showing the method by which momentum is reconstructed for agiven beam particle (red), as discussed in the text. Taken from [28]. The direction of the x axis isopposite to the convention used in this paper. K , p and d ) given the particle’s momentum, its mass, and assuming a distance of 28.575 m betweenthe TOF monitors. The large quantities of high-quality data collected by ProtoDUNE-SP enable many studies of theperformance of the TPC. This section describes the offline data preparation and noise suppression,charge calibration, noise measurement, signal processing, event reconstruction, signal-to-noiseperformance, and a measurement of the electron lifetime.
The ProtoDUNE-SP detector is typically triggered at a rate of 1-40 Hz where each trigger recordincludes synchronized contiguous samples from all TPC channels, typically with a length of 3 mscorresponding to 6000 ticks (ADC samples). Trigger records are processed independently ofone another, beginning with data preparation which converts the ADC waveform (ADC count foreach tick) for each channel to a charge waveform. The data preparation comprises evaluationof pedestals, charge calibration, mitigation of readout issues, tail removal and noise suppression.These operations are necessary in order to optimize the performance of subsequent stages of eventreconstruction. The data preparation steps are described in detail in the following subsections.– 17 – able 1 : A summary of beam line instrumentation logic used in the identification of particle types.Each cell reflects how a particular type of instrumentation is used at a given reference momentum.When time of flight is used, the values of the lower and upper cuts are given in nanoseconds. Inthe case of the high-pressure Cherenkov (XCET-H) and the low-pressure Cherenkov (XCET-L),zero and one represent the absence and presence of a signal respectively. When a given piece ofinstrumentation is not involved in a logic decision at a given momenta, a dash is used.
Momentum (GeV/ c )1 2 3 6 - 7 e TOF (ns) 0, 105 0, 105 – –XCET-L 1 1 1 1XCET-H – – 1 1 µ / π TOF (ns) 0, 110 0, 103 – –XCET-L 0 0 0 1XCET-H – – 1 1 K TOF (ns) – – – –XCET-L – – 0 0XCET-H – – 0 1 p TOF (ns) 110, 160 103, 160 – –XCET-L 0 0 0 0XCET-H – – 0 0
Voltage offsets are introduced at the inputs to the amplifier and ADC for each channel to keepthe signals in the appropriate range for each of these devices. These offsets and the gains of bothdevices vary from channel to channel and so there are channel-to-channel variations in the ADCpedestal, i.e. the mean ADC count that would be observed in the absence of signal. In addition, thepedestal is observed to have significant variation from one trigger record to another, presumablydue to low-frequency (compared to the 3 ms readout window) noise pickup before the amplifier. Tocope with this, the pedestal is evaluated independently for each channel and each trigger record.The pedestal is evaluated by histogramming the ADC count for all (typically 6000) ticks andfitting the observed peak with a Gaussian whose mean is used as the pedestal. The RMS of thefit Gaussian provides an initial estimate of the noise in the channel and is typically around fourto six ADC counts. Due to the sticky-code issues described in section 4.1.3, these ADC countdistributions are sometimes observed to have spikes at the offending sticky codes which can biasthe pedestal estimate. To reduce this bias, the peak bin is excluded from the fit if it holds more than20% of the samples.
Initial charge waveforms are obtained for each channel by subtracting the pedestal from each of theADC counts and multiplying this difference by the gain assigned to the channel. These gains may– 18 – ime of Flight [ns]
80 100 120 140 160 R e l a t i v e F r equen cy e p / m p DUNE:ProtoDUNE-SP
Beam Line Data (1 GeV/c) (a) Nominal beam momentum = 1 GeV/ c . Verti-cal lines represent the time of flight cuts used forelectrons (blue), and muons/pions (red). Time of Flight [ns]
85 90 95 100 105 110 115 R e l a t i v e F r equen cy e p / m p DUNE:ProtoDUNE-SP
Beam Line Data (2 GeV/c) (b) Nominal beam momentum = 2 GeV/ c . Verti-cal lines represent the time of flight cuts used forelectrons (blue), and muons/pions (red). Time of Flight [ns]
85 90 95 100 105 110 R e l a t i v e F r equen cy e p / m p/K DUNE:ProtoDUNE-SP
Beam Line Data (3 GeV/c) (c) Nominal beam momentum = 3 GeV/ c . Time of Flight [ns]
85 90 95 100 105 R e l a t i v e F r equen cy p / m e / Kp DUNE:ProtoDUNE-SP
Beam Line Data (6 GeV/c) (d) Nominal beam momentum = 6 GeV/ c . Figure 10 : Time of flight distributions for different reference momenta, separated by particle usingthe PID techniques listed in table 1. The distributions are normalized such that the maximum heightis equal to 1.be set to 1.0 to obtain a charge waveform in units of ADC counts or they may be taken from a chargecalibration. The standard ProtoDUNE-SP reconstruction makes use of the calibration discussed insection 4.2.Figure 12(a) shows an example event display consisting of waveforms on wires in the collectionplane of APA 3 shown side by side in a two-dimension color plot. Pedestal subtraction and chargecalibration have been applied. APA 3 instruments the upstream drift volume on the side of thecathode on which the beam enters.
A few percent of ADC ASIC channels suffer from an issue known as “sticky code,” in which certainADC values would be preferentially produced by the ADC independent of the input voltage causingthe readout channel to appear to “stick” at a particular value. The flaw in this ADC design is afailure of transistor matching at the transition from digitizing the six most significant bits to the sixleast significant bits. The sticky codes therefore tend to prefer values of zero or 63 plus a multipleof 64, though other sticky codes have been observed in the data as well. Sticky codes were observed– 19 – Momentum [GeV/c] T i m e o f F li gh t [ n s ] DUNE:ProtoDUNE-SP
Beam Line Data (1-7 GeV/c)e m p
K p d
Figure 11 : The distribution of particles’ time of flight against reconstructed momentum fromseveral runs at various beam reference momenta. The red curves are predictions for e , µ , π , K , p and deuterons ( d ) in order of increasing time of flight.in test-bench measurements in advance of installation, where the dynamic range of the ADC wastested with a calibrated source of charge.The pedestal histograms and a few waveforms for all channels were scanned by eye to obtain aninitial list of sticky codes and this list is extended when other problematic channels are uncovered.A total of 498 codes in 312 channels (of 15360) have been identified as sticky and are mitigated asdescribed in the following section.Approximately 70 channels are flagged as bad due to very high fractions of sticky codes orpopulation of multiple widely-separated sticky values. Another 35 are flagged as noisy due to seriousbut less-severe sticky-code issues. The solidly unresponsive channels mentioned in section 2.4 arealso flagged as bad. A total of 133 channels are flagged as bad or noisy. The data for these channelsare prepared like any others, but downstream processing such as deconvolution or track finding maychoose to ignore these channels or treat them in a special manner. For channels that have known sticky codes, if the ADC value on a particular sample is at one of thesticky values, it is replaced with a value interpolated from the nearest-neighboring non-sticky codes.If the two neighbors on either side exhibit a significant jump (20 ADC counts), the interpolationuses a quadratic fit. Otherwise, linear interpolation is used.– 20 – a) After pedestal subtraction and calibration. (b) After ADC sticky code and timing mitigation.(c) After tail removal. (d) After correlated noise removal.
Figure 12 : Example event displays for a collection plane showing background reduction in suc-cessive stages of data processing. The horizontal axis is the tick and vertical axis is the channelnumber. The color scale represents the charge for each channel averaged over five ticks with therange chosen to make the noise visible. Signals from charged tracks appear mostly in black and areoff scale, well above the noise level. Horizontal dashed lines indicate the boundaries between theten FEMBs used to read out the channels for this plane. The second from the bottom is FEMB 302referenced in the text.Figure 13 shows an example charge waveform before and after mitigation. This is fromFEMB 302 where sticky codes are particularly prevalent. A sticky code has been identified and thewaveform is significantly improved after mitigation. Sticky codes very close to the pedestal are notflagged.
One of the 120 FEMBs (FEMB 302) does not receive the master timing signal used to clock theADCs. The ADCs on that FEMB make use of a backup clock that resides on the FEMB. Althoughthe master and FEMB clocks both nominally run at 2 MHz, reconstructed signals show that theFEMB clock runs 0.07% slower than the master clock. The charge waveforms for FEMB 302 are– 21 –
50 100 150 200 250 300 350 400 450 500 Tick960980100010201040 A DC c oun t DUNE:ProtoDUNE-SP - - S i gna l [ A DC c oun t] DUNE:ProtoDUNE-SP
Figure 13 : Example of a raw ADC waveform with sticky codes (top) and the correspondingwaveform after pedestal subtraction and ADC sticky code mitigation (bottom). The dashed lines inthe top plot show the six-bit boundaries.corrected to match the sampling rate and offset for the other channels. The charge for each sampleis replaced with a linear interpolation of the original charges of two samples nearest in time.Figure 12(b) shows an event display made from mitigated waveforms for the same data withthe same scale and binning as figure 12(a). Both sticky-code and timing mitigations are added. Theshift in the FEMB 302 timing and reduction in noise are discernible. Channels flagged as bad ornoisy are zeroed and appear white in the display.
In each TPC channel, the amplifier and ADC are AC-coupled using a high-pass RC filter with atime constant of approximately τ RC = µ s (20-40 ticks) and this AC coupling implies the observed signal will be followed bya long tail of opposite sign whose area cancels that of the initial signal. The tails are much smallerand are neglected in induction-plane channels, where the signals are bipolar and thus integrate tozero.The decay time is comparable to the mean time between cosmic-ray signals, about 1500 ticks,and it is a significant fraction of the data readout time used to evaluate the pedestal, typically6000 ticks. Variations in cosmic arrival time and charge deposit per channel (in particular due tovarying angle of incidence) imply that all signals are superimposed on a fluctuating background ofaccumulated tails from preceding signals, many of which arrive before the readout window starts.Tails from these fluctuations are clearly visible in collection-plane waveforms and event displays.Large charge deposits in figure 12(b) are followed by blue regions indicating negative tails. Someother regions are positive (orange) because the initial pedestal estimate is biased by the negative– 22 –ails. The tails are removed in collection-plane channels using a time-domain correction, chosenbecause of the large fraction of channels that start each trigger record with a significant tail fromcharge that has arrived before the readout window starts.Before tail removal, the (nominal) pedestal-subtracted value (ADC count or calibrated charge)in sample i , d i , is the sum of signal, s i , and tail, ξ i , contributions and a pedestal offset, p o : d i = s i + ξ i + p o (4.1)The pedestal offset is not zero because the method used to evaluate the pedestal includes contribu-tions from tails. Because the tails are exponential, the tail in any sample may be expressed in termsof the signal and tail in the preceding sample: ξ i = βξ i − + α s i − (4.2)where β = e − / τ RC ( τ RC is the time constant in ticks) and α = − / β obtained by requiring theintegral of the tail cancel that of the signal. Equations 4.1 and 4.2 may be solved to obtain the signaland tail from the input data: eq. 4.1 is used to obtain s i from d i and ξ i and eq. 4.2 to obtain ξ i + from s i and ξ i for i = , , , ... . The tail correction replaces the input data with the evaluated signal: d i → s i .In addition to α , β , this solution depends on two parameters: the pedestal offset and the tailin the first sample, ξ . The latter is unknown because the data preceding the start of the triggeredreadout have not been recorded. These parameters are obtained by identifying signal-free regionsand choosing the values that minimize the sum of s i over those regions. This is done by iterativelyapplying the signal finder first to the input data and then to each signal estimate.Figure 12(c) shows an event display composed of waveforms after tail removal for the samedata with the same scale and binning as figure 12(b). Examples of two corrected waveforms fromProtoDUNE-SP data are shown in figure 14. A DC c oun t Uncorrected waveformAdditive correctionCorrected waveformPedestal
DUNE:ProtoDUNE-SP - - - A DC c oun t Uncorrected waveformAdditive correctionCorrected waveformPedestal
DUNE:ProtoDUNE-SP
Figure 14 : Examples of waveforms with tail removal. The black curve shows the original data { d i } and the red curve shows the corrected data, i.e. the signal { s i } . The blue curve is the correctionadded to the data, i.e. pedestal offset p o minus estimated tail { ξ i } . On the left, a very long pulseproduces a clearly visible tail. On the right, there is clear evidence of a tail from charge thatpreceded the readout window as well as tail from signal within the window. One of the most significant sources of excess noise observed in the ProtoDUNE-SP detector hasa frequency distribution with a peak around 45 kHz. This noise source is found to be highly– 23 –orrelated among a group of channels that share the same low-voltage regulator in the same FEMB.Following Ref. [37], a mitigation method is developed by dividing channels into groups. EachFEMB amplifies and digitizes 128 channels: 40 adjacent U-plane channels, 40 adjacent V-planechannels, and 48 adjacent collection-plane channels. The U-plane channels form a group, as dothe V-plane channels and the collection-plane channels. For each of the three groups, a correctionwaveform is constructed based on the median value of samples from the group at every time tick andit is subtracted from each channel’s waveform in that group. However, if the majority of waveformscontain signals of ionization electrons, it is necessary to protect this time region to avoid signalsuppression. A region of interest (ROI) is defined as the ADC counts above an expected thresholdas well as 8 (20) ticks before (after). As an example, event displays consisting of waveforms beforeand after the correlated noise removal (CNR) are shown in figure 12(c) and 12(d), respectively.The correlated noise is visible as vertical bands in figure 12(c), and it is suppressed in figure 12(d).Some sources of correlated noise remain in some portions of the detector, specifically those forwhich the spatial correlation does not coincide with FEMB boundaries.
The ProtoDUNE-SP electronics provide the capability to inject a known charge in short-duration( < µ s) pulses into each of the amplifiers connected to the TPC wires. The level of that charge iscontrolled by a six-bit voltage digital-to-analog converter (DAC) and is nearly linear with Q = SQ s where S is the DAC setting (0, 1, ..., 63) and the step charge Q s = Q = g A . The evaluation of charge for the bipolar TPCsignals in induction channels is more complicated but also proportional to the gain derived here.Special runs were taken with injected voltage regularly alternating between ground and theDAC level (one setting for each run) producing charge pulses of alternating sign. Fifty triggerrecords with typically 12 pulses of each sign are processed for each channel at each DAC setting.For each channel, the pedestal is evaluated for each event and a distribution of approximately 600pedestal-subtracted areas in units of (ADC count)-ticks is obtained for each charge sign. The meanof these signal area measurements are plotted as a function of DAC setting using data from manyruns, and a line constrained to pass through the origin is fit to DAC settings 1-7. The step chargedivided by the slope of this line provides the calibrated gain for each channel.Figure 15 shows the uncalibrated area vs. DAC setting and the fit for a typical collectionchannel. The response is fairly linear over the DAC setting range (-5, 20) with saturation settingin outside this range. Typical track charge deposits are one to four times the step charge andthis saturation is only an issue for very heavily ionizing tracks. The gain for this channel is g = ( . ∆ A = A − SQ s / g . These results are typical—most of the measured areas for positive– 24 – - - - - - t i ck ] · A r ea [ ( A DC c oun t ) – Slope: 909.41
DUNE:ProtoDUNE-SP
Figure 15 : Measured pulse area vs. DAC setting for a typical collection channel. The red lineshows the fit used to extract the gain.smaller ( S ≤ , Q (cid:46)
160 ke) pulses are within 1% of their fitted values. The systematic shift forhigher values is also typical and presumably reflects non-linearity in the DAC.All 15,360 ProtoDUNE channels were calibrated in this manner, and those gains are appliedearly (before the mitigation and noise removal) in the typical processing of data from the detector.Figure 17 shows the distribution of these gains for all channels. Channels flagged as bad orespecially noisy in an independent hand scan are shown separately. The gains for the remaininggood channels are contained in a narrow peak with an RMS of 5.1% reflecting channel-to-channelresponse variation in the ADCs and gain and shaping time variations in the amplifiers.
One very important goal for ProtoDUNE-SP is to demonstrate that noise levels are well belowsignals from charged tracks; this is found to be the case for nearly all of the channels in the detector.The noise is evaluated both for single ADC samples ( sample noise ) and for a contiguous range of50 samples ( integrated noise ). The latter range is chosen to be sufficient to obtain the area of thesignal from a charged track in the detector traveling in the y z plane. Tracks with other angles withrespect to the electric field will leave longer pulses on the sense wires.The noise is measured after initial data preparation. As discussed in section 4.1, the pedestalis evaluated for each trigger record, and the charge calibration is applied to the ADC count minuspedestal to obtain the initial charge measurement for each channel. Sticky codes are mitigated andthe AC-coupling tails are removed in the collection channels. The noise is evaluated both at this– 25 – - - - - - - t i ck ] · A r ea [ ( A DC c oun t ) D DUNE:ProtoDUNE-SP
Figure 16 : Measurement residuals (data - fit) for the same data as the preceding figure (black) plusthe same for data taken in the following months (colors). The dashed lines indicate deviations of ± Ar) signals, a signal finder is applied and the noise is defined to bethe RMS ADC value outside the signal regions. For the integrated noise measurement, integrationregions start every 50 ticks (i.e. at ticks 0, 50, 100, ...) and regions are discarded if they have anyoverlap with signal regions.The signal finder used for this study makes use of a variable sample threshold and retains aregion of (-30, +50) ticks around any tick with signal magnitude above that threshold. The thresholdis evaluated independently for each channel every trigger record. The threshold starts at 300 e and,if it is below five times the sample noise, is increased until it reaches that level. This allows efficientremoval of signals in quiet channels while retaining the noise in those that are noisier.Visual inspection of raw waveforms were performed to identify bad channels in the detector,mostly those with no signal or exceptionally high noise typically from sticky ADC codes. Thenumber of such channels is 90, i.e. 0.6% of the channels in the detector. These are excluded fromthe noise summary plots below.Figure 18 shows the distributions of sample and integrated noise levels before and after corre-lated noise removal for trigger records 1-1000 of run 5240 taken October 12, 2018. The collection-– 26 – .015 0.02 0.025 0.03 0.035 Gain [ke/(ADC count)/tick]020040060080010001200 N u m be r o f c hanne l s Good: count: 15227mean: 0.0234 RMS/mean: 0.051 Bad: count: 133
DUNE:ProtoDUNE-SP
Figure 17 : Distribution of fitted gains for good (blue) and bad/noisy (red) channels. The legendindicates the number of channels in each category and gives the mean (23.4 e/(ADC count)/tick))and RMS/mean (5.1%) for the good channels.plane and induction-plane channels are shown separately and, as expected, the noise levels are higherfor the induction-plane channels as the wires are longer. For the collection channels, the samplenoise is around 100 e before correlated noise removal falling to 80 e after the channel correlationsare removed. The corresponding values for the integrated noise are 1200 e and 900 e.For charge deposits much faster than the nominal 2 µ s shaping time of the amplifier, the area A of the resulting signal pulse is proportional to the height h and shaping time τ : A = K h τ . Theshape is well understood [37] and has been verified with fits of the ProtoDUNE-SP pulser signals.Numerical integration gives K = . / tick = . / µ s. For such fast signals, the charge maybe deduced directly from the pulse height and its standard deviation is called the ENC (equivalentnoise charge) [37]. The ProtoDUNE-SP signals are slower than this but the ENC is a standardmetric and is presented here to allow comparison with results from other detectors.The ratio of ENC to sample noise defined here is A / h = K τ . The actual shaping time variesfrom channel to channel but has central value around 2.2 µ s which gives a ratio of ENC to samplenoise of 5.58. With this factor and the above values for the sampling noise, the mean ENC for thecollection channels is 530 e before correlated noise removal and 430 e after. The correspondingnumbers for the induction channels are 620 e and 500 e. These noise values are similar to the 500-600 e values obtained from bench measurements with a prototype FEMB at LN2 temperature [3].Figure 19 shows the noise frequency power spectra for data collected at the same time asthose used for figure 18. For each channel, a signal finder with a dynamic threshold of five timesthe sample RMS in the non-signal region is used to identify signal samples. A discrete Fourier– 27 – N u m be r o f c hanne l s DUNE:ProtoDUNE-SP
Sample noise w/o CNRRun 5240 = 94 e æsÆ
Collection: = 111 e æsÆ
Induction: N u m be r o f c hanne l s DUNE:ProtoDUNE-SP
Integrated noise w/o CNRRun 5240 = 1216 e æsÆ
Collection: = 1664 e æsÆ
Induction: N u m be r o f c hanne l s DUNE:ProtoDUNE-SP
Sample noise with CNRRun 5240 = 82 e æsÆ
Collection: = 92 e æsÆ
Induction: N u m be r o f c hanne l s DUNE:ProtoDUNE-SP
Integrated noise with CNRRun 5240 = 889 e æsÆ
Collection: = 1057 e æsÆ
Induction:
Figure 18 : TPC sample (left) and 50-sample integrated (right) noise distributions before (top)and after (bottom) correlated noise removal. Each plot has one entry for each channel excludingbad channels with overflows shown in the last bin. Collection and induction channels are shownseparately.transform is performed on non-overlapping blocks of 1000 contiguous samples selected from thenon-signal regions. The power spectra for good channels are averaged separately for each of thefour wire types: TPC-side collection, cryostat-sided collection and the two induction orientations,U and V. These are normalized so that the sum over power terms or histogram entries is equal tothe RMS charge per sample, i.e. the sample noise shown on the left side of figure 18.As expected for effective removal of signals from the TPC, the power distributions are verysimilar for the TPC-side and cryostat-side collection channels. The induction wire distributionshave similar shapes. The small spike around 300 kHz is due to pickup noise in one of the APAs.The CNR effectively suppresses both that and the excess noise below 100 kHz.
The recorded waveform on each TPC readout channel is a linear transformation of the current onthe connected wire as a function of time. This transformation includes the effect of induced currentsdue to drifting and collecting charge, as well as the response of the front-end electronics. The goalof the signal-processing stage of the offline data processing chain is to produce distributions ofcharge arrival times and positions given the input waveforms. These charge arrival distributions are– 28 –
200 400 600 800 1000Frequency [kHz]0500100015002000 / ( k H z ) ] P o w e r /t i ck / c hanne l [ e = 93 e S W/o CNR: = 82 e S With CNR:
DUNE:ProtoDUNE-SP
C wiresRun 5240 0 200 400 600 800 1000Frequency [kHz]0500100015002000 / ( k H z ) ] P o w e r /t i ck / c hanne l [ e = 111 e S W/o CNR: = 92 e S With CNR:
DUNE:ProtoDUNE-SP
U wiresRun 52400 200 400 600 800 1000Frequency [kHz]0500100015002000 / ( k H z ) ] P o w e r /t i ck / c hanne l [ e = 97 e S W/o CNR: = 83 e S With CNR:
DUNE:ProtoDUNE-SP
Z wiresRun 5240 0 200 400 600 800 1000Frequency [kHz]0500100015002000 / ( k H z ) ] P o w e r /t i ck / c hanne l [ e = 113 e S W/o CNR: = 94 e S With CNR:
DUNE:ProtoDUNE-SP
V wiresRun 5240
Figure 19 : Noise frequency power spectra before and after CNR. Each plot is evaluated from1000-sample blocks in non-signal regions. Upper and lower left respectively show the cryostat- andTPC-side collection planes. The induction planes are on the right: top is U and and bottom is V.The first bin in each plot includes only the zero frequency component. The others are sums over20 kHz bins.used in subsequent reconstruction steps, such as hit finding. Because the response is linear in thearriving charge distribution, a deconvolution technique forms the core of the signal processing.A charge moving in the vicinity of an electrode can induce electric current. Shockley-Ramo’stheorem [38] states that the instantaneous electric current i on a particular electrode (wire) whichis held at constant voltage, is given by i = e ∇ φ · (cid:174) v e , (4.3)where e is the charge in motion, and (cid:174) v e is the charge velocity at a given location. The so-calledweighting potential φ of a selected electrode at a given location is determined by virtually removingthe charge and setting the potential of the selected electrode to unity while grounding all otherconductors.The field response is defined to be the induced current on different wires due to a movingpoint charge. The field response is an essential input to the signal processing procedure as will bediscussed below. For ProtoDUNE-SP, the field response is calculated with Garfield [39], a TPC driftsimulation code, in a 2D scheme as illustrated in figure 20(a). During the field response simulation,a point charge is positioned at different positions in a horizontal plane 10 cm away from the grid– 29 –lane and the drift path is recorded from the simulation as shown in figure 20(b). The electrondrift velocity can be determined from the electric field [40, 41], while the precomputed weightingpotential for a U-plane wire is also shown in figure 20(b). With the drift path and the weightingpotential, the field response of the point charge on the sense wire can be calculated according toeq. (4.3). This procedure is repeated for a series of point charges that spans a region of 21 wires withthe wire of interest at the center. In order to sample the rapidly-changing field response functionsadequately, point charges are simulated drifting in from positions on a grid with a spacing of onetenth of the wire pitch. After convolving the electronics response, the total response as a functionof time and wire pitch is presented in figure 21, where a “Log10” color scale is defined for the sakeof visibility: i in “Log10” = log ( i · ) , if i > × − , , if − × − ≤ i ≤ × − , − log (− · i · ) , if i < − × − . (4.4)As shown in figure 20(b), the weighting potential of the first induction (U) plane is significantlydifferent from zero over a region of a few wires even with the presence of the grid plane. As a result,the current induced on a sense wire contains contributions not only from charges passing betweenthe wire and its immediate neighbors, but also from moving charges that are farther away. A regionthat is 10 cm in front of the grid plane and ±
10 wires around the wire of interest in the simulationis sufficient to envelope the field response.In order to deconvolve the ionization electron distribution from the measured signal, it is naturalto mathematically describe this long-range effect as follows: M ( w i , t j ) = ∫ w i ∫ t j ∫ t j Q ( w i , t j ) · F ( w i − w i , t j − t j ) · E ( t j − t j ) · dt j · dt j · dt w i = ∫ w i ∫ t j Q ( w i , t j ) · R ( w i − w i , t j − t j ) · dt j · dt w i (4.5)where measured signal M ( w i , t j ) on the w i th wire and time t j is a convolution of i) the ionizationcharge distribution as a function of the position in wire number and the drift time: Q ( w i , t j ) , ii)the field response that describes the induced current on the wires when the ionization chargemove: F ( w i − w i , t j − t j ) , and iii) the electronics response that amplify and shape the inducedcurrent on the wire: E ( t j − t j ) . For simplicity, one can firstly convolve the field response andthe electronics response into the overall response function: R ( w i − w i , t j − t j ) , in which thefine-grained position-dependent field response function is averaged within one wire pitch.Because of the long-range induction effect, instead of a 1D deconvolution involving only thetime dimension, a two-dimensional (2D) deconvolution involving both the time and wire dimensionsis performed to extract the ionization electron distribution. In practice, the FFT algorithm isused to convert the data from the discrete 2D time and wire domain to a discrete 2D frequencydomain [42, 43]. To avoid amplifying high-frequency noise in the deconvolution, two Wiener-inspired filters are applied separately in both dimensions. In addition, to further reduce the noisecontamination and improve the charge resolution, a technique for identifying the signal regionsof interest (SROI) is adopted and adjusted accordingly. The application of SROIs are particularly– 30 – (a) x [ c m ] z [cm] (b) Figure 20 : (a) Illustration of the 2D ProtoDUNE-SP TPC scheme for the Garfield simulation,where ±
10 wires (large black dots) are considered for each wire plane and the electrons driftsimulation starts 10 cm away from the grid plane. The inset shows the spacing of the starting pointsof the simulated electrons; (b) Garfield simulation of electron drift paths (yellow lines) in a 2DProtoDUNE-SP TPC scheme and the equal weighting potential lines (green) for a given wire inthe first induction plane, where the latter is shown in percentage from 1% to 45%. A long-rangeinduction effect is noticed as the weighting potential has significant strength several wire spacingsaway from any particular wire.important to process the induction plane signals. As an example, a raw waveform that has beende-noised as described in section 4.1 and the corresponding extracted charge distribution after thedeconvolution are shown in figure 22(a) and figure 22(b), respectively.
There are two distinct steps in the ProtoDUNE-SP event reconstruction chain to go from thedeconvolved waveforms to fully reconstructed interactions: hit finding and pattern recognition.These steps are described in sections 4.5.1 and 4.5.2, respectively.
The hit finding algorithm fits peaks in the wire waveforms, where a hit represents a charge depositionon a single wire at a given time. Each hit corresponds to a fitted peak. Ideally, after the deconvolutionprocess described in section 4.4, the signals on all wires, regardless of whether they are induction-plane wires or collection-plane wires, will be waveforms containing possibly overlapping Gaussian-shaped peaks. The algorithm searches for candidate hits in the waveform and fits them to aGaussian shape to produce the hits. Situations can occur in which charge deposits do not forma simple Gaussian shape, for example when a particle trajectory is close to being in the plane– 31 –
Figure 21 : The overall response function convolved with electronics response. The color densityis shown in a modified, sign-dependent base 10 log scale, as described in the text.containing the wire under study and the electric field. If, after the candidate peak-finding, a verylarge number of candidate peaks are found in a given SROI then the algorithm bypasses the hit-fitting step and the pulse is instead divided into a number of evenly-spaced hits. An example of afitted waveform is shown in figure 23, where three hits have been reconstructed.The two induction planes consist of wires that are wrapped around the APA. As a result, itmust be determined on which segment of the wrapped wire that a given energy deposit was actuallymeasured. Firstly, triplets of wires (one on each plane) are formed using signals within a narrow time– 32 –
25 50 75 100 125 150 175 200U Wire No.0100200300400500600 T i m e [ s ] DUNE:ProtoDUNE-SP (a) After Noise Filtering T i m e [ s ] DUNE:ProtoDUNE-SP (b) After Deconvolution
Figure 22 : An interaction vertex from the 7-GeV charged particle beam data (run 5152, event89) measured on the induction U plane: (a) Raw waveform in ADC counts after noise filtering;(b) Ionization charge in number of electrons, scaled by 200, extracted with the 2D deconvolutiontechnique. s] m Time Tick [0.5 C ha r ge /t i ck [ e ] Deconvolved waveformFitted Gaussian hits
DUNE:ProtoDUNE-SP
Figure 23 : An example of a reconstructed waveform on a single wire from ProtoDUNE-SP data.window. Often, for a given collection wire, only a single pair of induction wires are matched, thusthe hits are disambiguated at this stage. Otherwise, there can be multiple induction wires consistentin time with the collection wire. In this case, the algorithm aims to minimise the difference incharge between the collection wire and the candidate induction wires in a deterministic manner.A full description of the method is given in Ref. [6]. Simulation studies show that this techniqueassigns more than 99% of hits to their correct wire segments.
Pattern recognition in ProtoDUNE-SP is performed using the Pandora software package [44], whichexecutes multiple algorithms to build up the overall picture of interactions in the detector. Pandorahas been used successfully in other liquid argon time projection chambers (LArTPCs) such asMicroBooNE [45]. New features have been developed for ProtoDUNE-SP since it differs from– 33 –icroBooNE with its multiple TPCs and drift volumes in addition to the need for a testbeamparticle specific reconstruction chain.Pandora contains chains of reconstruction algorithms that focus on specific topologies, but theyall follow a common pattern. The first step involves two-dimensional clustering of the reconstructedhits in each of the three detector readout planes separately. Dedicated algorithms then match setsof 2D clusters between the three views. If matching ambiguities are discovered, information fromall three views is used in order to motivate changes to the original 2D clustering. Once consistentmatches between 2D clusters have been made, three-dimensional hits are constructed and particleinteraction hierarchies are created.In the Pandora ProtoDUNE-SP reconstruction, all of the clusters are reconstructed first underthe cosmic-ray hypothesis using a set of algorithms designed to reconstruct track-like particles.Cosmic-ray candidates are subsequently identified and removed so that beam-particle analysis canproceed. One important feature of the cosmic-ray reconstruction step is the “stitching” of tracksacross the boundaries between neighboring drift volumes bounded by a CPA or an APA. Thestitching procedure is applied when two 3D clusters have been reconstructed in neighbouring driftvolumes that have consistent direction vectors and an equal but opposite shift in the drift directionfrom the CPA or APA. When the clusters are shifted by this amount, a single collinear clusterwith a known absolute position along the drift direction and time t relative to the trigger timeis produced. Figure 24 shows the reconstructed t distribution for data and simulation for thosecosmic-ray muon tracks that have been stitched at the cathode or anode. Cosmic-ray muons thatcross the cathode have t values between -2500 µ s and 500 µ s , and those that cross the anode have − µ s < t < µ s . Tracks satisfying one or more of the following criteria are identified as“clear” cosmic-ray candidates:• The particle enters through the top of the detector and exits through the bottom.• The measured t for stitched tracks is inconsistent with a particle coming from the beam.• Any of the reconstructed hits appear to be located outside of the detector when assuming t =
0, which indicates that the object is inconsistent with the timing of the beam.The hits from these clear cosmic-ray candidates are removed from the trigger record before thePandora reconstruction chain continues to further process the data. These tracks, and in particularthose with a measured t , form a critical component of the various detector calibrations detailed insection 6.Once the energy deposits from the clear cosmic rays have been removed, the reconstructioncontinues with a 3D slicing algorithm that divides the detector into spatial regions containing all ofthe hits from a single parent particle interaction. These 3D slices could contain beam particles orcosmic rays that were not clear enough to be removed in the first-pass cosmic-ray removal process.Two parallel reconstruction chains are applied to these slices - one is the aforementioned cosmic-rayreconstruction, and the other is a test-beam specific reconstruction.The test-beam specific reconstruction consists of a more complex chain of algorithms capableof reconstructing the intricate hierarchies of particles seen in hadronic interactions that can producenumerous track-like and shower-like topologies. Included in this reconstruction chain is a dedicated– 34 – - - - s] m [ Reconstructed t F r a c t i on o f e v en t s SimulationData
DUNE:ProtoDUNE-SP
Figure 24 : The Pandora reconstructed t distribution for cosmic-ray muons that cross either thecathode or anode in data (black points) and simulation (red).search for the primary interaction vertex of the test-beam particle. As well as being used to informthe clustering, the vertex is essential for constructing the correct particle hierarchy.Once the slices have been reconstructed under both hypotheses, cosmic-ray and test-beam, aboosted decision tree (BDT) algorithm is used to determine which, if any, of the slices are consistentwith being of test-beam origin. The input variables to the BDT primarily use topological informationto measure the consistency of the interaction with the test-beam particle hypothesis. The outputfrom the reconstruction is in the form of a particle hierarchy, where links are made between parentand child particles to give the flow of an interaction from the initial beam particle. Figure 25shows an example of a fully reconstructed particle hierarchy for a simulated beam interaction,where the incoming beam π + is shown as the magenta track. A suite of tools have been producedfor ProtoDUNE-SP analysers to easily access this hierarchical information in order to perform theanalyses.The charge deposition per unit length, dQ / dx is reconstructed for track-like objects such asmuons, charged pions, kaons, protons and the beginnings of electromagnetic showers. The charge Q is taken as the area of the Gaussian fit to the individual hit. The segment length dx is calculatedas the wire spacing divided by the cosine of the angle between the track direction and the directionnormal to the wire direction in the wire plane. The raw dQ / dx is further calibrated to removenonuniform detector effects and converted to energy loss dE / dx for energy measurement andparticle identification. This procedure is described in section 6.3. The measurement of the signal-to-noise ratio (S/N) for the ProtoDUNE-SP detector is carried outusing a selected cosmic-ray muon sample in Run 5432 taken on Oct. 20, 2018. Muon tracks crossing– 35 – eam ⇡ +
The ProtoDUNE-SP photon detector system (PDS) comprises 60 optical modules embedded withinthe six APA frames of the TPC. These modules view the LAr volume from each anode side oppositethe central cathode. Three different photon collection technologies proposed for DUNE’s fardetector modules [3] are implemented in ProtoDUNE-SP’s PD system. In each technology, incident– 37 –
50 100 150
Angle-Corrected Signal-to-Noise Ratio R e l a t i v e F r equen cy U Plane, RawV Plane, RawCollection Plane, RawU Plane, Noise-filteredV Plane, Noise-filteredCollection Plane, Noise-filtered
DUNE:ProtoDUNE-SP
Cosmics Data
Figure 27 : Angle-corrected S/N distributions before and after the noise filtering of the three planesusing the cosmic-ray muons for the characterization. The histograms are normalized such that themaximum frequency is one.LAr scintillation photons, which have wavelengths around 128 nm, are converted into longer-wavelength photons using photofluorescent compounds as wavelength shifters (WLS). Visible lightis trapped within the modules, a portion of which is eventually incident on an array of siliconphotomultiplier photosensors (SiPMs) [19].
Each APA contains ten support structures located behind the wire planes for the PDS modules.Each module is a long, thin bar oriented along the z axis. The spacing between modules in y is approximately 60 cm, as illustrated in figure 28. Of the 60 modules, two are based on theARAPUCA photon detector technology [46], 29 are dip-coated light guides [47, 48], and 29 aredouble-shift light guides [49]. All light-guide modules have the same dimensions. The opticalarea of a module is 207 . × . in size, and the light is read out on one end of the bar. TheARAPUCA modules are segmented longitudinally (along the z direction) into 12 cells, each with itsown readout. The first eight cells each have an optical area of 9 . × . and the remaining fourcells are double-area cells, each with an optical area of 19 . × . . One ARAPUCA moduleis located in the top half of the upstream APA in the beam-side drift volume. A second is locatedin the middle of the APA in the center of the opposite drift volume. The two light-guide designsfill the remaining modules in alternating positions in the APAs. One example of each module– 38 –ype is highlighted in figure 28, together with the photo-sensor arrays equipping the module. Theinstallation of the modules within an APA behind the wire planes and a grounding mesh is alsovisible in figure 28. PD Module Designs
Dip-Coated Light GuidesDouble-Shift Light GuidesARAPUCA (Light Trap)
Figure 28 : Picture of a ProtoDUNE-SP APA during assembly. Labels indicate the three types ofPDS modules inserted into the APA frame (left). Pictures of the three technologies (right) withdetails of the photo-sensor arrays equipping the modules (insets). From left to right, they are adip-coated light-guide module, a double-shift light-guide module, and an ARAPUCA module.The two light-guide designs convert incident VUV photons into the visible range using tetra-phenyl butadiene (TPB) (emission peak ∼
430 nm), while the ARAPUCA design uses p-terphenyl(PTP) (emission peak ∼
340 nm). In the dip-coated acrylic light guide, wavelength-shifted photonsare confined inside by total internal reflection and they are guided toward the end of the bar that isin optical contact with a photosensor array. The double-shift light guide contains an internal lightguide doped with the second WLS (490 nm emission) to facilitate trapping of double-convertedphotons within the module and guide them toward the photosensors at the end of the bar. TheARAPUCA uses a dichroic filter window (400 nm cutoff) to reflect photons from a second WLS(TPB) inside the cell underneath the window and prevent them from exiting before they are absorbedor detected by the photosensors distributed inside the cell.
Three silicon photosensor models, each with an active area of 6 × , are employed throughoutthe photon detection system. They were selected among those that were available commercially orthat were newly developed during the PDS material procurement phase: the SensL SiPM MicroFC-– 39 –0035-SMT (35 µ m pixel size) and two types of Hamamatsu MPPC S13360-6050 (50 µ m pixel size)- the CQ-type (Quartz windowed for Cryogenic application) and the VE-type (VErtical through-silicon via). Arrays of photosensors of the same model are passively ganged together in parallelforming large-area single channels for voltage supply and signal readout. The arrays formed bythree SensL SiPMs are indicated in the following as 3-S-SiPM, those formed by 3 HamamatsuMPPCs (VE-type) indicated as 3-H-MPPC and those with 12 Hamamatsu MPPC (CQ-type) as12-H-MPPC. Each of the light-guide modules is read out by four channels with three photosensorseach, either 3-S-SiPM or 3-H-MPPC, arranged in a strip of 12 photosensors in total at one end ofthe module. Each cell of each ARAPUCA module is read out by one 12-H-MPPC channel madeof 12 Hamamatsu MPPCs distributed in the plane opposite to the cell optical window, for a total of144 photosensors in the ARAPUCA module (12 cells). The numbers of channels of the differenttypes and their distribution in the PDS modules are summarized in table 3. A 12-H-MPPC arrayand a strip made by four 3-S-SiPM are shown in figure 28. Table 3 : Numbers of each type of PDS module installed in ProtoDUNE-SP, and the numbers ofsensors per channel and channels per module
Ph. sensors Type Channels Dip-coated Double-shift ARAPUCA Total channelsper channel of channel per module modules modules modules in PDS
Unamplified signals from the photosensors in the LAr are transmitted outside the cryostat on coppercables. A dedicated, custom module was built for receiving and processing silicon photosensorsignals for the trigger and DAQ. The module is called the SiPM Signal Processor (SSP). A self-contained, 1U SSP module reads out 12 independent PDS channels. Each channel has a voltageamplifier and a 14-bit, 150 megasamples per second ADC that digitizes the (current) output signalfrom photosensors into analog-to-digital units. The front-end amplifiers are configured to be fully-differential with high common-mode noise rejection. Each amplifier has on its input a terminationresistor that matches the 100 Ω characteristic impedance of the signal cable. The least-significantADC bit corresponds to 66 nA in this configuration. The digitized data are stored in pipelinesin the SSP, corresponding to as much as 13.3 µ s per trigger. The processing is performed byFPGA (Field-Programmable Gate Array). The FPGA implements an independent data processorfor each channel. The processing incorporates a leading-edge discriminator and a constant fractiondiscriminator for sub-clock timing resolution. A block diagram of the system is shown in Fig. 29.Each channel is individually triggerable. Triggers can come from a periodic trigger, an internaltrigger based on the leading-edge discriminator local to the individual channel, or an externalglobal trigger distributed by the timing system as the general triggers issued by the beam line– 40 – igure 29 : Block diagram of the ProtoDUNE-SP photon-detector readout module (SSP) withinterfaces to Trigger and DAQ systems.instrumentation (sections 3.1,3.3). If a trigger is present, the channel will produce a data packetconsisting of a header and a waveform (a sequence of values in ADU) of predefined length. AnartDAQ process in the PD DAQ system (SSP boardreader) generates a fragment when the timingsystem produces a trigger. This fragment contains all packets received from the SSP with timestampsin a window ± . The PDS incorporates a pulsed UV-light monitoring and calibration system to determine thephotosensors’ gains, linearities, and timing resolution, and to monitor the stability of the systemresponse over time.The system hardware consists of both warm and cold components. Figure 30 (left) shows aschematic of the ProtoDUNE-SP PD calibration and monitoring system. Diffusers mounted onthe cathode-plane assembly panels serve as point light sources that illuminate the APA with thePDS modules on the opposite side of the drift volume. The location of the diffusers on the CPApanel is indicated by magenta discs. Also shown are the quartz fibers from the top of the CPA tothe diffusers. The other CPA side holds a second set of five diffusers to calibrate the PDS in theopposite APA array.The active system component is a 1U rack mount Light Calibration Module (LCM) sittingoutside the cryostat. The LCM generates light pulses that propagate through the quartz fiber-optic cable to the diffusers at the CPA. The LCM consists of an FPGA-based control logic unitcoupled to an internal LED Pulser Module (LPM) and an additional bulk power supply. The LPM– 41 – igure 30 : The ProtoDUNE-SP photon-detector calibration and monitoring system installationon the CPA panels (left). The inset shows the actual lower left diffuser at time of installation.Waveforms in a LED calibration run displayed in persistence trace mode (right) showing recordedsingle and multi-photon signals. Each time tick (tt) represents 6.67 ns.utilizes multiple digital outputs from the control board to control the pulse characteristics, and itincorporates DACs to control the LPM pulse amplitude.The calibration system produces UV light flashes with predefined pulse amplitude, pulsewidth, repetition rate, and total number of pulses. A typical photosensor response to low amplitude,shortest duration calibration pulses is shown in figure 30 (right) where many recorded waveformsare overlaid with the color indicating the frequency, as in an oscilloscope persistence trace mode.The UV light flashes can be produced in pairs with a fixed time difference between the two pulsesto study timing properties of the photon system.
Silicon photomultipliers convert light flashes into analog electrical pulses. When a photon hitsand is absorbed in a microcell of the avalanche photodiode matrix, an electron is lifted into theconduction band. If the bias voltage exceeds the breakdown voltage, this photoelectron creates anavalanche multiplication, with amplification (gain) typically in the 10 range, which leads to a risingcurrent signal. Silicon photomultipliers are implemented as photosensors in the ProtoDUNE-SPPDS modules because of their compact design, low operating voltage and sensitivity to singlephotons. To overcome the limitation due to their small sensitive area (6 × ) and to limitthe number of channels, arrays of photosensors are passively ganged together in parallel forminga large-area single channel for voltage supply and signal readout. The capacitance of the arrayhowever increases with the number of photosensors connected in the array, and correspondinglyits recovery time. Therefore, the signal amplitude decreases and the intrinsic noise increases.Operating at cryogenic temperature helps reduce the dark count rate. On the other hand, correlatednoise — afterpulses in the same pixel and optical crosstalk in neighboring pixels, both generated– 42 –y the primary photoelectron event — is expected to grow at high signal gain settings. The adoptedmultiplicity of the arrays in the ProtoDUNE-SP PDS design (three per channel in the 3-S-SiPMand 3-H-MPPC for the electron bars and twelve per channel in the 12-H-MPPC for the ARAPUCAcells) and the working parameters for operation (bias voltage and gain) were determined withthe requirement of acceptable signal-to-noise ratio allowing for sensitivity to single photoelectrondiscrimination with minimal secondary effects.Fifteen channels out of a total of 256 show anomalous readings. There are two that appearto be disconnected. They fail their continuity checks and they have been unresponsive since theearliest tests after cabling. There are two that appear to respond anomalously to light signals. Theirgains are similar to the others, but are somewhat higher, and their peak signal amplitudes are muchhigher than those of similar channels. The remaining 11 pass their continuity checks but they donot respond to light signals. These anomalous channels (all of the 3-S-SiPM type) have not beeninvestigated further. No channels have changed state since detector operation began.The photosensor response is characterized using dedicated calibration runs that have fast, low-amplitude LED flash triggers. Data are collected at several different bias voltage settings. Thesecalibration runs are periodically repeated in time. Time [tt] - A m p li t ude [ A DU ] DUNE:ProtoDUNE-SP
Time [tt] - A m p li t ude [ A DU ] DUNE:ProtoDUNE-SP
Figure 31 : Sample waveforms of single photon signal from a 3-S-SiPM channel (left) and 12-H-MPPC channel (right) (three and twelve sensors passively ganged in parallel, respectively). Noisefiltered waveforms (red histogram) are superimposed, from noise-removal algorithms applied indata processing.Signal extraction and noise evaluation are performed for each recorded waveform. The wave-form consists of 2000 samples of the photosensor output (in ADC units - ADU) at discrete, evenlyspaced points in time ("time ticks" - tt). The sampling interval is 6.67 ns and the duration of thewaveform is 13.3 µ s. The trigger time, either from the global trigger or from the SSP internaltrigger, is at a fixed time relative to the start of the waveform. Typical recorded waveforms areshown in figure 31.The mean of the pedestal distribution, determined from a pre-sample portion of the waveformbefore the trigger, gives the baseline value and the spread ( σ N ) is an estimate of the noise in the– 43 –ecorded event. After baseline subtraction, the charge of the signal (in ADU × tt units, proportionalto number of electrons or fC, where 1 ADU × tt = 2750 electrons) is evaluated by integrationof the portion of the waveform starting from the trigger time and extending over a 7 µ s (1050tt) time window. De-noising algorithms that preserve the signal rise time and integral [50] areapplied to more precisely evaluate the maximum amplitude in the same window corresponding tothe photoelectron current of the signal (in ADU or µ A).During detector assembly, the photosensors for each of the three- and twelve-unit channels werepre-selected based on minimal difference in their nominal breakdown voltage (at warm temperature)from that listed in data sheets. After this selection, the spread in breakdown voltages among thesensors in the same 12-H-MPPC channel is typically ∆ V maxbd ≤
300 mV. All sensors in a channelare biased at the same common voltage V B , and the pre-selection thus enables the photosensors inthe channels to all operate in relatively similar working conditions.Typical charge (signal integral) and current (signal amplitude) distributions under pulsed LEDillumination and with a nominal operating V B setting are shown in figure 32 for a 12-H-MPPCchannel (top row) and for a 3-S-SiPM channel (bottom row). tt] · Charge [ADU E n t r i e s / b i n DUNE:ProtoDUNE-SP
Max Amplitude [ADU] E n t r i e s / b i n DUNE:ProtoDUNE-SP · tt] · Charge [ADU E n t r i e s / b i n DUNE:ProtoDUNE-SP
Max Amplitude [ADU] E n t r i e s / b i n DUNE:ProtoDUNE-SP
Figure 32 : Charge (left) and current (right) distribution for typical 12-H-MPPC channel ( V B = V )(top) and 3-S-SiPM channel ( V B = V ) (bottom) under low amplitude pulsed LED illumination.A fit of the charge distributions with a multi-Gaussian function giving peak positions and widths isshown in red.The multi-peak structure corresponds to detection of 0, 1, 2, . . . photoelectron-induced avalanches.The clear peak separation confirms good sensitivity to single PE detection for both the three-sensor– 44 –nd the twelve-sensor channels. The spread around the peaks in the charge spectra (left panels)is partly due to over-voltage difference among sensors in the array. The asymmetric distribu-tions around the peaks in the signal amplitude spectra (top right panel) may be due to secondaryavalanches from afterpulses and crosstalk in the sensors more visible in the H-MPPC due to thefaster recharge time. The 1-PE charge directly measures the overall gain of the photosensor arrayat the applied bias voltage. The gain g i thus provides the charge per avalanche issued by the i − thchannel.The gain as a function of bias voltage is shown in figure 33 for some of the 12-H-MPPCchannels and 3-S-SiPM channels. The gain response to varying V B is very uniform channel bychannel, as indicated by the slopes of the lines in figure 33. On the other hand, the intercept withthe horizontal axis, that defines the actual breakdown voltage V bd of the multi-sensor channel atLAr temperature, shows a relatively large spread, particularly for the 12-H-MPPC channels.At the reference bias setting adopted for PDS operation ( V B =
48 V for the 12-H-MPPC, V B =
26 V for the 3-S-SiPM - in either case within the range suggested by the different manufacturers)the two types of photosensors are operated at different over-voltages V oV = ( V B − V bd ) in the range(3.3 - 4.2) V for the 12-H-MPPC channels and (5.0 - 5.5) V for the 3-S-SiPM channels. Gains arecorrespondingly higher, by a factor ∼
2, for the 3-S-SiPM channels as indicated in figure 33.
44 46 48 50
Bias voltage [V] ] · G a i n [ DUNE:ProtoDUNE-SP
20 22 24 26
Bias voltage [V] ] × G a i n [ DUNE:ProtoDUNE SP
Figure 33 : Gain as a function of applied bias voltage for 12-H-MPPC channels (left), and for3-S-SiPM channels (right). Linearity of individual channel response is shown by the linear fit(red line) across the points at different bias voltage setting. The intercept of the fit line provides adirect evaluation of the breakdown voltage at LAr temperature for each 12-H-MPPC and 3-S-SiPMphotosensor.
The signal-to-noise ratio (SNR) is a good performance metric for the characterization of thephotosensor component of the PDS during normal operating conditions. The SNR of the individualchannel (three or twelve photosensors in parallel) at the reference bias voltage setting is here definedas: SNR = µ σ (5.1)– 45 –here, referring for example to figure 32 (left panels), the signal µ is the mean value from theGaussian fit of the one-PE peak (the minimal detectable signal), and the noise is evaluated from theGaussian spread, σ of the zero-PE peak. The SNR for all channels of the three types are shownin figure 34. For the 12-H-MPPC channels of the ARAPUCA modules, the SNR values are around6, while for the 3-S-SiPM channels of the double-shift and dip-coated bar modules the SNR is inthe range 10 to 12. The signal-to-noise ratio, as defined in equation 5.1, is directly proportional tothe gain and the higher SNR shown by the 3-S-SiPM channels is primarily due to their higher V oV setting adopted for operation. Channel S NR DUNE:ProtoDUNE SP
Channel S NR DUNE:ProtoDUNE-SP
Channel S NR DUNE:ProtoDUNE-SP
Figure 34 : Signal-to-Noise Ratio (SNR) for the 3-S-SiPM channels (left), for the 3-H-MPPCchannels (center), and for the 12-H-MPPC channels (right).
Calibration of the light response is necessary to convert the charge signal from the photosensorsinto the corresponding number of photons detected. The detector calibration LED pulser is flashedsynchronously with the data acquisition. Calibration runs at varying intensities of the flasher areneeded in order to produce a suitable illumination in each PDS element. For each channel, thedigitized waveform recorded in coincidence with the short LED pulse is baseline subtracted andthe photosensor charge output is measured by waveform integration over a predefined time window(see section 5.1). Typical charge distributions with multi-peak structure from a LED calibrationrun are shown in figure 32 (left panels, top for a 12-H-MPPC channel and bottom for a 3-S-SiPMchannel).Among the possible different calibration methods, the one adopted here relies on the statisticalfeatures of photon counting measurements under stable pulsed, low illumination conditions. Thenumber of detected photons ( n ) per light flash follows the Poisson distribution with λ , the expectedmean number of photons detected per flash, whose value is directly related to the probability ofdetecting 0-photons in that flash: P ( n ) = λ n e − λ n ! with P ( ) = e − λ → λ = − ln P ( ) (5.2)The probability P ( ) can be estimated by the relative frequency of detecting zero photoelectrons inmany LED trials, and from this the mean number of photons detected per flash is inferred: λ = − ln (cid:18) N N Tot (cid:19) (5.3)– 46 –here N is the observed number of counts under the zero-PE peak (1st peak in the charge distributionof figure 32 - left panels) and N Tot is the number of LED flashes in the calibration run.The mean number of photons per flash, λ , depends on the illumination level (LED flashamplitude). The illumination is maintained constant during the run and low enough to havesufficient probability of 0-photon detected. In addition to this, the measured rate is corrected for theaccidental background rate of environmental photons, which are not correlated to the LED flash,that may be detected in the trigger window. The background rate is measured in the portion of therecorded waveforms before the LED trigger. [V] oV V ] · C ha r ge [ e / ndf c – - p1 0.01547 – c – - p1 0.01547 – / ndf c – – – c – – – GainCalibration factor 12-H-MPPC
DUNE:ProtoDUNE-SP [V] oV V ] · C ha r ge [ e / ndf c – - p1 0.0388 – c – - p1 0.0388 – / ndf c – – – c – – – GainCalibration factor 3-S-SiPM
DUNE:ProtoDUNE-SP
Figure 35 : Charge signal per detected photon (blue points) and charge signal per avalanche (blackpoints) as a function of applied over-voltage V oV for a typical 12-H-MPPC channel (left), and 3-S-SiPM channel (right). The difference is due to the correlated noise contribution, mainly fromafterpulse and crosstalk in neighboring microcells of the photosensor. The vertical dotted line atthe operation over-voltage set point indicates the gain g i and the calibration factor c i used in dataanalysis for the i -th channel shown in the figure.The photosensor response to λ detected photons, estimated by equation (5.3), is the meancharge output per flash (cid:104) Q (cid:105) in the calibration run (average of the distribution shown in figure 32- left panels). The calibration factor for the i- t h PDS channel is thus determined by the ratio c i = (cid:104) Q (cid:105) i / λ i and represents the output charge per photon detected by the individual photosensorchannel.The charge issued when an incident photon is detected is expected to be, on average, larger thanthe single-avalanche induced charge. The comparison of the charge per photon detected (calibrationfactor c i - blue line) and charge per avalanche (gain g i - red line) as a function of the applied over-voltage V oV is shown in figure 35 for a typical 12-H-MPPC channel (left), and 3-S-SiPM channel(right). The difference is due to the correlated noise contribution to the signal formation in thephotosensor. This is found to grow exponentially with increasing voltage. A common feature of Si-photosensors is the generation of avalanche pulses subsequent to a primaryevent. The avalanche of a single microcell in a device has a finite probability of inducing an– 47 –valanche in neighboring microcells (optical crosstalk), or/and of re-triggering itself before themicrocell is fully recovered (afterpulse).
Channel < A v a l an c he > / P h DUNE:ProtoDUNE SP
Channel / P h æ A v a l an c he Æ DUNE:ProtoDUNE-SP
Channel / P h æ A v a l an c he Æ DUNE:ProtoDUNE-SP
Figure 36 : Afterpulse and crosstalk contribution to the photosensor signal expressed by the averagenumber of avalanches generated per detected photon for the 3-S-SiPM channels (left), for the 3-H-MPPC channels (center), and for the 12-H-MPPC channels (right).The rate of these secondary pulses increases at higher gain settings. The correlated noisedue to these effects is a well-known limiting factor for a precise photon counting with siliconphotosensors. The measurement of the charge per photon detected (the calibration factor c i , definedin section 5.2.3) and the charge per avalanche (the gain g i , defined in section 5.2.1) allows thecalculation of the crosstalk and afterpulse probability for each photosensor by the ratio c i / g i inunits of [Ava/Ph], average number of avalanches per photon detected. This ratio is sensitive tothe over-voltage on the photon detector and can be used to monitor for changes in the operatingcharacteristics of the photosensor as a measure of the stability of the PD system. Figure 36 shows themeasured avalanche/photon value for each channel in the PDS. An average of ∼ ∼ The sensor gain, the calibration factor and the size of the afterpulse and crosstalk component of thesignal can be used as a system monitor. Any drift in these parameters is an indication of instabilityin the system. The calibration data taken at various times during operation provide measurementsthat indicate the system stability as a function of time. Figure 37 shows the value of the gain fortypical 12-H-MPPC channels and 3-S-SiPM channels over the course of several months. Withinthe uncertainties of the measurements, neither the gain nor the other parameters were found to bedrifting over time for any of the sensors used in the ProtoDUNE-SP photon detector system.
The PD modules (ARAPUCA and light-guide bars) are exposed to scintillation light from ionizationevents in the drift volume. A fraction of the emitted photons impinge upon the optical surface of anygiven PDS module, and a charge signal is issued, proportional to the number of photons detectedby the photosensors of the module. The detection efficiency (cid:15) D of a PDS module is defined here asthe ratio of detected photons to impinging photons. Test-beam data from particles of known type,– 48 –
20 40 60 80 100
Time [days] ] × G a i n [ e DUNE:ProtoDUNE SP
12 H MPPC
Time [days] ] × G a i n [ e DUNE:ProtoDUNE SP
Figure 37 : Stability of the photo-sensor response over time: gain stability (charge signal peravalanche) for typical 12-H-MPPC channel (left) and 3-S-SiPM channel (right), from calibrationruns performed over ∼
100 days of operation. The shaded band corresponds to a ±
5% gain interval.Gain variations over time are contained well within the band. Statistical error bars are small, notvisible inside the symbolenergy and incident direction in the LAr volume are used to determine (cid:15) D and thus evaluate theperformance of the different detection technologies implemented in the PDS.For each beam event, the number of detected photons N Det j is evaluated from offline datareconstruction (baseline subtraction, waveform integration and charge-to-photon conversion) foreach of the 29 light-guide bars and for each of the 12 cells of the ARAPUCA module in thebeam side of the PDS. A Monte-Carlo simulation of test beam events is used for extracting thecorresponding number of photons incident N Inc j on each PDS element (light-guide module or cell inthe ARAPUCA module). This simulation is performed with the LArSoft toolkit [51], which has adetailed description of the geometry of the ProtoDUNE-SP detector, including a proper descriptionof the materials and the positions of the TPC components surrounding the LAr volume (APA, CPA,FC, as shown in figure 38).Simulation of beam events is performed with standard Geant4/LArG4 generator within LAr-Soft. This accounts for the well known features of scintillation in liquid argon ensuing ionizationprocesses. Photon emission : The emission spectrum is a narrow band in the Vacuum-UV (VUV) wave-length range peaking around λ =
128 nm (FWHM (cid:39) ∼ ∼ . − . µ s, with intensity ratio 0 . . × pho-tons/MeV for minimum ionizing particles is assumed in simulations, 60% of the maximum yieldmeasured at zero field. The relative uncertainty on the photon yield value is 8.5% [53]. The photonyield dependence on increasing linear energy transfer, the rate of energy deposited by ionizingparticles, is not included in the current simulation. Photon propagation : LAr is transparent to its own scintillation light. However, during prop-agation through LAr, VUV photons may undergo Rayleigh scattering, absorption from residualphoto-sensitive impurities diluted in LAr and reflections at the boundary surfaces that delimit the– 49 – R A P U C A d i p - c o a t e d L G d o u b l e - s h i f t L G A P A A P A A P A C e n t r a l C a t h o d e (Muon) Beam Entry Point Figure 38 : 3D event display made with the Wire-Cell BEE display [52] showing data from a7 GeV/ c beam muon crossing the whole TPC volume, fully reconstructed by the LArTPC. Onlytracks inside a predefined sub-volume (red box) are shown. The beam muon track enters near thecathode and propagates along the beam direction about 10 ◦ downward and 11 ◦ toward the anodeplane. Ten bars are located in each APA frame. The dip-coated bars are indicated in blue, thedouble-shift bars in green and the segmented ARAPUCA bar in orange.LAr volume. In the MC simulation, the Rayleigh scattering length, the reflectivities of materials forVUV photons, and the absorption length as a function of the impurity concentration are parametersthat are fixed at their best estimates from existing data. Tracking each of the large number of VUVphotons emitted in an event using Geant4 is computationally expensive, so a pre-computed opticallibrary is used to look up the probability that a photon produced at a particular location in the liquidargon volume is detected by a specific PDS channel. In order to create the optical library, the liquidargon volume is segmented into small sub-volumes (voxels) of size ∼ × × . For eachvoxel, a large number (of order 5 × ) of VUV photons is sampled with an isotropic angulardistribution. All photons are tracked using Geant4, recording how many reach the sensitive areaof each optical detector. In the simulation used to create the optical library, the Rayleigh scatteringlength for VUV photons in liquid argon is assumed to be 90 cm, according to the most recentexperimental determination [54, 55]. Due to the high level of purity during the beam run (Oxygenequivalent impurity concentration < 100 ppt), absorption by impurities is assumed to be negligible.Light reflection at VUV wavelength is low for perfectly polished metal surfaces (20% or less) andeffectively null for any other material. The actual reflectance of the (extruded, non polished) Al– 50 –rofiles of the field cage surrounding the LAr drift volume is unknown and therefore it is set to zeroin the current simulations. Once the library is created, ProtoDUNE-SP detector simulation jobsretrieve information from the library when the trajectory of a ionizing particle in the LAr volumeis simulated by Geant4, converting the number of emitted photons from energy deposited in eachvoxel directly into the number of photons impinging upon the area of each PD module coming fromthis given voxel. The uncertainty on the rate at which photons arrive at the detector after photontransport is dominated by the uncertainty on the Rayleigh scattering length. Neglecting reflectionsat the LAr volume boundaries is expected to be a subdominant effect. A relative uncertainty of5% is assigned to the number of photons incident on the detector surface by varying the Rayleighlength by 20% around the nominal value in the simulation. The uncertainty on the possible biasdue to the photon library parameterization is not included in the total uncertainty. Figure 39 : Schematic diagram illustrating photons impinging on a TPC wire plane (left) where thewire pitch p , the wire gauge d , and the incident photon angle ( γ ) are defined. On the right, the mapof transmission – color scale from 0 to 1 – through the set of parallel planes (TPC wire planes andthe mesh) as a function of the polar angles θ, φ of the incident photon direction (the planes lie onthe ( y , z ) plane, θ = γ when φ = ± π / Photon transmission at the anode plane : The optical surfaces of the PD modules lie immediatelybehind the four wire planes of the TPC and a fifth (grounding) plane made by the woven metallicmesh stretched across the APA frame (see section 2.2). A correction is applied to account for the lighttransmission through this series of parallel planes, which is not included in the detector simulationin LArSoft. The geometrical transparency of the mesh (percentage ratio of opening to total area,function of wire gauge and pitch) is 85% The transparency is reduced to 75% when the TPC wireplanes above the mesh plane are also considered. This corresponds to the transmission upper valuefor orthogonal incident light. Transmission at any angle is then obtained based on a geometricalmodel , as illustrated in figure 39. A stand-alone simplified MC simulation is then performedto evaluate the transmission of light from beam events. Optical photon emission is sampledover straight trajectories crossing the LAr volume along the beam direction, nearly representing A simplified geometrical model is used in which VUV photons intercepting a wire of the mesh are absorbed(no reflection). The transmission coefficient shows a dependence on the polar angle of incidence ( θ ) almost flat withT=0.75-0.7 for photons incoming with θ < ◦ and then decreasing above that angle. Only a small modulation in theazimuthal angle φ is expected across the whole range due to the geometrical orientation of the wires and mesh planes( φ = ± . ◦ , ◦ , ◦ ) and the gauge per pitch ratios (d / p). – 51 –eam muon tracks, or sampled according to a spatial parametrization of electromagnetic showers,representing the longitudinal and transverse energy deposition from incident beam electrons. Afterphoton propagation to the APAs, the angular distribution of incident photons on each PD moduleis folded with the transmission map to obtain the transmission coefficients for beam muons andbeam electrons. These were found in the 65-71% range, mildly depending on the position of the PDmodule. The relative uncertainty on the transmission coefficients is evaluated to be 7% (one-sided)to account for the simplified assumptions in the model (no reflection). The transmission coefficientsfor each module so determined are then used to scale down the number of photons arriving at theAPA from the Geant4 MC simulation of the beam events into the actual number of photons N Inc j incident on the surface of the PD module behind the APA. Runs with beam momentum settings from 2 to 7 GeV/ c are considered for the efficiency study.The muon and electron samples for the runs at different momenta are selected using the PIDinformation from the beam instrumentation and the recorded light signals passing quality cuts arefully reconstructed ( O (10k events/sample) for each run). Correspondingly, Monte Carlo runs weregenerated with muons or electrons entering the TPC volume from the beam-plug with the samemomentum (nominal value and spread) and direction to reproduce the features of the H4-VLE beamline (see section 3). The MC samples were generated with the same number of triggers as werecollected in the corresponding data samples. For each run the MC distribution of the number ofphotons in the event impinging upon the j -th PDS element (light-guide module or ARAPUCAcell) and the distribution from real data sample of photons detected by the same module/cell areextracted. Muon data : for each of the 12 cells of the ARAPUCA module located in APA 3 of the PDSbeam side, the mean value (cid:104) N Det j (cid:105) of the detected photon distribution from the muon data sampleswith beam momenta of 2, 3, 6 and 7 GeV/ c (open circles of assigned color) are displayed in figure40 (top-left), the mean values (cid:104) N Inc j (cid:105) of the photons incident on the cell surface from the MC muonevent samples is shown in the (center-left) panel. Statistical errors are small (few per-mille relativeto the mean values, not visible inside the symbols), systematic uncertainties not shown in the figureare discussed later in this section. The detection efficiency (cid:15) j = (cid:104) N Det j (cid:105)/(cid:104) N Inc j (cid:105) given by the ratio ofthe two mean values in the (bottom-left) panel, for each cell at all momenta. Cells in the ARAPUCAmodule corresponding to channels j = , . . . ,
12 are ordered along the z axis with the upstreamcell z =
0) into the LArTPC volume. Muons at all incident momenta areenergetic enough to cross the entire LAr volume and exit from the downstream side (see figure 38).The number of detected photons increases from cell to cell along z due to the increasing visibilityof the muon track from the cells deeper into the LAr volume. In every cell the number of detectedphotons is observed to increase with incident muon beam momentum (open circles of differentcolor in figure 40) due to the increase in the energy loss along the track for more energetic muons.Cells in the ARAPUCA module are of two types: the last four cells (channels j = , . . . ,
12) havedouble size but equal number of photosensors than the first eight. The high step in the number ofcollected photons N Inc j at j = N Det j (top). This is due to the halved– 52 – Cell æ D e t N Æ Muons2.0 GeV/c3.0 GeV/c6.0 GeV/c7.0 GeV/c
DUNE:ProtoDUNE-SP
ARAPUCA
Cell æ D e t N Æ Electrons2.0 GeV/c3.0 GeV/c6.0 GeV/c7.0 GeV/c
DUNE:ProtoDUNE-SP
ARAPUCA
Cell æ I n c N Æ Muons2.0 GeV/c3.0 GeV/c6.0 GeV/c7.0 GeV/c
DUNE:ProtoDUNE-SP
ARAPUCA
Cell æ I n c N Æ Electrons2.0 GeV/c3.0 GeV/c6.0 GeV/c7.0 GeV/c
DUNE:ProtoDUNE-SP
ARAPUCA
Cell E ff i c i en cy [ % ] Muons2.0 GeV/c3.0 GeV/c6.0 GeV/c7.0 GeV/c
DUNE:ProtoDUNE-SP
ARAPUCA
Cell E ff i c i en cy [ % ] Electrons2.0 GeV/c3.0 GeV/c6.0 GeV/c7.0 GeV/c
DUNE:ProtoDUNE-SP
ARAPUCA
Figure 40 : ARAPUCA cell efficiency as determined from beam muons (left) and beam electrons(right). Cells are of two types with the last four cells (channels j = , . . . ,
12) have double sizebut equal number of photosensors than the first eight. Top: average number of detected photonswith beams at different momenta. Center: average number of photons incident on the cell surfacefrom MC simulation of electron and muon beams at corresponding momenta. Bottom: efficiencyof the cell from the detected-to-incident ratio. Statistical error bars are small, not visible inside thesymbols. – 53 –hotocathode coverage partly mitigated by the light trapping in the ARAPUCA cell.For each light-guide module in the PDS beam side the number of detected photons and incidentphotons were evaluated in the same manner from the same beam muon samples (data and MC runswith beam momenta of 2, 3, 6 and 7 GeV/ c ). The efficiency from the ratio of the detected to incidentphotons is shown in figure 41 (left) for the 15 double-shift light-guide modules in APA3, 2 and 1and in figure 42 (left) for the 14 dip-coated light-guide modules. Statistical error bars are small,not visible inside the symbols. The locations of APAs 1, 2, and 3 in the ProtoDUNE-SP detectorare shown in figure 38. The efficiency of light-guide modules is expected to be proportional to thenumber of suitably placed photosensors, and inversely correlated with the length of the opticallyactive surface of a module with fixed width due to the attenuation of internally reflected opticalphotons. As a crude characterization, the smallness of the ratio of number of photosensors tooptically active surface area of the light-guide modules relative to the ARAPUCA cells underliesthe corresponding ratios of efficiencies. A factor of two can be gained by instrumenting both endsof the light guides, but improvement beyond that would require modification to the module designto enable effective deployment of additional photosensors. Module E ff i c i en cy [ % ] Muons3GeV/c6GeV/c7GeV/c
DUNE:ProtoDUNE SP
Double Shift Light Guide
Module E ff i c i en cy [ % ] Electrons3GeV/c6GeV/c7GeV/c
DUNE:ProtoDUNE SP
Double Shift Light Guide
Figure 41 : Efficiency measurements of 15 double-shift light-guide modules (PDS beam side), asdetermined from beam muons (left) and beam electrons (right) data at different momenta (onlymodules j = , ..,
10 in APA3, and 2 at the shorter distance from the shower and higher photoncounting are displayed).
Electron data : electrons with beam momenta of 2, 3, 6 and 7 GeV/ c provide data samples fora second independent set of efficiency measurements. Electrons deposit all their incident energyin showers localized in a limited portion of the LAr volume, unlike muons on long, throughgoingtracks. Electromagnetic showers develop in front of APA3, where the ARAPUCA module ispositioned nearly at the height of the entering beam (see figure 67 with a 3D display of 7 GeV/ c beam electron event). Light detected in the ARAPUCA cells, the MC estimate of the light arrivingon the cells’ optical surfaces and the corresponding detector efficiency are shown in the right-panelsof figure 40 (top), (center) and (bottom) respectively. For any given beam energy (open circlecolors in the plots), the distribution of the detected photons by the cells along the bar exhibitsa shower-like longitudinal profile, an indication of the position reconstruction capability of thesegmented ARAPUCA module. The number of detected photons is also clearly correlated with the– 54 –
10 15
Module E ff i c i en cy [ % ] Muons3GeV/c6GeV/c7GeV/c
DUNE:ProtoDUNE SP
Dip Coated Light Guide
Module E ff i c i en cy [ % ] Electrons3GeV/c6GeV/c7GeV/c
DUNE:ProtoDUNE SP
Dip Coated Light Guide
Figure 42 : Efficiency measurements of 14 dip-coated light-guide modules (PDS beam side), asdetermined from beam muons (left) and beam electrons (right) data at different momenta (onlymodules j = , .., c , excluding electron data at2 GeV/ c with the modules in APA1 at the farthest distance from the shower and low photoncounting. Results are shown in figure 41 (right) for the ten double-shift light-guide modules inAPA3 and APA2 and in figure 42 (right) for the nine dip-coated light-guide modules. Efficiency : the photon detection efficiency was evaluated through 8 independent measurementsusing muon data and electron data at four different beam momenta, for each element of the PDS (12cells in one ARAPUCA module, 15 double-shift light-guide modules and 14 dip-coated light-guidemodules of the PDS beam side). By comparing the results, efficiency estimated from the electrondata is found in all elements systematically higher than from the muon data, regardless of the energyof the particle [see figures 40 (bottom), 41 and 42]. The systematic difference may be due to biasin the MC simulation at the photon emission stage (e.g., an unaccounted deviation in scintillationyield for GeV-scale electrons and muons with respect to minimum-ionizing particles) and at thepropagation stage (e.g., a difference due to the computational method used to approximate thenumber of photons reaching the PD optical window from localized volumes (EM showers) andlong tracks (muons)). The mean value from all available measurements (cid:104) (cid:15) j (cid:105) for the j -th elementis taken as the best estimate of the efficiency of that element, and the standard deviation s j thatmeasures the dispersion around the mean is taken as an estimate of the systematic uncertaintyon the efficiency. The statistical uncertainty, evaluated from the standard errors of the meannumbers of detected and incident photons in the data and MC samples of muons and electrons atdifferent energies, is negligible. Comparing modules or cells of the same type, relative variationsin efficiency are within ±
6% for the ARAPUCA cells, ±
20% for the double-shift modules andgreater than ±
25% for the dip-coated modules. The median efficiency value with its statistical andsystematic uncertainty from each group of detectors ( ˜ (cid:15) A , ˜ (cid:15) A , ˜ (cid:15) DS , ˜ (cid:15) DC ) is selected to characterize– 55 – able 4 : Efficiencies of the detector technologies in the ProtoDUNE-SP PD system: median valueamong detectors of the same type, determined from the average of independent measurements withbeam muons and electrons at different energies. The error is from systematic uncertainty, withnegligible statistical uncertainty. The number of detectors of different types examined correspondto the fraction of PDS elements in the beam side upstream APAs ( N o of PDS elements examined Detector Type Efficiency (cid:15) A = ( . ± . ) %4 ARAPUCA cell (double area) ˜ (cid:15) A = ( . ± . ) %10 Double-shift module ˜ (cid:15) DS = ( . ± . ) %9 Dip-coated module ˜ (cid:15) DC = ( . ± . ) %the different technologies implemented in the ProtoDUNE-SP PD system. These value are reportedin table 4. The overall relative uncertainty on the efficiency for all detector types is thus found tobe 8 . (cid:46) σ (cid:15) / (cid:15) (cid:46) . The analysis of photon signals from cosmic-ray muons provides a check on the validity of thephoton simulation used to determine efficiency. Throughgoing cosmic-ray muons, or those whichenter through the upstream face and exit through the downstream face of the cryostat, are isolatedin the scintillation medium using CRT triggered events that have been matched to reconstructedtracks in the TPC. A trigger from this sub-detector involves a four-fold coincidence of the x - and y -measuring planes of the CRT over the span of 60 ns producing events that are very likely to containa throughgoing cosmic-ray muon. Two strategies were employed, both using an event definitiondescribed by the sum of all detected photons in the twelve cells of the non-beam side ARAPUCAmodule during a 13.33 µ s externally tagged PDS trigger. Before either analysis, the sum of photonsper event collected at the detector surface in the simulation were scaled by a fixed amount in orderto eliminate a systematic normalization difference with data.The first analysis, a comparison of the average light response as a function of transversedistance, was employed to observe some of the bulk effects of the medium. Here, the transversedistance is defined as the length of the segment between the reconstructed particle path and thecenter point of the ARAPUCA module as they occupy the same position along the z axis. Sincethe ARAPUCA module is in the central APA (APA6), this variable is exclusively a function of theparticle position in x and y as it bisects the detector in the z direction, exploiting the symmetryof the detector. The results of this analysis, shown in the left plot of figure 43, suggest that themeasured data favor simulation with Rayleigh scattering length hypothesis of 90 cm as opposedto a Rayleigh scattering length of 60 cm in the scintillation medium. A second analysis compareseach data track to a simulated track generated with matching position and trajectory in a mediumwith Rayleigh scattering length set at 90 cm. Results, which are shown in the right-hand plot of– 56 – .8 0.9 1.0 1.1 1.2 Data/Simulation T r an sv e r s e D i s t an c e [ c m ] =60cm R L =90cm R L DUNE:ProtoDUNE-SP
Cosmics T r an sv e r s e D i s t an c e [ c m ] Data/Simulation E v en t s DUNE:ProtoDUNE-SP
Cosmics
Figure 43 : The left-hand panel shows the ratio of the observed to the predicted PE yields as afunction of transverse distance, assuming two different values of the Rayleigh scattering length inthe simulation. The data agree more with simulation that employs a Rayleigh scattering length (L R )of 90 cm as opposed to the 60 cm prediction. On the right, an event-by-event comparison is shownfor roughly 43,000 events taken over four months with a simulated Rayleigh scattering length of90 cm. The dashed vertical lines in both plots represents where data and simulation agree. Thesolid vertical lines in the right-hand plot represent the bounds of the plot on the left.figure 43, demonstrate excellent agreement with simulation as a function of the transverse distancefrom the ARAPUCA to the reconstructed track. In comparisons of data to simulation, one standarddeviation in the difference between the reconstructed simulated light and the measured reconstructedlight is about 18.6%. These two comparisons show that photons are successfully reconstructed inProtoDUNE-SP and that the simulation of the optical properties of scintillation medium is in a goodagreement with the measurements. The timing performance of the PD system intrinsically depends upon a combination of factors, fromthe intrinsic time resolution of the photosensor, to the electronics response of the readout boardand signal digitization, to the features of the light propagation, wave-length shifting and photoncollection by the PD modules. The overall timing performance is evaluated here in two differentapplications: time resolution of two consecutive light signals and time matching between lightsignal and TPC signal.Resolving successive light signals in time is of importance in physics reconstruction of cor-related events, such as stopping muon with decay to Michel electron, or kaon decays, or nucleusde-excitation into gammas after neutrino interaction. Some of these correlations may be observedwith light signals in LAr depending on the PD timing performance. To explore this, data were takenwith an external trigger from the LCM (Light Calibration Module) producing two consecutive LED– 57 –ashes with a fixed time difference among them and from the common trigger time. The time dif-ference between the pulses was set in the few µ s range typical of the muon decay at rest, and muchlarger than the pulse width. In figure 44 (left) a recorded waveform from the LCM trigger is shownwith the two generated consecutive LED pulses as detected by an ARAPUCA cell/12-M-MPPCchannel and digitized by the SSP readout board. The rise time of each of the two signals from thecommon trigger was measured in the events collected with the LCM trigger and the distribution oftheir time difference ∆ t = ( t − t ) is shown in figure 44 (right). The time resolution to observe twoseparate light pulses is σ ∆ t (cid:39)
14 ns. Time jitter in the LCM pulse formation is small (sub-ns range)and the dominant factor is from digitization (6.67 ns sampling period). s] m Time [ A DC C oun t/ T i ck DUNE:ProtoDUNE-SP
Constant 7.5 – – – s] m t [ D E v en t s / T i ck Constant 7.5 – – – DUNE:ProtoDUNE-SP
Figure 44 : PDS timing measurements: Double pulse light signal using the photon detector calibra-tion system (left); Resolution in the time difference measurement between correlated light signals(right). - - - s) m track time ( t - - - s ) m P ho t on de t e c t o r t r a ck t i m e ( DUNE:ProtoDUNE-SP flash-matched trackstracks not matched to flash
Figure 45 : PDS Timing Measurements: Correlation between the TPC track t time and the PDSflash time. Non-matched tracks (red points) are mostly from CPA-crossing vertical muons (largenegative t ) whose flash was not recorded by the PDS at the opposite end of the drift distance.An efficient light flash-to-track matching in LArTPC is important for a correct event recon-struction, background rejection (especially for LArTPC’s operated on the surface) and low-energyunderground physics. – 58 –n average of approximately 70 cosmic-ray tracks are observed overlaying each beam eventduring TPC readout window. The cosmic-ray tracks arrive at random times relative to the beamtrigger. For some of these tracks, such as those that cross the cathode plane or cross one of theanode planes, their actual time of entering the LArTPC volume ( t time) can be reconstructed offlinefrom the TPC data by using the 3D track reconstruction algorithms and the geometrical features ofthese CPA or APA crossing tracks. With the ProtoDUNE-SP TPC at its nominal electric field of500 V/cm, the full drift time is 2.2 ms. Additional 2.5 ms (0.3 ms) are also recorded before (after)the drift time period, for a total of 5 ms TPC readout window per recorded event. Cathode or anodecrossing muons have t time distributed over the entire recorded window and reconstructed with aprecision of about 20 µ s.PDS detected light flashes corresponding to the t -determined TPC tracks are efficiently foundin the packets received from the SSP and contained in the PD fragment of the event. Matchingis performed in time inside the TPC readout range, looking for the closest flash timestamp to the t time of the track, within a given coincidence window. In the case of CPA/APA-crossing tracksthe matching efficiency depends on the SSP discriminator threshold for the packet recording andto the width of the coincidence window. An example of flash-to-track matching is given in figure45, showing the bisector correlation of PDS flash time and TPC track t time, for a 4500 APA/CPAcrossing track sample. Tracks not matched to a flash are mostly the shorter CPA-crossing trackswhose flash was below threshold. The high-quality ProtoDUNE-SP data will be used to measure particle-argon interaction crosssections. Figure 46 shows various candidate events in the collection plane from ProtoDUNE-SPdata after the noise mitigation described in section 4.1 and electronics gain calibration described insection 4.2. The color scale is in the unit of 1000 ionization electrons (ke).It is essential to understand the charge response of wires in a LArTPC for calorimetry. Inorder to measure the particle energy loss per unit length ( dE / dx ), it is important to correct fornonuniformities in the detector response and determine the energy scale to convert charge to energy.This section describes the procedure and results of the calibration of the charge and energy loss perunit length of the ProtoDUNE-SP LArTPC. Section 6.1 discusses the calibration of space chargeeffects caused by the ion accumulation in the TPC. Section 6.2 describes the measurement of driftelectron lifetime using TPC tracks and CRT information. Section 6.3 discusses the procedure tocorrect for remaining nonuniformities in the detector response and determine the energy scale.Section 6.4 shows the calibrated energy loss per unit length of different beam particles, including 1GeV/ c protons, muons, pions and electrons. As a detector located on the surface, ProtoDUNE-SP experiences a large flux of cosmic rays thatresults in a substantial amount of ionization produced in the detector per unit time. Along with theproduction of ionization electrons, argon ions are also produced in the detector by these cosmicrays. Because argon ions have drift velocities on the order of several millimeters per second at– 59 – T i c k
20 cmDUNE:ProtoDUNE-SP Run 5826 Event 83959 2.50.02.55.07.510.0 C h a r g e / t i c k / c h a nn e l ( k e ) (a) A 0.5 GeV/ c electron candidate. T i c k
50 cmDUNE:ProtoDUNE-SP Run 5770 Event 59001 2.50.02.55.07.510.0 C h a r g e / t i c k / c h a nn e l ( k e ) (b) A 6 GeV/ c electron candidate. T i c k
30 cmDUNE:ProtoDUNE-SP Run 5458 Event 21389 20246810 C h a r g e / t i c k / c h a nn e l ( k e ) (c) A 1 GeV/ c pion candidate. T i c k
50 cmDUNE:ProtoDUNE-SP Run 5772 Event 15132 20246810 C h a r g e / t i c k / c h a nn e l ( k e ) (d) A 6 GeV/ c pion candidate.
60 80 100 120 140 160 180 200Wire Number450046004700480049005000 T i c k
20 cmDUNE:ProtoDUNE-SP Run 5387 Event 89663 2.50.02.55.07.510.0 C h a r g e / t i c k / c h a nn e l ( k e ) (e) A 1 GeV/ c stopping proton candidate. T i c k
50 cmDUNE:ProtoDUNE-SP Run 5779 Event 12360 2.50.02.55.07.510.0 C h a r g e / t i c k / c h a nn e l ( k e ) (f) A 2 GeV/ c pion charge exchange candidate. T i c k
30 cmDUNE:ProtoDUNE-SP Run 5770 Event 29859 20246810 C h a r g e / t i c k / c h a nn e l ( k e ) (g) A 6 GeV/ c kaon candidate. T i c k
140 cmDUNE:ProtoDUNE-SP Run 5770 Event 50648 20246810 C h a r g e / t i c k / c h a nn e l ( k e ) (h) Large cosmic air shower candidate producingmany parallel muons. Figure 46 : Various candidate events from ProtoDUNE-SP data, with beam particles entering fromthe left. The x axis shows the wire number. The y axis shows the time tick in the unit of 0.5 µ s.The color scale represents the charge deposition. The beam particle starts approximately at wirenumber 90 and tick number 4800. – 60 –
500 V / cm in liquid argon, 2–4 × times slower than ionization electrons at the same electricfield, a considerable amount of positive space charge is expected in the detector as these argonions build up on a timescale of roughly ten minutes. A steady flux of cosmic rays ensures thatthe positive ions are constantly replenished in the detector, leading to persistent distortions of theelectric field in the TPC.These electric field distortions alter ionization electron drift paths in the detector, leading toionization charge being reconstructed at different positions in the detector than where the chargeoriginated from. The electric field distortions also impact the amount of prompt electron-ionrecombination experienced at the points of energy deposition in the detector. Both of these effectscan bias reconstructed particle energies and trajectories, and by modifying reconstructed dE / dx along a particle track (or at the beginning of an electromagnetic shower), can lead to complicationsin particle identification in a LArTPC detector. As a result, space charge effects should be carefullycharacterized at any large LArTPC detector operating at or near the surface, and calibrated out whenreconstructing particle trajectories, energies and dE / dx . The observation of transverse ionizationcharge migration during drift in early ProtoDUNE-SP data-taking, shown in figure 47, is consistentwith a large positive space charge density in the center of the TPC pulling ionization charge inwardtoward the middle of the detector during drift toward the anode planes. This observation highlightsthe fact that significant space charge effects are present at ProtoDUNE-SP and need to be addressedwhen calibrating the detector. In contrast to the case of ProtoDUNE-SP, space charge effects areexpected to be negligible at the single-phase DUNE far detector due to the very low cosmic-ray ratedeep underground. - - - - [cm] reco X [ c m ] r e c o Y DUNE:ProtoDUNE-SP [cm] reco Z - - - - [ c m ] r e c o X DUNE:ProtoDUNE-SP
Figure 47 : Projections of reconstructed and t -tagged cosmic-ray track end points in the x y plane(left) and zx plane (right) in ProtoDUNE-SP data; the selected tracks are t -tagged by requiringthat they cross the cathode plane ( x = zx plane projection(vertical streaks in the middle of the image).Figure 48 shows the magnitude of spatial offsets at four faces of the ProtoDUNE-SP TPC (top,– 61 –ottom, front or upstream with respect to the beam direction, back or downstream with respect tothe beam direction); specifically, spatial distortions in the direction normal to each detector face areshown. These spatial distortions are estimated using the ends of reconstructed tracks that have been t -tagged in order to know their position in x , the ionization drift coordinate. The transverse spatialdistortion is determined by measuring the distance between the end of the track and the location ofthe detector face in the direction orthogonal to the detector face being considered. As illustratedin figure 48, spatial distortions as large as 40 cm are observed in ProtoDUNE-SP data, largest nearthe faces of the TPC and furthest from the anode planes; the latter observation can be understoodas a result of charge originating further away from the anode experiencing space charge effects fora longer time, yielding a larger impact on drift path in the detector. reco Z - - - r e c o X Y [cm] D Data: Top Face
DUNE:ProtoDUNE-SP reco Z - - - r e c o X - - - - - - - - Y [cm] D Data: Bottom Face
DUNE:ProtoDUNE-SP - - - reco X r e c o Y - - - - - - - - Z [cm] D Data: Upstream Face
DUNE:ProtoDUNE-SP - - - reco X r e c o Y Z [cm] D Data: Downstream Face
DUNE:ProtoDUNE-SP
Figure 48 : Spatial distortions normal to the top detector face (upper left), bottom detector face(upper right), upstream detector face (lower left), and downstream detector face (lower right) inProtoDUNE-SP data. The color axis represents the additive correction (in cm) one must apply tothe start/end point of a track passing through the given detector face in order to correct its positionto the true entry/exit point at the side of the detector.A dedicated simulation of space charge effects was developed for ProtoDUNE-SP, using asoftware package originally developed for MicroBooNE [56, 57]. The analysis shown in figure 48is for data; the same analysis applied to Monte Carlo simulated events is shown in figure 49.– 62 –omparing figures 48 and 49, similar trends are observed in simulation as compared to data,building confidence that the spatial distortions observed in data are indeed a result of space chargeeffects. However, there are also several differences:• the magnitude of spatial distortions in data are generally larger than in simulation, by as muchas a factor of two at some locations in the detector;• there is an asymmetry in the magnitude of the spatial distortions about the cathode ( x = x > dE / dx ,as well as the inability for the dedicated space charge simulation to reproduce the observations seenin data, a data-driven simulation of space charge effects was produced for ProtoDUNE-SP using theresults shown in figures 48 and 49 as a starting point. This data-driven map of space charge effects(both spatial distortions and electric field distortions, each with three components, for a total of sixthree-dimensional maps) can be “inverted” to remove effects of space charge in a calibration stepfor both events in actual data and those produced with this data-driven simulation. The data-drivenspatial distortion and electric field distortion maps are produced using the following steps:• the two-dimensional transverse spatial offset maps at the four detector faces (top, bottom,upstream, and downstream) shown in figures 48 and 49 are used to form a “scale factor”map at each detector face by taking the ratio of the data map to the Monte Carlo map on apixel-by-pixel basis;• the scale factor maps are used to rescale the simulated three-dimensional spatial distortion mapby linearly interpolating the scale factors between the top and bottom detector faces for spatialdistortions in the y direction, linearly interpolating the scale factors between the upstream anddownstream detector faces for spatial distortions in the z direction, and performing the averageof the linear interpolations in these two directions for spatial distortions in the x direction; thevoxel-by-voxel scale factor obtained in this way is used as a multiplicative factor to rescalethe spatial distortion magnitude in the corresponding three-dimensional voxel (a “voxel” hererefers to a volumetric pixel);• the resulting data-driven spatial distortion maps are then inverted in order to obtain “invertedspatial distortion maps” that can be used to calibrate out spatial distortions in reconstructeddata or Monte Carlo events via repositioning reconstructed ionization charge space points inthree dimensions back to their point of original deposition (see more below); and– 63 –
100 200 300 400 500 600 700 reco Z - - - r e c o X Y [cm] D MC: Top Face
DUNE:ProtoDUNE-SP reco Z - - - r e c o X - - - - - - - - Y [cm] D MC: Bottom Face
DUNE:ProtoDUNE-SP - - - reco X r e c o Y - - - - - - - - Z [cm] D MC: Upstream Face
DUNE:ProtoDUNE-SP - - - reco X r e c o Y Z [cm] D MC: Downstream Face
DUNE:ProtoDUNE-SP
Figure 49 : Spatial distortions normal to the top detector face (upper left), bottom detector face(upper right), upstream detector face (lower left), and downstream detector face (lower right) in theoriginal ProtoDUNE-SP Monte Carlo simulation. The color axis represents the additive correction(in cm) one must apply to the start/end point of a track passing through the given detector face inorder to correct its position to the true entry/exit point at the side of the detector.• the gradient of the spatial distortion along the local drift direction, determined using theinverted spatial distortion maps, along with the known ionization electron drift velocity asa function of electric field, are used to obtain the electric field distortion maps (in threedimensions) using a method previously explored at MicroBooNE [58].The result of this procedure is a set of three-dimensional spatial distortion and electric field mapsthat are included in the ProtoDUNE-SP simulation used in the first results showcased in this work.The electric field magnitude variations in a couple of slices of the ProtoDUNE-SP TPC areshown in figure 50, comparing to the prediction of the original space charge effect simulation. It isobserved in figure 50 that the electric field magnitude variations in data are as large as 25% withrespect to the nominal drift electric field; an approximate estimate of the systematic uncertainty onthis number is 5% with respect to the nominal drift electric field magnitude (20% relative uncertaintywith respect to the full systematic effect), driven by the uncertainty in extrapolating spatial offsetsfrom the detector faces into the center of the detector. The simulation utilizes the data-driven spatial– 64 –istortion maps to modify the reconstructed position of ionization charge to better represent dataevents, while the data-driven electric field distortion maps are used to improve the prediction ofcharge yield after electron-ion recombination, as this effect is dependent on the local electric fieldmagnitude. - - - [cm] true X [ c m ] t r ue Y - - = 347 cm true Z [%]: EE/ D DUNE:ProtoDUNE-SP
Data [cm] true Z - - - [ c m ] t r ue X - - = 305 cm true Y [%]: EE/ D DUNE:ProtoDUNE-SP
Data - - - [cm] true X [ c m ] t r ue Y - - = 347 cm true Z [%]: EE/ D DUNE:ProtoDUNE-SP MC [cm] true Z - - - [ c m ] t r ue X - - = 305 cm true Y [%]: EE/ D DUNE:ProtoDUNE-SP MC Figure 50 : Two-dimensional slices of the three-dimensional electric field magnitude distortion mapin both ProtoDUNE-SP data (top row) and the original ProtoDUNE-SP Monte Carlo simulation(bottom row); the local electric field distortion magnitude is shown as a percentage of the nominaldrift electric field magnitude. Shown are slices in the z direction (left column) and y direction (rightcolumn), looking at the center of the detector in both slices.Additionally, a calibration of particle dE / dx was developed for both tracks and the track-like segments at the beginnings of electromagnetic showers measured by ProtoDUNE-SP. In thiscalibration, the spatial distortion map is used to correct for spatial squeezing/stretching of charge,impacting dx , and the electric field distortion map is used to correct for the electric-field-dependenceof electron-ion recombination in the liquid argon, impacting dE . The performance of this firstattempt at a calibration of space charge effects at ProtoDUNE-SP is demonstrated in several of theresults presented in this work in sections 6.3 and 6.4.The time dependence of space charge effects has been observed to be relatively small duringthe period of time that ProtoDUNE-SP was taking beam data, on the order of 5% of the total– 65 –patial distortion magnitude. A detailed study of the time dependence of space charge effects atProtoDUNE-SP will be presented in a future work. The liquid argon of the ProtoDUNE-SP detector contains impurities, such as water and oxygen, thatcan capture the ionized electrons as they drift towards the APA. Although the negative ions formedby the attached electrons still drift to the APA, they drift much more slowly than the unattachedelectrons and contribute negligibly to the signals measured on the APAs. The charge measured bythe APAs then becomes reduced due to the impurities capturing the electrons, lowering and biasingthe amount of charge measured on the wire planes. This effect is modeled as an exponential decayas a function of time: Q ( t ) = Q exp (−( t hit − t )/ τ ) , (6.1)where Q ( t ) is the charge measured on a wire, Q is the initial charge created by the ionization ofthe argon accounting for recombination, t is the time at which the ionization took place, t hit is thetime the drifting charge arrived the APA, and τ is the drift electron lifetime. A larger value of τ corresponds to higher liquid argon purity, as fewer drifting electrons will attach to impurities asthey drift to the APA.Purity monitors located inside the cryostat, but outside the field cage, measure the drift electronlifetimes for the argon inside their drift volumes, and thus are not expected to measure exactly thedrift electron lifetime in the TPC. Furtheremore, the electric field strength in the purity monitors islower than the electric field strength inside the TPC. Since the rate of drift electron attachment toimpurities depends on the electric field strength, the measured lifetimes in the purity monitors areexpected to further differ from that in the TPC. In situ measurements of the drift electron lifetimefrom signals in the TPC therefore are needed in order to calibrate the results of charge-basedanalyses.The drift electron lifetime inside the TPC is measured by fitting the dQ / dx of collection planehits from cosmic-ray tracks as a function of drift times. A sample of cosmic-ray tracks that passthrough the front and back faces of the TPC and the CRT are selected. CRT data are used tocalibrate track positions and to provide timestamps for TPC tracks.The electron lifetime measurement starts by matching a CRT track to a TPC track using thepositions of X and Y from both tracks. The timestamp from the CRT hits serve as the t for the TPCtrack. The dQ / dx of a hit is defined to be the hit charge ( Q ) obtained from the area of a Gaussianfit to the deconvolved signal divided by the step length from the previous collection plane hit to thecurrent collection plane hit.To avoid the space charge effect distortions on the location of a TPC hit, the CRT track isused to determine the hit’s position in X and Y as a function of Z, which is found through whichcollection plane wire the hit occurred. The track’s position is then fed into the electric fieldcalibration map from figure 50. This field map corrects the dQ / dx by a scale factor based onelectric field deviations from the space charge effect. The electric field distortions from the spacecharge effect were observed to cause at most a 1.75% difference in dQ / dx along the drift distance.These calibrated measurements of dQ calibrated / dx are fitted to a Landau function convolved with aGaussian to find the most probable value (MPV) for 100 µ s bin in time. An example of such a fit is– 66 –hown in figure 51. The function that describes the drift electron lifetime then can be quantified byevaluating dQ MPV / dx as such: dQ ( t ) MPV dx = dQ , MPV dx exp (−( t hit − t CRT )/ τ ) (6.2)where adjustments in the timing are made based on the timestamp provided by the CRT. / ndf c – – – – tick/cm] · dQ/dx [(ADC count) N u m be r o f H i t s / ndf c – – – – c – – – – DUNE:ProtoDUNE-SP <1.8 ms hit
Figure 51 : Distribution of dQ / dx for a slice of 100 µ s for a lower purity run in early November2018.Data was taken using the CRT once it became operational on November 1st, 2018 and runswere taken with the CRT during beam data-taking from November 1st, 2018 to November 11th,2018, the last day of beam. Fits to the MPV of the dQ / dx distributions as functions of hit timeare shown starting the first day in November and for the final day of beam data-taking in figure 52.During the end of October 2018, the pumps that circulate and purify the liquid argon were notoperating due to an external electrical issue. The pumps resumed recirculating and purifying onNovember 1st and typically take approximately a week to return the TPC back to its previous stateof liquid argon purity. Because of this circumstance, these drift electron lifetime measurementsshow the structure of charge attenuation for a lower purity run and a higher purity run during purityrecovery, respectively.While the drift electron lifetime can represent the purity, another useful metric is the ratio of dQ / dx at the anode and cathode or Q c Q a . This is calculated as follows: Q c Q a = e − t full drift τ (6.3)where t full drift is the time it takes to drift from the cathode to the anode, which was measured to be2.3 ms. Runs were taken with the CRT operating for the last week of beam data-taking. The Q c / Q a was measured for each day possible and occurred during a rise in purity in the detector.– 67 – .5 1.0 1.5 2.0 Hit Time [ms] t i ck / c m ] · d Q / d x [ ( A DC c oun t ) / ndf c –
288 Lifetime [ms] - e 0.2586 – c –
288 Lifetime [ms] - e 0.2586 – DUNE:ProtoDUNE-SP
Date: 1/11/2018
Hit Time [ms] t i ck / c m ] · d Q / d x [ ( A DC c oun t ) / ndf c – - e 14.32 – c – - e 14.32 – DUNE:ProtoDUNE-SP
Date: 11/11/2018
Figure 52 : Plot the MPV of the dQ / dx distribution as a function of the hit time, fit to an exponentialdecay function on November 1st, 2018 during a period of lower purity (top) and on November 11th,2018 during a period of higher purity (bottom). Only statistical errors are included.The systematic uncertainties determined for this measurement are from the uncertainty in theSCE calibration and impacts diffusion have on the hits as a function of drift time. The SCE effectuncertainty is estimated by evaluating the electron lifetime using a different SCE calibration mapmeasured using cosmic muons that cross the cathode and both anodes of the TPC. The differencebetween the lifetime value obtained using this alternate map and the lifetime value obtained usingthe SCE calibration discussed in section 6.1 is defined to be 1 σ of the SCE systematic uncertainty.– 68 –he uncertainty due to diffusion is estimated by turning diffusion off in the Monte Carlo simulationof the ProtoDUNE-SP detector, with a lifetime set at 35 ms. The difference in the electron lifetimevalues extracted with and without diffusion in the Monte Carlo simulation is taken to be the 1 σ variation due to the diffusion systematic, and is denoted σ diff . The fractional change in the lifetimedue to diffusion is σ diff τ = . Q c / Q a by 0.7%, assumingan electron lifetime of 35 ms. This value of the fractional uncertainty in the lifetime is assumed forall dates investigated.Towards the beginning of data-taking Q c / Q a was measured as low as 0.801 ± ± Q c / Q a of 0.9745 ± a / Q c Q DUNE:ProtoDUNE-SP
Figure 53 : Plot of Q c / Q a during November 2018 as measured with TPC tracks calibrated with CRTdata. Due to the run plan on November 6th, 2018, there was not enough data to make a precisionmeasurement of Q c / Q a on that day. Error bars include the statistical uncertainty, the uncertaintyfrom calibrating the SCE, and the uncertainty from diffusion’s impact on the measurement.These electron lifetimes also approximate the amount of impurity in the detector as expressed inunits of oxygen equivalent concentration. Measurements indicate that at approximately 500 V/cm,the impurity can be described as follows: N O = k a τ =
300 ppt ms τ (6.4)with N O being the concentration equivalent if all impurities were from oxygen and k a being theattachment constant for oxygen [15]. Considering the inverse relationship between the drift electronlifetime and the amount of oxygen equivalent impurity, the estimate predicts the impurity never wentabove 40 ppt equivalent of oxygen in the week of data-taking. At the end of beam data-taking on– 69 –ovember 11th, 2018, the impurity in the detector can be estimated to be approximately 3.4 ± The goal of detector calibration is to convert the measured charge in units of ADC counts toenergy in units of MeV, which provides important information for particle identification and energymeasurements. In order to get reliable calorimetric information, a two-step calibration procedure isemployed following the same method developed by the MicroBooNE collaboration [59]. In the firststep, the detector response is equalized using throughgoing cosmic-ray muons. In the second step,the absolute energy scale is determined using stopping cosmic-ray muons. In both steps muons thatcross the cathode are used because their t can be reconstructed (section 4.5.2). The two steps aredescribed in the following sections.The two steps are described in the following sections, using theresults for Run 5770 that was taken on Nov. 3, 2018 as an example. The charge deposition per unit length ( dQ / dx ) in a LArTPC is affected by a number of factorsincluding electronics gain variations, space charge effects, attenuation (due to electronegativeimpurities like O and H O), diffusion, and other effects. Some effects are calibrated out usingmeasurements described in previous sections such as electronics gains (section 4.2) and spacecharge effects (section 6.1). Calibrating the electron lifetime in situ via cathode-crossing muonsis complicated by the very complex space charge effects. Using the CRT allowed a much moreprecise calibration, but that system was not operable until late in the run and therefore those moreprecise lifetime measurements were not available for runs taken before then. The effect of diffusionhas not been measured yet. In the equalization step, cathode-crossing cosmic-ray muons are usedto calibrate the residual nonuniformity in the dQ / dx values throughout the TPC after the gain andspace charge effect calibrations. The following requirements are applied for track selection:• Fiducial volume requirements:
The fiducial volume FV1 is defined as a rectangular prismshaped as follows: the boundary from the anode planes is 10 cm, the boundary from theupstream and downstream ends is 40 cm, and the boundary from the top and bottom of theTPC is 40 cm. In order for a track to be selected, its start point and its end point must beoutside FV1.•
Angular requirements:
The reconstruction capability of LArTPCs is limited for tracks thatare parallel to the APA wires or contained in a plane containing a wire and the electric fielddirection. Figure 54 shows the dQ / dx distribution as a function of θ xz and θ yz , the twoangles are illustrated in figure 26. To get a sample of well reconstructed tracks, tracks with65 ◦ < | θ xz | < ◦ or 70 ◦ < | θ yz | < ◦ are removed, as indicated by the dashed lines infigure 54.Tracks passing the above selection criteria are used for dQ / dx calibration. Corrections are obtainedin the y z plane and as a function of the drift distance.– 70 – t i ck / c m ] · d Q / d x [ ( A DC c oun t ) - - [deg] XZ q - - [ deg ] Y Z q x>0 Cosmics data DUNE:ProtoDUNE-SP (a) x > t i ck / c m ] · d Q / d x [ ( A DC c oun t ) - - [deg] XZ q - - [ deg ] Y Z q x<0 Cosmics data DUNE:ProtoDUNE-SP (b) x < Figure 54 : Average dQ / dx distributions for ProtoDUNE-SP Run 5770 as functions of θ xz and θ yz in the collection plane. The color scale represents average dQ / dx for a track. The regions inside thedashed lines show the track incident angles excluded for the collection plane. 106764 throughgoingcosmic ray muon tracks were used in making the plots, which constitutes 24.8% of the total numberof cathode-crossing tracks in Run 5770.• YZ correction factors: dQ / dx values in the y z plane are affected by many factors includingnon-uniform wire response caused by nearby dead channels or disconnected wires, detectorfeatures such as the electron diverters and the wire support combs, and transverse diffusion.Figures 55(a) and 55(b) show the dQ / dx distribution in the y z plane separately for the x > x < x < dQ / dx because of the partially-charged disconnected G plane. The wire support combs also distort the dQ / dx averages [5],but only by around 5%, and only in very localized positions that are narrower than the binsizes in the figure. To correct for these non-uniformities we divide the y z plane in the twoProtoDUNE-SP drift volumes into a number of 5 × bins. Considering the dQ / dx valuesof all the hits lying in a particular bin, the median dQ / dx value is calculated and denoted ( dQ / dx ) localYZ . Further, the median dQ / dx value is calculated considering the hits throughouta drift volume, which is denoted ( dQ / dx ) globalYZ . The YZ correction factor is then defined as C ( y , z ) = ( dQ / dx ) globalYZ ( dQ / dx ) localYZ . (6.5)Figures 56(a) and 56(b) show the YZ correction factors for ProtoDUNE-SP Run 5770.• X correction factors:
The dQ / dx values along the drift direction are affected by factorssuch as attenuation due to electronegative impurities and longitudinal diffusion. Figure 57(a)shows the dQ / dx distribution as a function of x . The total drift volume is divided into 5 cmbins in the x coordinate. The dQ / dx values are first corrected using YZ correction factorsbased on the y and z coordinates of the hit. After the YZ correction, the median dQ / dx value ( dQ / dx ) localX is calculated for each bin. The median dQ / dx value for the whole TPC is– 71 –enoted ( dQ / dx ) globalX . The X correction factor is defined to be C ( x ) = ( dQ / dx ) globalX ( dQ / dx ) localX . (6.6)Figure 57(b) shows the X correction factors for ProtoDUNE-SP Run 5770. The dQ / dx valueis then normalized to the average value at the two anodes by defining the normalization factor N Q = ( dQ / dx ) anode ( dQ / dx ) global . (6.7)Finally, the corrected dQ / dx value is given by, ( dQ / dx ) corrected = N Q C ( y , z ) C ( x )( dQ / dx ) reconstructed (6.8) t i ck / c m ] · d Q / d x [ ( A DC c oun t ) Z Coordinate [cm] Y C oo r d i na t e [ c m ] DUNE:ProtoDUNE-SP x>0 Cosmics data (a) dQ / dx distribution in the y z plane, x > t i ck / c m ] · d Q / d x [ ( A DC c oun t ) Z Coordinate [cm] Y C oo r d i na t e [ c m ] DUNE:ProtoDUNE-SP x<0 Cosmics data (b) dQ / dx distribution in the y z plane, x < Figure 55 : dQ / dx distributions for ProtoDUNE-SP Run 5770 in the y z plane, for x > x < dQ / dx distribution for throughgoing cosmic-ray muons before and aftercharge calibration. Once the detector response is equalized, a sample of stopping cosmic-ray muonsare selected to determine the absolute energy scale. The conversion between ADC counts and the number of electrons is primarily determined by theelectronics response, including both the gain and the shaping time, and the field response. Eventhough the electronics gain is measured by the charge injection system (section 4.2) and the fieldresponse is calculated with Garfield (section 4.4), the estimated uncertainty on the measurementand calculation is at least a few percent. On the other hand, the energy loss per unit length for aminimum ionizing particle is known to better than 1%. Therefore, a sample of stopping cosmic-raymuons is selected to determine the absolute energy scale for both data and MC. The following cutsare used to select the stopping muon sample: – 72 – .81.01.21.41.61.82.0 Y Z c o rr e c t i on f a c t o r s Z Coordinate [cm] Y C oo r d i na t e [ c m ] DUNE:ProtoDUNE-SP x>0 Cosmics data (a) YZ correction factors, x > Y Z c o rr e c t i on f a c t o r s Z Coordinate [cm] Y C oo r d i na t e [ c m ] DUNE:ProtoDUNE-SP x<0 Cosmics data (b) YZ correction factors, x < Figure 56 : YZ correction factors for ProtoDUNE-SP Run 5770 in the y z plane, for x > x < - X coordinate [cm] t i ck / c m ] · d Q / d x [ ( A DC c oun t ) DUNE:ProtoDUNE-SP
Cosmics data (a) dQ / dx vs drift dimension x - X coordinate [cm] X c o rr e c t i on f a c t o r s DUNE:ProtoDUNE-SP
Cosmics data (b) X correction factors vs drift dimension x Figure 57 : dQ / dx distribution and X correction factors as a function of drift dimension x forProtoDUNE-SP Run 5770 (a) dQ / dx distribution and (b) X correction factors tick/cm] · dQ/dx [(ADC count) ) nu m be r o f h i t s ( Calibrated dQ/dxUncalibrated dQ/dx
DUNE:ProtoDUNE-SP
Cosmics data
Figure 58 : dQ / dx distribution for throughgoing cosmic-ray muons before and after calibration• Fiducial volume cuts:
Cathode-crossing tracks which start outside FV1 and end inside a– 73 –maller volume FV2 are used. The FV2 volume is a rectangular prism inside FV1 shaped asfollows: the boundary from the anode planes is 30 cm, the boundary from the upstream anddownstream ends is 50 cm, and the boundary from the top and bottom of the TPC is 50 cm.•
Angular cuts:
Tracks with 65 ◦ < | θ xz | < ◦ and tracks with 70 ◦ < | θ yz | < ◦ areremoved.• Removing broken tracks:
Some muons are reconstructed as two or more tracks, whichmimic a stopping muon. If the end points of the two tracks are within 30 cm and the anglebetween them is less than 14 ◦ , both tracks are removed. Additionally, any track which startsor stops within 5 cm of an APA boundary are removed.• Removing tracks with early and late hits : Tracks that are cut off by the 6000-tick TPCreadout window boundaries may mimic a stopping muon. If any hit associated with a trackhas a peak time less than 250 ticks or greater than 5900 ticks, the track is removed.•
Removing tracks with Michel hits attached:
The presence of an Michel electron canconfuse the reconstruction of the muon end point so muons that decay into Michel electronsare removed. The Michel activities are identified by looking for isolated hits close to muonend point. The number of hits within ± ±
50 ticks from the last hit of the muontrack and not belonging to the muon track or any other track longer than 100 cm is counted.If the count is greater than 0, such tracks are removed.After applying the above selection cuts, a highly pure sample of stopping muons remains.Defining the purity as the number of true stopping muons divided by the total number of candidatestopping muons in our sample, a purity of 99.74% is achieved based on Monte Carlo study. The dQ / dx values are corrected as described in the previous section. The most probable dE / dx valueas a function of residual range for stopping muon tracks in LAr is accurately predicted by Landau-Vavilov theory [60]. From the calibrated dQ / dx values (in ADC/cm) along the muon track inits MIP region (120 to 200 cm from stopping point), the dE / dx (in MeV/cm) values are fittedusing the modified Box model [61] function to correct for the recombination effect with the chargecalibration constant C cal (ADC × tick/cm → e/cm) as a free parameter in the χ minimization. C cal iseffectively a scaling factor that accounts for the electronics gain, ADC conversion and other residualeffects that are not explicitly calibrated out. The energy loss from the stopping muon sample and acomparison with the theoretical prediction in figure 59 show the result of the calibration procedurefor ProtoDUNE-SP Run 5770 and the corresponding Monte Carlo sample. (cid:18) dEdx (cid:19) calibrated = (cid:32) exp (cid:32) ( dQdx ) calibrated C cal β (cid:48) W ion ρ E (cid:33) − α (cid:33) (cid:18) ρ E β (cid:48) (cid:19) , (6.9)where C cal = Calibration constant used to convert ADC values to number of electrons, W ion = . × − MeV/electron (the work function of argon), E = E field based on the measured space charge map, ρ = (liquid argon density at a pressure of 124.106 kPa), α = β (cid:48) = )/MeV. – 74 – and β (cid:48) are the Modified Box model parameters which were measured by the ArgoNeuTexperiment at an electric field strength of 0.481 kV/cm [61].The calibration constant C cal is normalized so that the unit (“ADC × tick”) corresponds to 200electrons. In the case where the detector response is perfectly modeled (e.g. in the simulation), thecalibration constant C cal should be exactly 1/200 = 5 × − ADC × tick/e. The calibration constantsderived for the collection plane by fitting the stopping muon samples to the predicted dE / dx curveare shown in table 5. The uncertainties are statistical only. The difference between data and MCcalibration constants is caused by the uncertainties on the gain measurement and the simulation ofdetector response. Table 5 : Calibration constants for the collection plane in MC and data.Data MCFitted value of C cal (5.4 ± × − ADC × tick/e (5.03 ± × − ADC × tick/e Residual Range [cm] d E / d x [ M e V / c m ] DUNE:ProtoDUNE-SP
Cosmics data
Expectation (a) Data
Residual Range [cm] d E / d x [ M e V / c m ] DUNE:ProtoDUNE-SP
Cosmics MC
Expectation (b) MC dE/dx [MeV/cm] R e l a t i v e F r equen cy DATAMC
DUNE:ProtoDUNE-SP
Cosmics (c) dE / dx comparison Figure 59 : Stopping muon dE / dx distributions for the ProtoDUNE-SP cosmic-ray data and MC.The black curves in (a) and (b) are the predicted most probable values (using the Landau-Vavilovfunction) of dE / dx versus residual range and (c) is the dE / dx distribution for the stopping muonsample. The histograms in (c) are normalized such that the maximum frequency is one.– 75 – .4 Calorimetric energy reconstruction and particle identification The calibration constants derived from cosmic-ray muons are applied to beam particles. Thefollowing sections discuss the dE / dx distributions for beam muons and pions (section 6.4.1), beamprotons (section 6.4.2) and beam electrons (section 6.4.3). The identification of MIP particles(muons) and non-MIP particles (protons) is discussed in section 6.4.2. c beam pions andmuons Measurements of charged pion interactions with argon nuclei are an important physics goal ofthe ProtoDUNE-SP experiment. Accurate measurement of these interactions allows a more preciseunderstanding of neutrino interactions producing final-state pions, a key study channel of the DUNEexperiment. Reconstructing these final state pions and any particles produced by their secondaryinteractions is important for estimations of neutrino energy. This section describes the resultsobtained from ProtoDUNE-SP Run 5387 taken on Oct. 18, 2018 corresponding to approximately12 hours of exposure to a 1 GeV/ c beam. The electron drift lifetime of this run was approximately14 ms as measured by the purity monitor. Selection of events with reconstructed beam pions:
The beam line instrumentation as describedin section 3 can be used to find events where the beam line has delivered a pion to the TPC. For the1 GeV beam energy runs considered here, the beam line PID conditions for pions and muons canbe found in table 1. At a beam momentum of 1 GeV/ c the measured TOF of pions and muons isindistinguishable so the PID will select both pions and muons. Events are first removed for periodsof unstable HV or one or more inactive TPC readout boards. The Pandora pattern recognitionframework, described in section 4.5.2 is used to reconstruct the particle trajectories in the TPC aswell as identify a reconstructed particle as a likely candidate for the beam line track. To removeevents where a track has been incorrectly identified as the primary beam particle, quality cuts areplaced on the distance and angle between the end of the beam line particle’s reconstructed trajectoryand the start of the assigned reconstructed TPC beam particle track. These cuts remove backgroundsthat are not aligned with the measured beam line track such as cosmic rays and secondary particlesproduced by the beam particle interacting upstream of the TPC. The cuts used for data are as follows:• Beam quality cuts: angle
The cosine of the angle between the beam line track and reconstructed TPC track is requiredto be > . Beam quality cuts: position ◦ ≤ ( X StartTPC − X EndBeam ) ≤
10 cm, ◦ -5 cm ≤ ( Y StartTPC − Y EndBeam ) ≤
10 cm, ◦
30 cm ≤ ( Z StartTPC − Z EndBeam ) ≤
35 cm,where X StartTPC is the start position in X of the reconstructed candidate beam track in the TPCbefore SCE corrections. X EndBeam is the projected X location of the intersection of the trackfrom the beam line instrumentation with the TPC. The cuts are not centered around zero duethe SCE shifting the start points of the reconstructed TPC tracks.– 76 – replica selection is also placed on beam MC events where the true simulated beam particle iseither a pion or muon. In MC the truth information of the beam particle is used in place of the beamline information. The cuts used for MC are as follows:•
Beam quality cuts: angle
The cosine of the angle between the true beam particle and reconstructed TPC track is requiredto be > . Beam quality cuts: position ◦ -3 cm ≤ ( X StartTPC − X EndBeam ) ≤ ◦ -8 cm ≤ ( Y StartTPC − Y EndBeam ) ≤ ◦ ≤ ( Z StartTPC − Z EndBeam ) ≤ X EndBeam is the projected X location of the true beam particle’s intersection with the TPCand X StartTPC is same as for data.
Reconstructed Track Length/CSDA Range R e l a t i v e F r equen cy DUNE:ProtoDUNE-SP
Cut to select stopping muons
Beam Pions and Muons (1 GeV/c)
Figure 60 : The ratio of track length (SCE corrected) and continuous-slowing-down-approximation(CSDA) length for data candidates under the assumption the particle is a muon. The cut to selectthe stopping muons is indicated. The histogram is normalized such that the maximum frequency isone.
Selection of stopping muons:
The track lengths of the selected beam particles can be used toseparate the beam pions and muons. As 1 GeV/ c pions have an expected interaction length in argon of~1 m the majority of pions will interact before coming to a stop via ionization energy loss. Whereas1 GeV/ c muons will stop predominantly via ionization energy losses with an expected path lengthin argon of ~4 m. The measured beam momentum is used to approximate the stopping range ofeach particle under the assumption it is a muon using the continuous-slowing-down-approximation(CSDA) range [62]. The distribution of the beam tracks’ reconstructed lengths divided by their– 77 –alculated CSDA ranges is shown in figure 60. Requiring the ratio0 . < Track LengthCSDA Stopping Range < . , (6.10)selects a subsample of stopping particles, predominantly muons. A replica selection cut is placedon the MC sample using the the simulated momentum for the true beam particle in each event asinput to the ratio calculation. dE/dX [MeV/cm] R e l a t i v e F r equen cy DUNE:ProtoDUNE-SP
MC (SCE)Data
Pions (1 GeV/c) (a) dE/dX [MeV/cm] R e l a t i v e F r equen cy DUNE:ProtoDUNE-SP
MC (SCE)Data
Beam Stopping Muons (1 GeV/c) (b)
Figure 61 : Pion (a) and stopping muon (b) dE / dx distributions for the ProtoDUNE-SP beamdata after applying the calibration derived using cosmic-ray muons. The distribution for replicaselections on a beam Monte Carlo sample with space charge effect simulated is also shown. Thehistograms are normalized such that the maximum frequency is one. Calorimetric energy information of beam pions
The charge signal calibration produced fromstopping cosmic-ray muons collected during the same run period described in section 6.3 is appliedto the selected beam tracks to calculate the true deposited energy as a function of distance ( dE / dx )along their trajectory. Figure 61(a) shows the distribution of dE / dx values of all hits on thecollection plane from the selected beam pion candidates. That is, all candidates that are not selectedby the stopping muon cut (eq. 6.10) described above. The selected particles are MIPs. The MPVsof the calibrated data and MC samples agree to better than 1%. Calorimetric energy information of beam stopping muons
Figure 61(b) shows the distributionof calibrated dE / dx values of all hits on the collection plane from the beam muon candidatesselected by the stopping particle cut (eq. 6.10). As with the pions, the MPVs of the data and MCsamples agree to better than 1%. The distribution of the dE / dx vs residual range for the selectedstopping muons is shown for data in figure 62(a) and for MC in figure 62(b). A clear Bragg peak isseen at low residual range, as expected. The measured distribution displays good agreement withthe theoretical MPV curve for a stopping muon in argon, in both the minimum ionization regionand Bragg peak region of residual range. – 78 –
50 100 150 200
Residual Range [cm] d E / d X [ M e V / c m ] DUNE:ProtoDUNE-SP
Expectation
Stopping Beam Muons - Data (1 GeV/c) (a)
Stopping Beam Muons - MC (1 GeV/c)
Residual Range [cm] d E / d X [ M e V / c m ] DUNE:ProtoDUNE-SP
Expectation
Stopping Beam Muons - MC (1 GeV/c) (b)
Figure 62 : dE / dx vs residual range for selected stopping muons in the 1 GeV/ c beam after applyingthe calibration derived using cosmic-ray muons, in data (a) and MC (b). The expected most probablevalue of dE / dx is plotted as a function of residual range for both. c beam protons To understand the detector response to protons interacting in a LArTPC, an analysis procedure,including the selection of beam protons, detector calibration and calorimetric analysis, has beendeveloped. This section describes the results obtained from ProtoDUNE-SP Run 5387.Stopping protons are used for the detector characterization in terms of calorimetry and particleidentification. Protons are selected using the same beam-TPC matching criteria described insection 6.4.1. For the 1 GeV/ c beam momentum runs considered here, the beam line PID conditionsfor protons can be found in table 1.The measured beam momentum is used to approximate the stopping range under the assumptionit is a proton using the CSDA range. Figure 63 shows the distribution of the reconstructed protontracks, divided by their expected CSDA ranges. The distribution peaks at 0.88, which is dominatedby the stopping protons. The peak position is less than one because of the energy loss upstreamand the SCE. The tail on the left of the distribution is due to the interacting protons, since theirdrift distances inside the LAr are shorter than those of the stopping protons. The ratio cut, 0.74 ≤ (reconstructed proton track length/CSDA range) ≤ E -field distortions due to the SCE. After thesecorrections, the dQ / dx values (ADC/cm) of the stopping protons are converted to the corresponding dE / dx values (MeV/cm) using the calibration constants described in section 6.3. The same analysisprocedure is applied to a Monte Carlo sample.Figures 64(a) and 64(b) show the energy loss versus the residual range of the stopping protoncandidates for data and MC, respectively. The data and MC after the calibration procedure showgood agreement with the expected MPVs. The distributions of dE / dx in the data and the MC areshown in figure 64(c). The MPVs of the dE / dx distributions between data and MC agree to betterthan 1%. – 79 – .0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 Reconstructed Track Length/CSDA Range R e l a t i v e F r equen cy Cut to select stopping protons
DUNE:ProtoDUNE-SP
Protons (1 GeV/c)
Figure 63 : The distribution of the reconstructed proton track length divided by the associatedCSDA range. The histogram is normalized such that the maximum frequency is one. The incidentbeam momentum is 1 GeV/ c . The cut to select the stopping protons is indicated. dE / d x for 1 GeV/ c electrons It is important to understand the LArTPC response to electromagnetic showers since DUNE willmeasure electrons coming from oscillated neutrinos, produced via charged current interactions.Accurate measurement of the calorimetric response of electrons in the ProtoDUNE-SP TPC allowsa more precise understanding of e / γ separation and estimation of electron neutrino energy. The dE / dx for a photon-induced shower is expected to be twice the dE / dx of a single electron at thebeginning of the shower, due to the photon conversion into an electron-positron pair. This has beenverified in a LArTPC by the ArgoNeuT collaboration [63]. To successfully select ν e charged-currentinteractions in DUNE, a dE / dx metric can be used to remove electromagnetic-like background frominteractions such as neutral-current π production where the photons from π decay can mimic anelectron shower.Electrons are selected in Run 5809 taken on Nov. 8, 2018 using the same beam-TPC matchingcriteria described in section 6.4.1. For the 1 GeV/ c beam momentum runs considered here, thebeam line PID conditions for electrons can be found in table 1.The reconstruction of electron-induced showers in the detector follows the same procedure as intrack-like events. Signal processing (including deconvolution and noise removal) is followed by hitfinding and 2D cluster formation. The reconstruction framework Pandora [44] is used to reconstruct3D showers. The position and the direction of the shower are used to define the beginning of theshower, which is before the electromagnetic cascade develops. To ensure that the electron candidatehas not developed a cascade shower before entering the active TPC volume, a completeness cut wasrequired. The completeness is defined as the reconstructed shower energy divided by the incomingelectron’s momentum. Based on MC studies we expect to have losses due to energy loss upstreamand due to signal processing thresholds. If the completeness is at least 80%, the event is selected.– 80 – Residual Range [cm] d E / d x [ M e V / c m ] Expectation
DUNE:ProtoDUNE-SP
Proton Data (1 GeV/c) (a)
Residual Range [cm] d E / d x [ M e V / c m ] Expectation
DUNE:ProtoDUNE-SP
Proton MC (1 GeV/c) (b) dE/dx [MeV/cm] R e l a t i v e F r equen cy DataMC
DUNE:ProtoDUNE-SP
Protons (1 GeV/c) (c)
Figure 64 : Stopping proton dE / dx versus residual range distributions for the ProtoDUNE-SP beamdata (a) and MC (b), the expected most probable values are shown in red. The dE / dx distributionsafter the SCE corrections of data and MC are shown in (c). The histograms in (c) are normalizedsuch that the maximum frequency is one.To measure dE / dx , first, the charge deposition per unit length dQ / dx is measured on a singlewire at the collection plane. To calculate the effective pitch dx between hits, the direction ofthe shower is used to measure the actual distance that the electron traverses in the TPC betweenadjacent wires. Then, following the discussion in section 6.1 the SCE corrections are applied.The conversion from dQ / dx to dE / dx uses the calibration constants described in section 6.3. Tomeasure dE / dx at the beginning of the shower, only hits within 4 cm along the direction of theshower and 1 cm perpendicular to the shower are considered and the median dE / dx is computed.The same analysis procedure is applied to the Monte Carlo sample.The dE / dx distributions for 1 GeV/ c electron candidates are shown in figure 65. The dE / dx distributions in figure 65 follow the expected Gaussian-convolved Landau distribution with the dE / dx peak value corresponding to one single ionizing particle. This results demonstrates anunderstanding of the dE / dx metric for electrons that would be a valuable input for future analyses.Electrons are selected using the same beam-TPC matching criteria described in section 6.4.1. Forthe 1 GeV/ c beam momentum runs considered here, the beam line PID conditions for electrons can– 81 – dE/dx [MeV/cm] R e l a t i v e F r equen cy DataMC
DUNE:ProtoDUNE-SP
Positrons (1 GeV/c)
Figure 65 : dE / dx at the beginning of the shower. The histograms are normalized such that themaximum frequency is one.be found in table 1.The dE / dx distributions for various particle all show a good agreement between data and MCin the peak. However, the resolution of dE / dx is slightly overestimated in the MC. This could bedue to the imperfect modeling of physics processes and/or detector effects. Robust particle identification (PID) is of fundamental importance for the physics goals of ProtoDUNE-SP and the future DUNE experiment. The calorimetric-based PID method in a LArTPC uses thereconstructed energy deposits as a function of residual range for the stopping particles. Event selec-tions of the stopping muons and the stopping protons are described in section 6.3.2 and section 6.4.2,respectively. The stopping protons and muons are shown in figure 66(a). The highly-ionized protonsare clearly separated from the muons over the entire range from their stopping points.Based on the obtained calorimetry information, a likelihood-based parameter, ζ , is adoptedto quantify the PID performance of the ProtoDUNE-SP detector. The method is to compare theparticle species with respect to the stopping proton hypothesis. The parameter ζ is defined to be: ζ = n DoF (cid:213) j (cid:104) dE j dx j ( Data ) − dE j dx j ( MC Proton ) (cid:105) (cid:113) δ dE j dx j ( Data ) + δ dE j dx j ( MC Proton ) , (6.11)where j is the j -th hit of a track and n DoF is the number of degrees of freedom.Figure 66(b) shows the ζ distributions of the stopping protons and the stopping muons. Theprotons and the muons are well-separated. The PID performance for pions is expected to be similarto that of muons. – 82 –
20 40 60 80 100 120
Residual Range [cm] d E / d x [ M e V / c m ] Proton ExpectationMuon Expectation
DUNE:ProtoDUNE-SP
Data (a) z R e l a t i v e F r equen cy Proton DataMuon DataProton MCMuon MC
DUNE:ProtoDUNE-SP (b)
Figure 66 : (a) dE / dx versus residual range after the SCE corrections for the stopping protons(upper band) and the muons (lower band). The solid lines represent the expected most probablevalues for the protons (red) and the muons (blue). (b) The ζ distributions of the stopping protonsand muons. The histograms are normalized such that the maximum frequency is one. Homogeneous calorimeters are instrumented targets where the kinetic energy ( E ) of incident parti-cles is absorbed and transformed into a detectable signal. The deposited energy is typically detectedin the form of charge or light. When the energy is large enough, a shower of secondary particlesis produced (through electromagnetic or strong processes) with progressively reduced energy. Ifthe shower is fully contained and the output signal is efficiently collected, the calorimetric energyresolution is expected to be good, improving with energy as 1 /√ E . Calorimeters can also provideinformation on shower position, direction and size as well as arrival time t of the particle.A LArTPC is a sophisticated version of a homogeneous calorimeter, with additional imagingand particle identification capabilities. Energy deposition in the liquid argon target yields freecharge from ionization and it also yields fast scintillation light. The best energy resolution would beobtained by collecting both the charge and the light signals, which are anticorrelated by the random-ness of the recombination processes. With detectors based on LArTPC technology, calorimetrytypically relies only on the charge signal collection, while the use of the light signal is limited to t determination and triggering purposes. A first attempt to extend the use of scintillation lightfor calorimetry was recently performed in a low energy range with a small sized LArTPC [64].Operating protoDUNE-SP on the H4-VLE charged particle test beam offers the opportunity to di-rectly probe with light the calorimetric response of liquid argon to fully contained EM and hadronicshowers in the sub- to few-GeV energy range. As described in section 5.1, in ProtoDUNE-SP the photon detection system comprises a series ofoptical modules positioned inside the APA frames, behind the TPC wire planes and the groundedmesh. The active liquid argon volume is only on one side of the APA’s, on the side facing the central– 83 –athode. The total photo-sensitive area is ∼ .
5% of the boundary surface of the LAr volume.The relatively modest optical coverage and the one-sided geometry of the PD system, comparedfor example to the 4 π coverage of scintillation or Cherenkov detectors, are expected to limit thelight yield and the uniformity of the calorimetric response along the drift direction. In this section,beam electrons and data from the ARAPUCA module in the beam side of the PDS are utilized toinvestigate the light yield and resolution of the ProtoDUNE-SP PD system. Figure 67 : 3D event display of a real 7 GeV beam electron in the TPC volume [only tracks inside apredefined sub-volume (red box) are shown]. The beam electron enters near the cathode and an EMshower develops in LAr along the beam direction, about 10 ◦ downward and 11 ◦ toward the anodeplane, where the PDS beam side modules are located inside the APA frames. Scintillation photonsfrom energy deposits along the shower are detected by the ARAPUCA module. In a sequence of beam runs, data were collected with incident positive electrons ( e + ) with energiesof 0.3, 0.5, 1, 2, 3, 6 and 7 GeV, providing a large sample of EM showers developing in the LArvolume. The beam is delivered with a typical momentum spread of ±
5% around the nominalsetting. The beam line instrumentation (see section 3.3.1) provides an event-by-event particleidentification and momentum measurement with a precision of ∆ p / p (cid:39) .
5% [25]. Light signalfrom the single beam side ARAPUCA module is used for this calorimetric response study. TheARAPUCA module is positioned inside the upstream APA3 frame, nearly at the same height y ofthe entering beam and oriented along the z axis. In figure 67 a 3D display of a 7 GeV electronevent from the ProtoDUNE-SP data sample is shown as reconstructed by the TPC. The EM showerdevelops immediately downstream of the beam entry point in the LArTPC volume in front of theARAPUCA module, at ∼ x (drift) direction, and propagates longitudinally alongthe beam direction, about 10 ◦ downward and 11 ◦ toward the anode plane. Scintillation light is– 84 –mitted isotropically from every location at which ionization occurs along the shower. The totalphoto-sensitive area of the ARAPUCA module is ∼ . × − of the surface surrounding theLArTPC active volume (beam side). Summing over the twelve cells in the ARAPUCA module, . . . . . . . e-beam E Ph-detected N N u m be r o f e v en t s pe r bea m m o m en t u m v a l ue N o m i na l bea m m o m en t u m [ G e V / c ] ProtoDUNE - SP Electrons ARAPUCA
DUNE:ProtoDUNE - SP
Figure 68 : Distributions of the incident beam electron energies (left) and corresponding detectedphoton spectra (right) for the collected seven nominal beam energies (Gaussian fit superimposed -red line). Fit parameters are used in figure 69. The photon spectra are relative to the sum of photonsdetected by the 12 cells of the ARAPUCA PD module.the total number of photons detected N Ph = (cid:205) N Det j is evaluated event by event, for each beam run.Monte Carlo events, already used for the efficiency study, are also available, generated as describedin section 5.3 with incident electrons as in the beam runs (same energy distributions and direction).Scintillation light from the EM shower development in LAr is propagated to the photo-detector(s)and the total number of photons incident on the surface of the ARAPUCA module is evaluated foreach event of the MC simulated momentum run. In figure 68, the energy distribution (left) of theincident electrons from the beam spectrometer of the CERN H4-VLE beam line for each beam runand the corresponding calorimetric response from the ARAPUCA detector (right) expressed by thenumber of detected photons are shown. A Gaussian fit of both distributions (red lines in figure 68)gives the average electron energy (cid:104) E e (cid:105) and the corresponding average photon counting (cid:104) N Ph (cid:105) , andtheir spreads ( σ E , σ N ), for each run. The average number of detected photons as a function of thebeam energy is shown in figure 69. This relationship gives the calorimetric energy response fromlight data. Correspondingly, the response as obtained from the MC simulation, expressed by the– 85 –verage number of detectable photons (incident at the detector surface) from EM showers at givenelectron beam energy, is presented in figure 70 (left).To a first approximation, the average light response is a linear function of the energy over theentire range of tested beam energies as shown in figure 69 (left) with the result of the linear fit.The slope of the fit p gives the light yield Y light = . Y light is relativeto a diffuse light source (EM shower) at a distance of about 3 m (see figure 67). The non-zero(negative) intercept ( p = − . − ±
14 MeV from the nominal value for all beam energies. From the CERN H4-VLE beam lineMC simulations (section 3.2 and 3.3) beam electrons are expected to release 10-20 MeV in thematerial in the portion of the beam line downstream the spectrometer and additional ∼ ±
14 MeV, in better agreement with the expected beam electron energy loss in thematerials before entering the TPC - details of this study can be found in [65]. [GeV] beam æ e E Æ de t e c t ed æ P h N Æ p 1.4 – - p 1.5 – p 1.4 – - p 1.5 – c c ElectronsDetected photonsLinear Fit
DUNE:ProtoDUNE-SP
ARAPUCA [GeV] beam æ e E Æ de t e c t ed æ P h N Æ / N s k 0.003 – k 0.008 – k 0.009 – k 0.003 – k 0.008 – k 0.009 – c c ElectronsDetected photonsFit function Eq.(7.1)
DUNE:ProtoDUNE-SP
ARAPUCA
Figure 69 : Number of detected photons (Gaussian fit mean value, from fig.68 right) as a functionof incident electron energy (Gaussian fit mean value, from fig.68 - left) (left panel). Reconstructedenergy resolution from the detected photon distributions (Gaussian fit standard deviation to themean ratio) (right panel). A line of slope p and intercept p is fit to the data in the left-hand plot,and the function in equation (7.1) is fit to the data in the right-hand plot.Based on the linearity of the light response, the relative calorimetric energy resolution σ E / E e – 86 –s obtained from the σ N / N Ph ratio of the light response (figure 69 - right). The energy resolution,as for a homogeneous calorimeter, can be expressed in a general form [66] depending on threedifferent contributions: σ E E = k ⊕ k √ E ⊕ k E (7.1)where the symbol ⊕ indicates a quadratic sum and E is in GeV. The terms on the right-hand sideare referred to below as the “constant term”, the “stochastic term” and the “noise term”. Therelative weight of the three terms depends on the energy of the incident particle. The stochasticterm contribution to the energy resolution comes from the statistical fluctuations in the number ofphotons detected. The relatively large value ( k = . ) - when compared to typical homogeneouscalorimeters - is ascribed to the limited photo-sensitive coverage of the ARAPUCA module. Thenoise term comes from the electronic noise of the readout chain. Its value ( k = .
057 GeV) isexactly as expected from the measured signal-to-noise-ratio of the ARAPUCA readout (section5.2.2). The constant term is large ( k = . σ E / E e = ( . ± . ) %. An additional contribution comes fromfluctuations in the energy loss in the materials before electrons enter the TPC. Since the energy lossoccurs downstream of the momentum spectrometer, this energy degradation and its fluctuation donot appear in the incident beam energy spectra and it is evaluated by simulations ( ∼ [GeV] beam æ e E Æ · i n c i den t æ P h N Æ q 78 – - q 86 – q 78 – - q 86 – c c ElectronsSimulation
DUNE:ProtoDUNE-SP
ARAPUCA [GeV] beam æ e E Æ s i m u l a t ed æ P h N Æ / de t e c t ed æ P h N Æ Electrons
DUNE:ProtoDUNE-SP
ARAPUCA
Figure 70 : Monte Carlo simulation of light response to EM showers: average number of incidentphotons at the ARAPUCA bar surface from simulation vs average electron beam energy (left) andData/MC comparison (right) by the ratio of average number of photons detected to the correspondingnumber of photons from MC simulation, at the different beam energies.ARAPUCA module is adequate. An extrapolation of the performance to a PDS system consistingentirely of ARAPUCA modules, obtained by scaling the detected photon response of the lightguide bars with the ratio of the ARAPUCA-to-light guide efficiencies (presented in section 5.3.1),indicates that it can perform calorimetric energy reconstruction with an expected light yield of– 87 –.9 photons/MeV. This performance exceeds the specifications of the DUNE Far Detector [5] byalmost a factor of four.A comparison of electron data and the corresponding MC simulation can be used to validate thesimulation of the light propagation and collection. The average number of photons incident on thesurface of the ARAPUCA module from the MC (shown in figure 70 - left) is scaled by a normalizationfactor η , an average value over the ARAPUCA cells’ efficiency shown in section 5.3.1, to give thesimulated detected photons (cid:104) N Ph (cid:105) simulated = η (cid:104) N Ph (cid:105) incident . The ratio (cid:104) N Ph (cid:105) detected /(cid:104) N Ph (cid:105) simulated isshown as a function of incident beam energy in figure 70 (right). A systematic deviation, between5 and ≤ This paper summarizes the first results on the performance of the ProtoDUNE-SP LArTPC usinglarge samples of data from a test-beam run at the CERN Neutrino Platform. The dedicated H4-VLEbeam line delivers electrons, pions, protons and kaons in the 0.3 – 7 GeV/ c momentum range,which are crucial to the study of detector performance and the measurement of particle-argon crosssections. In table 6 the detector’s high-level performance parameters from studies and findingspresented in this report are shown and they are compared with the corresponding DUNE SP FarDetector design specifications. For each of the categories shown, the ProtoDUNE-SP performancemeets or exceeds the DUNE specification, in several cases by a large margin. This successfulperformance demonstrates the effectiveness of the single-phase detector design and the executionof the fabrication, assembly, installation, commissioning, and operations phases [11]. Table 6 : ProtoDUNE-SP performance for main parameters and corresponding DUNE specifica-tions.
Detector parameter ProtoDUNE-SP performance DUNE specification
Average drift electric field 500 V/cm 250 V/cm (min)500 V/cm (nominal)LAr e-lifetime >
20 ms > < (cid:104) SNR (cid:105) (C) 48.7, (I) 21.2 (w/CNR)CE dead channels 0.2% < > <
100 nsThe electric field in the TPC drift volume was stable at the nominal level of 500 V/cm with– 88 – ( ± ) ms for the last day of beamdata-taking. This corresponds to a concentration of impurity in the liquid argon of 3.4 ± equivalent.The TPC and cold electronics show excellent signal-to-noise performance. The signal-to-noiseratios corresponding to the most-probable-value ionization of a minimum ionizing particle are 40.3,15.1 and 18.6, for collection, U and V wires, respectively, after noise filtering and signal processing.The number of solidly unresponsive TPC channels was initially 29 out of 15360 and rose to36 over the course of a year and five months of operations. Approximately 105 additional channelsare noisy or have other issues with the electronics so that they were not included in the analysespresented in this report.Three different photon detection technologies were implemented in the PDS and characterizedwith muon and electron beam data. The ARAPUCA technology showed 2% efficiency, the highestamong the three, with a light response to EM energy deposit linear over the entire range of beamenergies. A PDS system consisting entirely of ARAPUCA modules, with an expected light yieldof 1.9 photons/MeV, will exceeds the specifications of the DUNE Far Detector.Space-charge effects were predicted to be prominent in ProtoDUNE-SP. Spatial distortions inthe apparent positions of tracks of up to 40 cm are observed in the data, based on the points of entryand exit into the TPC. Changes in the magnitude of the electric field by up to 25% are inferred fromthe spatial distortion measurements. Data-based, three-dimensional maps of spatial offsets andelectric field strengths are made and are used to correct the observed data positions and ionizationstrengths for use in precision analyses.The measured resolutions of the calibrated TPC and photon detector responses to protons,muons, electrons, and charged pions are similar to or better than those in the simulations that areused to predict the performance of the first DUNE far detector module.The data collected by ProtoDUNE-SP during beam runs and cosmic-ray runs will allow detailedstudies of detector characteristics such as fluid flow, and they will also allow the measurement ofargon-hadron cross sections. The results of these studies and measurements will be reported infuture publications. Acknowledgments
The ProtoDUNE-SP detector was constructed and operated on the CERN Neutrino Platform. Wegratefully acknowledge the support of the CERN management, and the CERN EP, BE, TE, EN andIT Departments for NP04/ProtoDUNE-SP. This document was prepared by the DUNE collaborationusing the resources of the Fermi National Accelerator Laboratory (Fermilab), a U.S. Departmentof Energy, Office of Science, HEP User Facility. Fermilab is managed by Fermi Research Alliance,LLC (FRA), acting under Contract No. DE-AC02-07CH11359. This work was supported byCNPq, FAPERJ, FAPEG and FAPESP, Brazil; CFI, IPP and NSERC, Canada; CERN; MŠMT,Czech Republic; ERDF, H2020-EU and MSCA, European Union; CNRS/IN2P3 and CEA, France;– 89 –NFN, Italy; FCT, Portugal; NRF, South Korea; CAM, Fundación “La Caixa” and MICINN, Spain;SERI and SNSF, Switzerland; TÜBİTAK, Turkey; The Royal Society and UKRI/STFC, UnitedKingdom; DOE and NSF, United States of America. This research used resources of the NationalEnergy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office ofScience User Facility operated under Contract No. DE-AC02-05CH11231.
References [1] DUNE collaboration, S. Jones et al.,
Deep Underground Neutrino Experiment (DUNE), Far DetectorTechnical Design Report, Volume 1 Introduction to DUNE , .[2] ICARUS collaboration, S. Amerio et al., Design, construction and tests of the ICARUS T600 detector , Nucl. Instrum. Meth.
A527 (2004) 329–410.[3] DUNE collaboration, B. Abi et al.,
The Single-Phase ProtoDUNE Technical Design Report , .[4] F. Pietropaolo, Review of Liquid-Argon Detectors Development at the CERN Neutrino Platform , J.Phys. Conf. Ser. (2017) 012038.[5] DUNE collaboration, S. Jones et al.,
Deep Underground Neutrino Experiment (DUNE), Far DetectorTechnical Design Report, Volume IV Far Detector Single-phase Technology , .[6] DUNE collaboration, B. Abi et al., Deep Underground Neutrino Experiment (DUNE), Far DetectorTechnical Design Report, Volume II DUNE Physics , .[7] C. Anderson et al., The ArgoNeuT Detector in the NuMI Low-Energy beam line at Fermilab , JINST (2012) P10019, [ ].[8] C. Bromberg et al., Design and Operation of LongBo: a 2 m Long Drift Liquid Argon TPC , JINST (2015) P07015, [ ].[9] MicroBooNE collaboration, R. Acciarri et al., Design and Construction of the MicroBooNEDetector , JINST (2017) P02017, [ ].[10] D. Adams et al., Design and performance of a 35-ton liquid argon time projection chamber as aprototype for future very large detectors , JINST (2020) P03035, [ ].[11] DUNE collaboration, Design, construction and operation of the ProtoDUNE-SP liquid argon TPC. Inpreparation. , 2020.[12] D. Montanari et al.,
Development of membrane cryostats for large liquid argon neutrino detectors , IOP Conf. Ser. Mater. Sci. Eng. (2015) 012049.[13] P. Benetti et al.,
Argon purification in the liquid phase , Nucl. Instrum. Meth. A (1993) 567–570.[14] M. Adamowski et al.,
The Liquid Argon Purity Demonstrator , JINST (2014) P07005, [ ].[15] A. Bettini, A. Braggiotti, F. Casagrande, P. Casoli, P. Cennini, S. Centro et al., A study of the factorsaffecting the electron lifetime in ultra-pure liquid argon , Nuclear Instruments and Methods in PhysicsResearch Section A: Accelerators, Spectrometers, Detectors and Associated Equipment (1991)177–186.[16] G. J. Michna, S. P. Gent, D. Pederson, and C. Streff, “CFD Analysis of the Fluid, Heat, and ImpurityFlows in ProtoDUNE Single Phase Detector.” DUNE-Doc-17481-v1 (2019), unpublished. – 90 –
17] G. De Geronimo et al.,
Front-end ASIC for a Liquid Argon TPC , IEEE Transactions on NuclearScience (2011) 1376–1385.[18] D. Adams et al., The ProtoDUNE-SP LArTPC Electronics Production, Commissioning, andPerformance , JINST (2020) P06017, [ ].[19] F. Acerbi and S. Gundacker, Understanding and simulating SiPMs , Nucl. Instrum. Meth.
A926 (2019)16–35.[20] E. M. Conover,
Muon-induced backgrounds in the Double Chooz neutrino oscillation experiment .PhD thesis, The University of Chicago, 2014.[21] R. Herbst et al.,
Design of the SLAC RCE Platform: A general purpose ATCA based data acquisitionsystem , in
Proceedings, 21st Symposium on Room-Temperature Semiconductor X-ray and Gamma-rayDetectors (RTSD 2014): Seattle, WA, USA, November 8-15, 2014 , p. 7431254, 2016. DOI.[22] J. Anderson et al.,
FELIX: a PCIe based high-throughput approach for interfacing front-end andtrigger electronics in the ATLAS Upgrade framework , JINST (2016) C12023.[23] K. Biery, C. Green, J. Kowalkowski, M. Paterno and R. Rechenmacher, artdaq: An Event-Building,Filtering, and Processing Framework , IEEE Trans. Nucl. Sci. (2013) 3764 – 3771.[24] N. Charitonidis and I. Efthymiopoulos, Low energy tertiary beam line design for the cern neutrinoplatform project , Phys. Rev. Accel. Beams (Nov, 2017) 111001.[25] A. C. Booth, N. Charitonidis, P. Chatzidaki, Y. Karyotakis, E. Nowak, I. Ortega-Ruiz et al., Particleproduction, transport, and identification in the regime of − / c , Phys. Rev. Accel. Beams (Jun, 2019) 061003.[26] I. Ortega, Accurate profile measurement of the low intensity secondary beams in the CERNExperimental Areas.
PhD thesis, Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, 2018.10.5075/epfl-thesis-8278.[27] “FMC Time to Digital Converter | FMC TDC 1ns 5cha.” https://ohwr.org/project/fmc-tdc/wikis/home .[28] N. Charitonidis, I. Efthymiopoulos and Y. Karyotakis,
Beam performance and instrumentationstudies for the ProtoDUNE-DP experiment of CENF , Tech. Rep. CERN-ACC-NOTE-2016-0052. 27,CERN, Geneva, Jul, 2016.[29] N. Charitonidis, Y. Karyotakis and L. Gatignon,
Estimation of the R134a gas refractive index for useas a Cherenkov radiator, using a high energy charged particle beam , Nuclear Instruments andMethods in Physics Research Section B: Beam Interactions with Materials and Atoms (2017) 134– 138.[30] D. Carey, K. Brown and F. Rothacker, "third-order transport: A computer program for designingcharged particle beam transport systems" , technical report, Stanford Linear Accelerator Center,Menlo Park, CA (US), 1995. 10.2172/97260.[31] “Methodical Accelerator Design CERN.” http://mad.web.cern.ch/mad/ .[32] P. K. Skowronski, F. Schmidt and E. Forest,
Advances in MAD X using PTC , Conf. Proc.
C070625 (2007) 3381.[33] P. Chatzidaki,
Optics optimization of tertiary particle beamlines and efficiency measurement ofprototype scintillating fiber detectors . Diploma thesis, Natl. Tech. U., Athens, Oct, 2018.[34] T. J. Roberts, K. B. Beard, D. Huang, S. Ahmed, D. M. Kaplan and L. K. Spentzouris,
G4BeamlineParticle Tracking in Matter-dominated Beam Lines , Conf. Proc.
C0806233 (2008) WEPP120. – 91 –
35] T. Böhlen, F. Cerutti, M. Chin, A. Fassò, A. Ferrari, P. Ortega et al.,
The FLUKA Code:Developments and Challenges for High Energy and Medical Applications , Nuclear Data Sheets (2014) 211 – 214.[36] A. Ferrari, P. R. Sala, A. Fassò and J. Ranft,
FLUKA: A multi-particle transport code (programversion 2005) . CERN Yellow Reports: Monographs. CERN, Geneva, 2005,10.5170/CERN-2005-010.[37] MicroBooNE collaboration, R. Acciarri et al.,
Noise Characterization and Filtering in theMicroBooNE Liquid Argon TPC , JINST (2017) P08003, [ ].[38] S. Ramo, Currents induced by electron motion , Proc. Ire. (1939) 584–585.[39] R. Veenhof, GARFIELD, recent developments , Nucl. Instrum. Meth.
A 419 (1998) 726.[40] Y. Li et al.,
Measurement of Longitudinal Electron Diffusion in Liquid Argon , Nucl. Instrum. Meth.
A816 (2016) 160–170, [ ].[41] C. Zhang, “Summary of Liquid Argon Properties.” http://lar.bnl.gov/properties/ .[42] MicroBooNE collaboration, C. Adams et al.,
Ionization electron signal processing in single phaseLArTPCs. Part I. Algorithm Description and quantitative evaluation with MicroBooNE simulation , JINST (2018) P07006, [ ].[43] MicroBooNE collaboration, C. Adams et al., Ionization electron signal processing in single phaseLArTPCs. Part II. Data/simulation comparison and performance in MicroBooNE , JINST (2018)P07007, [ ].[44] J. S. Marshall and M. A. Thomson, The Pandora Software Development Kit for Pattern Recognition , Eur. Phys. J.
C75 (2015) 439, [ ].[45] MicroBooNE collaboration, R. Acciarri et al.,
The Pandora multi-algorithm approach to automatedpattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector , Eur. Phys.J.
C78 (2018) 82, [ ].[46] A. A. Machado and E. Segreto,
ARAPUCA a new device for liquid argon scintillation light detection , JINST (2016) C02004.[47] Z. Moss, J. Moon, L. Bugel, J. M. Conrad, K. Sachdev, M. Toups et al., A Factor of Four Increase inAttenuation Length of Dipped Lightguides for Liquid Argon TPCs Through Improved Coating , .[48] L. Bugel, J. M. Conrad, C. Ignarra, B. J. P. Jones, T. Katori, T. Smidt et al., Demonstration of aLightguide Detector for Liquid Argon TPCs , Nucl. Instrum. Meth.
A640 (2011) 69–75, [ ].[49] B. Howard, S. Mufson, D. Whittington, B. Adams, B. Baugh, J. R. Jordan et al.,
A Novel Use of LightGuides and Wavelength Shifting Plates for the Detection of Scintillation Photons in Large LiquidArgon Detectors , Nucl. Instrum. Meth.
A907 (2018) 9–21, [ ].[50] L. Condat,
A Direct Algorithm for 1D Total Variation Denoising , IEEE Signal Processing Letters (2013) 1054–1057.[51] E. D. Church, LArSoft: A Software Package for Liquid Argon Time Projection Drift Chambers , .[52] C. Zhang, “Wire-Cell BEE event display.” .[53] T. Doke, A. Hitachi, J. Kikuchi, K. Masuda, H. Okada and E. Shibamura, Absolute Scintillation Yieldsin Liquid Argon and Xenon for Various Particles , Jap. J. Appl. Phys. (2002) 1538–1545. – 92 –
54] M. Babicz et al.,
Experimental study of the propagation of scintillation light in Liquid Argon , Nucl.Instrum. Meth.
A936 (2019) 178–179.[55] M. Babicz et al.,
Light Propagation in Liquid Argon , .[56] M. Mooney, The MicroBooNE Experiment and the Impact of Space Charge Effects , in
Proceedings,Meeting of the APS Division of Particles and Fields (DPF 2015): Ann Arbor, Michigan, USA, 4-8Aug 2015 , 2015. .[57] MicroBooNE Collaboration, “Measurement of Space Charge Effects in MicroBooNE.” http://microboone.fnal.gov/public-notes/ (see MICROBOONE-NOTE-1018-PUB).[58] MicroBooNE collaboration, C. Adams et al.,
A Method to Determine the Electric Field of LiquidArgon Time Projection Chambers Using a UV Laser System and its Application in MicroBooNE , .[59] MicroBooNE collaboration, C. Adams et al., Calibration of the charge and energy loss per unitlength of the MicroBooNE liquid argon time projection chamber using muons and protons , JINST (2020) P03022, [ ].[60] Particle Data Group collaboration, M. Tanabashi, K. Hagiwara, K. Hikasa, K. Nakamura,Y. Sumino, F. Takahashi et al., Review of particle physics , Phys. Rev. D (Aug, 2018) 030001.[61] ArgoNeuT collaboration, R. Acciarri et al., A Study of Electron Recombination Using HighlyIonizing Particles in the ArgoNeuT Liquid Argon TPC , JINST (2013) P08005, [ ].[62] “Muon Stopping Power and Range Tables 10 MeV - 100TeV.” http://pdg.lbl.gov/2019/AtomicNuclearProperties/adndt.pdf .[63] ArgoNeuT Collaboration collaboration, R. Acciarri, C. Adams, J. Asaadi, B. Baller, T. Bolton,C. Bromberg et al., First observation of low energy electron neutrinos in a liquid argon timeprojection chamber , Phys. Rev. D (Apr, 2017) 072005.[64] LArIAT collaboration, W. Foreman et al., Calorimetry for low-energy electrons using charge andlight in liquid argon , Phys. Rev.
D101 (2020) 012010, [ ].[65] D. Totani and F. Cavanna,
First Calorimetric Energy Reconstruction of Beam Events with ARAPUCALight Detector in ProtoDUNE-SP , JINST (2020) C03033.[66] C. W. Fabjan and F. Gianotti, Calorimetry for particle physics , Rev. Mod. Phys. (2003) 1243–1286.(2003) 1243–1286.