Measurement of inclusive charged-particle jet production in Au+Au collisions at \sqrt{s_{NN}}=200 GeV
STAR Collaboration, J. Adam, L. Adamczyk, J. R. Adams, J. K. Adkins, G. Agakishiev, M. M. Aggarwal, Z. Ahammed, I. Alekseev, D. M. Anderson, A. Aparin, E. C. Aschenauer, M. U. Ashraf, F. G. Atetalla, A. Attri, G. S. Averichev, V. Bairathi, K. Barish, A. Behera, R. Bellwied, A. Bhasin, J. Bielcik, J. Bielcikova, L. C. Bland, I. G. Bordyuzhin, J. D. Brandenburg, A. V. Brandin, J. Butterworth, H. Caines, M. Calderón de la Barca Sánchez, D. Cebra, I. Chakaberia, P. Chaloupka, B. K. Chan, F-H. Chang, Z. Chang, N. Chankova-Bunzarova, A. Chatterjee, D. Chen, J. H. Chen, X. Chen, Z. Chen, J. Cheng, M. Cherney, M. Chevalier, S. Choudhury, W. Christie, X. Chu, H. J. Crawford, M. Csanád, M. Daugherity, T. G. Dedovich, I. M. Deppner, A. A. Derevschikov, L. Didenko, X. Dong, J. L. Drachenberg, J. C. Dunlop, T. Edmonds, N. Elsey, J. Engelage, G. Eppley, R. Esha, S. Esumi, O. Evdokimov, A. Ewigleben, O. Eyser, R. Fatemi, S. Fazio, P. Federic, J. Fedorisin, C. J. Feng, Y. Feng, P. Filip, E. Finch, Y. Fisyak, A. Francisco, L. Fulek, C. A. Gagliardi, T. Galatyuk, F. Geurts, A. Gibson, K. Gopal, D. Grosnick, W. Guryn, A. I. Hamad, A. Hamed, S. Harabasz, J. W. Harris, S. He, W. He, X. He, S. Heppelmann, S. Heppelmann, N. Herrmann, E. Hoffman, L. Holub, Y. Hong, S. Horvat, Y. Hu, et al. (265 additional authors not shown)
MMeasurement of inclusive charged-particle jet productionin Au+Au collisions at √ s NN = 200 GeV J. Adam, L. Adamczyk, J. R. Adams, J. K. Adkins, G. Agakishiev, M. M. Aggarwal, Z. Ahammed, I. Alekseev,
3, 35
D. M. Anderson, A. Aparin, E. C. Aschenauer, M. U. Ashraf, F. G. Atetalla, A. Attri, G. S. Averichev, V. Bairathi, K. Barish, A. Behera, R. Bellwied, A. Bhasin, J. Bielcik, J. Bielcikova, L. C. Bland, I. G. Bordyuzhin, J. D. Brandenburg,
49, 6
A. V. Brandin, J. Butterworth, H. Caines, M. Calder´on de la Barca S´anchez, D. Cebra, I. Chakaberia,
29, 6
P. Chaloupka, B. K. Chan, F-H. Chang, Z. Chang, N. Chankova-Bunzarova, A. Chatterjee, D. Chen, J. H. Chen, X. Chen, Z. Chen, J. Cheng, M. Cherney, M. Chevalier, S. Choudhury, W. Christie, H. J. Crawford, M. Csan´ad, M. Daugherity, T. G. Dedovich, I. M. Deppner, A. A. Derevschikov, L. Didenko, X. Dong, J. L. Drachenberg, J. C. Dunlop, T. Edmonds, N. Elsey, J. Engelage, G. Eppley, R. Esha, S. Esumi, O. Evdokimov, A. Ewigleben, O. Eyser, R. Fatemi, S. Fazio, P. Federic, J. Fedorisin, C. J. Feng, Y. Feng, P. Filip, E. Finch, Y. Fisyak, A. Francisco, L. Fulek, C. A. Gagliardi, T. Galatyuk, F. Geurts, A. Gibson, K. Gopal, D. Grosnick, W. Guryn, A. I. Hamad, A. Hamed, S. Harabasz, J. W. Harris, S. He, W. He, X. He, S. Heppelmann, S. Heppelmann, N. Herrmann, E. Hoffman, L. Holub, Y. Hong, S. Horvat, Y. Hu, H. Z. Huang, S. L. Huang, T. Huang, X. Huang, T. J. Humanic, P. Huo, G. Igo, D. Isenhower, P. M. Jacobs, W. W. Jacobs, C. Jena, A. Jentsch, Y. JI, J. Jia,
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K. Jiang, S. Jowzaee, X. Ju, E. G. Judd, S. Kabana, M. L. Kabir, S. Kagamaster, D. Kalinkin, K. Kang, D. Kapukchyan, K. Kauder, H. W. Ke, D. Keane, A. Kechechyan, M. Kelsey, Y. V. Khyzhniak, D. P. Kiko(cid:32)la, C. Kim, B. Kimelman, D. Kincses, T. A. Kinghorn, I. Kisel, A. Kiselev, A. Kisiel, M. Kocan, L. Kochenda, L. K. Kosarzewski, L. Kramarik, P. Kravtsov, K. Krueger, N. Kulathunga Mudiyanselage, L. Kumar, R. Kunnawalkam Elayavalli, J. H. Kwasizur, R. Lacey, S. Lan, J. M. Landgraf, J. Lauret, A. Lebedev, R. Lednicky, J. H. Lee, Y. H. Leung, C. Li, W. Li, W. Li, X. Li, Y. Li, Y. Liang, R. Licenik, T. Lin, Y. Lin, M. A. Lisa, F. Liu, H. Liu, P. Liu, P. Liu, T. Liu, X. Liu, Y. Liu, Z. Liu, T. Ljubicic, W. J. Llope, R. S. Longacre, N. S. Lukow, S. Luo, X. Luo, G. L. Ma, L. Ma, R. Ma, Y. G. Ma, N. Magdy, R. Majka, D. Mallick, S. Margetis, C. Markert, H. S. Matis, J. A. Mazer, N. G. Minaev, S. Mioduszewski, B. Mohanty, M. M. Mondal, I. Mooney, Z. Moravcova, D. A. Morozov, M. Nagy, J. D. Nam, Md. Nasim, K. Nayak, D. Neff, J. M. Nelson, D. B. Nemes, M. Nie, G. Nigmatkulov, T. Niida, L. V. Nogach, T. Nonaka, G. Odyniec, A. Ogawa, S. Oh, V. A. Okorokov, B. S. Page, R. Pak, A. Pandav, Y. Panebratsev, B. Pawlik, D. Pawlowska, H. Pei, C. Perkins, L. Pinsky, R. L. Pint´er, J. Pluta, J. Porter, M. Posik, N. K. Pruthi, M. Przybycien, J. Putschke, H. Qiu, A. Quintero, S. K. Radhakrishnan, S. Ramachandran, R. L. Ray, R. Reed, H. G. Ritter, J. B. Roberts, O. V. Rogachevskiy, J. L. Romero, L. Ruan, J. Rusnak, N. R. Sahoo, H. Sako, S. Salur, J. Sandweiss, S. Sato, W. B. Schmidke, N. Schmitz, B. R. Schweid, F. Seck, J. Seger, M. Sergeeva, R. Seto, P. Seyboth, N. Shah, E. Shahaliev, P. V. Shanmuganathan, M. Shao, F. Shen, W. Q. Shen, S. S. Shi, Q. Y. Shou, E. P. Sichtermann, R. Sikora, M. Simko, J. Singh, S. Singha, N. Smirnov, W. Solyst, P. Sorensen, H. M. Spinka, B. Srivastava, T. D. S. Stanislaus, M. Stefaniak, D. J. Stewart, M. Strikhanov, B. Stringfellow, A. A. P. Suaide, M. Sumbera, B. Summa, X. M. Sun, X. Sun, Y. Sun, Y. Sun, B. Surrow, D. N. Svirida, P. Szymanski, A. H. Tang, Z. Tang, A. Taranenko, T. Tarnowsky, J. H. Thomas, A. R. Timmins, D. Tlusty, M. Tokarev, C. A. Tomkiel, S. Trentalange, R. E. Tribble, P. Tribedy, S. K. Tripathy, O. D. Tsai, Z. Tu, T. Ullrich, D. G. Underwood, I. Upsal,
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G. Van Buren, J. Vanek, A. N. Vasiliev, I. Vassiliev, F. Videbæk, S. Vokal, S. A. Voloshin, F. Wang, G. Wang, J. S. Wang, P. Wang, Y. Wang, Y. Wang, Z. Wang, J. C. Webb, P. C. Weidenkaff, L. Wen, G. D. Westfall, H. Wieman, S. W. Wissink, R. Witt, Y. Wu, Z. G. Xiao, G. Xie, W. Xie, H. Xu, N. Xu, Q. H. Xu, Y. F. Xu, Y. Xu, Z. Xu, Z. Xu, C. Yang, Q. Yang, S. Yang, Y. Yang, Z. Yang, Z. Ye, Z. Ye, L. Yi, K. Yip, H. Zbroszczyk, W. Zha, D. Zhang, S. Zhang, S. Zhang, X. P. Zhang, Y. Zhang, Y. Zhang, Z. J. Zhang, Z. Zhang, Z. Zhang, J. Zhao, C. Zhong, C. Zhou, X. Zhu, Z. Zhu, M. Zurek, and M. Zyzak (STAR Collaboration) Abilene Christian University, Abilene, Texas 79699 AGH University of Science and Technology, FPACS, Cracow 30-059, Poland Alikhanov Institute for Theoretical and Experimental Physics NRC ”Kurchatov Institute”, Moscow 117218, Russia Argonne National Laboratory, Argonne, Illinois 60439 a r X i v : . [ nu c l - e x ] M a y American University of Cairo, New Cairo 11835, New Cairo, Egypt Brookhaven National Laboratory, Upton, New York 11973 University of California, Berkeley, California 94720 University of California, Davis, California 95616 University of California, Los Angeles, California 90095 University of California, Riverside, California 92521 Central China Normal University, Wuhan, Hubei 430079 University of Illinois at Chicago, Chicago, Illinois 60607 Creighton University, Omaha, Nebraska 68178 Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic Technische Universit¨at Darmstadt, Darmstadt 64289, Germany ELTE E¨otv¨os Lor´and University, Budapest, Hungary H-1117 Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany Fudan University, Shanghai, 200433 University of Heidelberg, Heidelberg 69120, Germany University of Houston, Houston, Texas 77204 Huzhou University, Huzhou, Zhejiang 313000 Indian Institute of Science Education and Research (IISER), Berhampur 760010 , India Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India Indian Institute Technology, Patna, Bihar 801106, India Indiana University, Bloomington, Indiana 47408 Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000 University of Jammu, Jammu 180001, India Joint Institute for Nuclear Research, Dubna 141 980, Russia Kent State University, Kent, Ohio 44242 University of Kentucky, Lexington, Kentucky 40506-0055 Lawrence Berkeley National Laboratory, Berkeley, California 94720 Lehigh University, Bethlehem, Pennsylvania 18015 Max-Planck-Institut f¨ur Physik, Munich 80805, Germany Michigan State University, East Lansing, Michigan 48824 National Research Nuclear University MEPhI, Moscow 115409, Russia National Institute of Science Education and Research, HBNI, Jatni 752050, India National Cheng Kung University, Tainan 70101 Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic Ohio State University, Columbus, Ohio 43210 Institute of Nuclear Physics PAN, Cracow 31-342, Poland Panjab University, Chandigarh 160014, India Pennsylvania State University, University Park, Pennsylvania 16802 NRC ”Kurchatov Institute”, Institute of High Energy Physics, Protvino 142281, Russia Purdue University, West Lafayette, Indiana 47907 Rice University, Houston, Texas 77251 Rutgers University, Piscataway, New Jersey 08854 Universidade de S˜ao Paulo, S˜ao Paulo, Brazil 05314-970 University of Science and Technology of China, Hefei, Anhui 230026 Shandong University, Qingdao, Shandong 266237 Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800 Southern Connecticut State University, New Haven, Connecticut 06515 State University of New York, Stony Brook, New York 11794 Instituto de Alta Investigaci´on, Universidad de Tarapac´a, Chile Temple University, Philadelphia, Pennsylvania 19122 Texas A&M University, College Station, Texas 77843 University of Texas, Austin, Texas 78712 Tsinghua University, Beijing 100084 University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan United States Naval Academy, Annapolis, Maryland 21402 Valparaiso University, Valparaiso, Indiana 46383 Variable Energy Cyclotron Centre, Kolkata 700064, India Warsaw University of Technology, Warsaw 00-661, Poland Wayne State University, Detroit, Michigan 48201 Yale University, New Haven, Connecticut 06520
The STAR Collaboration at the Relativistic Heavy Ion Collider reports the first measurementof inclusive jet production in peripheral and central Au+Au collisions at √ s NN = 200 GeV. Jetsare reconstructed with the anti- k T algorithm using charged tracks with pseudo–rapidity | η | < . and transverse momentum 0 . < p T <
30 GeV /c , with jet resolution parameter R = 0.2, 0.3,and 0.4. The large background yield uncorrelated with the jet signal is observed to be dominatedby statistical phase space, consistent with a previous coincidence measurement. This backgroundis suppressed by requiring a high transverse-momentum (high- p T ) leading hadron in accepted jetcandidates. The bias imposed by this requirement is assessed, and the p T -region in which thebias is small is identified. Inclusive charged-particle jet distributions are reported in peripheraland central Au+Au collisions for 5 < p chT , jet <
25 GeV /c and 5 < p chT , jet <
30 GeV /c respectively.The charged-particle jet inclusive yield is suppressed for central Au+Au collisions, compared toboth the peripheral Au+Au yield from this measurement and to the pp yield calculated using thePYTHIA event generator. The magnitude of the suppression is consistent with that of inclusivehadron production at high p T , and that of semi-inclusive recoil jet yield when expressed in terms ofenergy loss due to medium-induced energy transport. Comparison of inclusive charged-particle jetyields for different values of R exhibits no significant evidence for medium-induced broadening ofthe transverse jet profile for R < . PACS numbers: 25.75.Bh, 13.87.-a, 12.38.Mh
I. INTRODUCTION
Collisions of heavy nuclei at high energy generate aQuark-Gluon Plasma (QGP), a state of matter withtemperature and energy density similar to those of theuniverse a few microseconds after the Big Bang, andwhose dynamics are governed by the interactions of sub-hadronic quanta ([1] and references therein). Extensivemeasurements of the QGP have been carried out withnuclear collisions at the Relativistic Heavy Ion Collider(RHIC) and the Large Hadron Collider (LHC). Compar-ison of these measurements with theoretical calculationsindicates that the QGP is an inviscid fluid exhibitingcollective behavior [2]. The QGP is likewise found tobe opaque to penetrating probes carrying color charge,a phenomenon known as “jet quenching” ([3] and refer-ences therein).Jets in high-energy collisions are generated by the hard(high momentum-transfer Q ) scattering of quarks andgluons (collectively, partons) from the incoming projec-tiles. The scattered partons fragment into correlatedsprays of stable hadrons that are observed in the detec-tor. Jet production has been measured extensively in ppcollisions, with theoretical calculations based on high-order perturbative Quantum Chromodynamics (pQCD)describing such measurements accurately over a widekinematic range [4–8].Jets are likewise generated in high-energy nuclear col-lisions, with production rates that are accurately calcula-ble using pQCD methods [9]. Because high- Q processesoccur early in the evolution of a nuclear collision, jetsprobe the QGP at its highest temperature and energydensity. Jet quenching, which arises from the interactionof energetic partons with the QGP via elastic and radia-tive processes, is expected to generate modifications inobserved jet production rates and internal structure [10].Measurement of reconstructed jets in heavy-ion colli-sions is challenging: a jet, which comprises ∼
10 corre-lated particles at RHIC energies, must be distinguishedfrom the many hundreds of particles generated by uncor-related processes [11]. High transverse-momentum (high- p T ) hadrons, which are the leading fragments of jets, canbe more readily distinguished from this background thanfully reconstructed jets. The production rate of high- p T hadrons was also predicted to be suppressed due to jetquenching [12], and suppression of inclusive productionand correlations of high- p T hadrons due to jet quench-ing has indeed been observed at RHIC [13–20] and theLHC [21–25]. The comparison of inclusive hadron sup-pression measurements with theoretical calculations hasbeen used to constrain the QGP transport parameterˆ q [3], which characterizes the momentum transfer be-tween a jet probe and the QGP medium.High- p T hadron suppression provides limited insightinto the mechanisms and dynamics of jet quenching, how-ever. High- p T hadrons arise predominantly from jets thathave lost relatively little energy in-medium, due to the in-terplay of the jet- p T distribution, jet fragmentation, andjet energy loss [26–33]. The contribution to the inclusivehadron yield of jets undergoing significant modificationdue to quenching is thereby suppressed.Broader exploration of jet quenching requires mea-surements with reconstructed jets. At the LHC,reconstructed-jet measurements in Pb+Pb collisions havebeen reported for inclusive production [34–38], cor-relations [39–43], and jet substructure [44–46]. AtRHIC, reconstructed-jet measurements in Au+Au colli-sions have been reported for correlations [47, 48]. Whilethe inclusive jet and dijet production cross sections havebeen reported for pp collisions at RHIC [4, 5], the mea-surement of inclusive jet production in Au+Au collisionsat RHIC has not been reported to date.This manuscript presents the first measurementof inclusive jet production in Au+Au collisions at √ s NN = 200 GeV. Jets are reconstructed in central (0-10 percentile bin of the inelastic cross section) and pe-ripheral (60-80 percentile bin) Au+Au collisions usingcharged tracks with transverse momentum p constT > . /c and pseudo–rapidity | η track | < .
0, using the anti- k T algorithm [49] with resolution parameter R = 0.2, 0.3,and 0.4. Uncorrelated background yield is suppressed bya cut on the leading (highest p T ) hadron of each jet can-didate, p T , lead > p minT , lead , which imposes a bias on thefragmentation pattern of the reported jet population; welabel the resulting jet population “quasi-inclusive”. Theeffect of the bias is determined by varying the value of p minT , lead . The distribution of the jet population arisingfrom the large uncorrelated background is well-describedby a model calculation based on statistical phase space,without taking into account any multi-particle correla-tions whatsoever. This observation is consistent with theaccurate description of the background to semi-inclusiverecoil jet yields by event mixing [48].Quasi-inclusive charged-particle jet distributions arereported in the range 5 < p chT , jet <
30 GeV /c for cen-tral Au+Au collisions. Charged-particle jet yield sup-pression is quantified by comparing the quasi-inclusivedistribution measured in central Au+Au collisions tothat measured in peripheral Au+Au collisions, and tothe inclusive charged-particle jet distribution for pp col-lisions generated using the PYTHIA Monte Carlo genera-tor [50], which has been validated by comparison to inclu-sive measurements of pions and fully-reconstructed jetsat RHIC [51]. These measurements are also compared tosimilar inclusive jet measurements at the LHC, to semi-inclusive hadron+jet measurements at RHIC, and to the-oretical calculations of jet quenching.The manuscript is organized as follows: Sect. II de-scribes the experiment and data selection; Sect. IIIpresents considerations for heavy-ion jet analysis and themeasurement approach; Sect. IV presents the jet recon-struction; Sect. V presents raw jet spectra; Sect. VIpresents the corrections due to background fluctuationsand detector effects; Sect. VII presents the systematicuncertainties; Sect. VIII presents the parametrized model(PM) and closure test; Sect. IX presents the referencespectrum from pp collisions calculated using PYTHIA;Sect. X describes the theoretical calculations used forcomparison; Sect. XI presents the results; and Sect. XIIpresents the summary. II. DETECTOR AND DATASET
The STAR detector is described in [52]. STAR isa large, general-purpose collider detector with high-precision tracking, particle identification, electromag-netic calorimetry, and forward detectors. The centralregion is immersed in a 0.5 T solenoidal magnetic field.The data for this analysis were recorded during the 2011RHIC run with Au+Au collisions at √ s NN = 200 GeV.Events were selected online using a Minimum Bias (MB)trigger that requires signals in both forward scintillatorVertex Position Detectors (VPD), with a timing cut toconstrain the primary vertex position within | z vtx | < µ s before or after thetriggered collision, consistent with the drift time of theTime Projection Chamber (TPC) [53].Charged particle tracks are reconstructed offline usingthe TPC, which has an inner radius of 50 cm and anouter radius of 200 cm, and covers the full azimuth within | η track | <
1. TPC tracks have a maximum number of 45space points.Global tracks, which do not include the primary eventvertex in the momentum fit, are accepted if they havemore than 14 space points, with the ratio of the numberof space points to the number of potential space pointsgreater than 0.52. The location of the primary vertexis determined using global tracks. The primary vertexposition resolution along the beam direction is 350 µ mfor the most central Au+Au events used in the analysis.Jet reconstruction utilizes primary tracks, which areglobal tracks whose momenta have been refit with inclu-sion of the primary event vertex. Primary tracks with0 . < p constT <
30 GeV /c and which have distance ofclosest approach (DCA) to the primary vertex in thetransverse plane DCA xy < | z vtx | <
30 cm of the nominalcenter of STAR along the beamline, and within 2 cm ofthe beam axis in the transverse plane. After offline eventselection cuts, a total of ∼ ∼ µb − .Events are classified offline in percentile bins of cen-trality, based on charged-particle multiplicity measuredin | η track | < .
5. The accepted event population has ∼
47M central collision events and ∼
94M peripheral col-lision events. The online trigger efficiency is consistentwith 100% for central Au+Au collisions and is approxi-mately 70% for peripheral Au+Au collisions.Simulated events for pp collisions at √ s = 200 GeVwere generated using PYTHIA 6.428, tune Perugia2012 [50]. Simulated events without instrumental effectsare denoted “particle level,” whereas events incorporat-ing instrumental effects are denoted “detector level”; seeSect. VI. The largest instrumental effects in the measure-ment of charged-particle jets are tracking efficiency andtrack momentum resolution. Fast simulation events aregenerated by applying a p T -dependent parameterizationof these effects to PYTHIA-generated events.Tracking efficiency is determined by embedding singletracks simulated at the detector level into real Au+Auevents. Tracking efficiency depends on particle species;tracking efficiency for non-identified charged tracks there-fore depends on the relative population of differentspecies. In order to assess the magnitude of this depen-dence, two different assumptions are made for the relativeyield of charged pions, charged kaons, protons and anti-protons comprising the charged track population: therelative yields measured in pp collisions [54, 55], andthose measured in Au+Au collisions [55–57]. The relativeyields for Au+Au collisions are used in the principal anal-ysis, giving tracking efficiency for primary charged tracksof 68% at p T = 0.5 GeV /c and 72% for p T > /c incentral Au+Au collisions; and 85% at p T = 0.5 GeV /c and 88% for p T > /c in peripheral Au+Au colli-sions. The relative yields from pp collisions give trackingefficiency that is 1% lower for p T < /c , with neg-ligible differences for p T > /c . This variation issmaller than the overall systematic uncertainty assignedto the tracking efficiency, which is discussed below.Primary track momentum resolution, which is alsodetermined by embedding simulated tracks into realAu+Au events, is parametrized for p T > . /c as σ p T = − .
026 + 0 . · p T + 0 . · ( p T ) [ p T in unitsof GeV /c ], with a variation σ p T = 0 . · ( p T ) used forsystematic uncertainty.Comparison of inclusive jet spectra at different cen-tralities requires the scaling of yields by the centrality-dependent nuclear thickness factor (cid:104) T AA (cid:105) , which is cal-culated using Glauber modeling [58]. In this analysis, (cid:104) T AA (cid:105) has the value 22 . ± . − for central Au+Aucollisions and 0 . ± .
14 mb − for peripheral Au+Aucollisions. III. ANALYSIS STRATEGY
Jet reconstruction algorithms provide a systematicallywell-controlled approach to jet measurements and corre-sponding theoretical calculations in pp collisions at col-lider energies [5, 7, 8]. Jet measurements in heavy-ioncollisions are significantly more complex, however, due tothe large uncorrelated background in such events. In thissection we discuss the main considerations for a theoret-ically interpretable measurement of the inclusive jet dis-tribution in the large-background environment of heavy-ion collisions, and the consequent strategy for this anal-ysis.The constituents of a jet reconstructed in a high-energynuclear collision arise from multiple different sources,which we classify qualitatively as due to hard processes( Q > few GeV ) or to soft processes (all others). Multi-ple hard processes can occur in a single nuclear collision;in the framework of QCD factorization they are consid-ered to be incoherent. These processes can generate mul-tiple energetic jets that overlap in ( η, φ )-space, whosehadronic fragments are thereby clustered by a jet re-construction algorithm into a single jet candidate. Eachsuch jet candidate will also contain copiously producedhadrons from soft processes. Jet candidates in centralhigh-energy nuclear collisions therefore have a significantcontribution from hadrons due to soft processes, and mayalso contain hadronic fragments of one or more primor-dial jets arising from hard processes.For an inclusive jet measurement in central high-energynuclear collisions to be theoretically interpretable, itmust report the distribution of a unique, well-defined jetpopulation arising from hard processes. The measure- ment must therefore exclude the yield of purely combi-natorial jet candidates arising solely due to contributionsfrom soft processes, and disentangle the effects of mul-tiple overlapping primordial jets arising from hard pro-cesses. It should also correct for the shift and smearingof the jet p T -scale due to the large number of hadronsarising from soft processes in each identified hard-jet can-didate.In semi-inclusive hadron jet analyses [39, 48] thesecorrections are implemented in three distinct steps: (i)approximate adjustment event-by-event of jet candidate p T , jet for the uncorrelated background contribution; (ii)rejection of background yield not correlated with the trig-ger, giving the raw trigger-correlated jet yield; and (iii)final correction via unfolding of the jet p T , jet for shift andfluctuations in the background energy density. Steps (ii)and (iii) are carried out at the level of ensemble-averageddistributions (“statistical correction”). This approachenables the measurement of trigger-normalized recoil jetdistributions for large jet radius R and low p T , jet in themost central A + A collisions, without imposing fragmen-tation bias on the reported jet population [39, 48].The inclusive jet distribution that is the goal of thisanalysis is not defined with respect to a trigger, how-ever, and a different approach is needed for step (ii) toidentify jet candidates that arise from hard processes.We therefore accept for analysis only those jet candi-dates whose highest- p T hadronic constituent (“leadinghadron”) has p T , lead > p minT , lead [35, 59]. No cut is madeon p T , jet in this analysis, in contrast to other currentmeasurements of inclusive jet distributions in heavy-ioncollisions [34, 35, 37, 38].There are competing considerations for the value of p minT , lead [59]: • The value of p minT , lead must be sufficiently high thatthe probability for such a hadron to arise frompurely combinatorial jet is negligible; i.e. with highprobability it is the fragment of a hard process. • The value of p minT , lead must be sufficiently high thatthe probability for multiple hadrons to satisfy thiscut in a central Au+Au collision is negligible. Theprobability of two hard jets in an event passingthis acceptance cut is therefore also negligible; withhigh probability there will be at most one such jetcandidate in an event. This selection thereby iden-tifies a unique, well-defined jet population arisingfrom a specified hard process, as required. • The value of p minT , lead should be as low as possible,to minimize the bias imposed on the accepted jetpopulation.The bias relative to the inclusive jet population im-posed by the p minT , lead cut must be determined experi-mentally, for the measurement to be theoretically inter-pretable. The value of p minT , lead is consequently varied inthe analysis, and the p T , jet range in which the correctedinclusive jet distribution does not depend significantly on p minT , lead is found. This is identified as the range where thebias is small. IV. JET RECONSTRUCTION
Jet reconstruction utilizes the k T [60] and anti- k T [49]algorithms with the boost-invariant p T -recombinationscheme [60], applied to all accepted charged tracks. Thejet area is calculated by the Fastjet algorithm [61] withghost particle area of 0.01. The jet centroid is calculatedas the sum of the four-vectors of its constituents [60].This analysis employs several types of charged-particlejet, which are referred to using the notation definedin [48]: the raw transverse momentum of reconstructedjets is denoted p raw , chT , jet ; jet transverse momentum afterthe event-wise adjustment for uncorrelated backgrounddensity is denoted p reco , chT , jet ; and jet transverse momentumafter full correction for instrumental effects and back-ground fluctuations is denoted p chT , jet .Jet reconstruction is carried out twice for each event.The first jet reconstruction pass applies the k T algorithmwith R = 0.3 to calculate ρ , the estimated transverse-momentum density of background in the event [62], ρ = median (cid:40) p raw , iT , jet A ijet (cid:41) , (1)where index i labels the charged-particle jet candidatesin the event from this reconstruction pass, and p raw , iT , jet and A ijet are the transverse momentum and area of the i th jet.For central Au+Au collisions, the two jets with largest p raw , iT , jet are excluded from the median calculation, whilefor peripheral collisions the single jet with largest p raw , iT , jet is excluded. Different choices for the number of excludedjets are used for systematic variation (Sect. VII D).The second reconstruction pass, which generates jetcandidates for the measured distributions, applies theanti- k T algorithm with R = 0.2, 0.3, or 0.4. Jet can-didates are accepted for further analysis if their centroidlies within | η jet | < − R , due to the TPC acceptance.The value of p raw , iT , jet is adjusted according to [62] p reco , iT , jet = p raw , iT , jet − ρ · A ijet , (2)where i in this case labels the jet candidates from thesecond reconstruction pass, and ρ is determined fromEq. 1. The value of ρ varies event-to-event: for cen-tral Au+Au collisions in this analysis its most probablevalue is 31 GeV/( c -sr), with RMS = 3 GeV/( c -sr); forperipheral Au+Au collisions its most probable value is 0,with RMS = 1 GeV/( c -sr).The definition of ρ in Eq. 1 requires algorithmic choicesthat are not unique, including reconstruction algorithm, jet-resolution parameter R , and the number of jet candi-dates excluded from the median calculation. The adjust-ment to p raw , chT , jet in Eq. 2 is therefore only an estimate ofthe event-wise pedestal due to uncorrelated background.The absolute jet energy scale is imposed in the unfoldingstep described below (see also [39, 48]).Figure 1, upper panels, show distributions of p reco , chT , jet vs. jet area in central Au+Au collisions for the inclusivecharged-particle jet population without a leading parti-cle cut (indicated by p minT , lead = 0; note that tracks have p constT > . /c ) with R = 0.2 and 0.4. Jets withsmall area predominantly have p reco , chT , jet ∼
0. The mid-dle and lower panels show area projections of these dis-tributions, together with those for jets in pp collisionssimulated using PYTHIA with p chT , jet = 10 and 20GeV /c that have been embedded into real events, andfor single-particle “jets” (SP, Sect. VI B). The area dis-tributions for PYTHIA-generated and SP jets in centralAu+Au collisions are similar, with negligible dependenceon p chT , jet . The area distributions for PYTHIA-generatedand SP jets are similar in peripheral Au+Au collisions(not shown).Figure 1 shows that, for jets with p chT , jet >
10 GeV /c ,the jet area is largely a geometric quantity, with lit-tle dependence on the pattern of jet fragmentation intohadrons. The area distribution for embedded jets ispeaked at A jet ∼ π · R , while the inclusive jet popula-tion exhibits a tail towards small area, which arises frompurely combinatorial jets without a hard component. Acut on jet area is therefore applied to suppress purelycombinatorial jet candidates, while preserving high effi-ciency for jets that include a hard component [48]. Jetcandidates are rejected if A jet < .
07 sr for R = 0.2, A jet < . R = 0.3, and A jet < . R = 0.4. V. UNCORRECTED JET DISTRIBUTIONS
Figure 2 shows measured p reco , chT , jet distributions for in-clusive jet candidates with R = 0.2, 0.3 and 0.4 whichpass the jet area cut, in peripheral and central Au+Aucollisions. The distributions for central Au+Au collisionshave significant yield in the region p reco , chT , jet <
0. This fea-ture is also observed in hadron-triggered semi-inclusiveanalyses [39, 48], where it is attributed predominantly tocombinatorial jet candidates generated by soft processesthat are uncorrelated with the trigger.The distributions exhibit a change in slope at p reco , chT , jet ∼
10 GeV /c for all R in peripheral Au+Au collisions, andat p reco , chT , jet ∼
15 GeV /c for R = 0.2 in central Au+Au col-lisions, suggesting two distinct contributions to the spec-trum that are visible for the configurations with small-est background. In this picture the yield at low p reco , chT , jet is dominated by combinatorial jet candidates, similar todistributions in the hadron+jet analysis, while the yieldat large p reco , chT , jet is dominated by jets arising from hardprocesses. Area (sr) ) c ( G e V / r e c o T , j e t p - - = 200 GeV NN sAu+Au Central (0-10%), R = 0.2 T k anti-| < 1 - R jet h | Area (sr) ) c ( G e V / r e c o T , j e t p - - = 200 GeV NN sAu+Au Central (0-10%), R = 0.4 T k anti-| < 1 - R jet h | Area (sr) a r b . un i t s NN sAu+Au Central (0-10%), R = 0.2 T k anti-| < 1 - R jet h | STAR data: inclusive jetsEmbed. (PYTHIA): c =10 GeV/ embT p c =20 GeV/ embT p Area (sr) a r b . un i t s NN sAu+Au Central (0-10%), R = 0.4 T k anti-| < 1 - R jet h | STAR data: inclusive jetsEmbed. (PYTHIA): c =10 GeV/ embT p c =20 GeV/ embT p Area (sr) a r b . un i t s NN sAu+Au Central (0-10%), R = 0.2 T k anti-| < 1 - R jet h | STAR data: inclusive jetsEmbedding (SP): c =10 GeV/ embT p c =20 GeV/ embT p Area (sr) a r b . un i t s NN sAu+Au Central (0-10%), R = 0.4 T k anti-| < 1 - R jet h | STAR data: inclusive jetsEmbedding (SP): c =10 GeV/ embT p c =20 GeV/ embT p FIG. 1. (Color online) Distribution of p reco , chT , jet and jet area for the inclusive charged-particle jet population ( p minT , lead = 0) incentral Au+Au collisions. Upper panels: p reco , chT , jet vs jet area for R = 0.2 (left) and R = 0.4 (right). Middle and lower panels:projection onto the jet area axis. Also shown are area distributions for PYTHIA-generated (middle) and SP jets (lower) with p embT = 10 and 20 GeV /c , embedded into real Au+Au data for central collisions. The vertical dashed lines show the jet areacut. ) c (GeV/ reco, chT, jet p - - - ) c ( G e V / j e t h d c h T , j e t p N / d d p / e v en t s / N - - - - - - - - -
10 110 = 200 GeV NN sAu+Au Peripheral (60-80%)| < 1 - R jet h | T k anti- R = 0.2, A > 0.07 srR = 0.3, A > 0.3 srR = 0.4, A > 0.4 sr ) c (GeV/ reco, chT, jet p - - - ) c ( G e V / j e t h d c h T , j e t p N / d d p / e v en t s / N - - - - - - - - -
10 110 = 200 GeV NN sAu+Au Central (0-10%)| < 1 - R jet h | T k anti- R = 0.2, A > 0.07 srR = 0.3, A > 0.3 srR = 0.4, A > 0.4 sr
FIG. 2. (Color online) Distribution of inclusive charged-particle jet candidates passing the jet area cut as a functionof p reco , chT , jet for R = 0.2, 0.3 and 0.4, in peripheral (upper) andcentral (lower) Au+Au collisions. Figure 3 shows the effect of the cut p T , lead > p minT , lead on p reco , chT , jet distributions in peripheral and central Au+Aucollisions. The p minT , lead cut suppresses the yield moststrongly for large negative values of p reco , chT , jet , with muchreduced suppression at large positive values of p reco , chT , jet .Larger values of p minT , lead generate larger suppression,with correspondingly larger bias expected in the fullycorrected distributions. Section III specifies the com-peting criteria for optimizing the value of p minT , lead . Theoptimum value of p minT , lead for this analysis is found to be p minT , lead = 5 GeV /c , which is the lowest value that givesstable unfolding results (Sect. VI C) and successful clo-sure (Sect. VIII). The value p minT , lead = 7 GeV /c is usedfor systematic variation, to determine the range in p chT , jet over which the bias is small (Sect. XI). VI. CORRECTIONS
The raw distributions are corrected for the effects of in-strumental response and background fluctuations, usingregularized unfolding [63–65]. We utilize the approachand notation described in [48].
A. Instrumental response matrix R det The instrumental response matrix R det is constructedusing PYTHIA-generated events for pp collisions at √ s = 200 GeV. A detector-level event is generated byapplying the fast simulator to each particle-level event.Jet reconstruction is then carried out with the anti- k T algorithm at both the particle and detector levels,and jets are selected by applying the fiducial acceptance( | η jet | < − R ) and p minT , lead cuts. Jets at the particleand detector levels are matched following the procedurein [48].The instrumental response is determined by comparingmatched jets at the particle and detector levels. Figure 4shows the distribution of p detT , jet for u-quark-initiated jetswith several values of p partT , jet , with detector-level effectscorresponding to those for central Au+Au collisions. Thecut p minT , lead = 5 GeV /c is applied both at the particle anddetector levels for the primary analysis, with p minT , lead = 7GeV /c used to correct the corresponding analysis usedfor systematic variation (not shown). The distributionsfor gluon-initiated jets are very similar, suggesting thatthe instrumental response does not depend significantlyon the specific mixture of light quark- and gluon-initiatedjets in the population.The instrumental response in Fig. 4 is asymmetric,with a tail for p detT , jet < p partT , jet that arises predomi-nantly from the loss of a single charged hadron withhigh momentum-fraction (high-z) due to tracking inef-ficiency [48]. This asymmetric response cannot be char-acterized fully by a Jet Energy Resolution (JER) figure,and so the entire distribution shown in Fig. 4 is used tocorrect the spectrum for instrumental effects. Neverthe-less, as an approximation to the JER, we fit the mainpeak of these distributions with a Gaussian function andreport its relative width, as shown in the figure. For jetswith 7 < p partT , jet <
40 GeV /c , the relative width has val-ues between 4 and 10%, with negligible dependence onfragmentation model or jet resolution parameter R .A detector-level jet corresponding to a particle-level jetin the experimental acceptance can be lost due to fiducialcuts and instrumental response. The most significantcontribution to this loss is tracking inefficiency, especiallyfor low- p T jets containing few tracks. The jet area cuthas negligible inefficiency for p partT , jet > /c .Figure 5 shows the jet reconstruction efficiency. Thenominal calculation is carried out for a mixture of u-quark and gluon jets with yield ratio 2:1, and the nominaltracking efficiency. The efficiencies for pure u-quark or ) c (GeV/ reco, chT, jet p - - - - ) c ( G e V / j e t h d c h T , j e t p N / d d p / e v en t s / N - - - - - - - - -
10 110 = 200 GeV NN sAu+Au Peripheral (60-80%), R = 0.2 T k anti-| < 1 - R jet h | = minT,lead p c c c c ) c (GeV/ reco, chT, jet p - - - - ) c ( G e V / j e t h d c h T , j e t p N / d d p / e v en t s / N - - - - - - - - -
10 110 = 200 GeV NN sAu+Au Central (0-10%), R = 0.2 T k anti-| < 1 - R jet h | = minT,lead p c c c c ) c (GeV/ reco, chT, jet p - - - - ) c ( G e V / j e t h d c h T , j e t p N / d d p / e v en t s / N - - - - - - - - -
10 110 = 200 GeV NN sAu+Au Peripheral (60-80%), R = 0.3 T k anti-| < 1 - R jet h | = minT,lead p c c c c ) c (GeV/ reco, chT, jet p - - - - ) c ( G e V / j e t h d c h T , j e t p N / d d p / e v en t s / N - - - - - - - - -
10 110 = 200 GeV NN sAu+Au Central (0-10%), R = 0.3 T k anti-| < 1 - R jet h | = minT,lead p c c c c ) c (GeV/ reco, chT, jet p - - - - ) c ( G e V / j e t h d c h T , j e t p N / d d p / e v en t s / N - - - - - - - - -
10 110 = 200 GeV NN sAu+Au Peripheral (60-80%), R = 0.4 T k anti-| < 1 - R jet h | = minT,lead p c c c c ) c (GeV/ reco, chT, jet p - - - - ) c ( G e V / j e t h d c h T , j e t p N / d d p / e v en t s / N - - - - - - - - -
10 110 = 200 GeV NN sAu+Au Central (0-10%), R = 0.4 T k anti-| < 1 - R jet h | = minT,lead p c c c c FIG. 3. (Color online) Distribution of p reco , chT , jet measured in peripheral (left) and central (right) Au+Au collisions at √ s NN = 200GeV, for p minT , lead = 0, 3, 5 and 7 GeV /c . Upper: R = 0.2; middle: R = 0.3; lower: R = 0.4. The distributions for p minT , lead = 0are the same as those in Fig. 2. gluon populations are also shown, as is the jet-finding ef-ficiency for ±
5% relative variation in tracking efficiency,corresponding to its systematic uncertainty. The single-track efficiency is also shown, which corresponds to thejet reconstruction efficiency for p partT , jet = p minT , lead .A particle-level jet without a sufficiently hard leading track may be accepted at the detector-level due to trackmomentum smearing. This jet feed-up increases the jetfinding efficiency for the lowest p T , jet values by 1-2% (ab-solute). Figure 5 includes this effect.A track from a displaced vertex arising from a weakdecay may be assigned an incorrect momentum that sit-0 (GeV/c) detT,jet p p r obab ili t y -
10 1 =6% T p / T p d , c =7 GeV/ partT,jet p PYTHIA 6u-quark, R = 0.3 T k anti- = 5 GeV/c minT,lead p det. effects forcentral Au+Au =5% T p / T p d , c =10 GeV/ partT,jet p PYTHIA 6u-quark, R = 0.3 T k anti- = 5 GeV/c minT,lead p det. effects forcentral Au+Au =6% T p / T p d , c =15 GeV/ partT,jet p PYTHIA 6u-quark, R = 0.3 T k anti- = 5 GeV/c minT,lead p det. effects forcentral Au+Au =6% T p / T p d , c =20 GeV/ partT,jet p PYTHIA 6u-quark, R = 0.3 T k anti- = 5 GeV/c minT,lead p det. effects forcentral Au+Au =5% T p / T p d , c =30 GeV/ partT,jet p PYTHIA 6u-quark, R = 0.3 T k anti- = 5 GeV/c minT,lead p det. effects forcentral Au+Au =6% T p / T p d , c =40 GeV/ partT,jet p PYTHIA 6u-quark, R = 0.3 T k anti- = 5 GeV/c minT,lead p det. effects forcentral Au+Au FIG. 4. (Color online) Instrumental jet response: distributionof p detT , jet for u-quark jets generated by PYTHIA with variousvalues of p partT , jet , with detector effects corresponding to those incentral Au+Au collisions. Jets have R = 0.3 and are selectedwith the requirement p minT , lead = 5 GeV /c at both the particleand detector level. The red distributions show a Gaussianfunctional fit to the peak region of each distribution, withrelative width of the fit as shown. uates it inside or outside of a jet cone differently thanits parent particle. However, such effects are found to benegligible [48] and no correction for them is applied.Figure 6, left panel, shows the matrix R det for cen-tral Au+Au collisions. Contributions in the region p detT , jet < p partT , jet are due primarily to tracking efficiency,which causes tracks to be lost from the jet. Contribu-tions in the region p detT , jet > p partT , jet , which are less probable,arise primarily from the effect of momentum resolutionfor cases in which the fraction of p T , jet lost due to track-ing inefficiency is small.The Jet Energy Scale (JES) uncertainty due to instru-mental effects, which is dominated by the uncertainty ofthe tracking efficiency, is ∼
5% for R = 0.2 and 0.3, and7% for R = 0.4, in central Au+Au collisions; and 3% for R = 0.2, 0.3 and 0.4 in peripheral Au+Au collisions. Thedependence of JES on p detT , jet is negligible. B. Uncorrelated background response matrix R bkg The response matrix representing fluctuations in en-ergy density uncorrelated with a jet arising from a hardprocess is calculated by embedding detector-level sim-ulated jets into real events, reconstructing the hybridevents, and then matching each embedded jet with a re-constructed jet. The matching of particle- and detector-level jets likewise follows the procedure in Ref. [48]. Theresponse matrix corresponds to the probability distribu-tion for δp T , where ) c (GeV/ partT, jet p j e t e nominalu-quarkgluon-5% track ˛ +5% track ˛ tracking eff. PYTHIA u+g (2:1) jetsPeripheral (60-80%), R = 0.3 T k anti- c = 5 GeV/ minT,lead p ) c (GeV/ partT, jet p j e t e nominalu-quarkgluon-5% track ˛ +5% track ˛ tracking eff. PYTHIA u+g (2:1) jetsCentral (0-10%), R = 0.3 T k anti- c = 5 GeV/ minT,lead p FIG. 5. (Color online) Jet reconstruction efficiency in periph-eral (upper) and central (lower) Au+Au collisions, for R = 0.3and p minT , lead = 5 GeV /c , for u-quarks and gluons in the ratio2:1 (labeled “nominal”), pure u, pure g, and variation of therelative tracking efficiency by ±
5% for the nominal popula-tion. The orange line shows the single-track efficiency. δp T = p reco , chT , jet − p embT . (3)Jet reconstruction algorithms are infrared andcollinear-safe (IRC-safe) in elementary collision systems,i.e. they measure energy flow and are insensitive to thespecific pattern of jet fragmentation into hadrons. Inthis analysis we likewise seek to measure energy flow forcharged-particle jets in heavy-ion collisions, without biastowards specific patterns of jet fragmentation. That goalrequires the δp T distribution not to have significant de-pendence on the jet fragmentation pattern.In order to test this dependence, we calculate the δp T distribution in Eq. 3 with two significantly differentjet fragmentation models: light quark jets generated byPYTHIA (PYlq), utilizing the PYTHIA fragmentation1 ) c (GeV/ detT, jet p - - - ) c ( G e V / pa r t T , j e t p detector RM - - - detector RM ) c (GeV/ recoT, jet p - - - ) c ( G e V / de t T , j e t p RM T p d - - - = 200 GeV NN sAu+Au Central (0-10%) c = 5 GeV/ minT,lead p , R = 0.3 T k anti- RM T p d ) c (GeV/ recoT, jet p - - - ) c ( G e V / pa r t T , j e t p detector RM · RM T p d - - - detector RM · RM T p d FIG. 6. (Color online) Response matrices (RM) for charged jets with p minT , lead = 5 GeV /c and R = 0.3. Left: detector effects R det ; center: background fluctuations R bkg (SP embedding); right: R total = R bkg × R det . ) c (GeV/ T p d - - ) c p r obab ili t y / ( G e V / - - - - -
10 1 = 200 GeV NN sSTAR Au+Au Central (0-10%), R = 0.2 T k anti- c = 5 GeV/ minT,lead p | < 1 - R jet h | c = 20 GeV/ embT p =0 v SP, =0.04 v ˜ SP =0 v PYlq, ) c (GeV/ T p d - - ) c p r obab ili t y / ( G e V / - - - - -
10 1 = 200 GeV NN sSTAR Au+Au Central (0-10%), R = 0.4 T k anti- c = 5 GeV/ minT,lead p | < 1 - R jet h | c = 20 GeV/ embT p =0 v SP, =0.04 v ˜ SP =0 v PYlq, ) c (GeV/ T p d - - ) c p r obab ili t y / ( G e V / - - - - -
10 1 = 200 GeV NN sSTAR Au+Au Central (0-10%), R = 0.2 T k anti- c = 5 GeV/ minT,lead p | < 1 - R jet h | c = 5 GeV/ embT p c = 10 GeV/ embT p c = 20 GeV/ embT p ) c (GeV/ T p d - - ) c p r obab ili t y / ( G e V / - - - - -
10 1 = 200 GeV NN sSTAR Au+Au Central (0-10%), R = 0.4 T k anti- c = 5 GeV/ minT,lead p | < 1 - R jet h | c = 5 GeV/ embT p c = 10 GeV/ embT p c = 20 GeV/ embT p FIG. 7. (Color online) δp T distributions calculated by embedding various types of simulated jet in central Au+Au collisionsat √ s NN = 200 GeV, for R = 0.2 (left) and R = 0.4 (right). Upper panels: p embT = 20 GeV /c for SP jets, SP jets with v -modulated background, and light quark jets generated by PYTHIA. Lower panels: SP jets for several values of p embT . Seetext for details. p T is carried by a single hard particle [66]. Figure 7, up-per panels, compare the δp T probability distributions forthe SP and PYlq fragmentation models for R = 0.2 and0.4 jets with p chT , jet = 20 GeV /c embedded into centralAu+Au collisions; the cut p minT , lead = 5 GeV /c is appliedto the PYlq jets. The two fragmentation models indeedgenerate very similar δp T distributions. Figure 1 showsthat the jet area distributions for these two fragmenta-tion models are also similar.Figure 6, middle panel, shows the background responsematrix R bkg , whose elements are the δp T probability dis-tribution as a function of p T , jet , calculated by SP embed-ding.High- p T hadrons may be correlated in azimuth withthe Event Plane (EP) orientation [67]. The strength ofthis correlation is characterized by v , the second-orderFourier coefficient of the azimuthal distribution betweenthe hadron and the EP. Non-zero v for hadrons with p T > p minT , lead will bias the orientation of the accepted jetpopulation relative to the EP, thereby biasing the level ofuncorrelated background. This bias is taken into accountin the calculation of the δp T probability distribution byweighting each embedded jet with a weight w , w = 1 + 2 · v · cos(2∆ φ ) , (4)where ∆ φ is the azimuthal angle of the leading hadronrelative to the EP axis. Figure 7, upper panels, show δp T probability distributions with SP embedding for p T , jet = 20 GeV /c , for v = 0 and for v = 0.04, withthe latter value consistent with hadron v measured inthe region p T > p minT , lead [67]. This variation in v is seento generate negligible variation in the δp T distributions,and its effect is likewise negligible in the final correctedspectra. This is the only contribution of azimuthal asym-metry effects to the analysis.Figure 7, lower panels, show δp T probability distri-butions for SP jets with embedded p T , jet = 5, 10 and20 GeV /c . These distributions exhibit negligible depen-dence on p T , jet for R = 0.2, and minor dependence for R = 0.4.Figure 7 shows that the response matrix for back-ground fluctuations in central Au+Au collisions is largelyindependent of both p T , jet and the fragmentation modelused in the calculation. A similar lack of dependenceon fragmentation model is found for peripheral Au+Aucollisions. This indicates that jet reconstruction in thisanalysis indeed measures energy flow within jets, as re-quired. The small residual variations seen in Fig. 7 aretaken into account in the systematic uncertainty of thecorrected spectra. C. Unfolding
The unfolding procedure utilizes the cumulative re-sponse matrix (Fig. 6, right panel), which is the productof R bkg and R det . Two different unfolding methods areused: an iterative method based on Bayes’s Theorem [68],and a method based on Singular Value Decomposition(SVD) [64].Several different functional forms are used for theprior distribution: a power-law distribution, p T − n , with n = 4.5, 5.0, and 5.5; the inclusive jet distribution gen-erated by PYTHIA for pp collisions at √ s = 200 GeV,with p minT , lead = 5 GeV /c ; and the Tsallis function [48, 69],with n varying between 4 and 20 and T varying between0.6 and 1.2.The unfolding procedure is regularized, which imposesa smoothness constraint on the solution [63–65]. Thebackfolded distribution, which is the unfolded distribu-tion smeared by the response matrix, is used to optimizethe regularization. For iterative Bayesian unfolding, reg-ularization corresponds to limiting the number of iter-ations i ; optimization of the regularization is based oncomparison of unfolded distributions for two successiveiterations, and comparison of the backfolded and uncor-rected distributions. For SVD unfolding, regularizationcorresponds to truncation of the expansion at k terms;optimization of the value of k is determined by comparingthe backfolded and uncorrected distributions.Values of i or k are accepted if the distance between theunfolded and backfolded histogram (or between succes-sive iterations in case of i ) is sufficiently small. The his-togram distance is quantified using the average relativedistance between the central values of two distributions a and b , d rel = 1 n n (cid:88) i =1 | a i − b i | min( a i , b i ) , (5)where n is the number of bins, and a i and b i denote thecentral values in bin i .This approach is based on PM simulations (Sect. VIII)which show that a small distance between backfolded andunfolded solutions, or between successive iterations forBayesian unfolding, corresponds to a small distance be-tween the unfolded and generated spectra. The d rel met-ric is found to provide better discrimination than χ andKolmogorov-Smirnov metrics. D. Magnitude of corrections
In this section we estimate the magnitude of correc-tions to the quasi-inclusive jet spectrum, to provide con-text for the systematic uncertainties discussed below.This estimate utilizes PYTHIA-generated events for ppcollisions at √ s = 200 GeV, with instrumental effects cor-responding to central Au+Au collisions. The detector-level spectrum is smeared to account for background3fluctuations and is scaled by (cid:104) T AA (cid:105) , likewise for centralAu+Au collisions. (GeV/c) jetT p - - - ) c ) ( G e V / j e t h d T , j e t p d p N / ( d e v en t s / N - - - - - - PYTHIA 6.4.28charged jets = 5 GeV/c minT, lead p | < 1 - R jet h |R = 0.3 particle level instr. eff. ˜ T p d instr. eff. + ˜ FIG. 8. (Color online) Estimated magnitude of correctionsfor charged jets with R = 0.3 and p minT , lead = 5.0 GeV /c , forcentral Au+Au collisions. See text for details. Figure 8 shows the results of this calculation: the dis-tribution of charged jets with R = 0.3 and p minT , lead = 5.0GeV /c at the particle level (green dashed line), whichis modified cumulatively by instrumental effects (bluesolid line) and background fluctuations ( δ p T , red dashed).Correction by unfolding for this case corresponds totransforming the red-dashed to the green-dashed distri-bution. At fixed values of p T , jet , the effect of the unfold-ing correction for p chT , jet >
15 GeV /c is a factor ∼ p chT , jet is signif-icantly larger, due predominantly to the effect of back-ground fluctuations that transport yield to the region p chT , jet < VII. SYSTEMATIC UNCERTAINTIES
Systematic uncertainties arise from corrections for in-strumental response and background fluctuations, andfrom the unfolding procedure. We distinguish two cat-egories of systematic uncertainty: correlated uncertain-ties, which do not change the shape of the distribution,and shape uncertainties.Table I shows the significant contributions to the sys-tematic uncertainty. For each component, the corre-sponding contribution to the response matrix is variedand the full correction procedure was carried out. Theresulting variation in the corrected spectrum gives thesystematic uncertainty due to that component.
A. Tracking
The largest instrumental uncertainty is due to trackingefficiency (“tracking efficiency” Tab. I), whose relativeuncertainty is ±
5% [70].
B. Fragmentation model for R det The calculation of R det incorporates a fragmentationmodel to determine the instrumental response to a jet.The primary analysis utilizes a relative population oflight quarks and gluons in the ratio 2:1 at all p detT , jet . Sys-tematic variations utilize 100% light quark or 100% gluonfragmentation. The corresponding entry in Tab. I is la-beled “fragmentation for R det .” C. δp T for R bkg The primary analysis utilizes SP jets to calculate δp T . For systematic variation, δp T distributions are cal-culated utilizing PYTHIA-generated fragmentation forlight-quark jets. The requirement that accepted jets have p minT , lead = 5 GeV /c biases the background distribution,since hadrons with p T > /c may be correlatedin azimuth with the EP (Eq. 4). The primary analysisutilizes v = 0.04, which is the maximum value compat-ible with the current measurement [67], while v = 0 isused for systematic variation. The corresponding entryin Tab. I is labeled “ δp T ”. D. Median background density ρ The calculation of ρ (Eq. 1) is varied relative to that forthe primary analysis by using R = 0.2 or 0.4 for the firstjet reconstruction pass, or by excluding only the singlemost energetic jet for central Au+Au collisions and nojets for peripheral Au+Au collisions. The correspondingentry in Tab. I is labeled “ ρ ”. E. Unfolding
Systematic variation of the unfolding procedure corre-sponds to variation of its components: algorithm, priordistribution, and regularization criterion. The compo-nents are varied independently and the unfolding proce-dure is carried out for each such variant. The unfoldedsolution from a variant is accepted if it satisfies the samequality criteria as those used in the primary analysis (seeSect. VI C).The algorithm is varied by using the Bayesian andSVD approaches. Variation of the prior distribution isdiscussed in Sect. VI C. Variation of the regularization4parameter corresponds to variation of the number of it-erations i for Bayesian unfolding and the number of terms k in the series expansion for SVD unfolding: both i and k were increased by 1 relative to their optimum valuesfound in the primary analysis.For each bin in p chT , jet , the central value of the reporteddistribution is the mean of all accepted unfolded distri-butions from this variation procedure. The systematicuncertainty due to unfolding is the corresponding RMS,calculated separately for positive and negative excursionsrelative to the mean; the resulting uncertainty is there-fore asymmetric. The corresponding entry in Tab. I islabeled “unfolding”. F. (cid:104) T AA (cid:105) The uncertainties of the nuclear thickness factor (cid:104) T AA (cid:105) are specified in Sect. II. G. Cumulative uncertainty
The total correlated systematic uncertainty in Table Iis the quadrature sum of the individual component con-tributions for each bin in p chT , jet . The most significantsources of systematic uncertainty in both peripheral andcentral collisions are the unfolding procedure, tracking ef-ficiency, and the choice of δp T probe. Other uncertaintysources generate smaller contributions. VIII. PARAMETRIZED MODEL ANDCLOSURE TEST
The contribution of uncorrelated background to semi-inclusive hadron+jet distributions in central Au+Au col-lisions at √ s NN = 200 GeV is well-described by a mixed-event population [48]. This indicates that such back-ground distributions are largely statistical in nature, withdynamically-generated correlations having small or neg-ligible influence. In this manuscript we explore a relatedapproach to describe the uncorrelated background to theinclusive jet distribution, utilizing a parametrized model(PM) calculation that accurately describes the event-wisedistributions of mean- p T ( (cid:104) p T (cid:105) ) and mean transverse en-ergy ( (cid:104) E T (cid:105) ) in high-energy nuclear collisions [71–73] (seealso [74–77]). We apply this model in a closure test ofthis analysis, which assesses the precision with which aknown signal is reproduced by the full measurement pro-cedure.For trigger-normalized coincidence measurements, aclosure test can be carried out by embedding simulatedsignal pairs into real events, reconstructing the hybridevent, and executing the full analysis chain [48]. If therate per real event of the process of interest is much less than unity, identification of the embedded signal trig-ger can be made without significant ambiguity in such aprocedure. In contrast, for an inclusive jet analysis, thejet distribution is normalized per event, not per trigger,and such an embedding procedure effectively modifies theinclusive jet distribution found in real events. The clo-sure test in this approach then corresponds to measuringthis modification. The modification is, however, not well-defined, since the intrinsic jet spectrum of real events isunknown in central Au+Au collisions; indeed, measur-ing it is the goal of the analysis. A different approachto the closure test is therefore required for inclusive jetdistributions.The inclusive jet measurement closure test therefore re-quires the analysis of fully simulated events, whose globalproperties mimic those of Au+Au collisions and whoseinclusive jet distribution is known by construction. Oneapproach for the closure test is to generate events us-ing established Monte Carlo event generators such as HI-JING [78] or PYQUEN [79], which reproduce the globalfeatures of heavy-ion collisions at RHIC and the LHC.However, the statistical precision of a meaningful closuretest must be similar to that of the real data analysis,which is difficult to achieve with such MC calculations.We therefore utilize events generated by the PM, whichis computationally more efficient than MC generators,and which likewise reproduces the global properties ofAu+Au collisions at √ s NN = 200 GeV and has a spec-ified inclusive jet distribution. Comparison of the PMcalculation with data has the additional benefit of pro-viding insight into the nature of the backgrounds in thismeasurement.The following considerations motivate a statisticalapproach to modeling the background in this analy-sis. Event-wise distributions of (cid:104) p T (cid:105) and (cid:104) E T (cid:105) in lim-ited acceptance have been measured in high-energy nu-clear collisions [71–77]. These distributions are well-described by mixed-event analyses [71, 73–76], and bycalculations based on uncorrelated particle emission [71–73, 80]. The uncorrelated background in semi-inclusivehadron+jet distributions at √ s NN = 200 GeV is likewisewell-reproduced by a mixed-event approach [48], showingthat the background distribution in heavy-ion jet mea-surements is predominantly statistical, with dynamically-generated correlations on the scale of the resolution pa-rameter R , due to jets and other QCD mechanisms, play-ing a smaller, even negligible, role.In the PM, hadrons are generated from twosources [59]: a soft physics component based on uncor-related particle emission; and the production and frag-mentation of hard jets based on a PYTHIA calculationfor pp collisions. All generated “hadrons” are identical,with zero mass and charge.The soft hadronic component comprises M indepen-dent particles distributed uniformly in azimuth (0 < ϕ < π ) and pseudo-rapidity ( | η | < p T according to a Boltzmann function,5 TABLE I. Components of the systematic uncertainty for jets with R = 0.2, 0.3 and 0.4 in central and peripheral Au+Aucollisions. See text for details. Systematic uncertainty (%)central Au+Au collisions, √ s NN = 200 GeV peripheral Au+Au collisions, √ s NN = 200 GeV R p chT , jet [GeV /c ] [14,16] [20,25] [14,16] [20,25] [14,16] [20,25] [14,16] [18,20] [14,16] [18,20] [14,16] [18,20] correlated tracking efficiency +15 −
12 +16 −
10 +16 −
13 +12 −
22 +14 −
11 +18 −
12 +6 − −
12 +12 −
11 +14 −
12 +13 −
12 +16 − fragmentation for R det +1 − − − − − − − − − − − − δp T +8 − − − − − − − − − − − − ρ +1 − − − − − − − − − − − − total correlated +17 −
13 +24 −
10 +19 −
13 +21 −
23 +17 −
11 +26 −
13 +12 −
10 +18 −
14 +15 −
11 +18 −
13 +15 −
12 +20 − shape unfolding +17 −
14 +12 −
10 +24 −
19 +25 −
18 +46 −
29 +51 −
31 +14 −
11 +8 − − −
12 +4 − − TABLE II. Model parameters for central Au+Au collisions.Figure 9 shows the comparison of PM distributions usingthese parameters to measured STAR data.PM parameters, Au+Au collisions, √ s NN = 200 GeV (cid:104) p T (cid:105) /cM R AA , R = 0.2 0.2 R AA , R = 0.4 0.2-0.5 dN AAsoft d p T ∝ · p T (cid:104) p T (cid:105) · e − · p T / (cid:104) p T (cid:105) , (6)where the parameters (cid:104) p T (cid:105) and M are constants. Thisapproach provides an accurate description of the event-wise distribution of transverse energy E T in high-energynuclear collisions [71–73].The hard jet yield per Au+Au collision isdN AAjet d p T , jet = d σ jetpp d p T , jet · (cid:104) T AA (cid:105) · R AA · C ( p T , jet ) , (7)where dσ jetpp dp T , jet is the inclusive charged-particle jet crosssection within | η jet | < √ s = 200GeV, calculated by PYTHIA; (cid:104) T AA (cid:105) has value 22.8mb − for central Au+Au collisions; R AA is the jet yieldsuppression due to quenching, with value chosen suchthat the hard tail of the reconstructed jet distributionsmatches the data at high- p T , jet ; and C ( p T , jet ) is a func-tion that cuts the dN AAjet d p T , jet distribution off smoothly for p chT , jet (cid:46) /c , in order not to double-count soft par-ticle production.Table II shows the PM parameters used to model cen-tral Au+Au collisions at √ s NN = 200 GeV. R AA is con-stant for R = 0.2 and a linear function of p reco , chT , jet for R = 0.4, to provide model variation that spans inclu-sive hadron measurements at RHIC and the LHC andjet measurements at the LHC (see Fig. 16). For theseparameters, the integral of Eq. 7 for p T , jet > /c is 0.126, which is the average rate of such hard jets percentral Au+Au collision. For PM event generation, thenumber of hard jets in each event is Poisson-distributedabout this average, with p T , jet distributed according toEq. 7, and with uniform distribution over the full az-imuth and | η | <
1. PYTHIA fragmentation is then runfor either a light quark or a gluon jet, chosen in ratio 2:1,with transverse momentum equal to p T , jet . The chargedparticles generated by this procedure are the “hadrons”of the PM, comprising the hard jet component of PMevents.Figure 9 shows (quasi-)inclusive jet p reco , chT , jet distribu-tions for various values of p minT , lead , for PM-generatedevents and for the STAR measurements in centralAu+Au collisions shown in Fig. 3. The good level ofagreement of the PM-generated distributions with datais notable, in light of the very simple nature of the model.For p minT , lead = 5 GeV /c , the PM-generated distributionsagree with data within 10%, except in the extreme tails,over three orders of magnitude variation in yield. For p minT , lead = 0, the level of agreement is poorer, though theyields in this case vary by six orders of magnitude overthe range of comparison. While the agreement of themodel with data could be improved further by introduc-ing additional parameters, the focus of this analysis is on p minT , lead = 5 GeV /c , where the agreement is already good,and we therefore choose not to do so.Figure 9 shows that the background distribution inthis analysis is driven predominantly by gross featuresof the collisions and measurement – acceptance, trackmultiplicity M , and (cid:104) p T (cid:105) – with dynamical correlationsdue to both soft and hard QCD processes playing a sec-ondary or even negligible role. This picture, in which thebackground distribution is determined largely by statisti-cal phase-space, is consistent with that derived from theMixed Event background analysis in [48].We turn now to the closure test, to assess the validity of6 - - - ) c ( G e V / j e t h d c h T , j e t p / d j e t N d p / e v en t s / N - - - - - - - -
10 110 = 200 GeV NN sAu+Au Central (0-10%)charged jets T k anti-| < 1 - R jet h | R = 0.2 : minT,lead p STAR, c c c c - - P M / S T A R -
10 1 : minT,lead p c c - - - ) c ( G e V / j e t h d c h T , j e t p / d j e t N d p / e v en t s / N - - - - - - - -
10 110 - - - - - - - - R = 0.4 : minT,lead p PM, c c c c ) c (GeV/ reco, chT, jet p - - P M / S T A R -
10 1 - FIG. 9. (Color online) (Quasi-)inclusive jet p reco , chT , jet distributions for various values of p minT , lead for R = 0.2 (left) and R = 0.4(right), for PM-generated events and for the STAR measurements of central Au+Au collisions (data from Fig. 3). Lower panelsshow the ratio of the PM and data distributions, for p minT , lead = 0 and p minT , lead = 5 GeV /c . ) c (GeV/ chT,jet p un f o l ded / gene r a t ed Parametrized Model = 200 GeV NN sAu+Au central (0-10%) T k charged jets, anti- c = 5 GeV/ minT,lead p | < 1 - R jet h | R=0.2R=0.4
FIG. 10. (Color online) Closure test for PM-generated eventscorresponding to central Au+Au collisions at √ s NN = 200GeV. the correction procedure described above. Closure of thiscorrection procedure for instrumental effects was shownin [48]. The focus of this closure test is therefore thelarge smearing of the jet spectrum due to fluctuations ofuncorrelated background, which are well-represented bythe PM generator (Fig. 9).The closure test utilizes 20M PM-generated events modeling central Au+Au collisions, which has simi-lar statistical precision to the real dataset. The cut p minT , lead = 5 GeV /c is imposed on all jet candidates. Thefull analysis to generate the p chT , jet distribution and tocorrect background fluctuations was then run, includinggeneration of δp T distributions, unfolding, and the deter-mination of systematic uncertainties.Figure 10 shows the ratio of the corrected distributionsfrom this procedure to the reconstructed hard jet distri-bution without background or detector effects (“Truth”),for R = 0.2 and R = 0.4. The ratio in the range p chT , jet >
15 GeV /c is consistent with unity within un-certainties for both values of R . The ratio is, however,significantly above unity in the first bin at threshold, p minT , lead = 5 GeV /c . This feature is expected, since byconstruction the generated distribution has magnitudezero for p T , jet < p minT , lead and its magnitude is small andchanging rapidly for p T , jet just above p minT , lead , while theoutput of a regularized unfolding procedure cannot varyarbitrarily rapidly. In Sect. XI, the first bin at p T , jet = 5GeV /c in the corrected distributions is therefore notshown. For larger values of p T , jet , Fig. 10 validates thecorrection procedure for background fluctuations in thisanalysis.7 ) c - ( m b G e V p / d s d E - - - - - - - - -
10 110 = 200 GeVsp+p : p PYTHIA PHENIX PRL91 (2003) 241803: + p PYTHIA STAR: - p PYTHIA STAR } PRL108 (2012) 072302 ) c (GeV/ T p da t a / PY T H I A FIG. 11. (Color online) Upper panel: inclusive pion crosssection in pp collisions at √ s = 200 GeV from measure-ments [4, 55, 81] and from a PYTHIA simulation (see textfor details). Lower panel: ratio of data and PYTHIA. IX. REFERENCE SPECTRUM FROM PPCOLLISIONS
The modification of inclusive jet production due toquenching is quantified by comparing measurements incentral Au+Au collisions to those in smaller systems,specifically peripheral Au+Au and pp collisions. R AA is the ratio of inclusive yields in A + A and pp collisions,with the latter scaled by (cid:104) T AA (cid:105) to account for the effectsof nuclear geometry. R CP is a similar ratio, in which theyield for peripheral Au+Au collisions is used as normal-ization.The inclusive charged-particle jet spectrum in pp col-lisions at √ s = 200 GeV is not currently available withstatistical precision comparable to the inclusive charged-particle jet spectrum in central Au+Au collisions re-ported here. We therefore simulate the charged-particlejet distribution for pp collisions at √ s = 200 GeV us-ing PYTHIA Monte Carlo generator version 6.428 [82],with the Perugia 2012 tune (370) and CTEQ6L1 LO par-ton distribution functions [50]. However, a calculationof charged-pion yields using this PYTHIA tune overesti-mates the measured pion distribution by up to 30%. Itwas found that changing the PYTHIA parameter thatcontrols the energy dependence of the low momentum cut-off for underlying event generation (PARP(90)) fromits default value of 0.24 to 0.213 improves the agreementof the calculated inclusive pion yields with data, for bothcharged and neutral pions [51].Figure 11 shows the comparison of PYTHIA-generateddistributions using this tune with modified PARP(90) toinclusive pion measurements [55, 81]; agreement of modeland data is seen to be within 10% for p T > /c .This configuration of PYTHIA is also in good agreementwith measurements of inclusive jet yields, hadron dis-tributions within jets, electromagnetic jet energy frac-tion, and dijet properties measured in pp collisions at √ s = 510 GeV [51], and the underlying event measuredin pp collisions at √ s = 200 GeV [83]. These compar-isons validate this PYTHIA-based calculation with mod-ified tune for calculating inclusive jet R AA in the Au+Auanalysis presented here.The systematic uncertainty of the inclusive jet crosssection generated by PYTHIA was estimated using sev-eral alternative PYTHIA tunes [82]: tune pairs 371 and372 with α s ( p ⊥ ) and α s (2 p ⊥ ) to vary the magnitudeof initial- and final-state radiation; tune 374 with re-duced color re-connection; tunes 376 and 377 with modi-fied longitudinal and transverse fragmentation; and tune383 with Innsbruck hadronization parameters. The tunepair 371 and 372, which bracket the distribution gener-ated by the default PYTHIA tune and those of the othertunes, are used as the systematic uncertainty of the refer-ence jet pp spectrum, corresponding to 22% for R = 0.2;20% for R = 0.3; and 18% for R = 0.4, with negligibledependence on p T , jet . X. THEORETICAL CALCULATIONS OF JETQUENCHING
We compare our results to several theoretical calcula-tions incorporating jet quenching, which are labeled asfollows: • NLO [84]: a next-to-leading-order (NLO) pQCDcalculation that accounts for initial-state nu-clear modification (EMC effect, initial-state energyloss) [85, 86], with collisional partonic energy lossin the QGP calculated using a weak-coupling ap-proach. This calculation provides a good descrip-tion of the inclusive jet cross section for R = 0.4in pp collisions at √ s = 200 GeV [4], and predictsthat inclusive jet R AA for R = 0.2 in Au+Au colli-sions at √ s NN = 200 GeV should be similar to R AA for neutral pions [19]. • SCET [87, 88]: soft-collinear effective theoryextended to describe jet propagation in matter(SCET G ) [89–91]; initial-state effects include dy-namical nuclear shadowing, Cronin effect, andinitial-state partonic energy loss. This approachdescribes well the measurement of charged-hadron R AA in Pb+Pb collisions at √ s NN = 2.76 TeV [22,824, 92, 93], though a similar level of agreement canbe achieved with different parameter choices for ini-tial state energy loss and Cronin effect, which areanti-correlated with R AA in the model. From thetwo SCET implementations available we use theone with slightly larger Cronin effect and smallerenergy loss (SCET1). The error band for this modelreflects two values of coupling constant g betweenthe jet and the medium; the lower edge of the bandcorresponds to g = 2.2, while the upper edge cor-responds to g = 2.0. • Hybrid Model [94]: combines several processesgoverning the evolution and interaction of jet show-ers in the medium. The production and evolu-tion of the jet shower uses a weakly-coupled ap-proach based on PYTHIA, while the interactionof shower partons with the QGP uses a strongly-coupled holographic approach based on N =4 super-symmetric Yang-Mills theory. The model includes p T -broadening of the shower in the QGP, and back-reaction of the medium due to passage of the jet.Three different variants are compared:1. no response from the medium2. medium response including only the positivecontribution from the wake3. full medium responseThe value of κ sc , the free parameter in the model,was fixed by using LHC hadron and jet data as de-scribed in [95]. We note that calculations based onthis global fit to LHC data disagree with measure-ments of high- p T hadron suppression at RHIC atthe 3 σ level, suggesting stronger jet-medium inter-action at RHIC. XI. RESULTS
Figure 12 shows fully-corrected quasi-inclusive chargedjet distributions in central and peripheral Au+Au colli-sions at √ s NN = 200 GeV, for R = 0.2, 0.3 and 0.4, andfor p minT , lead = 5 and 7 GeV /c . The entire dataset is usedfor each distribution, which are therefore not statisticallyindependent.The requirement p T , lead > p minT , lead imposes a bias on thereported jet population. This bias must be quantified inorder to compare these data to other jet measurementsand to theoretical calculations. The magnitude of thebias is expected to increase monotonically with increas-ing value of p minT , lead , and we utilize that expectation todetermine the range in p chT , jet in which the corrected dis-tributions do not depend significantly on the value chosenfor p minT , lead .We first explore the effect of the bias in pp collisionsat √ s = 200 GeV, using PYTHIA simulations. Fig-ure 13 shows the ratios of quasi-inclusive charged jet cross sections with R = 0.2 and 0.4 from this simulationfor p minT , lead = 5 GeV /c relative to the unbiased distribu-tion (labeled “5/0”), and p minT , lead = 7 GeV /c relative to p minT , lead = 5 GeV /c (labeled “7/5”). The ratio rises morerapidly above threshold for R = 0.2 than for R = 0.4,and more rapidly for 5/0 than 7/5. The bias due to p minT , lead = 5 GeV /c is less than 10% ( i.e., the ratio 5/0 islarger than 0.9) for p chT , jet >
13 GeV /c for R = 0.2 and p chT , jet >
17 GeV /c for R = 0.4. The relative bias dueto p minT , lead = 7 GeV /c relative to p minT , lead = 5 GeV /c isless than 10% ( i.e., the ratio 7/5 is larger than 0.9) for p chT , jet >
19 GeV /c for R = 0.2 and p chT , jet >
24 GeV /c for R = 0.4. It is evident that measurement of the 7/5 ratioprovides a conservative estimate of the range over whichthe bias due to choosing the value p minT , lead = 5 GeV /c issmall.Figure 14 shows the ratios of distributions from Fig. 12for p minT , lead = 7 GeV /c and p minT , lead = 5 GeV /c for R = 0.2,0.3 and 0.4 in peripheral and central Au+Au collisions.The systematic uncertainty of the ratio accounts for thecorrelated systematic uncertainties of numerator and de-nominator. For uncorrected distributions such a ratiomust have value unity or below since the numerator isdrawn from a subset of the data used in the denominator;however, the figure shows the ratio of corrected distribu-tions, and such a constraint has not been imposed.Figure 14 also shows the corresponding 7/5 ratios forpp collisions simulated by PYTHIA (Fig. 13). The ratiosfor pp collisions rise more slowly as a function of p chT , jet than those for peripheral Au+Au collisions and centralAu+Au collisions, indicating differences in the distribu-tion of high- p T jet fragments.As discussed above for pp collisions, the 7/5 ratio pro-vides a conservative estimate of the region in which thebias due to the choice of value p minT , lead = 5 GeV /c is small.The ratios in Fig. 14 are consistent with or larger than0.9 in the range p chT , jet >
15 GeV /c for jets with R = 0.2and p chT , jet >
17 GeV /c for jets with R = 0.3 and 0.4. Inthe following figures we indicate these ranges by the label“ ∼ unbiased”.Jet quenching may induce energy transport to angleslarger than R with respect to the jet axis, effectively sup-pressing the jet yield at a given value of p chT , jet . In the nextsections we discuss measurements of jet yield modifica-tion in central Au+Au collisions, using both the R CP and R AA observables. A. Yield suppression: R CP Figure 15 shows the distribution of R CP from this mea-surement, for R = 0.2, 0.3 and 0.4. Given the close simi-larity of the 7/5 ratio for central and peripheral Au+Aucollisions shown in Fig. 14, we show R CP over the fullmeasured range of p chT , jet , without specification of an “Un-biased” region. The systematic uncertainty of R CP takesinto account the correlated uncertainties of numerator9 ) c (GeV/ chT,jet p - ) c ) ( G e V / j e t h d T , j e t p d T , j e t p p / ( j e t N d e v en t s / N - - - - - - - - - · · STAR Au+Au = 200 GeV NN s T k charged jets, anti-peripheral (60-80%) c = 5 GeV/ minT,lead p | < 1 - R jet h | · · STAR Au+Au = 200 GeV NN s T k charged jets, anti-peripheral (60-80%) c = 5 GeV/ minT,lead p | < 1 - R jet h | · · R=0.2R=0.3R=0.4 correlated unc.shape unc.
STAR Au+Au = 200 GeV NN s T k charged jets, anti-peripheral (60-80%) c = 5 GeV/ minT,lead p | < 1 - R jet h | ) c (GeV/ chT,jet p - ) c ) ( G e V / j e t h d T , j e t p d T , j e t p p / ( j e t N d e v en t s / N - - - - - - - - - - · · STAR Au+Au = 200 GeV NN s T k charged jets, anti-central (0-10%) c = 5 GeV/ minT,lead p | < 1 - R jet h | · · STAR Au+Au = 200 GeV NN s T k charged jets, anti-central (0-10%) c = 5 GeV/ minT,lead p | < 1 - R jet h | · · R=0.2R=0.3R=0.4 correlated unc.shape unc.
STAR Au+Au = 200 GeV NN s T k charged jets, anti-central (0-10%) c = 5 GeV/ minT,lead p | < 1 - R jet h | ) c (GeV/ chT,jet p - ) c ) ( G e V / j e t h d T , j e t p d T , j e t p p / ( j e t N d e v en t s / N - - - - - - - - - · · STAR Au+Au = 200 GeV NN s T k charged jets, anti-peripheral (60-80%) c = 7 GeV/ minT,lead p | < 1 - R jet h | · · STAR Au+Au = 200 GeV NN s T k charged jets, anti-peripheral (60-80%) c = 7 GeV/ minT,lead p | < 1 - R jet h | · · R=0.2R=0.3R=0.4 correlated unc.shape unc.
STAR Au+Au = 200 GeV NN s T k charged jets, anti-peripheral (60-80%) c = 7 GeV/ minT,lead p | < 1 - R jet h | ) c (GeV/ chT,jet p - ) c ) ( G e V / j e t h d T , j e t p d T , j e t p p / ( j e t N d e v en t s / N - - - - - - - - - - · · STAR Au+Au = 200 GeV NN s T k charged jets, anti-central (0-10%) c = 7 GeV/ minT,lead p | < 1 - R jet h | · · STAR Au+Au = 200 GeV NN s T k charged jets, anti-central (0-10%) c = 7 GeV/ minT,lead p | < 1 - R jet h | · · R=0.2R=0.3R=0.4 correlated unc.shape unc.
STAR Au+Au = 200 GeV NN s T k charged jets, anti-central (0-10%) c = 7 GeV/ minT,lead p | < 1 - R jet h | FIG. 12. (Color online) Corrected quasi-inclusive charged-particle jet distributions in Au+Au collisions at √ s NN =200 GeV, for R = 0.2, 0.3, and 0.4. Left: peripheral; right: central Au+Au collisions. Upper: p minT , lead = 5 GeV /c ; lower: p minT , lead = 7 GeV /c .Correlated and shape systematic uncertainties are shown separately. The value of p chT , jet is shifted horizontally within each binto account for the spectrum shape. and denominator. The uncertainty in the ratio due to (cid:104) T AA (cid:105) is independent of p chT , jet and is dominated by theuncertainty in (cid:104) T AA (cid:105) for peripheral collisions. We ob-serve that R CP ∼ . R , with at most a weakdependence on p chT , jet .Figure 16 compares R CP from Fig. 15 to that forcharged jets with R = 0.2 and 0.3 measured in Pb+Pbcollisions at √ s NN = 2.76 TeV [34], and to R CP for charged hadrons measured in Au+Au collisionsat √ s NN = 200 GeV [15] and Pb+Pb collisions at √ s NN = 2.76 TeV [93]. Note that for this measurement,central and peripheral collisions correspond to the 0-10%and 60-80% percentile intervals of the Au+Au inelastic cross section, respectively, while for the LHC jet mea-surements in the figure the corresponding intervals are0-10% and 50-80%; and for the charged hadron measure-ments at both RHIC and the LHC the centrality intervalsare 0-5% and 60-80%.The values of charged-hadron R CP at RHIC and theLHC agree within uncertainties over their common rangein p T . The magnitude of charged-particle jet R CP is like-wise consistent within uncertainties at RHIC and LHC,though their p chT , jet intervals do not overlap. (Note thatthe bias due to p minT , lead = 5 GeV /c is small for p chT , jet > /c ; see Fig. 14.) The apparent lack of dependenceof charged-particle jet R CP on p chT , jet is in contrast to the0 ) c (GeV/ chT,jet p ) T , c u t p > m i n T , l ead p ( h d T p / d c h , j e t d N ) T , c u t p > m i n T , l ead p ( h d T p / d c h , j e t d N = 200 GeVs p+p ) c (GeV/ T,cut2 p / T,cut1 p R = 0.2 7/5 5/0R = 0.4 7/5 5/0PYTHIA 6.428
FIG. 13. (Color online) Ratio of quasi-inclusive charged-particle jet cross sections simulated by PYTHIA for pp col-lisions at √ s = 200 GeV, η jet = 0, for R = 0.2 (red) and0.4 (blue), for p minT , lead = 5 GeV /c relative to the unbiased dis-tribution (“5/0”, dashed) and p minT , lead = 7 GeV /c relative to p minT , lead = 5 GeV /c (“7/5”, solid).TABLE III. p T -shift between jet yield distributions in periph-eral and central collisions normalized by the average numberof binary collisions for quasi-inclusive jets (left) and semi-inclusive recoil jets (right).Au+Au collisions, √ s NN = 200 GeV p T -shift peripheral → central [GeV /c ] R quasi-inclusive jet (this analysis) h+jet [48]15 < p chT , jet <
25 GeV /c < p chT , jet <
20 GeV /c − . ± . stat ± . sys − . ± . stat ± . sys − . ± . stat ± . sys − . ± . stat ± . sys − . ± . stat ± . sys − . ± . stat ± . sys significant p T -dependence of charged-hadron R CP .The inclusive charged-hadron distribution at high- p T arises predominantly from the leading hadron of the cor-responding jet. The correlation between hadron p T andits parent jet p chT , jet has a distribution that reflects thefragmentation process, and which may generate different p T -dependence of R CP for hadrons and jets. The com-parison of hadron and jet suppression in Fig. 16 thusprovides new constraints on theoretical descriptions ofjet quenching.The suppression of R CP as a function of p chT , jet can beexpressed equivalently as a p T -shift of the spectrum incentral, relative to peripheral, Au+Au collisions. Thisrepresentation enables direct comparison of different sup-pression measurements since it removes the effect of thespectrum shape. The shift can be interpreted as thepopulation-averaged energy transport out of the jet cone due to jet quenching [39, 48]. Table III shows the p T -shift values corresponding to R CP in Fig. 15 in the range15 < p chT , jet <
25 GeV /c , chosen to minimize the effectof the bias due to the p minT , lead cut. The uncertainty inthe value of the p T -shift takes into account the corre-lated uncertainties of the central and peripheral Au+Audistributions.Table III compares the p T -shift measured in this anal-ysis to that for semi-inclusive recoil jet yield suppressionmeasured using hadron+jet correlations in Au+Au colli-sions at √ s NN = 200 GeV [48]. Note that the in-mediumpath-length distribution of jets contributing to the twomeasurements may differ [48]. While the central valuesof the p T -shift for the inclusive jet distributions are con-sistently smaller than those for recoil jets, no significantdifference in p T -shift is observed within the uncertainties. B. Yield suppression: R PythiaAA
This section presents measurements of R PythiaAA , inwhich the reference is the inclusive charged-particle jetdistribution for pp collisions at √ s = 200 GeV calcu-lated by PYTHIA, which was validated by comparing toother STAR hadron and jet measurements (Sect. IX). No p minT , lead cut is imposed on this reference jet population.Figure 17 shows R PythiaAA for quasi-inclusive jets in cen-tral Au+Au collisions at √ s NN = 200 GeV, for R = 0.2,0.3 and 0.4. The region where the bias due to the p minT , lead cut is small for the central Au+Au collisions is indicatedby the vertical dashed line.Figure 18 compares R PythiaAA from Fig. 17 to charged-hadron and π R AA measured in Au+Au collisions at √ s NN = 200 GeV. The values of π and jet R AA agreewithin uncertainties in this region.We next compare theoretical calculations to the mea-sured charged-particle jet R AA (see Fig. 19). The Hybridmodel calculation [94] is carried out for charged jets,while the SCET [87, 88] and NLO pQCD [84] calcula-tions are carried out for fully reconstructed jets. The p T , jet -dependence of full jet R AA is weak, however, sothat comparison of these calculations with the charged-particle jet measurement is meaningful. The three the-oretical calculations exhibit only small differences in thepredicted magnitude of jet R AA and its p T -dependence.The SCET calculation has the largest dependence on jetresolution parameter R , with the NLO pQCD calcula-tion having smaller R dependence and the Hybrid modelnegligible R dependence. All calculations are consistentwith the measured inclusive jet R AA within uncertain-ties, except for minor differences for R = 0.2. Improvedsystematic precision and measurements at larger R arerequired to discriminate between the models based oninclusive jet R AA .1 c = G e V / m i n T , l ead p / c = G e V / m i n T , l ead p -
10 1 --> ~ UNBIASED
R=0.2 = 200 GeV NN sSTAR Au+Au T k charged jets, anti-peripheral (60-80%)| < 1 - R jet h | c = G e V / m i n T , l ead p / c = G e V / m i n T , l ead p -
10 1 --> ~ UNBIASED
R=0.3 correlated unc.shape unc. ) c (GeV/ chT,jet p c = G e V / m i n T , l ead p / c = G e V / m i n T , l ead p -
10 1 --> ~ UNBIASED
R=0.4
STAR Au+AuPYTHIA p+p c = G e V / m i n T , l ead p / c = G e V / m i n T , l ead p -
10 1 --> ~ UNBIASED
R=0.2 = 200 GeV NN sSTAR Au+Au T k charged jets, anti-central (0-10%)| < 1 - R jet h | c = G e V / m i n T , l ead p / c = G e V / m i n T , l ead p -
10 1 --> ~ UNBIASED
R=0.3 correlated unc.shape unc. ) c (GeV/ chT,jet p c = G e V / m i n T , l ead p / c = G e V / m i n T , l ead p -
10 1 --> ~ UNBIASED
R=0.4
STAR Au+AuPYTHIA p+p
FIG. 14. (Color online) Ratio of distributions from Fig. 12 for p minT , lead = 7 GeV /c and 5 GeV /c , for R = 0.2, 0.3, and 0.4in peripheral (upper) and central (lower) Au+Au collisions. The red lines show the corresponding ratios from a PYTHIAsimulation of pp collisions (Fig. 13). ( - % ) / ( - % ) C P R -
10 1 R=0.2 = 200 GeV NN sSTAR Au+Au T k charged jets, anti- c = 5 GeV/ minT,lead p | < 1 - R jet h | ( - % ) / ( - % ) C P R -
10 1 R=0.3 correlated unc.shape unc. uncertainty AA T ) c (GeV/ chT,jet p ( - % ) / ( - % ) C P R -
10 1 R=0.4
FIG. 15. (Color online) R CP ((0 − / (60 − √ s NN = 200 GeV, for R = 0.2, 0.3 and 0.4. Seetext for details. C. Medium-induced jet broadening
The dependence of the inclusive jet yield on resolu-tion parameter R is sensitive to the jet energy profiletransverse to its axis. Ratios of inclusive cross sectionsare of particular interest for measuring the transverse jet energy profile and its modification due to jet quenchingsince there is significant cancellation of systematic un-certainties in the ratio, both experimenally [6, 97] andtheoretically [94, 98, 99].The ratio of inclusive jet cross sections for small R ( R = 0.2) and large R ( R = 0.4 or 0.5) is found to2 C P R -
10 1 , R=0.2 T k anti- c = 5 GeV/ minT, lead p = 200 GeV NN sAu+Au ch. jets 0-10% / 60-80% ch. hadrons 0-5% / 60-80% ) c (GeV/ chT p , chT, jet p C P R -
10 1
R=0.3=2.76 TeV NN sPb+Pb ch. jets 0-10% / 50-80% ch. hadrons 0-5% / 60-80% FIG. 16. (Color online) R CP distributions from Fig. 15 compared to that measured in Pb+Pb collisions at √ s NN = 2.76TeV [34], for R = 0.2 (left) and R = 0.3 (right). Also shown are R CP for inclusive charged hadrons in Au+Au collisions at √ s NN = 200 GeV [15] and in Pb+Pb collisions at √ s NN = 2.76 TeV [93]. Data from RHIC are in blue; data from the LHCare in red. The charged hadrons R CP distributions are the same in the two panels. The different choices of centrality class arediscussed in the text. be less than unity in pp collisions at √ s = 2.76 and 7TeV [6, 97, 100], consistent with pQCD calculations atNLO and next-to-next-to-leading-order (NNLO) [98, 99].The value of this ratio less than unity is expected quali-tatively because of the transverse jet energy profile: theareal energy density in a jet is on average largest nearthe jet axis, decreasing with increasing distance from theaxis. The ratio of semi-inclusive recoil jet yields for differ-ent R is likewise measured to be less than unity in pp col-lisions at √ s = 7 TeV [39], with the ratio described betterby PYTHIA than a pQCD calculation at NLO [39, 101].In nucleus-nucleus collisions, broadening of the trans-verse jet energy profile due to quenching has been ex-plored by measuring the ratio of charged-particle jet in-clusive cross sections with different R in Pb+Pb colli-sions at √ s NN = 2.76 TeV [34], and the ratio of semi-inclusive recoil jet yields with different R in Pb+Pb col-lisions at √ s NN = 2.76 TeV [39] and in Au+Au collisionsat √ s NN = 200 GeV [48]. In both measurements, no sig-nificant medium-induced broadening is observed. Notethat this observable is different from the jet shape observ-able employed in [102, 103], with different experimentaland theoretical uncertainties.Figure 20 shows the ratio of distributions from Fig. 12for R = 0.2 and 0.4, for central and peripheral Au+Aucollisions. The measured ratio is less than unity for bothcentralities, as observed in pp collisions [6, 39, 97, 100].The panels also show calculations for pp collisions at √ s = 200 GeV from PYTHIA and HERWIG, whichagree within uncertainties with the ratios measured inAu+Au collisions. This indicates that there is no sig-nificant modification of the transverse jet profile due toquenching in central Au+Au collisions at √ s NN = 200GeV, consistent with related measurements at RHIC andLHC [39, 48, 100]. This observation is in contrast to measurements of dijetasymmetry A J at RHIC [47], which find that energy lostdue to quenching for jets with R = 0.2 is largely recoveredfor jets with R = 0.4, indicating a significant medium-induced modification of the transverse profile for the jetpopulation selected in that analysis. However, that pop-ulation differs significantly from the jet population usedin the analysis reported here. Assessment of the twoanalyses and interpretation of their observed differencesin terms of transverse jet profile modification requires themodeling of both measurements in a common theoreticalframework (e.g. [105]).Figure 20, right panel, also shows theoretical calcula-tions incorporating jet quenching compared to the mea-surement for central Au+Au collisions. The SCET andHybrid Model predictions agree with the measurementwithin uncertainties. While these two models predict adifferent p T -dependence of the ratio, the current datauncertainties do not discriminate between them. In con-trast, the NLO calculation predicts a larger ratio that isinconsistent with the data within uncertainties. Compar-ison with models of this observable will likewise benefitfrom improved systematic precision and measurementsat larger R . XII. SUMMARY
We have reported the first measurement of inclusivecharged-particle jet production in central and peripheralAu+Au collisions at √ s NN = 200 GeV, over the range5 < p chT , jet <
30 GeV /c . The large uncorrelated back-ground is suppressed by the requirement that the lead-ing hadron in the jet satisfies p T , lead > p minT , lead , where p minT , lead = 5 GeV /c . The bias imposed by this requirement3 P y t h i a AA R -
10 1 --> ~ UNBIASED
R=0.2 = 200 GeV NN sSTAR Au+Au T k charged jets, anti-peripheral (60-80%) c = 5 GeV/ minT,lead p | < 1 - R jet h | P y t h i a AA R -
10 1 --> ~ UNBIASED
R=0.3 correlated unc.shape unc. uncertainty AA TPythia uncertainty ) c (GeV/ chT,jet p P y t h i a AA R -
10 1 --> ~ UNBIASED
R=0.4
STAR Au+Au, unbiasedSTAR Au+Au, biased P y t h i a AA R -
10 1 --> ~ UNBIASED
R=0.2 = 200 GeV NN sSTAR Au+Au T k charged jets, anti-central (0-10%) c = 5 GeV/ minT,lead p | < 1 - R jet h | P y t h i a AA R -
10 1 --> ~ UNBIASED
R=0.3 correlated unc.shape unc. uncertainty AA TPythia uncertainty ) c (GeV/ chT,jet p P y t h i a AA R -
10 1 --> ~ UNBIASED
R=0.4
STAR Au+Au, unbiasedSTAR Au+Au, biased
FIG. 17. (Color online) R PythiaAA for quasi-inclusive charged jets in peripheral (upper) and central (lower) Au+Au collisionsat √ s NN = 200 GeV, for R = 0.2, 0.3 and 0.4. The reference spectrum for pp collisions at √ s = 200 GeV is generated byPYTHIA; see text for details. The region where the bias due to the p minT , lead cut is small is indicated by the vertical dashed line. P y t h i a AA R -
10 110 = 200 GeV NN sAu+Au Central (0-10%) T k anti- R=0.2 P y t h i a AA R -
10 110
STAR charged jets c = 5 GeV/ minT, lead p jet normalization unc.STAR ch. hadrons (0-5%) STAR hadr. norm. unc. R=0.3 ) c (GeV/ T p , chT, jet p P y t h i a AA R -
10 110
PHENIX ch. hadrons p PHENIX PHENIX hadr. norm. unc.
R=0.4
FIG. 18. (Color online) Comparison of R PythiaAA from Fig. 17 (stars) to charged hadron [15, 96] and π [19] R AA at √ s NN = 200GeV. Only points from the region where the bias in the data due to the p minT , lead cut is small are shown. is quantified by comparing distributions for p minT , lead = 5and 7 GeV /c , and the region of the measurement wherethe bias is small is identified.A Parametrized Model (PM) is developed, incorporat-ing uncorrelated soft particle emission and a PYTHIA-generated jet distribution, motivated by the excellent de-scription by such an approach of event-by-event trans-verse energy fluctuations in A + A collisions over a wide range in √ s NN . The PM describes the uncorrectedjet distributions in this analysis well, indicating thatthe background underlying jet measurements in centralAu+Au collisions at RHIC is to a large extent statisti-cally distributed, with dynamical correlations playing amuch lesser role. This picture is also supported by anearlier Mixed Event analysis of semi-inclusive hadron-jetdistributions at RHIC.4 P y t h i a AA R -
10 110 = 200 GeV NN sAu+Au Central (0-10%) T k anti- R=0.2 P y t h i a AA R -
10 110
STAR charged jets c = 5 GeV/ minT, lead p jet normalization unc.Full jets: SCET NLO pQCD R=0.3 ) c (GeV/ T, jet p , chT, jet p P y t h i a AA R -
10 110
Hybrid model, ch. jets: no medium resp. pos. resp. from wake full medium resp.
R=0.4
FIG. 19. (Color online) Comparison of R PythiaAA from Fig. 17 (stars) to theoretical calculations for full jets labeled as NLO [84](dashed line), SCET [87, 88](solid line), and Hybrid Model [94] (filled bands) for charged jets. See text for details of thecalculations. Only points from the region where the bias in the data due to the p minT , lead cut is small are shown. The theoreticalcalculations do not have a cut on p minT , lead . ) c (GeV/ chT, jet p or T, jet p R = . / R = . -
10 1 peripheral (60-80%) --> ~ UNBIASED T k charged jets, anti- = 200 GeV NN sAu+Au c = 5 GeV/ minT,lead p STAR, STAR, biased region = 200GeVsp+p Herwig PYTHIA ) c (GeV/ chT, jet p or T, jet p -
10 1 central (0-10%) --> ~ UNBIASED = 200 GeV NN sAu+Au Hybrid model, ch. jets: no medium resp. positive resp. from wake full medium resp.Full jets: NLO pQCD SCET = 200 GeV NN sAu+Au Hybrid model, ch. jets: no medium resp. positive resp. from wake full medium resp.Full jets: NLO pQCD SCET FIG. 20. (Color online) Ratio of quasi-inclusive yields for charged jets with R = 0.2 and R = 0.4, for peripheral (left) andcentral (right) Au+Au collisions at √ s NN = 200 GeV and p minT , lead = 5 GeV/ c . Calculations are shown for charged jets in ppcollisions at √ s = 200 GeV simulated by PYTHIA [82] and HERWIG [104]; both panels show the same distributions from thesecalculations. Also shown are predictions for reconstructed jets in Au+Au collisions from the theoretical calculations describedin Sect. X. The simulated distributions do not have a cut on p minT , lead . The region where the bias due to the p minT , lead cut is smallis indicated by the vertical dashed line. Comparison of the charged-particle jet yield in centraland peripheral Au+Au collisions reveals a suppressionfor central Au+Au collisions, with magnitude of the sup-pression similar to that in central Pb+Pb collisions atthe LHC. No significant p chT , jet -dependence of inclusivejet suppression is observed, in contrast to the marked p T -dependence of inclusive hadron suppression in central A + A collisions at both RHIC and the LHC. Jet yield suppression at fixed p chT , jet can be expressedequivalently as a shift in the yield distribution as a func-tion of p chT , jet , where the magnitude of the shift corre-sponds to medium-induced energy transport out of thejet cone. The p chT , jet -shift for the inclusive jet populationwith R = 0.4 is -3.3 ± . stat ± . sys GeV /c , consistentwith that measured for semi-inclusive recoil jets. We notethat in-medium path-length distributions for these two5measurements may differ.The charged-particle jet yield in central Au+Au col-lisions is also compared to that for pp collisions gener-ated by PYTHIA, which was validated using a STARmeasurement of fully reconstructed jets in pp collisionsand inclusive single particle spectra. The magnitude ofsuppression of inclusive π and jet production from thiscomparison are consistent within uncertainties.Comparison is also made to several theoretical cal-culations of jet quenching (NLO pQCD, SCET, Hy-brid Model), which are consistent with the measurementwithin uncertainties. Greater precision is needed to dis-criminate the models.Finally, medium-induced broadening of the jet trans-verse energy distribution is explored by measuring theratio of inclusive yields for R = 0.2 and 0.4. No signif-icant medium-induced modification is observed in cen-tral Au+Au collisions, consistent with similar measure-ments at the LHC. In comparison to jet quenching calcu-lations, NLO predicts a larger ratio than that observed,but SCET and the Hybrid Model are consistent with themeasurement. The absence of medium-induced broad-ening in this inclusive jet analysis is in contrast to thebroadening observed in di-jet asymmetry measurementsat RHIC. Interpretation of this difference requires mod-eling to carefully assess underlying biases in each of thetwo analyses.The results presented here provide new constraints ontheoretical models of jet quenching, and new insights intothe nature of the large backgrounds to jet measurementsin heavy-ion collisions. XIII. ACKNOWLEDGMENTS
We thank Daniel Pablos, Krishna Rajagopal and IvanVitev for providing theoretical calculations. We thankthe RHIC Operations Group and RCF at BNL, theNERSC Center at LBNL, and the Open Science Gridconsortium for providing resources and support. Thiswork was supported in part by the Office of NuclearPhysics within the U.S. DOE Office of Science, the U.S.National Science Foundation, the Ministry of Educa-tion and Science of the Russian Federation, NationalNatural Science Foundation of China, Chinese Academyof Science, the Ministry of Science and Technology ofChina and the Chinese Ministry of Education, the HigherEducation Sprout Project by Ministry of Education atNCKU, the National Research Foundation of Korea,Czech Science Foundation and Ministry of Education,Youth and Sports of the Czech Republic, HungarianNational Research, Development and Innovation Office,New National Excellency Programme of the HungarianMinistry of Human Capacities, Department of AtomicEnergy and Department of Science and Technology ofthe Government of India, the National Science Centre ofPoland, the Ministry of Science, Education and Sportsof the Republic of Croatia, RosAtom of Russia and German Bundesministerium fur Bildung, Wissenschaft,Forschung and Technologie (BMBF), Helmholtz Associ-ation, Ministry of Education, Culture, Sports, Science,and Technology (MEXT) and Japan Society for the Pro-motion of Science (JSPS).6 [1] W. Busza, K. Rajagopal, and W. van der Schee, Ann.Rev. Nucl. Part. Sci. , 339 (2018), arXiv:1802.04801[hep-ph].[2] U. Heinz and R. Snellings, Ann. Rev. Nucl. Part. Sci. , 123 (2013), arXiv:1301.2826 [nucl-th].[3] K. M. Burke et al. , Phys.Rev. C90 , 014909 (2014),arXiv:1312.5003 [nucl-th].[4] B. I. Abelev et al. (STAR), Phys. Rev. Lett. , 252001(2006), arXiv:hep-ex/0608030.[5] L. Adamczyk et al. (STAR), Phys. Rev. D95 , 071103(2017), arXiv:1610.06616 [hep-ex].[6] B. Abelev et al. (ALICE), Phys.Lett.
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