Search for long-lived particles using displaced jets in proton-proton collisions at s √ = 13 TeV
EEUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH (CERN)
CERN-EP-2020-2022020/12/04
CMS-EXO-19-021
Search for long-lived particles using displaced jets inproton-proton collisions at √ s =
13 TeV
The CMS Collaboration * Abstract
An inclusive search is presented for long-lived particles using displaced jets. Thesearch uses a data sample collected with the CMS detector at the CERN LHC in 2017and 2018, from proton-proton collisions at a center-of-mass energy of 13 TeV. Theresults of this search are combined with those of a previous search using a data sam-ple collected with the CMS detector in 2016, yielding a total integrated luminosityof 132 fb − . The analysis searches for the distinctive topology of displaced tracksand displaced vertices associated with a dijet system. For a simplified model, wherepair-produced long-lived neutral particles decay into quark-antiquark pairs, pair pro-duction cross sections larger than 0.07 fb are excluded at 95% confidence level (CL)for long-lived particle masses larger than 500 GeV and mean proper decay lengths be-tween 2 and 250 mm. For a model where the standard model-like Higgs boson decaysto two long-lived scalar particles that each decays to a quark-antiquark pair, branch-ing fractions larger than 1% are excluded at 95% CL for mean proper decay lengthsbetween 1 mm and 340 mm. A group of supersymmetric models with pair-producedlong-lived gluinos or top squarks decaying into various final-state topologies contain-ing displaced jets is also tested. Gluino masses up to 2500 GeV and top squark massesup to 1600 GeV are excluded at 95% CL for mean proper decay lengths between 3 and300 mm. The highest lower bounds on mass reach 2600 GeV for long-lived gluinosand 1800 GeV for long-lived top squarks. These are the most stringent limits to dateon these models. Submitted to Physical Review D © 2020 CERN for the benefit of the CMS Collaboration. CC-BY-4.0 license * See Appendix B for the list of collaboration members a r X i v : . [ h e p - e x ] D ec The existence of long-lived particles (LLPs) that have macroscopic decay lengths is a commonfeature in both the standard model (SM) and beyond-the-SM (BSM) scenarios. There are nu-merous alternative BSM physics cases for the production of LLPs at the CERN LHC. Examplesinclude, but are not limited to: split supersymmetry (SUSY) [1–7], where the gluino decays aresuppressed by heavy scalars; SUSY with weak R -parity violation (RPV) [8–12], where the de-cays of the lightest supersymmetric particle are suppressed by small RPV couplings; SUSY withgauge-mediated SUSY breaking (GMSB) [13–15], where the decays of the next-to-lightest su-persymmetric particle are suppressed by a large SUSY breaking scale; “stealth SUSY” [16, 17];“Hidden Valley” models [18–20]; dark matter models [21–28]; models with heavy neutral lep-tons that have small mixing parameters [29–32]; and models incorporating neutral natural-ness [33–38]. In the examples listed above, it is very common for the LLPs to further decay intofinal states containing jets, giving rise to displaced-jets signatures.Given the large variety of the BSM scenarios that lead to displaced-jets signatures, it is im-portant to make the displaced-jets search as model independent as possible. In this paper, wepresent an inclusive search for LLPs decaying into jets, with at least one LLP having a decayvertex within the tracker acceptance, but which is displaced from the production vertex by upto 550 mm in the transverse plane. The search looks for a pair of jets known as dijets, wherethe jets are clustered from energy deposits in the calorimeters. For jets arising from the de-cay of an LLP, the associated tracks are usually displaced from the primary vertices (PVs), andthe decay vertex can be reconstructed from the displaced tracks. The properties of the tracksand the decay vertex can provide discrimination power to distinguish long-lived signals fromSM backgrounds. As mentioned above, a large number of models predict LLPs decaying intodisplaced jets. Our tests for some of these will be discussed in detail in Section 3.Events used in this analysis were collected with the CMS detector [39] at the LHC from proton-proton (pp) collisions at a center-of-mass energy of 13 TeV in 2017 and 2018, correspondingto an integrated luminosity of 95.9 fb − . The results are combined with those of a previousdisplaced-jets search using the events collected in 2016 [40], yielding a total integrated lumi-nosity of 132 fb − . For the models that were not studied in the 2016 displaced-jets search, ad-ditional simulated signal samples have been produced following the 2016 run condition of theCMS detector. These additional samples are then processed with the reconstruction and selec-tion procedures described in Ref. [40] to compute the additional signal yields and systematicuncertainties for the 2016 data that are used in the combination.Compared to the 2016 displaced-jets search, a set of new techniques that significantly improvesthe sensitivity to long-lived signatures is implemented in this analysis. The new techniquesinclude one additional dedicated trigger aimed at selecting events containing displaced jetsto recover efficiencies for high-mass LLPs, an auxiliary nuclear interactions (NIs) veto map toimprove background rejection, a dedicated variable based on the sum of signed impact pa-rameters, and the use of machine learning techniques to improve signal-to-background dis-crimination. With these new techniques, compared to the 2016 search, we have reduced thebackground rate by approximately a factor of three, while significantly increasing the signalefficiencies for almost all signal points in different LLP models. Results of searches for similarLLP signatures with hadronic decays at √ s =
13 TeV have also been reported by ATLAS [41–45]and CMS [46–48].The paper is organized as follows. A brief description of the CMS detector is introduced inSection 2. The data and the simulated samples are described in Section 3. Section 4 details theevent reconstruction and the preselection criteria. Section 5 describes the event selections and the background estimation methods. The systematic uncertainties are summarized in Section 6.The observation and the interpretation of the results are described in Section 7. The paper issummarized in Section 8.
The central feature of the CMS apparatus is a superconducting solenoid of 6 m internal diame-ter, providing a magnetic field of 3.8 T. Within the solenoid volume are a silicon pixel and striptracker, a lead tungstate crystal electromagnetic calorimeter (ECAL), and a brass and scintilla-tor hadron calorimeter (HCAL), each composed of a barrel and two endcap detectors. Muonsare detected in gas-ionization chambers embedded in the steel flux-return yoke outside thesolenoid.The silicon tracker measures charged particles within the pseudorapidity range | η | < < p T <
10 GeVand | η | < p T , and 25–75 µ m in the transverseimpact parameter [49].In the region | η | < ∆ η = ∆ φ = η - φ plane, and for | η | < × | η | > ∆ η and ∆ φ . Within each tower, the energy deposits in ECAL andHCAL cells are summed to define the calorimeter tower energies, and are subsequently usedto provide the energies and directions of hadronic jets.Events of interest are selected using a two-tiered trigger system [50]. The first level, composedof custom hardware processors, uses information from the calorimeters and muon detectors toselect events at a rate of around 100 kHz within a time interval of less than 4 µ s. The secondlevel, known as the high-level trigger (HLT), consists of a farm of processors running a versionof the full event reconstruction software optimized for fast processing, and reduces the eventrate to around 1 kHz before data storage.A more detailed description of the CMS detector, together with a definition of the coordinatesystem used and the relevant kinematic variables, can be found in Ref. [39]. Data were collected with two dedicated triggers aimed at selecting events containing displacedjets. At the HLT, jets are reconstructed from the energy deposits in the calorimeter towers,clustered using the anti- k T algorithm [51, 52] with a distance parameter of 0.4. In this process,the contribution from each calorimeter tower is assigned a momentum, the absolute value andthe direction of which are given by the energy measured in the tower and the coordinatesof the tower. The raw jet energy is obtained from the sum of the tower energies, and theraw jet momentum from the vector sum of the tower momenta, which results in a nonzerojet mass. The raw jet energies are then corrected [53] to establish a uniform relative response ofthe calorimeter in η and a calibrated absolute response in transverse momentum p T .Events may contain multiple PVs, corresponding to multiple pp collisions occurring in the same bunch crossing. The candidate vertex with the largest value of summed physics-object p is taken to be the primary pp interaction vertex. The physics objects are the jets, clusteredusing the jet finding algorithm [51, 52] with the tracks assigned to candidate vertices as inputs,and the associated missing transverse momentum, taken as the negative vector sum of the p T of those jets. More details are given in Section 9.4.1 of Ref. [54].The first trigger, referred to as the “displaced” trigger, requires H T >
430 GeV, where H T is thescalar sum of the jet p T for all jets that have p T >
40 GeV and | η | < • p T >
40 GeV and | η | < • at most two associated prompt tracks with p T > ) with respect to the leading PVsmaller than 1.0 mm; and • at least one associated displaced track with p T > larger than 0.5 mm and an impact parameter significance(Sig [ IP ] ) larger than 5.0. The significance is defined as the ratio between the impactparameter and its uncertainty.The second trigger, referred to as the “inclusive” trigger, requires H T >
650 GeV and the pres-ence of at least two jets, each of them satisfying: • p T >
60 GeV and | η | < • at most two associated prompt tracks with p T > (cid:46) (cid:38)
300 mm) mean proper decay lengths ( c τ ).The background sources in this search include NIs between outgoing particles and detectormaterial, long-lived SM hadrons, and misreconstructed displaced vertices formed by acciden-tally crossing tracks. The background events mainly arise from SM events containing jets pro-duced through the strong interaction, referred to as quantum chromodynamics (QCD) mul-tijet events. The QCD multijet Monte Carlo (MC) sample is simulated at leading order withM AD G RAPH MC @ NLO
PYTHIA
PYTHIA parameters for the underlyingevent modeling are set to be the CP5 tune [58]. The set of parton distribution functions (PDFs)used for the production is the NNPDF3.1 NNLO PDF set [59]. The QCD multijet MC sample ismainly used to inspire the analysis strategy and to estimate systematic uncertainties, while thebackground estimation for this search is purely determined from data.Feynman diagrams for the benchmark models studied in this paper are summarized in Fig. 1.One of the benchmark signal models is a simplified model, where long-lived scalar neutralparticles X are pair produced through a scattering process mediated by an off-shell Z boson.In this model, each X particle decays to a quark-antiquark pair, assuming equal branchingfractions to u, d, s, c, and b quark pairs. The decays to top quark pairs are excluded to provide asimple final-state topology for this model, but it was important that the analysis strategy wouldstill be sensitive to a variety of other models. We checked the impact on the signal efficienciesof excluding decays to top quark pairs, and found it to be small, where the relative changes ofthe signal efficiencies are generally at the order of a few percents. This model is referred to as the jet-jet model. The samples are produced with different resonance masses ranging from 50to 1500 GeV, and with different proper decay lengths ranging from 1 to 10 mm.Another signature we consider is the case where LLPs arise from exotic decays of an SM-likeHiggs boson, which can happen in many BSM scenarios (a review can be found in the SectionIV.6.6 of Ref. [60]), including “Hidden Valley” models [18, 19], Twin Higgs models [36], and theFolded SUSY model [61]. For the simulation, we use POWHEG 2.0 [62–65] to generate eventscontaining a 125 GeV Higgs boson produced through gluon-gluon fusion. The 125 GeV Higgsboson then decays to two long-lived scalar particles S, and each scalar particle then decays to aquark-antiquark pair. The samples are produced with the scalar particle mass m S set to be 15,40, or 55 GeV, while the c τ of S varies from 1 to 3000 mm.We also consider a group of SUSY models with different final-state topologies. The first oneis a GMSB SUSY model [66] in the general gauge mediation scenario [14, 15], where gluinosare pair produced and the gravitino is the lightest SUSY particle, while the gluino is the next-to-lightest supersymmetric particle. After the gluino is produced, it decays to a gluon and agravitino, producing a single displaced jet and missing transverse momentum. This decay issuppressed by the SUSY-breaking scale, and therefore the gluino is long lived. The model inwhich this process occurs is referred to as the (cid:101) g → g (cid:101) G model. The samples are produced withgluino masses from 800 to 2500 GeV, and with the c τ of the gluino varying from 1 to 10 mm.The second SUSY model we consider is a mini-split SUSY model [6, 7], referred to as the (cid:101) g → qq (cid:101) χ model. In this model the gluino decays to a quark-antiquark pair and the light-est neutralino ( (cid:101) χ ), with equal branching fractions to u, d, s, and c quark pairs. This decay ismediated by a squark, which is much heavier than the gluino. The squark’s large mass sup-presses the gluino decay, making it long lived. The mass of the neutralino is assumed to be100 GeV, the samples are produced with gluino masses from 1400 to 3000 GeV, and the c τ ofthe gluino varies from 1 to 10 mm.The third SUSY model is an RPV SUSY model [67] with minimal flavor violation, where gluinosare pair produced and long lived. Each long-lived gluino decays to top, bottom, and strangeantiquarks through the RPV coupling λ (cid:48)(cid:48) and the mediation of a virtual top squark [12], lead-ing to a multijet final-state topology. This model is referred to as the (cid:101) g → tbs model. Thesamples are produced with gluino masses from 1200 to 3000 GeV, and a c τ varying from 1 to10 mm.We also consider two other RPV SUSY models [68] with semileptonic decays, in which long-lived top squarks are pair produced, and each top squark decays to a bottom quark (downquark) and a charged lepton via RPV couplings λ (cid:48) , λ (cid:48) , and λ (cid:48) ( λ (cid:48) , λ (cid:48) , and λ (cid:48) ) [12].The decay rate to each of the three lepton flavors is assumed to be equal. The two models arereferred to as the (cid:101) t → b (cid:96) ( (cid:101) t → d (cid:96) ) models. The samples are produced with different top squarkmasses from 600 to 2000 GeV, and a c τ varying from 1 to 10 mm.Finally, we consider another SUSY model, referred to as the (cid:101) t → dd model, motivated by dy-namical RPV (dRPV) [69, 70], where long-lived top squarks are pair produced, and each topsquark decays to two down antiquarks via a nonholomorphic RPV coupling η (cid:48)(cid:48) [71]. The non-holomorphic RPV coupling is suppressed by some large scale M . The samples are producedwith different top squark masses from 800 to 1800 GeV, and a c τ varying from 1 to 10 mm. PYTHIA
PYTHIA parameters for the underlying event modeling areset to be the CP2 tune [58]. In the SUSY models, a long-lived gluino or top squark can forma hadronic state through strong interactions, an R -hadron [9, 72, 73], which is simulated with pp Z ∗ XX q qqq pp H SS q qqq pp e g e g g e G e Gg pp e g e g q q e χ e χ qq pp e g e g e t e t t bssbt λ ′′ λ ′′ pp e t e t b ℓℓ b λ ′ x λ ′ x pp e t e t d ℓℓ d λ ′ x λ ′ x pp e t e t d ddd η ′′ Mη ′′ M Figure 1: The Feynman diagrams for the different long-lived models considered, including thejet-jet model (upper left), models with an exotic decay of the SM-like Higgs boson (upper right),general gauge mediation models with (cid:101) g → g (cid:101) G decay (second row, left), mini-split SUSY with (cid:101) g → qq (cid:101) χ decay (second row, right), RPV SUSY with (cid:101) g → tbs decay (third row, left), RPVSUSY with (cid:101) t → b (cid:96) decay (third row, right), RPV SUSY with (cid:101) t → d (cid:96) decay (lower left), anddRPV SUSY with (cid:101) t → dd decay (lower right). PYTHIA . The interactions of the R -hadron with matter were studied following the simulationdescribed in Ref. [74, 75], and were found to have negligible impact on this analysis, since theyhave very little influence on the vertex reconstruction.The simulated background and signal events are processed with a G EANT
This search examines dijet candidates in a given event. The algorithms for the offline jet recon-struction and PV selection are the same as those applied at the HLT (as described in Section 3),except that the full offline information is used. To make sure that the online H T and jet p T re-quirements in the displaced-jet triggers reach full efficiency, we apply selections on the offline H T , jets p T , and η . After the trigger selection, if an event passes the displaced trigger, we requirethe event to have offline H T >
500 GeV, and dijet candidates are formed from all possible pairsof jets in the event, with the jets satisfying p T >
50 GeV and | η | < H T >
700 GeV, and thedijet candidates are formed from all possible pairs of jets in the event, with the jets satisfying p T >
80 GeV and | η | < p T > χ of thetrack fit, the impact parameters, and the number of hits in different tracker layers) to reduce thefraction of misreconstructed tracks, and the selection is optimized separately for each iterationof the tracking [49], so that it is efficient for selecting tracks with different displacements. Moredetails of the high-purity selection can be found in Ref. [49]. The η and φ of a given trackare determined by the direction of its momentum vector at the closest approach point to theleading PV. For a given dijet candidate, we associate track candidates with each jet by requiringthat ∆ R < ∆ R = √ ( ∆ η ) + ( ∆ φ ) is the angular distance between the jet axis andthe track direction. When a track satisfies ∆ R < ∆ R .After associating track candidates with each jet, the next step is to reconstruct a secondaryvertex (SV) for each dijet candidate. From all the tracks associated with a dijet candidate, weselect displaced tracks that satisfy IP > [ IP ] > χ per degree of freedom ( χ /n dof ) of less than 5.0. In order to suppress long-lived SMmesons and baryons, the invariant mass of the vertex is required to be larger than 4 GeV, andthe transverse momentum of the vertex is required to be larger than 8 GeV, where the four-momentum of the vertex is calculated assuming the pion mass for all assigned tracks.We only consider dijet candidates that have a reconstructed SV satisfying the above require- ments. Furthermore, SVs in background events tend to have only one track with a high valuefor IP , corresponding to the tail of the impact parameter distribution. We therefore con-sider the track with the second-highest Sig [ IP ] among the tracks that are assigned to the SV,since this provides a more sensitive discriminant for identifying displaced jets. We require thesecond-highest Sig [ IP ] to be larger than 15.We also compute another quantity (cid:101) , which is the ratio between the sum of energy for all thetracks assigned to the SV and the sum of the energy for all the tracks associated with the twojets: (cid:101) = ∑ track ∈ SV E track ∑ track ∈ dijet E track . (1)Since (cid:101) is expected to be large for displaced-jet signatures, dijet candidates with (cid:101) smaller than0.15 are rejected.An additional variable, ζ , is defined to characterize the contribution of prompt activity to thejets. For each track associated with a jet, we identify the PV (including the leading PV and thepileup vertices) with the minimum three-dimensional (3D) impact parameter significance tothe track. If this minimum value is smaller than 5, we assign the track to this PV. Then for eachjet, we compute the track energy contribution from each PV, and the PV with the largest trackenergy contribution to the jet is chosen. Finally, we define ζ as ζ = ∑ track ∈ PV E Jet track + ∑ track ∈ PV E Jet track E Jet + E Jet , (2)where ∑ track ∈ PV i E Jet i track is the sum of the track energy coming from the most compatible PV fora given jet, while E Jet i is the energy of a given jet, thus ζ is the charged energy fraction of thedijet associated with the most compatible PVs. For displaced-jet signatures, ζ tends to be smallsince the jets are not compatible with PVs. Dijet candidates with ζ larger than 0.2 are rejected.To suppress the background events arising from NIs in the tracker material, we compare thepositions of the SVs with a map of the distribution of material in the inner tracker. The mapwas obtained from the distribution of NI candidate vertices, which are reconstructed using theadaptive vertex fitter on a sample of events collected with isolated single-muon triggers. TheNI candidates are required to satisfy the following criteria: • The tracks are required to be associated with dijet candidates, which are formedfrom the jets having p T >
10 GeV and | η | < • The associated tracks must have p T > > [ IP ] > • vertex track multiplicity is larger than 3; • the ratio (cid:101) of the energy sum for SV tracks to that for all tracks is less than 0.15 forthe dijet candidate; • vertex L xy significance is larger than 200, where the transverse decay length L xy isthe distance between the SV and the leading PV; and • vertex χ /n dof is smaller than 3.0.After these selections, the distribution of the NI vertex candidates is transferred to an NI-vetomap in the transverse plane, with | x | and | y | <
25 cm, as shown in Fig. 2. To suppress misrecon-structed vertices and the displaced vertices produced by decaying long-lived SM mesons andbaryons, we only select the region where the NI vertex density is above a threshold that varies for different layers of the pixel detector. In the NI-veto map, we can clearly see the structuresof the beam pipe (at r = √ x + y ≈
23 mm), the four pixel layers (at r ≈ ≈ ≈ ≈
160 mm), and the support rails (at r ≈
200 mm). In our search, any SV candidate that over-laps with the NI-veto map is rejected. The loss of the fiducial volume within r <
300 mm due tothe veto is around 4%, and the efficiencies for signal events to pass this selection are generallywell above 90%. In the veto no requirement is placed on the z coordinates of the SVs, but theimpact of restricting the veto to the barrel region of the pixel detector ( | z | <
27 cm) is negligibleon the signal efficiencies. A similar study on the structure of the CMS inner tracking systemusing a more sophisticated NI reconstruction technique with 2016 data has been reported inRef. [78]. - - - - - x [mm] - - - - - y [ mm ] CMS r [mm] E ff i c i en cy CMS
Figure 2: Left: the NI-veto map based on the NI vertex reconstruction in the 2017 and 2018 datacollected by the CMS detector, the map corresponds to the geometry of the CMS pixel detectorused in 2017–2018 data taking [79]. The structures of the different pixel layers can be clearlyseen. Right: the efficiency for a given vertex candidate to pass the NI-veto as a function ofradius r .The preselection criteria for this search, summarized in Table 1, are efficient for a wide range oflong-lived models with different final-state topologies.Table 1: Summary of the preselection criteria.SV/dijet variable RequirementVertex χ /n dof < > > significance > (cid:101) (SV track energy fraction in the dijet) > ζ (energy fraction from compatible PVs) < x - y plane no overlap with the NI-veto map After reconstructing the SV using the adaptive vertex fitter, we employ an auxiliary algorithmto check the consistency between the SV system and the dijet system. For each displaced track (having IP > [ IP ] > x - y ) plane. The dijet direction is the spacedirection of the four-momentum-sum of the two jets, for which the production vertex of thetwo jets is taken to be the SV. For each crossing point, an expected transverse decay length( L exp xy ) is computed with respect to the leading PV. The L exp xy is positive if the crossing point is atthe same side of the dijet direction, otherwise it is negative. The associated displaced tracks arethen clustered based on their L exp xy , using a hierarchical clustering algorithm [80]. During theclustering, two clusters are merged when the smallest L exp xy difference between the two clustersis smaller than 15% of the L xy of the SV. After the clustering procedure is finished, if more thanone cluster is formed, the one closest to the SV is selected. The cluster root-mean-square (RMS),taken to be the relative RMS of individual tracks L exp xy with respect to the SV L xy , is computedto provide signal-to-background discrimination:RMS cluster = (cid:118)(cid:117)(cid:117)(cid:116) N tracks N tracks ∑ i = ( L exp xy ( i ) − L xy ) L xy , (3)where N tracks is the number of tracks in the selected cluster.For each track assigned to the SV, a sign is given to the IP and Sig [ IP ] based on the anglebetween the dijet direction and the impact parameter vector that points from the leading PV tothe closest approach point (with respect to the leading PV) of the track in the transverse plane.The sign is positive if this angle is smaller than π /2; otherwise the sign is negative. A newvariable, κ , is then introduced as the signed Sig [ IP ] sum of the six leading tracks from the SV(where the tracks are ordered by the absolute values of their Sig [ IP ] ): κ = ∑ i = Sig [ IP ( track i )] . (4)For background processes, since the tracks assigned to the SV are uncorrelated with the dijetdirection, the signed Sig [ IP ] of different tracks tend to cancel each other, therefore κ peakssharply around zero. On the other hand, for displaced jets that originate from the SV, the direc-tions of the tracks will be highly correlated with the dijet direction, therefore κ is significantlydifferent from zero and | κ | tends to be large.To improve signal-to-background discrimination and to define a region with signal events en-riched, we proceed to construct a multivariate discriminant based on the following variablesfor the vertex/dijet candidates: • Vertex track multiplicity; • Vertex L xy significance; • Cluster RMS; • The magnitude of the signed Sig [ IP ] sum of the six leading tracks | κ | .The distributions of the four variables are shown in Fig. 3, with displaced-jet triggers, offline H T , and offline jet kinematic variables selections (described in Section 4) applied. For the mul-tivariate discriminant we utilize the Gradient Boosted Decision Tree (GBDT) algorithm [81–83],with cross entropy as the loss function. The GBDT algorithm is implemented using the TMVA(Toolkit for Multivariate Data Analysis) package [84] interfaced with S CIKIT - LEARN [85]. Giventhe large cross section of the QCD multijet process and the relatively low H T threshold of our displaced-jet triggers, the event count of the simulated QCD multijet sample (after preselec-tions) is insufficient for the GBDT training, since it is much smaller than the number of expectedQCD multijet events in the analyzed data sample. Therefore, for the background sample in theGBDT training, we use the data in the following region: • events are selected by the displaced-jet triggers, and pass the offline H T and jet kine-matic variables selections; • (cid:101) < • the veto using the NI-veto map is not applied; • all the other preselection criteria are satisfied.For the signal sample in the GBDT training, simulated jet-jet model events that pass the prese-lection criteria are used, with m X = c τ =
1, 10, 100, 1000 mm.If there is more than one dijet/SV candidate passing the selection criteria in a given event, theone with the largest track multiplicity is chosen for the training. If the track multiplicities arethe same, the one with the smallest SV χ /n dof is chosen. An event weight is assigned sepa-rately to each signal point with a given m X and c τ such that the sum of weights is identicalfor each point, thus each signal point has the same priority in the training. Twenty percent ofthe events in the signal and background samples are randomly selected to validate the perfor-mance of the GBDT and to make sure it is not overtrained.The GBDT output values or scores for data, simulated QCD multijet events, and simulatedsignal events are shown in Fig. 4. The signal efficiencies for this search are measured with sim-ulated signal events produced separately. The background prediction is purely based on someother control samples in data, which are different from the one used for the GBDT training.Although only the jet-jet model is used as the signal sample in the GBDT training, the GBDT ishighly efficient in selecting signatures of other LLP models with different final-state topologies,since we have explicitly chosen the input variables to make the GBDT as model-independentas possible.In addition to the GBDT score g , we use another variable N in the final event selection,which is the number of 3D prompt tracks in a single jet, where the 3D prompt tracks are thetracks that have 3D impact parameters with respect to the leading PV smaller than 0.3 mm.If more than one dijet candidate passes the preselection criteria described in Section 4, the onewith the largest GBDT score is selected. If the GBDT scores are the same, the one with thesmallest SV χ /n dof is selected. In the final signal region, the candidate is further required topass three final selection criteria, which are: • Selection 1: for the leading jet with larger p T , N is smaller than 3; • Selection 2: for the subleading jet, N is smaller than 3; and • Selection 3: the GBDT score g is larger than 0.988.The numerical values for the selection criteria are chosen by optimizing the discovery poten-tial of 5 standard deviations based on the Punzi significance [86] for the jet-jet model and the (cid:101) g → g (cid:101) G model across different LLP masses and lifetimes. The chosen models encompass thedisplaced-dijet and displaced-single-jet signatures, with m X = m (cid:101) g = (cid:101) g → g (cid:101) G model, while c τ istaken to be 1, 10, 100, and 1000 mm. Thus there are 24 signal points considered in total for theselection optimization.Based on the three selection criteria, we can define eight nonoverlapping regions A–H, which E v en t s / . un i t s (13 TeV) -1 CMS
Jet-jet model
DataQCD multijet MC = 3 mm t = 300 GeV, c X m = 30 mm t = 300 GeV, c X m = 300 mm t = 300 GeV, c X m Vertex track multiplicity D a t a / M C E v en t s / . un i t s (13 TeV) -1 CMS
Jet-jet model
DataQCD multijet MC = 3 mm t = 300 GeV, c X m = 30 mm t = 300 GeV, c X m = 300 mm t = 300 GeV, c X m significance xy Vertex L D a t a / M C E v en t s / . un i t s (13 TeV) -1 CMS
Jet-jet model
DataQCD multijet MC = 3 mm t = 300 GeV, c X m = 30 mm t = 300 GeV, c X m = 300 mm t = 300 GeV, c X m Cluster RMS D a t a / M C E v en t s / . un i t s (13 TeV) -1 CMS
Jet-jet model
DataQCD multijet MC = 3 mm t = 300 GeV, c X m = 30 mm t = 300 GeV, c X m = 300 mm t = 300 GeV, c X m | k | D a t a / M C Figure 3: The distributions of the vertex track multiplicity (upper left), vertex L xy significance(upper right), cluster RMS (lower left), and the magnitude of the signed Sig [ IP ] sum | κ | (lower right), for data, simulated QCD multijet events, and simulated signal events. Data andsimulated events are selected with the displaced-jet triggers and with the offline H T , jets p T ,and η selections applied. For a given event, if there is more than one SV candidate being recon-structed, the one with the largest vertex track multiplicity is chosen. If the track multiplicitiesare the same, the one with the smallest χ /n dof is chosen. The lower panels show the ratiosbetween the data and the simulated QCD multijet events. The blue shaded error bands andvertical bars represent the statistical uncertainties. Three benchmark signal distributions areshown (dashed lines) for the jet-jet model with m X =
300 GeV and varying c τ . For visualiza-tion purposes, each signal process is given a cross section that yields 10 events produced inthe analyzed data sample. E v en t s / . un i t s (13 TeV) -1 CMS
Jet-jet model
DataQCD multijet MC = 3 mm t = 300 GeV, c X m = 30 mm t = 300 GeV, c X m = 300 mm t = 300 GeV, c X m GBDT score D a t a / M C Figure 4: The distributions of the GBDT output score for data, simulated QCD multijet events,and simulated signal events. Data and simulated events are selected with the displaced-jet trig-gers and with the offline H T , jets p T , and η selections applied. For a given event, if there is morethan one SV candidate being reconstructed, the one with the largest vertex track multiplicity ischosen. If the track multiplicities are the same, the one with the smallest χ /n dof is chosen. Thelower panel shows the ratio between the data and the simulated QCD multijet events. The blueshaded error bands and vertical bars represent the statistical uncertainties. Three benchmarksignal distributions are shown (dashed lines) for the jet-jet model with m X =
300 GeV and vary-ing c τ . For visualization purposes, each signal process is given a cross section correspondingto 10 events produced in the analyzed data sample. The signal events shown in this plot arenot used in the GBDT training. include the final signal region. The event counts in different regions are N f f f , N p f f , · · · , and N ppp , for regions A, B, · · · , and H, respectively, as shown in Table 2. The region H is the regionwhere the events pass all the three selection criteria, and thus is the final signal region. Eventsin the remaining regions (A–G) fail one or more of the three selection criteria. Since the threeselection criteria have little correlation between them for the background events, a propertythat has been verified with simulated QCD multijet events, the background yield in the signalregion H can be estimated by different ratios of event counts in regions A–G, where the ratio b nominal = N pp f ( N f f p + N f pp + N p f p ) / ( N f f f + N p f f + N f p f ) uses the fraction of events passingto those failing the GBDT selection (Selection 3) and is taken as the central value of the predictedbackground yields. Three additional ratios are computed using the events failing either one ofthe first two selections: • Cross-check 1: N pp f ( N f f p + N f pp ) / ( N f f f + N f p f ) ; • Cross-check 2: N pp f ( N f f p + N p f p ) / ( N f f f + N p f f ) ; • Cross-check 3: N pp f ( N f pp + N p f p ) / ( N f p f + N p f f ) .Table 2: The definitions of the different regions used in the background estimation.Region Selection 1 Selection 2 Selection 3 Event countA Fail Fail Fail N f f f B Pass Fail Fail N p f f C Fail Pass Fail N f p f D Fail Fail Pass N f f p E Fail Pass Pass N f pp F Pass Fail Pass N p f p G Pass Pass Fail N pp f H Pass Pass Pass N ppp These cross-checks provide an important test of the robustness of the background predictionand the assumption that the three selection criteria are minimally correlated. Differences be-tween the predictions obtained with the nominal ratio b nominal and the cross-checks are used toestimate the systematic uncertainties in the background prediction.The method described above can be generalized to predict the background yield in arbitraryintervals of the GBDT score g , where the first two selections are also satisfied. In this way wecan verify our background prediction method in the different bins of GBDT score g . The back-ground prediction method is first tested with simulated QCD multijet events and simulatedsignal events, and is found to be robust both with and without signal contaminations. We thentest the background prediction in data, for which we define a control region by inverting the se-lection on (cid:101) , requiring (cid:101) to be smaller than 0.15. In order to improve the statistical precision, weremove the selection requirement that uses the NI-veto map. The predicted background yieldsand observed events in the control region are shown in Fig. 5, which shows a good agreementbetween the predicted and observed yields. The predicted background yields in the differentbins of GBDT score are correlated, since the events that are used for background predictions inlower bins are also used in the background predictions in higher bins. The systematic uncertainty in the background prediction is taken to be the largest deviation ofthe three cross checks from the nominal prediction b nominal , and is found to be 52% in the finalsignal region where the GBDT score g is larger than 0.988. . £ . < g . £ . < g . £ . < g . £ . < g . £ . < g . £ . < g . £ . < g £ . < g GBDT score g E v en t s Observed eventsBackground predictions (13 TeV) -1 CMS
Control region
Figure 5: The predicted background yields and the numbers of observed events in the controlregion, for different bins of the GBDT scores. The background predictions in different bins arecorrelated, since the events that are used for background predictions in lower bins are also usedin the background predictions in higher bins. The error bands for the predictions representstatistical uncertainties and systematic uncertainties added in quadrature. The error bars forthe observed events represent statistical uncertainties, assuming Poisson statistics. The systematic uncertainties in the integrated luminosity for 13 TeV pp collision data are 2.3%and 2.5% for the 2017 and 2018 [87, 88] data taking periods, respectively, and are modeled asuncorrelated nuisance parameters between the years.The systematic uncertainty in signal yields due to the online H T requirements of the displaced-jet triggers is estimated by comparing the efficiency of the online H T requirements measuredin data with the one measured in the QCD multijet MC sample. The efficiencies are measuredusing the events collected with an isolated single-muon trigger. The discrepancies between themeasurements in data and MC simulation are parameterized as functions of offline H T , andcorresponding corrections are applied to the simulated signal events. The signal yields arethen recalculated for different masses and mean proper decay lengths. The largest correctionin the signal yield for a given model is taken as the corresponding systematic uncertainty, andis found to be smaller than 2%.The systematic uncertainty in signal yields due to the online jet p T requirements of the displaced-jet triggers is estimated by measuring the per-jet efficiencies of the online jet p T requirementsin data and in the QCD multijet MC sample, using events collected with a prescaled H T triggerthat requires H T >
425 GeV. Corrections for the discrepancies between the measurements indata and MC simulation are applied to the simulated signal events, and the signal yields arerecalculated. The correction is more significant for low-mass LLPs (with m ∼
50 GeV), and issmaller than 8%, which is taken as the corresponding systematic uncertainty.To estimate the systematic uncertainty due to the online tracking requirements of the displaced-jet triggers, we measure the per-jet efficiencies of the online tracking requirements as functionsof the number of prompt tracks and displaced tracks, using events collected with the prescaled H T trigger. The efficiencies obtained in data are found to be consistent with the efficienciesobtained in MC simulations. Therefore no corresponding systematic uncertainty is assigned.To estimate the impact of the possible mismodeling of the GBDT score in MC simulation onsignal yields, we compare the distribution of the GBDT score in simulated QCD multijet eventswith the distribution measured in data, using events collected with the prescaled H T trigger.The discrepancies between data and MC simulation are taken into account by varying theGBDT scores in the simulated samples by the same magnitude. The largest variation in thesignal efficiency for a given model is taken as the corresponding systematic uncertainty in sig-nal yields, and is found to be in the range of 4–15%.Similarly, the uncertainty in signal yields due to impact parameter modeling is estimated bycomparing the distribution of the impact parameters in simulated QCD multijet events withthe distribution measured in data, also using events collected with the prescaled H T trigger.The impact parameters in simulated QCD multijet events are varied until the discrepanciesbetween data and MC simulation are covered by the variation. The impact parameters in thesimulated signal samples are then varied by the same magnitude. The largest variation in thesignal efficiency for a given model is taken as the corresponding systematic uncertainty, and isfound to be in the range of 8–18%.The impact of the jet energy scale uncertainty on the signal yields is estimated by varying the jetenergy and p T by one standard deviation of the jet energy scale uncertainty [53]. The variationsof the signal efficiencies are smaller than 3%, which is taken as the corresponding systematicuncertainty.The impact of the PDF uncertainty on the signal acceptance is estimated by reweighting thesimulated signal events with NNPDF, CT14 [89] and MMHT14 [90] PDF sets, and their associ-ated uncertainty sets [91, 92], following the PDF4LHC recommendation [91]. The uncertainty in signal efficiency for a given signal model is quantified by comparing the efficiencies calcu-lated with alternative PDF sets and the ones with the nominal NNPDF set, and is found to bein the range of 4–6%.The uncertainty in the signal yields due to the selection of the PV is estimated by replacingthe leading PV with the subleading PV when calculating impact parameters and vertex dis-placement. The largest variation of the signal efficiency for a given signal model is taken as thecorresponding systematic uncertainty, and is found to be in the range of 8–15%.The various systematic uncertainties in the signal yields are summarized in Table 3.Table 3: Summary of the systematic uncertainties in the signal yields.Source Uncertainties (%)Integrated luminosity 2.3–2.5Online H T requirement 0–2Online jet p T requirement 0–8Offline vertexing 4–15Track impact parameter modeling 8–18Jet energy scale 0–3PDF 4–6Primary vertex selection 8–15Total 17–25 The predicted background yields and the numbers of observed events in different GBDT ranges,after all the preselection criteria are applied, are shown in Fig. 6, with N smaller than 3 forboth jets. The final signal region is defined by a GBDT score larger than 0.988, and the pre-dicted background yield is 0.75 ± ± > m LLP (cid:38)
300 GeV and 1 (cid:46) c τ (cid:46) × by the GBDT selection.Event yields in data after different selection requirements have been applied are shown in Ta-ble 4. We observe one event in the final signal region, which is consistent with the predictedbackground yield. The observed event has an offline H T of 570 GeV, and an SV candidate withan L xy of ≈
26 cm and 8 associated tracks. The position of the SV is close to one of the siliconstrip layers, and was likely produced by NIs with the silicon strip detector.
The signal yields in the final signal region are used to set limits on a variety of models. The sig-nal efficiencies for representative signal points in different models can be found in Tables A.1–A.9 and Fig. A.1 of Appendix A. The results obtained with the 2017–2018 analysis are furthercombined in this paper with the results from the displaced-jets search using the pp collisiondata collected with the CMS experiment in 2016 [40], for which the systematic uncertaintiesarising from the same source are taken to be fully correlated, while the other systematic un-certainties and the statistical uncertainties in signal yields or expected background yields are .2 Interpretation of the results . £ . < g . £ . < g . £ . < g . £ . < g . £ . < g . £ . < g . £ . < g £ . < g GBDT score g -
10 110 E v en t s Observed eventsBackground predictions = 3 mm t = 300 GeV, c X m = 30 mm t = 300 GeV, c X m = 300 mm t = 300 GeV, c X m (13 TeV) -1 CMS
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Figure 6: The predicted background yields and the number of observed events for the data inthe signal region, with N smaller than 3 for both jets, shown for different bins of the GBDTscores. The background predictions in different bins are correlated, since the events that areused for background predictions in lower bins are also used in the background predictions inhigher bins. For comparison, three benchmark signal points are also shown (dashed lines) forthe jet-jet model with m X =
300 GeV and different lifetimes. For visualization purposes, eachsignal process is given a cross section that yields 100 events produced in the analyzed datasample.8
300 GeV and different lifetimes. For visualization purposes, eachsignal process is given a cross section that yields 100 events produced in the analyzed datasample.8 Table 4: Event yields after different selection requirements have been applied for data collectedin 2017 and 2018. Signal efficiencies for the jet-jet model with m X = c τ are also shown for comparison. Selection requirements are cumulative from the first row to thelast. Selections Observed events Signal efficiency (%) m X = c τ H T selectionsOffline jet p T and η selections, 8387775 68.9 90.7 73.5vertex χ /n dof < p T > > [ IP ] >
15 422449 66.0 89.0 60.9Charged energy fraction from the SV (cid:101) > ζ < N < > taken to be uncorrelated. The total integrated luminosity is 132 fb − . For the models that werenot studied in the 2016 displaced-jets search, we have produced additional signal MC sam-ples following the 2016 run condition of the CMS detector. We then process these sampleswith the reconstruction and selection procedures implemented in the 2016 search to computethe additional signal yields and systematic uncertainties for the 2016 data that are used in thecombination.Upper limits on the cross section for a given model are presented with different masses andlifetimes by computing the 95% confidence level (CL) associated with each signal point usingthe CL s prescription [93–96], for which an LHC-style profile likelihood ratio [95, 96] is taken asthe test statistic. Systematic uncertainties are incorporated through the use of nuisance param-eters treated according to the frequentist paradigm. The asymptotic approximation [95] is usedfor the calculation of the CL s values, which have been verified with full-frequentist results forrepresentative signal points. Since the background yields of this search are small, the impact ofthe associated statistical or systematic uncertainties on the upper limits are also small.The expected and observed upper limits on the pair production cross section for the jet-jetmodel at different values of c τ and LLP mass m X are shown in Fig. 7, where a branching frac-tion of 100% for X to decay into a quark-antiquark pair is assumed. For a fixed LLP mass m X ,the limits are most restrictive for c τ between 3 and 300 mm. For smaller c τ , the limits becomeless stringent, because we only select displaced tracks to reconstruct SVs and we veto dijet can-didates with a large number of prompt tracks. The limits also become less restrictive for larger c τ ( c τ >
300 mm), because the tracking efficiency becomes worse with large displacement ofthe SV. For high-mass LLPs ( m X >
500 GeV), pair production cross sections larger than 0.07 fbare excluded for c τ between 2 and 250 mm. The lowest pair production cross section excludedis 0.04 fb, at c τ =
30 mm and m X > .2 Interpretation of the results [mm] t c - -
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ObservedMedian expected68% expected = 50 GeV X m = 100 GeV X m = 300 GeV X m = 1000 GeV X m [GeV] X m - -
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ObservedMedian expected68% expected = 3 mm t c = 30 mm t c = 300 mm t c = 3000 mm t c Figure 7: The 95% CL upper limits on the pair production cross section of the LLP X, where a100% branching fraction for X to decay to a quark-antiquark pair is assumed. Left: the upperlimits as functions of c τ for different masses. Right: the upper limits as functions of the particlemass for different c τ . The solid (dashed) curves show the observed (median expected) limits.The shaded bands indicate the regions containing 68% of the distributions of the limits expectedunder the background-only hypothesis.times to a quark-antiquark pair of a specific flavor. The upper limits on the branching frac-tion are calculated assuming the gluon-gluon fusion production cross section of a 125 GeVHiggs boson at 13 TeV [60]. When the long-lived scalar particle decays to a light-flavor quark-antiquark pair, branching fractions larger than 1% are excluded for c τ between 1 and 340 mmwith m S ≥
40 GeV. When the long-lived scalar particle decays to a bottom quark-antiquarkpair, branching fractions larger than 10% are excluded for c τ between 1 and 530 mm with m S ≥
40 GeV. These are the most stringent limits to date on this model for c τ between 1 and1000 mm. For m S =
15 GeV, where the track multiplicity of the SV is small, and the tracks arecollimated due to the boost of S, the limits become worse. The limits are also worse for thecase where the scalar particle decays to a bottom quark-antiquark pair, because the decays of bhadrons can produce tertiary vertices, which can be missed by the SV reconstruction we deployin this search.The expected and observed upper limits on the pair production cross section of long-livedgluinos in the GMSB (cid:101) g → g (cid:101) G model are shown in Fig. 9, where a branching fraction of 100%for the gluino to decay into a gluon and a gravitino is assumed. Since we do not require thereconstructed SV to have associated tracks from both jets, the two separate displaced singlejets produced by the decays of the two long-lived gluinos in the (cid:101) g → g (cid:101) G model can be pairedtogether and pass the selections, therefore the search is sensitive to the models with similarsignatures. When the gluino mass is 2400 GeV, signal efficiencies are around 21, 53, and 41%in the 2017 and 2018 analysis for c τ =
3, 30, and 300 mm, respectively. With the data samplescollected in 2016–2018, gluino pair production cross sections larger than 0.1 fb are excluded for c τ between 7 and 600 mm at m (cid:101) g = c τ according to the upper limits on the pair production cross section, and acalculation at next-to-next-to-leading logarithmic precision matched to the approximated next-to-next-to-leading order predictions (NNLO approx +NNLL) of the gluino pair production crosssection at √ s =
13 TeV [97–102]. Gluino masses up to 2450 GeV are excluded for c τ between 6 [mm] t c - - - -
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95% CL upper limits b b fi SS, S fi H = 125 GeV H H, m fi gg ObservedMedian expected68% expected = 40 GeV S m = 55 GeV S m Figure 8: The expected and observed 95% CL upper limits on the branching fraction of the SM-like Higgs boson to decay to two long-lived scalar particles, assuming the gluon-gluon fusionHiggs boson production cross section of 49 pb at 13 TeV with m H =
125 GeV, shown at differentmasses and c τ for the scalar particle S. Left: the upper limits when each scalar particle decaysto a down quark-antiquark pair. Right: the upper limits when each scalar particle decays toa bottom quark-antiquark pair. The solid (dashed) curves represent the observed (median ex-pected) limits. The shaded bands represent the regions containing 68% of the distributions ofthe expected limits under the background-only hypothesis.and 550 mm. The largest gluino mass excluded is 2560 GeV with a c τ of 30 mm. These limitsare the most restrictive to date on this model for c τ between 1 and 1000 mm.Figure 10 shows the expected and observed upper limits on the pair production cross sectionof the long-lived gluinos in the mini-split (cid:101) g → qq (cid:101) χ model, assuming a branching fraction of100% for the gluino to decay into a quark-antiquark pair and the lightest neutralino. The neu-tralino mass is assumed to be 100 GeV. When the gluino mass is 2400 GeV, signal efficienciesare around 31, 69, and 51% in the 2017 and 2018 analysis for c τ =
3, 30, and 300 mm, re-spectively. With the data samples collected in 2016–2018, gluino pair production cross sectionslarger than 0.1 fb are excluded for proper decay lengths between 3 and 900 mm. The upperlimits on the pair production cross sections are then translated into upper limits on the gluinomass for different c τ , based on the NNLO approx +NNLL gluino pair production cross sections.Gluino masses up to 2500 GeV are excluded for c τ between 7 and 360 mm. The largest gluinomass excluded is 2610 GeV with a c τ of 30 mm. These bounds are the most stringent to date onthis model for c τ between 10 and 1000 mm.The expected and observed upper limits on the pair production cross section of the long-livedgluinos in the (cid:101) g → tbs model are shown in Fig. 11 , where a branching fraction of 100% forthe gluino to decay into top, bottom, and strange quarks is assumed. When the gluino massis 2400 GeV, signal efficiencies are around 41, 81, and 66% in the 2017 and 2018 analysis for c τ =
3, 30, and 300 mm, respectively. With the data samples collected in 2016–2018, gluinopair production cross sections larger than 0.1 fb are excluded for c τ between 3 and 1490 mmat m (cid:101) g = c τ according to the upper limits on the pair production cross section and the calculation of theNNLO approx +NNLL gluino pair production cross sections. Gluino masses up to 2500 GeV areexcluded for c τ between 3 and 1000 mm. The largest gluino mass excluded is 2640 GeV with a .2 Interpretation of the results [mm] t c - -
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95% CL upper limits )g~g~ fi (pp s +NNLL approx NNLO = 1600 GeV g~ m = 2400 GeV g~ m G~ g fi g~GMSB ObservedMedian expected68% expected = 1000 GeV g~ m = 2400 GeV g~ m (arXiv:1906.06441)CMS delayed jets /mm) t (c log [ G e V ] g ~ m - - c r o ss s e c t i on [f b ] GMSBG~ g fi g~, g~g~ fi pp +NNLL exclusion approx
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Figure 9: Left: the 95% CL upper limits on the pair production cross section for the long-livedgluinos with m (cid:101) g = (cid:101) g → g (cid:101) G decaysis assumed. The NNLO approx +NNLL gluino pair production cross sections for m (cid:101) g = (cid:101) g → g (cid:101) G model as a function ofthe mean proper decay length c τ and the gluino mass m (cid:101) g . The thick solid black (dashed red)curve shows the observed (median expected) 95% CL limit on the gluino mass as a functionof c τ , assuming the NNLO approx +NNLL cross sections. The thin dashed red curves indicatethe region containing 68% of the distribution of the limits expected under the background-onlyhypothesis. The thin solid black curves represent the change in the observed limit when thesignal cross sections are varied according to their theoretical uncertainties. [mm] t c - -
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95% CL upper limits )g~g~ fi (pp s +NNLL approx NNLO = 1600 GeV g~ m = 2400 GeV g~ m c~ q q fi g~mini-split = 100 GeV c~ m ObservedMedian expected68% expected = 1600 GeV g~ m = 2400 GeV g~ m /mm) t (c log [ G e V ] g ~ m - - c r o ss s e c t i on [f b ] = 100 GeV mini-split c~ m c~ q q fi g~, g~g~ fi pp +NNLL exclusion approx
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Figure 10: Left: the 95% CL upper limits on the pair production cross section for the long-livedgluinos with m (cid:101) g = (cid:101) g → qq (cid:101) χ de-cays is assumed. The NNLO approx +NNLL gluino pair production cross sections for m (cid:101) g = (cid:101) g → qq (cid:101) χ model as a function of the mean proper decay length c τ and the gluinomass m (cid:101) g . The thick solid black (dashed red) curve shows the observed (median expected) 95%CL limits on the gluino mass as a function of c τ , assuming the NNLO approx +NNLL cross sec-tions. The thin dashed red curves indicate the region containing 68% of the distribution of thelimits expected under the background-only hypothesis. The thin solid black curves representthe change in the observed limit when the signal cross sections are varied according to theirtheoretical uncertainties. .2 Interpretation of the results c τ of 30 mm. These limits are the most stringent to date on this model for c τ between 30 and10 mm. [mm] t c - -
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95% CL upper limits )g~g~ fi (pp s +NNLL approx NNLO = 1600 GeV g~ m = 2400 GeV g~ m '' l tbs fi g~RPV ObservedMedian expected68% expected = 1400 GeV g~ m = 2000 GeV g~ m /mm) t (c log [ G e V ] g ~ m - - c r o ss s e c t i on [f b ] RPV '' l tbs fi g~, g~g~ fi pp +NNLL exclusion approx
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Figure 11: Left: the 95% CL upper limits on the pair production cross section for the long-livedgluinos with m (cid:101) g = (cid:101) g → tbs de-cays is assumed. The NNLO approx +NNLL gluino pair production cross sections for m (cid:101) g = (cid:101) g → tbs model as a function of the mean proper decay length c τ and the gluinomass m (cid:101) g . The thick solid black (dashed red) curve shows the observed (median expected) 95%CL limits on the gluino mass as a function of c τ , assuming the NNLO approx +NNLL cross sec-tions. The thin dashed red curves indicate the region containing 68% of the distribution of thelimits expected under the background-only hypothesis. The thin solid black curves representthe change in the observed limit when the signal cross sections are varied according to theirtheoretical uncertainties.The expected and observed upper limits on the pair production cross section of the long-livedtop squarks in the RPV (cid:101) t → b (cid:96) model are shown in Fig. 12, where a branching fraction of 100%for the top squark to decay into a bottom quark and a charged lepton is assumed, with equalbranching fractions for e, µ , and τ . When the top squark mass is 1600 GeV, signal efficiencies arearound 22, 43, and 26% in the 2017 and 2018 analysis for c τ =
3, 30, and 300 mm, respectively.With the data samples collected in 2016–2018, top squark pair production cross sections largerthan 0.1 fb are excluded for c τ between 8 and 160 mm at m (cid:101) t = c τ according to the upper limits on thepair production cross section and the calculation of the NNLO approx +NNLL top squark pairproduction cross sections. Top squark masses up to 1600 GeV are excluded for c τ between 5and 240 mm. The largest top squark mass excluded is 1720 GeV with a c τ of 30 mm. Theselimits are the most stringent to date on this model in the tested c τ range.The expected and observed upper limits on the pair production cross section of the long-livedtop squarks in the RPV (cid:101) t → d (cid:96) model are shown in Fig. 13, where a branching fraction of 100%for the top squark to decay into a down quark and a charged lepton, with equal branchingfractions for e, µ , and τ . When the top squark mass is 1600 GeV, signal efficiencies are around25, 48, and 29% in the 2017 and 2018 analysis for c τ =
3, 30, and 300 mm, respectively. With the [mm] t c - -
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95% CL upper limits )t~t~ fi (pp s +NNLL approx NNLO = 1000 GeV t~ m = 1600 GeV t~ m x33 ' l bl fi t~RPV ObservedMedian expected68% expected = 800 GeV t~ m = 1600 GeV t~ m /mm) t (c log [ G e V ] t ~ m - c r o ss s e c t i on [f b ] RPV x33 ' l bl fi t~, t~ t~ fi pp +NNLL exclusion approx
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Figure 12: Left: the 95% CL upper limits on the pair production cross section for the long-livedtop squarks with m (cid:101) t = (cid:101) t → b (cid:96) decays is assumed, with equal branching fractions for e, µ , and τ . The NNLO approx +NNLL topsquark pair production cross sections for m (cid:101) t = (cid:101) t → b (cid:96) model as a func-tion of the mean proper decay length c τ and the top squark mass m (cid:101) t . The thick solid black(dashed red) curve shows the observed (median expected) 95% CL limits on the top squarkmass as a function of c τ , assuming the NNLO approx +NNLL cross sections. The thin dashed redcurves indicate the region containing 68% of the distribution of the limits expected under thebackground-only hypothesis. The thin solid black curves represent the change in the observedlimit when the signal cross sections are varied according to their theoretical uncertainties. .2 Interpretation of the results data samples collected in 2016–2018, and top squark pair production cross sections larger than0.1 fb are excluded for c τ between 7 and 220 mm. We then compute the upper limits on the topsquark mass for different c τ according to the upper limits on the pair production cross sectionand the calculation of the NNLO approx +NNLL top squark pair production cross sections. Topsquark masses up to 1600 GeV are excluded for c τ between 3 and 360 mm. The largest topsquark mass excluded is 1740 GeV with a c τ of 30 mm. These limits are the most restrictive todate on this model in the tested c τ range. [mm] t c - -
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95% CL upper limits )t~t~ fi (pp s +NNLL approx NNLO = 1000 GeV t~ m = 1600 GeV t~ m x31 ' l dl fi t~RPV ObservedMedian expected68% expected = 800 GeV t~ m = 1600 GeV t~ m /mm) t (c log [ G e V ] t ~ m - c r o ss s e c t i on [f b ] RPV x31 ' l dl fi t~, t~ t~ fi pp +NNLL exclusion approx
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Figure 13: Left: the 95% CL upper limits on the pair production cross section for the long-livedtop squarks with m (cid:101) t = (cid:101) t → d (cid:96) decays is assumed, with equal branching fractions for e, µ , and τ . The NNLO approx +NNLL topsquark pair production cross sections for m (cid:101) t = (cid:101) t → d (cid:96) model as a func-tion of the mean proper decay length c τ and the top squark mass m (cid:101) t . The thick solid black(dashed red) curve shows the observed (median expected) 95% CL limits on the top squarkmass as a function of c τ , assuming the NNLO approx +NNLL cross sections. The thin dashed redcurves indicate the region containing 68% of the distribution of the limits expected under thebackground-only hypothesis. The thin solid black curves represent the change in the observedlimit when the signal cross sections are varied according to their theoretical uncertainties.The expected and observed upper limits on the pair production cross section of the long-livedtop squarks in the dRPV (cid:101) t → dd model are shown in Fig. 14, where a branching fraction of100% for the top squark to decay into two down antiquarks is assumed. When the top squarkmass is 1600 GeV, signal efficiencies are around 43, 76, and 53% in the 2017 and 2018 analysis for c τ =
3, 30, and 300 mm, respectively. With the data samples collected in 2016–2018, top squarkpair production cross sections larger than 0.1 fb are excluded for c τ between 3 and 820 mmat m (cid:101) t = c τ according to the upper limits on the pair production cross section and the calculation ofthe NNLO approx +NNLL top squark pair production cross sections. Top squark masses up to1600 GeV are excluded for c τ between 2 and 1320 mm. The largest top squark mass excludedis 1820 GeV with a c τ of 30 mm. These bounds are the most stringent to date on this model for c τ between 10 and 10 mm. [mm] t c - -
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95% CL upper limits )t~t~ fi (pp s +NNLL approx NNLO = 1600 GeV t~ m = 1000 GeV t~ m '' h dd fi t~dRPV ObservedMedian expected68% expected = 1600 GeV t~ m = 800 GeV t~ m /mm) t (c log [ G e V ] t ~ m - c r o ss s e c t i on [f b ] dRPV '' h dd fi t~, t~ t~ fi pp +NNLL exclusion approx
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Figure 14: Left: the 95% CL upper limits on the pair production cross section for the long-lived top squarks with m (cid:101) t = (cid:101) t → dd decays is assumed. The NNLO approx +NNLL top squark pair production cross sections for m (cid:101) t = (cid:101) t → dd model as a function of the mean proper decay length c τ and the top squark mass m (cid:101) t . The thick solid black (dashed red) curve shows the observed(median expected) 95% CL limits on the top squark mass as a function of c τ , assuming theNNLO approx +NNLL cross sections. The thin dashed red curves indicate the region containing68% of the distribution of the limits expected under the background-only hypothesis. The thinsolid black curves represent the change in the observed limit when the signal cross sections arevaried according to their theoretical uncertainties. A search has been presented for long-lived particles decaying to jets, using proton-proton col-lision data collected with the CMS experiment at a center-of-mass energy of 13 TeV in 2017and 2018. The results are combined with those of a previous CMS search for displaced jetsusing proton-proton collision data from 2016, accumulating to a total integrated luminosityof 132 fb − . After all selections, one event is observed in the data collected in 2017 and 2018,which is consistent with the predicted background yield. The search is designed to be modelindependent, and is sensitive to a large number of models predicting displaced-jets signatureswith different final-state topologies.The best current limits are set on a variety of models that have long-lived particles with meanproper decay lengths between 1 mm and 10 m. All limits are computed at the 95% confidencelevel. For a simplified model where pair-produced long-lived neutral particles decay to quark-antiquark pairs, pair production cross sections larger than 0.07 fb are excluded for mean properdecay lengths between 2 and 250 mm at high mass ( m X >
500 GeV). For a model where thestandard model-like Higgs boson decays to two long-lived scalar particles and each long-livedscalar particle decays to a down (bottom) quark-antiquark pair, branching fractions for theexotic Higgs boson decay larger than 1% (10%) are excluded for mean proper decay lengthsbetween 1 and 340 mm (530 mm) when the scalar particle mass is larger than 40 GeV. For asupersymmetric (SUSY) model in the general gauge mediation scenario, where the long-livedgluino decays to a gluon and a lightest SUSY particle, gluino masses up to 2450 GeV are ex-cluded for mean proper decay lengths between 6 and 550 mm. For another SUSY model in themini-split scenario, where the long-lived gluino can decay to a quark-antiquark pair and thelightest neutralino, gluino masses up to 2500 GeV are excluded for mean proper decay lengthsbetween 7 and 360 mm. An R -parity violating (RPV) SUSY model is also tested, where thelong-lived gluino can decay to top, bottom, and strange antiquarks, and gluino masses up to2500 GeV are excluded for mean proper decay lengths between 3 and 1000 mm. Another RPVSUSY model is studied, where the long-lived top squark can decay to a bottom quark and acharged lepton, and top squark masses up to 1600 GeV are excluded for mean proper decaylengths between 5 and 240 mm. For an RPV SUSY model, where the long-lived top squark candecay to a down quark and a charged lepton, top squark masses up to 1600 GeV are excludedfor mean proper decay lengths between 3 and 360 mm. Finally, for a dynamical-RPV SUSYmodel, where the long-lived top squark can decay to two down antiquarks, top squark massesup to 1600 GeV are excluded for mean proper decay lengths between 2 and 1320 mm. These arethe most stringent limits to date on these models. Acknowledgments
We congratulate our colleagues in the CERN accelerator departments for the excellent perfor-mance of the LHC and thank the technical and administrative staffs at CERN and at other CMSinstitutes for their contributions to the success of the CMS effort. In addition, we gratefullyacknowledge the computing centers and personnel of the Worldwide LHC Computing Gridfor delivering so effectively the computing infrastructure essential to our analyses. Finally,we acknowledge the enduring support for the construction and operation of the LHC and theCMS detector provided by the following funding agencies: BMBWF and FWF (Austria); FNRSand FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria);CERN; CAS, MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croa-tia); RIF (Cyprus); SENESCYT (Ecuador); MoER, ERC PUT and ERDF (Estonia); Academy ofFinland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF8
We congratulate our colleagues in the CERN accelerator departments for the excellent perfor-mance of the LHC and thank the technical and administrative staffs at CERN and at other CMSinstitutes for their contributions to the success of the CMS effort. In addition, we gratefullyacknowledge the computing centers and personnel of the Worldwide LHC Computing Gridfor delivering so effectively the computing infrastructure essential to our analyses. Finally,we acknowledge the enduring support for the construction and operation of the LHC and theCMS detector provided by the following funding agencies: BMBWF and FWF (Austria); FNRSand FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria);CERN; CAS, MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croa-tia); RIF (Cyprus); SENESCYT (Ecuador); MoER, ERC PUT and ERDF (Estonia); Academy ofFinland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF8 (Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland);INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LAS (Lithuania); MOE and UM(Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Mon-tenegro); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal);JINR (Dubna); MON, RosAtom, RAS, RFBR, and NRC KI (Russia); MESTD (Serbia); SEIDI,CPAN, PCTI, and FEDER (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland);MST (Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK (Turkey);NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA).Individuals have received support from the Marie-Curie program and the European ResearchCouncil and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, and 765710 (Euro-pean Union); the Leventis Foundation; the A.P. Sloan Foundation; the Alexander von Hum-boldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation `a laRecherche dans l’Industrie et dans l’Agriculture (FRIA-Belgium); the Agentschap voor Inno-vatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) un-der the “Excellence of Science – EOS” – be.h project n. 30820817; the Beijing Municipal Science &Technology Commission, No. Z191100007219010; the Ministry of Education, Youth and Sports(MEYS) of the Czech Republic; the Deutsche Forschungsgemeinschaft (DFG) under Germany’sExcellence Strategy – EXC 2121 “Quantum Universe” – 390833306; the Lend ¨ulet (“Momen-tum”) Program and the J´anos Bolyai Research Scholarship of the Hungarian Academy of Sci-ences, the New National Excellence Program ´UNKP, the NKFIA research grants 123842, 123959,124845, 124850, 125105, 128713, 128786, and 129058 (Hungary); the Council of Science and In-dustrial Research, India; the HOMING PLUS program of the Foundation for Polish Science,cofinanced from European Union, Regional Development Fund, the Mobility Plus program ofthe Ministry of Science and Higher Education, the National Science Center (Poland), contractsHarmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406; the National Priorities ResearchProgram by Qatar National Research Fund; the Ministry of Science and Higher Education,project no. 0723-2020-0041 (Russia); the Tomsk Polytechnic University Competitiveness En-hancement Program; the Programa Estatal de Fomento de la Investigaci ´on Cient´ıfica y T´ecnicade Excelencia Mar´ıa de Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa delPrincipado de Asturias; the Thalis and Aristeia programs cofinanced by EU-ESF and the GreekNSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn Universityand the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thai-land); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the WelchFoundation, contract C-1845; and the Weston Havens Foundation (USA). References [1] N. Arkani-Hamed and S. Dimopoulos, “Supersymmetric unification without lowenergy supersymmetry and signatures for fine-tuning at the LHC”,
JHEP (2005) 073, doi:10.1088/1126-6708/2005/06/073 , arXiv:hep-th/0405159 .[2] G. F. Giudice and A. Romanino, “Split supersymmetry”, Nucl. Phys. B (2004) 65, doi:10.1016/j.nuclphysb.2004.08.001 , arXiv:hep-ph/0406088 . [Erratum: doi:10.1016/j.nuclphysb.2004.11.048 ].[3] J. L. Hewett, B. Lillie, M. Masip, and T. G. Rizzo, “Signatures of long-lived gluinos insplit supersymmetry”, JHEP (2004) 070, doi:10.1088/1126-6708/2004/09/070 , arXiv:hep-ph/0408248 . eferences [4] N. Arkani-Hamed, S. Dimopoulos, G. F. Giudice, and A. Romanino, “Aspects of splitsupersymmetry”, Nucl. Phys. B (2005) 3, doi:10.1016/j.nuclphysb.2004.12.026 , arXiv:hep-ph/0409232 .[5] P. Gambino, G. F. Giudice, and P. Slavich, “Gluino decays in split supersymmetry”, Nucl. Phys. B (2005) 35, doi:10.1016/j.nuclphysb.2005.08.011 , arXiv:hep-ph/0506214 .[6] A. Arvanitaki, N. Craig, S. Dimopoulos, and G. Villadoro, “Mini-split”, JHEP (2013)126, doi:10.1007/JHEP02(2013)126 , arXiv:1210.0555 .[7] N. Arkani-Hamed et al., “Simply unnatural supersymmetry”, (2012). arXiv:1212.6971 .[8] P. Fayet, “Supergauge invariant extension of the Higgs mechanism and a model for theelectron and its neutrino”, Nucl. Phys. B (1975) 104, doi:10.1016/0550-3213(75)90636-7 .[9] G. R. Farrar and P. Fayet, “Phenomenology of the production, decay, and detection ofnew hadronic states associated with supersymmetry”, Phys. Lett. B (1978) 575, doi:10.1016/0370-2693(78)90858-4 .[10] S. Weinberg, “Supersymmetry at ordinary energies. 1. masses and conservation laws”, Phys. Rev. D (1982) 287, doi:10.1103/PhysRevD.26.287 .[11] L. J. Hall and M. Suzuki, “Explicit R -parity breaking in supersymmetric models”, Nucl.Phys. B (1984) 419, doi:10.1016/0550-3213(84)90513-3 .[12] R. Barbier et al., “ R -parity violating supersymmetry”, Phys. Rept. (2005) 1, doi:10.1016/j.physrep.2005.08.006 , arXiv:hep-ph/0406039 .[13] G. F. Giudice and R. Rattazzi, “Theories with gauge mediated supersymmetrybreaking”, Phys. Rept. (1999) 419, doi:10.1016/S0370-1573(99)00042-3 , arXiv:hep-ph/9801271 .[14] P. Meade, N. Seiberg, and D. Shih, “General gauge mediation”, Prog. Theor. Phys. Suppl. (2009) 143, doi:10.1143/PTPS.177.143 , arXiv:0801.3278 .[15] M. Buican, P. Meade, N. Seiberg, and D. Shih, “Exploring general gauge mediation”, JHEP (2009) 016, doi:10.1088/1126-6708/2009/03/016 , arXiv:0812.3668 .[16] J. Fan, M. Reece, and J. T. Ruderman, “Stealth supersymmetry”, JHEP (2011) 012, doi:10.1007/JHEP11(2011)012 , arXiv:1105.5135 .[17] J. Fan, M. Reece, and J. T. Ruderman, “A stealth supersymmetry sampler”, JHEP (2012) 196, doi:10.1007/JHEP07(2012)196 , arXiv:1201.4875 .[18] M. J. Strassler and K. M. Zurek, “Echoes of a hidden valley at hadron colliders”, Phys.Lett. B (2007) 374, doi:10.1016/j.physletb.2007.06.055 , arXiv:hep-ph/0604261 .[19] M. J. Strassler and K. M. Zurek, “Discovering the Higgs through highly-displacedvertices”, Phys. Lett. B (2008) 263, doi:10.1016/j.physletb.2008.02.008 , arXiv:hep-ph/0605193 . [20] T. Han, Z. Si, K. M. Zurek, and M. J. Strassler, “Phenomenology of hidden valleys athadron colliders”, JHEP (2008) 008, doi:10.1088/1126-6708/2008/07/008 , arXiv:0712.2041 .[21] D. E. Kaplan, M. A. Luty, and K. M. Zurek, “Asymmetric dark matter”, Phys. Rev. D (2009) 115016, doi:10.1103/PhysRevD.79.115016 , arXiv:0901.4117 .[22] L. J. Hall, K. Jedamzik, J. March-Russell, and S. M. West, “Freeze-in production of FIMPdark matter”, JHEP (2010) 080, doi:10.1007/JHEP03(2010)080 , arXiv:0911.1120 .[23] I.-W. Kim and K. M. Zurek, “Flavor and collider signatures of asymmetric dark matter”, Phys. Rev. D (2014) 035008, doi:10.1103/PhysRevD.89.035008 , arXiv:1310.2617 .[24] Y. Cui and B. Shuve, “Probing baryogenesis with displaced vertices at the LHC”, JHEP (2015) 049, doi:10.1007/JHEP02(2015)049 , arXiv:1409.6729 .[25] R. T. Co, F. D’Eramo, L. J. Hall, and D. Pappadopulo, “Freeze-In dark matter withdisplaced signatures at colliders”, JCAP (2015) 024, doi:10.1088/1475-7516/2015/12/024 , arXiv:1506.07532 .[26] L. Calibbi, L. Lopez-Honorez, S. Lowette, and A. Mariotti, “Singlet-Doublet dark matterfreeze-in: LHC displaced signatures versus cosmology”, JHEP (2018) 037, doi:10.1007/JHEP09(2018)037 , arXiv:1805.04423 .[27] Y. Cui, L. Randall, and B. Shuve, “A WIMPy baryogenesis miracle”, JHEP (2012) 075, doi:10.1007/JHEP04(2012)075 , arXiv:1112.2704 .[28] Y. Cui and R. Sundrum, “Baryogenesis for weakly interacting massive particles”, Phys.Rev. D (2013) 116013, doi:10.1103/PhysRevD.87.116013 , arXiv:1212.2973 .[29] A. Atre, T. Han, S. Pascoli, and B. Zhang, “The search for heavy Majorana neutrinos”, JHEP (2009) 030, doi:10.1088/1126-6708/2009/05/030 , arXiv:0901.3589 .[30] M. Drewes, “The phenomenology of right handed neutrinos”, Int. J. Mod. Phys. E (2013) 1330019, doi:10.1142/S0218301313300191 , arXiv:1303.6912 .[31] F. F. Deppisch, P. S. Bhupal Dev, and A. Pilaftsis, “Neutrinos and collider physics”, NewJ. Phys. (2015) 075019, doi:10.1088/1367-2630/17/7/075019 , arXiv:1502.06541 .[32] Y. Cai, T. Han, T. Li, and R. Ruiz, “Lepton number violation: seesaw models and theircollider tests”, Front. Phys. (2018) 40, doi:10.3389/fphy.2018.00040 , arXiv:1711.02180 .[33] Z. Chacko, H.-S. Goh, and R. Harnik, “Natural electroweak breaking from a mirrorsymmetry”, Phys. Rev. Lett. (2006) 231802, doi:10.1103/PhysRevLett.96.231802 , arXiv:hep-ph/0506256 .[34] H. Cai, H.-C. Cheng, and J. Terning, “A quirky little Higgs model”, JHEP (2009) 045, doi:10.1088/1126-6708/2009/05/045 , arXiv:0812.0843 .[35] N. Craig, S. Knapen, and P. Longhi, “Neutral naturalness from orbifold Higgs models”, Phys. Rev. Lett. (2015) 061803, doi:10.1103/PhysRevLett.114.061803 , arXiv:1410.6808 . eferences [36] N. Craig, A. Katz, M. Strassler, and R. Sundrum, “Naturalness in the dark at the LHC”, JHEP (2015) 105, doi:10.1007/JHEP07(2015)105 , arXiv:1501.05310 .[37] D. Curtin and C. B. Verhaaren, “Discovering uncolored naturalness in exotic Higgsdecays”, JHEP (2015) 072, doi:10.1007/JHEP12(2015)072 , arXiv:1506.06141 .[38] S. Alipour-Fard et al., “The second Higgs at the lifetime frontier”, JHEP (2020) 029, doi:10.1007/JHEP07(2020)029 , arXiv:1812.09315 .[39] CMS Collaboration, “The CMS experiment at the CERN LHC”, JINST (2008) S08004, doi:10.1088/1748-0221/3/08/S08004 .[40] CMS Collaboration, “Search for long-lived particles decaying into displaced jets inproton-proton collisions at √ s =
13 TeV”,
Phys. Rev. D (2019) 032011, doi:10.1103/PhysRevD.99.032011 , arXiv:1811.07991 .[41] ATLAS Collaboration, “Search for long-lived, massive particles in events with displacedvertices and missing transverse momentum in √ s = 13 TeV pp collisions with theATLAS detector”, Phys. Rev. D (2018) 052012, doi:10.1103/PhysRevD.97.052012 , arXiv:1710.04901 .[42] ATLAS Collaboration, “Search for long-lived particles produced in pp collisions at √ s =
13 TeV that decay into displaced hadronic jets in the ATLAS muon spectrometer”,
Phys. Rev. D (2019) 052005, doi:10.1103/PhysRevD.99.052005 , arXiv:1811.07370 .[43] ATLAS Collaboration, “Search for long-lived neutral particles in pp collisions at √ s = 13TeV that decay into displaced hadronic jets in the ATLAS calorimeter”, Eur. Phys. J. C (2019) 481, doi:10.1140/epjc/s10052-019-6962-6 , arXiv:1902.03094 .[44] ATLAS Collaboration, “Search for long-lived, massive particles in events with adisplaced vertex and a muon with large impact parameter in pp collisions at √ s = Phys. Rev. D (2020) 032006, doi:10.1103/PhysRevD.102.032006 , arXiv:2003.11956 .[45] ATLAS Collaboration, “Search for long-lived neutral particles produced in pp collisionsat √ s =
13 TeV decaying into displaced hadronic jets in the ATLAS inner detector andmuon spectrometer”,
Phys. Rev. D (2020) 052013, doi:10.1103/PhysRevD.101.052013 , arXiv:1911.12575 .[46] CMS Collaboration, “Search for new long-lived particles at √ s = 13 TeV”, Phys. Lett. B (2018) 432, doi:10.1016/j.physletb.2018.03.019 , arXiv:1711.09120 .[47] CMS Collaboration, “Search for long-lived particles with displaced vertices in multijetevents in proton-proton collisions at √ s =
13 TeV”,
Phys. Rev. D (2018) 092011, doi:10.1103/PhysRevD.98.092011 , arXiv:1808.03078 .[48] CMS Collaboration, “Search for long-lived particles using nonprompt jets and missingtransverse momentum with proton-proton collisions at √ s =
13 TeV”,
Phys. Lett. B (2019) 134876, doi:10.1016/j.physletb.2019.134876 , arXiv:1906.06441 .[49] CMS Collaboration, “Description and performance of track and primary-vertexreconstruction with the CMS tracker”, JINST (2014) P10009, doi:10.1088/1748-0221/9/10/P10009 , arXiv:1405.6569 . [50] CMS Collaboration, “The CMS trigger system”, JINST (2017) P01020, doi:10.1088/1748-0221/12/01/P01020 , arXiv:1609.02366 .[51] M. Cacciari, G. P. Salam, and G. Soyez, “The anti- k T jet clustering algorithm”, JHEP (2008) 063, doi:10.1088/1126-6708/2008/04/063 , arXiv:0802.1189 .[52] M. Cacciari, G. P. Salam, and G. Soyez, “FastJet user manual”, Eur. Phys. J. C (2012)1896, doi:10.1140/epjc/s10052-012-1896-2 , arXiv:1111.6097 .[53] CMS Collaboration, “Jet energy scale and resolution in the CMS experiment in ppcollisions at 8 TeV”, JINST (2017) P02014, doi:10.1088/1748-0221/12/02/P02014 , arXiv:1607.03663 .[54] CMS Collaboration, “Technical proposal for the Phase-II upgrade of the CMS detector”,CMS Technical proposal CERN-LHCC-2015-010, CMS-TDR-15-02, 2015.[55] J. Alwall et al., “The automated computation of tree-level and next-to-leading orderdifferential cross sections, and their matching to parton shower simulations”, JHEP (2014) 079, doi:10.1007/JHEP07(2014)079 , arXiv:1405.0301 .[56] T. Sj ¨ostrand et al., “An introduction to PYTHIA 8.2”, Comput. Phys. Commun. (2015)159, doi:10.1016/j.cpc.2015.01.024 , arXiv:1410.3012 .[57] J. Alwall et al., “Comparative study of various algorithms for the merging of partonshowers and matrix elements in hadronic collisions”, Eur. Phys. J. C (2008) 473, doi:10.1140/epjc/s10052-007-0490-5 , arXiv:0706.2569 .[58] CMS Collaboration, “Extraction and validation of a new set of CMS PYTHIA8 tunesfrom underlying-event measurements”, Eur. Phys. J. C (2020) 4, doi:10.1140/epjc/s10052-019-7499-4 , arXiv:1903.12179 .[59] NNPDF Collaboration, “Parton distributions from high-precision collider data”, Eur.Phys. J. C (2017) 663, doi:10.1140/epjc/s10052-017-5199-5 , arXiv:1706.00428 .[60] LHC Higgs Cross Section Working Group, “Handbook of LHC Higgs cross sections: 4.Deciphering the nature of the Higgs sector”, CERN Report CERN-2017-002-M, 2016. doi:10.23731/CYRM-2017-002 , arXiv:1610.07922 .[61] G. Burdman, Z. Chacko, H.-S. Goh, and R. Harnik, “Folded supersymmetry and theLEP paradox”, JHEP (2007) 009, doi:10.1088/1126-6708/2007/02/009 , arXiv:hep-ph/0609152 .[62] P. Nason, “A new method for combining NLO QCD with shower Monte Carloalgorithms”, JHEP (2004) 040, doi:10.1088/1126-6708/2004/11/040 , arXiv:hep-ph/0409146 .[63] S. Frixione, P. Nason, and C. Oleari, “Matching NLO QCD computations with partonshower simulations: the POWHEG method”, JHEP (2007) 070, doi:10.1088/1126-6708/2007/11/070 , arXiv:0709.2092 .[64] S. Alioli, P. Nason, C. Oleari, and E. Re, “A general framework for implementing NLOcalculations in shower Monte Carlo programs: the POWHEG BOX”, JHEP (2010)043, doi:10.1007/JHEP06(2010)043 , arXiv:1002.2581 . eferences [65] E. Bagnaschi, G. Degrassi, P. Slavich, and A. Vicini, “Higgs production via gluon fusionin the POWHEG approach in the SM and in the MSSM”, JHEP (2012) 088, doi:10.1007/JHEP02(2012)088 , arXiv:1111.2854 .[66] Z. Liu and B. Tweedie, “The fate of long-lived superparticles with hadronic decays afterLHC Run 1”, JHEP (2015) 042, doi:10.1007/JHEP06(2015)042 , arXiv:1503.05923 .[67] C. Csaki, Y. Grossman, and B. Heidenreich, “Minimal flavor violation supersymmetry: anatural theory for R -parity violation”, Phys. Rev. D (2012) 095009, doi:10.1103/PhysRevD.85.095009 , arXiv:1111.1239 .[68] P. W. Graham, D. E. Kaplan, S. Rajendran, and P. Saraswat, “Displaced supersymmetry”, JHEP (2012) 149, doi:10.1007/JHEP07(2012)149 , arXiv:1204.6038 .[69] C. Csaki, E. Kuflik, and T. Volansky, “Dynamical R -parity violation”, Phys. Rev. Lett. (2014) 131801, doi:10.1103/PhysRevLett.112.131801 , arXiv:1309.5957 .[70] C. Csaki, E. Kuflik, O. Slone, and T. Volansky, “Models of dynamical R -parity violation”, JHEP (2015) 045, doi:10.1007/JHEP06(2015)045 , arXiv:1502.03096 .[71] C. Csaki et al., “Phenomenology of a long-lived LSP with R -parity violation”, JHEP (2015) 016, doi:10.1007/JHEP08(2015)016 , arXiv:1505.00784 .[72] G. R. Farrar and P. Fayet, “Bounds on R -hadron production from calorimetryexperiments”, Phys. Lett. B (1978) 442, doi:10.1016/0370-2693(78)90402-1 .[73] M. Fairbairn et al., “Stable massive particles at colliders”, Phys. Rept. (2007) 1, doi:10.1016/j.physrep.2006.10.002 , arXiv:hep-ph/0611040 .[74] R. Mackeprang and A. Rizzi, “Interactions of coloured heavy stable particles in matter”, Eur. Phys. J. C (2007) 353, doi:10.1140/epjc/s10052-007-0252-4 , arXiv:hep-ph/0612161 .[75] R. Mackeprang and D. Milstead, “An updated description of heavy-hadron interactionsin GEANT-4”, Eur. Phys. J. C (2010) 493, doi:10.1140/epjc/s10052-010-1262-1 , arXiv:0908.1868 .[76] GEANT4 Collaboration, “G EANT
Nucl. Instrum. Meth. A (2003) 250, doi:10.1016/S0168-9002(03)01368-8 .[77] R. Fr ¨uhwirth, W. Waltenberger, and P. Vanlaer, “Adaptive vertex fitting”,
J. Phys. G (2007) N343, doi:10.1088/0954-3899/34/12/N01 .[78] CMS Collaboration, “Precision measurement of the structure of the CMS inner trackingsystem using nuclear interactions”, JINST (2018) P10034, doi:10.1088/1748-0221/13/10/P10034 , arXiv:1807.03289 .[79] CMS Collaboration, “CMS technical design report for the pixel detector upgrade”,Technical Report CERN-LHCC-2012-016, CMS-TDR-011, 2012. doi:10.2172/1151650 .[80] S. C. Johnson, “Hierarchical clustering schemes”, Psychometrika (1967) 241, doi:10.1007/BF02289588 . [81] Y. Freund and R. E. Schapire, “Experiments with a new boosting algorithm”, in Proceedings of the Thirteenth International Conference on International Conference on MachineLearning , ICML’96, p. 148. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA,1996.[82] J. Friedman, T. Hastie, and R. Tibshirani, “Additive logistic regression: a statistical viewof boosting (with discussion and a rejoinder by the authors)”,
Ann. Statist. (2000)337, doi:10.1214/aos/1016218223 .[83] J. H. Friedman, “Greedy function approximation: A gradient boosting machine.”, Ann.Statist. (2001) 1189, doi:10.1214/aos/1013203451 .[84] H. Voss, A. H ¨ocker, J. Stelzer, and F. Tegenfeldt, “TMVA, the toolkit for multivariate dataanalysis with ROOT”, in XIth International Workshop on Advanced Computing and AnalysisTechniques in Physics Research (ACAT) , p. 40. 2007. arXiv:physics/0703039 . doi:10.22323/1.050.0040 .[85] F. Pedregosa et al., “Scikit-learn: machine learning in Python”, J. Mach. Learn. Res. (2011) 2825, arXiv:1201.0490 .[86] G. Punzi, “Sensitivity of searches for new signals and its optimization”, in Statisticalproblems in particle physics, astrophysics and cosmology. Proceedings, Conference, PHYSTAT2003, Stanford, USA, September 8-11, 2003 , p. MODT002. 2003. arXiv:physics/0308063 . [eConf C030908/MOTD002].[87] CMS Collaboration, “CMS luminosity measurement for the 2017 data-taking period at √ s =
13 TeV”, CMS Physics Analysis Summary CMS-PAS-LUM-17-004, 2018.[88] CMS Collaboration, “CMS luminosity measurement for the 2018 data-taking period at √ s =
13 TeV”, CMS Physics Analysis Summary CMS-PAS-LUM-18-002, 2019.[89] S. Dulat et al., “New parton distribution functions from a global analysis of quantumchromodynamics”,
Phys. Rev. D (2016) 033006, doi:10.1103/PhysRevD.93.033006 , arXiv:1506.07443 .[90] L. A. Harland-Lang, A. D. Martin, P. Motylinski, and R. S. Thorne, “Parton distributionsin the LHC era: MMHT 2014 PDFs”, Eur. Phys. J. C (2015) 204, doi:10.1140/epjc/s10052-015-3397-6 , arXiv:1412.3989 .[91] J. Butterworth et al., “PDF4LHC recommendations for LHC Run II”, J. Phys. G (2016)023001, doi:10.1088/0954-3899/43/2/023001 , arXiv:1510.03865 .[92] A. Buckley et al., “LHAPDF6: parton density access in the LHC precision era”, Eur.Phys. J. C (2015) 132, doi:10.1140/epjc/s10052-015-3318-8 , arXiv:1412.7420 .[93] T. Junk, “Confidence level computation for combining searches with small statistics”, Nucl. Instrum. Meth. A (1999) 435, doi:10.1016/S0168-9002(99)00498-2 , arXiv:hep-ex/9902006 .[94] A. L. Read, “Presentation of search results: The CL s technique”, J. Phys. G (2002)2693, doi:10.1088/0954-3899/28/10/313 . eferences [95] G. Cowan, K. Cranmer, E. Gross, and O. Vitells, “Asymptotic formulae forlikelihood-based tests of new physics”, Eur. Phys. J. C (2011) 1554, doi:10.1140/epjc/s10052-011-1554-0 , arXiv:1007.1727 . [Erratum: doi:10.1140/epjc/s10052-013-2501-z ].[96] ATLAS and CMS Collaborations, The LHC Higgs Combination group, “Procedure forthe LHC Higgs boson search combination in Summer 2011”, Technical ReportCMS-NOTE-2011-005, ATL-PHYS-PUB-2011-11, 2011.[97] W. Beenakker, R. Hopker, M. Spira, and P. M. Zerwas, “Squark and gluino production athadron colliders”, Nucl. Phys. B (1997) 51, doi:10.1016/S0550-3213(97)80027-2 , arXiv:hep-ph/9610490 .[98] A. Kulesza and L. Motyka, “Threshold resummation for squark-antisquark andgluino-pair production at the LHC”, Phys. Rev. Lett. (2009) 111802, doi:10.1103/PhysRevLett.102.111802 , arXiv:0807.2405 .[99] A. Kulesza and L. Motyka, “Soft gluon resummation for the production of gluino-gluinoand squark-antisquark pairs at the LHC”, Phys. Rev. D (2009) 095004, doi:10.1103/PhysRevD.80.095004 , arXiv:0905.4749 .[100] W. Beenakker et al., “Squark and gluino hadroproduction”, Int. J. Mod. Phys. A (2011) 2637, doi:10.1142/S0217751X11053560 , arXiv:1105.1110 .[101] C. Borschensky et al., “Squark and gluino production cross sections in pp collisions at √ s = 13, 14, 33 and 100 TeV”, Eur. Phys. J. C (2014) 3174, doi:10.1140/epjc/s10052-014-3174-y , arXiv:1407.5066 .[102] W. Beenakker et al., “NNLL-fast: predictions for coloured supersymmetric particleproduction at the LHC with threshold and Coulomb resummation”, JHEP (2016)133, doi:10.1007/JHEP12(2016)133 , arXiv:1607.07741 . A Signal efficiencies for representative signal points in differentmodels
Table A.1: Signal efficiencies for the jet-jet model in the 2017 and 2018 analysis at different meanproper decay lengths c τ and different masses m X . Selection requirements are cumulative fromthe first row to the last for each value of m X . Uncertainties are statistical only. Efficiency (%) m X (GeV) c τ ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± Table A.2: Signal efficiencies for the model where the SM-like Higgs boson decays to two long-lived scalar particles S in the 2017 and 2018 analysis at different mean proper decay lengths c τ and with m S =
55 GeV. The long-lived scalar particle is assumed to decay to a down quark-antiquark pair (S → dd). Selection requirements are cumulative from the first row to the last.Uncertainties are statistical only. Efficiency × m S (GeV) c τ ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± Table A.3: Signal efficiencies for the model where the SM-like Higgs boson decays to two long-lived scalar particles S in the 2017 and 2018 analysis at different mean proper decay lengths c τ and with m S =
55 GeV. The long-lived scalar particle is assumed to decay to a bottom quark-antiquark pair (S → bb). Selection requirements are cumulative from the first row to the last.Uncertainties are statistical only. Efficiency × m S (GeV) c τ ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± /mm) t (c log [ G e V ] g ~ m - - E ff i c i en cy CMS
Simulation
G~ g fi g~, g~g~ fi pp /mm) t (c log [ G e V ] g ~ m - - E ff i c i en cy CMS
Simulation c~ q q fi g~, g~g~ fi pp /mm) t (c log [ G e V ] g ~ m - - E ff i c i en cy CMS
Simulation tbs fi g~, g~g~ fi pp /mm) t (c log [ G e V ] t ~ m - - E ff i c i en cy CMS
Simulation bl fi t~, t~t~ fi pp /mm) t (c log [ G e V ] t ~ m - - E ff i c i en cy CMS
Simulation dl fi t~, t~t~ fi pp /mm) t (c log [ G e V ] t ~ m - - E ff i c i en cy CMS
Simulation dd fi t~, t~t~ fi pp Figure A.1: The signal efficiencies as functions of the long-lived particle mass and mean properdecay length in the 2017 and 2018 analysis, for the (cid:101) g → g (cid:101) G model (upper left), the (cid:101) g → qq (cid:101) χ model (upper right), the (cid:101) g → tbs model (middle left), the (cid:101) t → b (cid:96) model (middle right), the (cid:101) t → d (cid:96) model (lower left), and the (cid:101) t → dd model (lower right).8
Simulation dd fi t~, t~t~ fi pp Figure A.1: The signal efficiencies as functions of the long-lived particle mass and mean properdecay length in the 2017 and 2018 analysis, for the (cid:101) g → g (cid:101) G model (upper left), the (cid:101) g → qq (cid:101) χ model (upper right), the (cid:101) g → tbs model (middle left), the (cid:101) t → b (cid:96) model (middle right), the (cid:101) t → d (cid:96) model (lower left), and the (cid:101) t → dd model (lower right).8 Table A.4: Signal efficiencies for the (cid:101) g → g (cid:101) G model in the 2017 and 2018 analysis at differentmean proper decay lengths c τ and different masses m (cid:101) g . Selection requirements are cumulativefrom the first row to the last for each value of m (cid:101) g . Uncertainties are statistical only. Efficiency (%) m (cid:101) g (GeV) c τ ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± Table A.5: Signal efficiencies for the (cid:101) g → qq (cid:101) χ model ( m (cid:101) χ =
100 GeV) in the 2017 and 2018analysis at different mean proper decay lengths c τ and different masses m (cid:101) g . Selection require-ments are cumulative from the first row to the last for each value of m (cid:101) g . Uncertainties arestatistical only. Efficiency (%) m (cid:101) g (GeV) c τ ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± Table A.6: Signal efficiencies for the (cid:101) g → tbs model in the 2017 and 2018 analysis at differentmean proper decay lengths c τ and different masses m (cid:101) g . Selection requirements are cumulativefrom the first row to the last for each value of m (cid:101) g . Uncertainties are statistical only. Efficiency (%) m (cid:101) g (GeV) c τ ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± Table A.7: Signal efficiencies for the (cid:101) t → b (cid:96) model in the 2017 and 2018 analysis at differentmean proper decay lengths c τ and different masses m (cid:101) t . Selection requirements are cumulativefrom the first row to the last for each value of m (cid:101) t . Uncertainties are statistical only. Efficiency (%) m (cid:101) t (GeV) c τ ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± Table A.8: Signal efficiencies for the (cid:101) t → d (cid:96) model in the 2017 and 2018 analysis at differentmean proper decay lengths c τ and different masses m (cid:101) t . Selection requirements are cumulativefrom the first row to the last for each value of m (cid:101) t . Uncertainties are statistical only. Efficiency (%) m (cid:101) t (GeV) c τ ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± Table A.9: Signal efficiencies for the (cid:101) t → dd model in the 2017 and 2018 analysis at differentmean proper decay lengths c τ and different masses m (cid:101) t . Selection requirements are cumulativefrom the first row to the last for each value of m (cid:101) t . Uncertainties are statistical only. Efficiency (%) m (cid:101) t (GeV) c τ ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± B The CMS Collaboration
Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan † , A. Tumasyan Institut f ¨ur Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, T. Bergauer, M. Dragicevic, J. Er ¨o, A. Escalante Del Valle, R. Fr ¨uhwirth ,M. Jeitler , N. Krammer, L. Lechner, D. Liko, T. Madlener, I. Mikulec, F.M. Pitters, N. Rad,J. Schieck , R. Sch ¨ofbeck, M. Spanring, S. Templ, W. Waltenberger, C.-E. Wulz , M. Zarucki Institute for Nuclear Problems, Minsk, Belarus
V. Chekhovsky, A. Litomin, V. Makarenko, J. Suarez Gonzalez
Universiteit Antwerpen, Antwerpen, Belgium
M.R. Darwish , E.A. De Wolf, D. Di Croce, X. Janssen, T. Kello , A. Lelek, M. Pieters,H. Rejeb Sfar, H. Van Haevermaet, P. Van Mechelen, S. Van Putte, N. Van Remortel Vrije Universiteit Brussel, Brussel, Belgium
F. Blekman, E.S. Bols, S.S. Chhibra, J. D’Hondt, J. De Clercq, D. Lontkovskyi, S. Lowette,I. Marchesini, S. Moortgat, A. Morton, Q. Python, S. Tavernier, W. Van Doninck, P. Van Mulders
Universit´e Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, B. Clerbaux, G. De Lentdecker, B. Dorney, L. Favart, A. Grebenyuk,A.K. Kalsi, I. Makarenko, L. Moureaux, L. P´etr´e, A. Popov, N. Postiau, E. Starling, L. Thomas,C. Vander Velde, P. Vanlaer, D. Vannerom, L. Wezenbeek
Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, M. Gruchala, I. Khvastunov , M. Niedziela, C. Roskas, K. Skovpen,M. Tytgat, W. Verbeke, B. Vermassen, M. Vit Universit´e Catholique de Louvain, Louvain-la-Neuve, Belgium
G. Bruno, F. Bury, C. Caputo, P. David, C. Delaere, M. Delcourt, I.S. Donertas, A. Giammanco,V. Lemaitre, K. Mondal, J. Prisciandaro, A. Taliercio, M. Teklishyn, P. Vischia, S. Wuyckens,J. Zobec
Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
G.A. Alves, G. Correia Silva, C. Hensel, A. Moraes
Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
W.L. Ald´a J ´unior, E. Belchior Batista Das Chagas, H. BRANDAO MALBOUISSON,W. Carvalho, J. Chinellato , E. Coelho, E.M. Da Costa, G.G. Da Silveira , D. De Jesus Damiao,S. Fonseca De Souza, J. Martins , D. Matos Figueiredo, M. Medina Jaime , M. Melo De Almeida,C. Mora Herrera, L. Mundim, H. Nogima, P. Rebello Teles, L.J. Sanchez Rosas,A. Santoro, S.M. Silva Do Amaral, A. Sznajder, M. Thiel, E.J. Tonelli Manganote ,F. Torres Da Silva De Araujo, A. Vilela Pereira Universidade Estadual Paulista a , Universidade Federal do ABC b , S˜ao Paulo, Brazil C.A. Bernardes a , L. Calligaris a , T.R. Fernandez Perez Tomei a , E.M. Gregores b , D.S. Lemos a ,P.G. Mercadante b , S.F. Novaes a , Sandra S. Padula a Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia,Bulgaria
A. Aleksandrov, G. Antchev, I. Atanasov, R. Hadjiiska, P. Iaydjiev, M. Misheva, M. Rodozov,M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
M. Bonchev, A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov, A. Petrov
Beihang University, Beijing, China
W. Fang , Q. Guo, H. Wang, L. Yuan Department of Physics, Tsinghua University, Beijing, China
M. Ahmad, Z. Hu, Y. Wang
Institute of High Energy Physics, Beijing, China
E. Chapon, G.M. Chen , H.S. Chen , M. Chen, D. Leggat, H. Liao, Z. Liu, R. Sharma, A. Spiezia,J. Tao, J. Thomas-wilsker, J. Wang, H. Zhang, S. Zhang , J. Zhao State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing, China
A. Agapitos, Y. Ban, C. Chen, A. Levin, Q. Li, M. Lu, X. Lyu, Y. Mao, S.J. Qian, D. Wang,Q. Wang, J. Xiao
Sun Yat-Sen University, Guangzhou, China
Z. You
Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beamApplication (MOE) - Fudan University, Shanghai, China
X. Gao Zhejiang University, Hangzhou, China
M. Xiao
Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C. Florez, J. Fraga, A. Sarkar, M.A. Segura Delgado
Universidad de Antioquia, Medellin, Colombia
J. Jaramillo, J. Mejia Guisao, F. Ramirez, J.D. Ruiz Alvarez, C.A. Salazar Gonz´alez,N. Vanegas Arbelaez
University of Split, Faculty of Electrical Engineering, Mechanical Engineering and NavalArchitecture, Split, Croatia
D. Giljanovic, N. Godinovic, D. Lelas, I. Puljak, T. Sculac
University of Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac
Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, D. Majumder, M. Roguljic, A. Starodumov , T. Susa University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, E. Erodotou, A. Ioannou, G. Kole, M. Kolosova, S. Konstantinou,G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H. Rykaczewski, H. Saka,D. Tsiakkouri
Charles University, Prague, Czech Republic
M. Finger , M. Finger Jr. , A. Kveton, J. Tomsa Escuela Politecnica Nacional, Quito, Ecuador
E. Ayala
Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab Republic of Egypt, EgyptianNetwork of High Energy Physics, Cairo, Egypt
S. Abu Zeid , S. Elgammal , A. Ellithi Kamel Center for High Energy Physics (CHEP-FU), Fayoum University, El-Fayoum, Egypt
M.A. Mahmoud, Y. Mohammed National Institute of Chemical Physics and Biophysics, Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M. Kadastik,M. Raidal, C. Veelken
Department of Physics, University of Helsinki, Helsinki, Finland
P. Eerola, L. Forthomme, H. Kirschenmann, K. Osterberg, M. Voutilainen
Helsinki Institute of Physics, Helsinki, Finland
E. Br ¨ucken, F. Garcia, J. Havukainen, V. Karim¨aki, M.S. Kim, R. Kinnunen, T. Lamp´en,K. Lassila-Perini, S. Laurila, S. Lehti, T. Lind´en, H. Siikonen, E. Tuominen, J. Tuominiemi
Lappeenranta University of Technology, Lappeenranta, Finland
P. Luukka, T. Tuuva
IRFU, CEA, Universit´e Paris-Saclay, Gif-sur-Yvette, France
C. Amendola, M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F. Ferri, S. Ganjour,A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry, B. Lenzi, E. Locci, J. Malcles,J. Rander, A. Rosowsky, M. ¨O. Sahin, A. Savoy-Navarro , M. Titov, G.B. Yu Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut Polytechniquede Paris, Palaiseau, France
S. Ahuja, F. Beaudette, M. Bonanomi, A. Buchot Perraguin, P. Busson, C. Charlot, O. Davignon,B. Diab, G. Falmagne, R. Granier de Cassagnac, A. Hakimi, I. Kucher, A. Lobanov,C. Martin Perez, M. Nguyen, C. Ochando, P. Paganini, J. Rembser, R. Salerno, J.B. Sauvan,Y. Sirois, A. Zabi, A. Zghiche
Universit´e de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France
J.-L. Agram , J. Andrea, D. Bloch, G. Bourgatte, J.-M. Brom, E.C. Chabert, C. Collard, J.-C. Fontaine , D. Gel´e, U. Goerlach, C. Grimault, A.-C. Le Bihan, P. Van Hove Universit´e de Lyon, Universit´e Claude Bernard Lyon 1, CNRS-IN2P3, Institut de PhysiqueNucl´eaire de Lyon, Villeurbanne, France
E. Asilar, S. Beauceron, C. Bernet, G. Boudoul, C. Camen, A. Carle, N. Chanon,D. Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M. Gouzevitch, B. Ille, Sa. Jain,I.B. Laktineh, H. Lattaud, A. Lesauvage, M. Lethuillier, L. Mirabito, L. Torterotot, G. Touquet,M. Vander Donckt, S. Viret
Georgian Technical University, Tbilisi, Georgia
I. Bagaturia , Z. Tsamalaidze RWTH Aachen University, I. Physikalisches Institut, Aachen, Germany
L. Feld, K. Klein, M. Lipinski, D. Meuser, A. Pauls, M. Preuten, M.P. Rauch, J. Schulz,M. Teroerde
RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany
D. Eliseev, M. Erdmann, P. Fackeldey, B. Fischer, S. Ghosh, T. Hebbeker, K. Hoepfner, H. Keller,L. Mastrolorenzo, M. Merschmeyer, A. Meyer, P. Millet, G. Mocellin, S. Mondal, S. Mukherjee, D. Noll, A. Novak, T. Pook, A. Pozdnyakov, T. Quast, M. Radziej, Y. Rath, H. Reithler, J. Roemer,A. Schmidt, S.C. Schuler, A. Sharma, S. Wiedenbeck, S. Zaleski
RWTH Aachen University, III. Physikalisches Institut B, Aachen, Germany
C. Dziwok, G. Fl ¨ugge, W. Haj Ahmad , O. Hlushchenko, T. Kress, A. Nowack, C. Pistone,O. Pooth, D. Roy, H. Sert, A. Stahl , T. Ziemons Deutsches Elektronen-Synchrotron, Hamburg, Germany
H. Aarup Petersen, M. Aldaya Martin, P. Asmuss, I. Babounikau, S. Baxter, O. Behnke,A. Berm ´udez Mart´ınez, A.A. Bin Anuar, K. Borras , V. Botta, D. Brunner, A. Campbell,A. Cardini, P. Connor, S. Consuegra Rodr´ıguez, V. Danilov, A. De Wit, M.M. Defranchis,L. Didukh, D. Dom´ınguez Damiani, G. Eckerlin, D. Eckstein, T. Eichhorn, L.I. Estevez Banos,E. Gallo , A. Geiser, A. Giraldi, A. Grohsjean, M. Guthoff, A. Harb, A. Jafari , N.Z. Jomhari,H. Jung, A. Kasem , M. Kasemann, H. Kaveh, C. Kleinwort, J. Knolle, D. Kr ¨ucker, W. Lange,T. Lenz, J. Lidrych, K. Lipka, W. Lohmann , R. Mankel, I.-A. Melzer-Pellmann, J. Metwally,A.B. Meyer, M. Meyer, M. Missiroli, J. Mnich, A. Mussgiller, V. Myronenko, Y. Otarid,D. P´erez Ad´an, S.K. Pflitsch, D. Pitzl, A. Raspereza, A. Saggio, A. Saibel, M. Savitskyi,V. Scheurer, P. Sch ¨utze, C. Schwanenberger, A. Singh, R.E. Sosa Ricardo, N. Tonon, O. Turkot,A. Vagnerini, M. Van De Klundert, R. Walsh, D. Walter, Y. Wen, K. Wichmann, C. Wissing,S. Wuchterl, O. Zenaiev, R. Zlebcik University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, K. De Leo, T. Dreyer, A. Ebrahimi, M. Eich, F. Feindt,A. Fr ¨ohlich, C. Garbers, E. Garutti, P. Gunnellini, J. Haller, A. Hinzmann, A. Karavdina,G. Kasieczka, R. Klanner, R. Kogler, V. Kutzner, J. Lange, T. Lange, A. Malara, C.E.N. Niemeyer,A. Nigamova, K.J. Pena Rodriguez, O. Rieger, P. Schleper, S. Schumann, J. Schwandt,D. Schwarz, J. Sonneveld, H. Stadie, G. Steinbr ¨uck, B. Vormwald, I. Zoi
Karlsruher Institut fuer Technologie, Karlsruhe, Germany
M. Baselga, S. Baur, J. Bechtel, T. Berger, E. Butz, R. Caspart, T. Chwalek, W. De Boer,A. Dierlamm, A. Droll, K. El Morabit, N. Faltermann, K. Fl ¨oh, M. Giffels, A. Gottmann,F. Hartmann , C. Heidecker, U. Husemann, M.A. Iqbal, I. Katkov , P. Keicher, R. Koppenh ¨ofer,S. Maier, M. Metzler, S. Mitra, D. M ¨uller, Th. M ¨uller, M. Musich, G. Quast, K. Rabbertz,J. Rauser, D. Savoiu, D. Sch¨afer, M. Schnepf, M. Schr ¨oder, D. Seith, I. Shvetsov, H.J. Simonis,R. Ulrich, M. Wassmer, M. Weber, R. Wolf, S. Wozniewski Institute of Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia Paraskevi,Greece
G. Anagnostou, P. Asenov, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas, G. Paspalaki,A. Stakia
National and Kapodistrian University of Athens, Athens, Greece
M. Diamantopoulou, D. Karasavvas, G. Karathanasis, P. Kontaxakis, C.K. Koraka,A. Manousakis-katsikakis, A. Panagiotou, I. Papavergou, N. Saoulidou, K. Theofilatos,K. Vellidis, E. Vourliotis
National Technical University of Athens, Athens, Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis, A. Zacharopoulou
University of Io´annina, Io´annina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, K. Manitara,N. Manthos, I. Papadopoulos, J. Strologas MTA-ELTE Lend ¨ulet CMS Particle and Nuclear Physics Group, E ¨otv ¨os Lor´and University,Budapest, Hungary
M. Bart ´ok , R. Chudasama, M. Csanad, M.M.A. Gadallah , S. L ¨ok ¨os , P. Major, K. Mandal,A. Mehta, G. Pasztor, O. Sur´anyi, G.I. Veres Wigner Research Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath , F. Sikler, V. Veszpremi, G. Vesztergombi † Institute of Nuclear Research ATOMKI, Debrecen, Hungary
S. Czellar, J. Karancsi , J. Molnar, Z. Szillasi, D. Teyssier Institute of Physics, University of Debrecen, Debrecen, Hungary
P. Raics, Z.L. Trocsanyi, B. Ujvari
Eszterhazy Karoly University, Karoly Robert Campus, Gyongyos, Hungary
T. Csorgo, F. Nemes, T. Novak
Indian Institute of Science (IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri, D. Kumar, L. Panwar, P.C. Tiwari
National Institute of Science Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati , D. Dash, C. Kar, P. Mal, T. Mishra, V.K. Muraleedharan Nair Bindhu,A. Nayak , D.K. Sahoo , N. Sur, S.K. Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, N. Dhingra , R. Gupta, A. Kaur, S. Kaur,P. Kumari, M. Lohan, M. Meena, K. Sandeep, S. Sharma, J.B. Singh, A.K. Virdi University of Delhi, Delhi, India
A. Ahmed, A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, A. Kumar,M. Naimuddin, P. Priyanka, K. Ranjan, A. Shah
Saha Institute of Nuclear Physics, HBNI, Kolkata, India
M. Bharti , R. Bhattacharya, S. Bhattacharya, D. Bhowmik, S. Dutta, S. Ghosh, B. Gomber ,M. Maity , S. Nandan, P. Palit, A. Purohit, P.K. Rout, G. Saha, S. Sarkar, M. Sharan, B. Singh ,S. Thakur Indian Institute of Technology Madras, Madras, India
P.K. Behera, S.C. Behera, P. Kalbhor, A. Muhammad, R. Pradhan, P.R. Pujahari, A. Sharma,A.K. Sikdar
Bhabha Atomic Research Centre, Mumbai, India
D. Dutta, V. Kumar, K. Naskar , P.K. Netrakanti, L.M. Pant, P. Shukla Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, R. Kumar Verma, U. Sarkar
Tata Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, S. Karmakar, S. Kumar,G. Majumder, K. Mazumdar, S. Mukherjee, D. Roy, N. Sahoo
Indian Institute of Science Education and Research (IISER), Pune, India
S. Dube, B. Kansal, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, A. Rastogi, S. Sharma
Department of Physics, Isfahan University of Technology, Isfahan, Iran
H. Bakhshiansohi Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
S. Chenarani , S.M. Etesami, M. Khakzad, M. Mohammadi Najafabadi University College Dublin, Dublin, Ireland
M. Felcini, M. Grunewald
INFN Sezione di Bari a , Universit`a di Bari b , Politecnico di Bari c , Bari, Italy M. Abbrescia a , b , R. Aly a , b ,39 , C. Aruta a , b , A. Colaleo a , D. Creanza a , c , N. De Filippis a , c ,M. De Palma a , b , A. Di Florio a , b , A. Di Pilato a , b , W. Elmetenawee a , b , L. Fiore a , A. Gelmi a , b ,M. Gul a , G. Iaselli a , c , M. Ince a , b , S. Lezki a , b , G. Maggi a , c , M. Maggi a , I. Margjeka a , b , J.A. Merlin a ,S. My a , b , S. Nuzzo a , b , A. Pompili a , b , G. Pugliese a , c , A. Ranieri a , G. Selvaggi a , b , L. Silvestris a ,F.M. Simone a , b , R. Venditti a , P. Verwilligen a INFN Sezione di Bologna a , Universit`a di Bologna b , Bologna, Italy G. Abbiendi a , C. Battilana a , b , D. Bonacorsi a , b , L. Borgonovi a , b , S. Braibant-Giacomelli a , b ,R. Campanini a , b , P. Capiluppi a , b , A. Castro a , b , F.R. Cavallo a , M. Cuffiani a , b , G.M. Dallavalle a ,T. Diotalevi a , b , F. Fabbri a , A. Fanfani a , b , E. Fontanesi a , b , P. Giacomelli a , L. Giommi a , b ,C. Grandi a , L. Guiducci a , b , F. Iemmi a , b , S. Lo Meo a ,40 , S. Marcellini a , G. Masetti a ,F.L. Navarria a , b , A. Perrotta a , F. Primavera a , b , A.M. Rossi a , b , T. Rovelli a , b , G.P. Siroli a , b , N. Tosi a INFN Sezione di Catania a , Universit`a di Catania b , Catania, Italy S. Albergo a , b ,41 , S. Costa a , b , A. Di Mattia a , R. Potenza a , b , A. Tricomi a , b ,41 , C. Tuve a , b INFN Sezione di Firenze a , Universit`a di Firenze b , Firenze, Italy G. Barbagli a , A. Cassese a , R. Ceccarelli a , b , V. Ciulli a , b , C. Civinini a , R. D’Alessandro a , b , F. Fiori a ,E. Focardi a , b , G. Latino a , b , P. Lenzi a , b , M. Lizzo a , b , M. Meschini a , S. Paoletti a , R. Seidita a , b ,G. Sguazzoni a , L. Viliani a INFN Laboratori Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, D. Piccolo
INFN Sezione di Genova a , Universit`a di Genova b , Genova, Italy M. Bozzo a , b , F. Ferro a , R. Mulargia a , b , E. Robutti a , S. Tosi a , b INFN Sezione di Milano-Bicocca a , Universit`a di Milano-Bicocca b , Milano, Italy A. Benaglia a , A. Beschi a , b , F. Brivio a , b , F. Cetorelli a , b , V. Ciriolo a , b ,20 , F. De Guio a , b ,M.E. Dinardo a , b , P. Dini a , S. Gennai a , A. Ghezzi a , b , P. Govoni a , b , L. Guzzi a , b , M. Malberti a ,S. Malvezzi a , D. Menasce a , F. Monti a , b , L. Moroni a , M. Paganoni a , b , D. Pedrini a , S. Ragazzi a , b ,T. Tabarelli de Fatis a , b , D. Valsecchi a , b ,20 , D. Zuolo a , b INFN Sezione di Napoli a , Universit`a di Napoli ’Federico II’ b , Napoli, Italy, Universit`a dellaBasilicata c , Potenza, Italy, Universit`a G. Marconi d , Roma, Italy S. Buontempo a , N. Cavallo a , c , A. De Iorio a , b , F. Fabozzi a , c , F. Fienga a , A.O.M. Iorio a , b ,L. Layer a , b , L. Lista a , b , S. Meola a , d ,20 , P. Paolucci a ,20 , B. Rossi a , C. Sciacca a , b , E. Voevodina a , b INFN Sezione di Padova a , Universit`a di Padova b , Padova, Italy, Universit`a di Trento c ,Trento, Italy P. Azzi a , N. Bacchetta a , A. Boletti a , b , A. Bragagnolo a , b , R. Carlin a , b , P. Checchia a ,P. De Castro Manzano a , T. Dorigo a , F. Gasparini a , b , U. Gasparini a , b , S.Y. Hoh a , b , M. Margoni a , b ,A.T. Meneguzzo a , b , M. Presilla b , P. Ronchese a , b , R. Rossin a , b , F. Simonetto a , b , G. Strong,A. Tiko a , M. Tosi a , b , H. YARAR a , b , M. Zanetti a , b , P. Zotto a , b , A. Zucchetta a , b , G. Zumerle a , b INFN Sezione di Pavia a , Universit`a di Pavia b , Pavia, Italy A. Braghieri a , S. Calzaferri a , b , D. Fiorina a , b , P. Montagna a , b , S.P. Ratti a , b , V. Re a , M. Ressegotti a , b ,C. Riccardi a , b , P. Salvini a , I. Vai a , P. Vitulo a , b INFN Sezione di Perugia a , Universit`a di Perugia b , Perugia, Italy M. Biasini a , b , G.M. Bilei a , D. Ciangottini a , b , L. Fan `o a , b , P. Lariccia a , b , G. Mantovani a , b ,V. Mariani a , b , M. Menichelli a , F. Moscatelli a , A. Rossi a , b , A. Santocchia a , b , D. Spiga a ,T. Tedeschi a , b INFN Sezione di Pisa a , Universit`a di Pisa b , Scuola Normale Superiore di Pisa c , Pisa Italy,Universit`a di Siena d , Siena, Italy K. Androsov a , P. Azzurri a , G. Bagliesi a , V. Bertacchi a , c , L. Bianchini a , T. Boccali a , R. Castaldi a ,M.A. Ciocci a , b , R. Dell’Orso a , M.R. Di Domenico a , b , S. Donato a , L. Giannini a , c , A. Giassi a ,M.T. Grippo a , F. Ligabue a , c , E. Manca a , c , G. Mandorli a , c , A. Messineo a , b , F. Palla a , G. Ramirez-Sanchez a , c , A. Rizzi a , b , G. Rolandi a , c , S. Roy Chowdhury a , c , A. Scribano a , N. Shafiei a , b ,P. Spagnolo a , R. Tenchini a , G. Tonelli a , b , N. Turini a , A. Venturi a , P.G. Verdini a INFN Sezione di Roma a , Sapienza Universit`a di Roma b , Rome, Italy F. Cavallari a , M. Cipriani a , b , D. Del Re a , b , E. Di Marco a , M. Diemoz a , E. Longo a , b , P. Meridiani a ,G. Organtini a , b , F. Pandolfi a , R. Paramatti a , b , C. Quaranta a , b , S. Rahatlou a , b , C. Rovelli a ,F. Santanastasio a , b , L. Soffi a , b , R. Tramontano a , b INFN Sezione di Torino a , Universit`a di Torino b , Torino, Italy, Universit`a del PiemonteOrientale c , Novara, Italy N. Amapane a , b , R. Arcidiacono a , c , S. Argiro a , b , M. Arneodo a , c , N. Bartosik a , R. Bellan a , b ,A. Bellora a , b , C. Biino a , A. Cappati a , b , N. Cartiglia a , S. Cometti a , M. Costa a , b , R. Covarelli a , b ,N. Demaria a , B. Kiani a , b , F. Legger a , C. Mariotti a , S. Maselli a , E. Migliore a , b , V. Monaco a , b ,E. Monteil a , b , M. Monteno a , M.M. Obertino a , b , G. Ortona a , L. Pacher a , b , N. Pastrone a ,M. Pelliccioni a , G.L. Pinna Angioni a , b , M. Ruspa a , c , R. Salvatico a , b , F. Siviero a , b , V. Sola a ,A. Solano a , b , D. Soldi a , b , A. Staiano a , D. Trocino a , b INFN Sezione di Trieste a , Universit`a di Trieste b , Trieste, Italy S. Belforte a , V. Candelise a , b , M. Casarsa a , F. Cossutti a , A. Da Rold a , b , G. Della Ricca a , b ,F. Vazzoler a , b Kyungpook National University, Daegu, Korea
S. Dogra, C. Huh, B. Kim, D.H. Kim, G.N. Kim, J. Lee, S.W. Lee, C.S. Moon, Y.D. Oh, S.I. Pak,B.C. Radburn-Smith, S. Sekmen, Y.C. Yang
Chonnam National University, Institute for Universe and Elementary Particles, Kwangju,Korea
H. Kim, D.H. Moon
Hanyang University, Seoul, Korea
B. Francois, T.J. Kim, J. Park
Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, S. Ha, B. Hong, K. Lee, K.S. Lee, J. Lim, J. Park, S.K. Park, J. Yoo
Kyung Hee University, Department of Physics, Seoul, Republic of Korea
J. Goh, A. Gurtu
Sejong University, Seoul, Korea
H.S. Kim, Y. Kim
Seoul National University, Seoul, Korea
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, S. Ko, H. Kwon, H. Lee, K. Lee, S. Lee,K. Nam, B.H. Oh, M. Oh, S.B. Oh, H. Seo, U.K. Yang, I. Yoon8
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, S. Ko, H. Kwon, H. Lee, K. Lee, S. Lee,K. Nam, B.H. Oh, M. Oh, S.B. Oh, H. Seo, U.K. Yang, I. Yoon8 University of Seoul, Seoul, Korea
D. Jeon, J.H. Kim, B. Ko, J.S.H. Lee, I.C. Park, Y. Roh, D. Song, I.J. Watson
Yonsei University, Department of Physics, Seoul, Korea
H.D. Yoo
Sungkyunkwan University, Suwon, Korea
Y. Choi, C. Hwang, Y. Jeong, H. Lee, Y. Lee, I. Yu
College of Engineering and Technology, American University of the Middle East (AUM),Kuwait
Y. Maghrbi
Riga Technical University, Riga, Latvia
V. Veckalns Vilnius University, Vilnius, Lithuania
A. Juodagalvis, A. Rinkevicius, G. Tamulaitis
National Centre for Particle Physics, Universiti Malaya, Kuala Lumpur, Malaysia
W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli
Universidad de Sonora (UNISON), Hermosillo, Mexico
J.F. Benitez, A. Castaneda Hernandez, J.A. Murillo Quijada, L. Valencia Palomo
Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz , R. Lopez-Fernandez,A. Sanchez-Hernandez Universidad Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, M. Ramirez-Garcia, F. Vazquez Valencia
Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada
Universidad Aut ´onoma de San Luis Potos´ı, San Luis Potos´ı, Mexico
A. Morelos Pineda
University of Montenegro, Podgorica, Montenegro
J. Mijuskovic , N. Raicevic University of Auckland, Auckland, New Zealand
D. Krofcheck
University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler
National Centre for Physics, Quaid-I-Azam University, Islamabad, Pakistan
A. Ahmad, M.I. Asghar, M.I.M. Awan, H.R. Hoorani, W.A. Khan, M.A. Shah, M. Shoaib,M. Waqas
AGH University of Science and Technology Faculty of Computer Science, Electronics andTelecommunications, Krakow, Poland
V. Avati, L. Grzanka, M. Malawski
National Centre for Nuclear Research, Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. G ´orski, M. Kazana, M. Szleper, P. Traczyk,P. Zalewski Institute of Experimental Physics, Faculty of Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk , K. Doroba, A. Kalinowski, M. Konecki, J. Krolikowski,M. Olszewski, M. Walczak Laborat ´orio de Instrumenta¸c˜ao e F´ısica Experimental de Part´ıculas, Lisboa, Portugal
M. Araujo, P. Bargassa, D. Bastos, P. Faccioli, M. Gallinaro, J. Hollar, N. Leonardo, T. Niknejad,J. Seixas, K. Shchelina, O. Toldaiev, J. Varela
Joint Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, P. Bunin, Y. Ershov, M. Gavrilenko, A. Golunov, I. Golutvin, N. Gorbounov,I. Gorbunov, V. Karjavine, A. Lanev, A. Malakhov, V. Matveev , P. Moisenz, V. Palichik,V. Perelygin, M. Savina, S. Shmatov, S. Shulha, V. Smirnov, O. Teryaev, N. Voytishin,B.S. Yuldashev , A. Zarubin Petersburg Nuclear Physics Institute, Gatchina (St. Petersburg), Russia
G. Gavrilov, V. Golovtcov, Y. Ivanov, V. Kim , E. Kuznetsova , V. Murzin, V. Oreshkin,I. Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Volkov, A. Vorobyev Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov, N. Krasnikov,A. Pashenkov, G. Pivovarov, D. Tlisov † , A. Toropin Institute for Theoretical and Experimental Physics named by A.I. Alikhanov of NRC‘Kurchatov Institute’, Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, A. Nikitenko , V. Popov, G. Safronov,A. Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A. Zhokin Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev
National Research Nuclear University ’Moscow Engineering Physics Institute’ (MEPhI),Moscow, Russia
O. Bychkova, M. Chadeeva , D. Philippov, E. Popova, V. Rusinov P.N. Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin, M. Kirakosyan, A. Terkulov
Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,Russia
A. Baskakov, A. Belyaev, E. Boos, V. Bunichev, M. Dubinin , L. Dudko, A. Ershov, A. Gribushin,V. Klyukhin, O. Kodolova, I. Lokhtin, S. Obraztsov, V. Savrin Novosibirsk State University (NSU), Novosibirsk, Russia
V. Blinov , T. Dimova , L. Kardapoltsev , I. Ovtin , Y. Skovpen Institute for High Energy Physics of National Research Centre ‘Kurchatov Institute’,Protvino, Russia
I. Azhgirey, I. Bayshev, V. Kachanov, A. Kalinin, D. Konstantinov, V. Petrov, R. Ryutin, A. Sobol,S. Troshin, N. Tyurin, A. Uzunian, A. Volkov
National Research Tomsk Polytechnic University, Tomsk, Russia
A. Babaev, A. Iuzhakov, V. Okhotnikov, L. Sukhikh
Tomsk State University, Tomsk, Russia
V. Borchsh, V. Ivanchenko, E. Tcherniaev University of Belgrade: Faculty of Physics and VINCA Institute of Nuclear Sciences,Belgrade, Serbia
P. Adzic , P. Cirkovic, M. Dordevic, P. Milenovic, J. Milosevic Centro de Investigaciones Energ´eticas Medioambientales y Tecnol ´ogicas (CIEMAT),Madrid, Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, A. ´Alvarez Fern´andez, I. Bachiller, M. Barrio Luna,Cristina F. Bedoya, J.A. Brochero Cifuentes, C.A. Carrillo Montoya, M. Cepeda, M. Cerrada,N. Colino, B. De La Cruz, A. Delgado Peris, J.P. Fern´andez Ramos, J. Flix, M.C. Fouz,A. Garc´ıa Alonso, O. Gonzalez Lopez, S. Goy Lopez, J.M. Hernandez, M.I. Josa,J. Le ´on Holgado, D. Moran, ´A. Navarro Tobar, A. P´erez-Calero Yzquierdo, J. Puerta Pelayo,I. Redondo, L. Romero, S. S´anchez Navas, M.S. Soares, A. Triossi, L. Urda G ´omez, C. Willmott
Universidad Aut ´onoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Troc ´oniz, R. Reyes-Almanza
Universidad de Oviedo, Instituto Universitario de Ciencias y Tecnolog´ıas Espaciales deAsturias (ICTEA), Oviedo, Spain
B. Alvarez Gonzalez, J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras, I. Gonza-lez Caballero, E. Palencia Cortezon, C. Ram ´on ´Alvarez, J. Ripoll Sau, V. Rodr´ıguez Bouza,S. Sanchez Cruz, A. Trapote
Instituto de F´ısica de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, J. Duarte Campderros, M. Fernandez,P.J. Fern´andez Manteca, G. Gomez, C. Martinez Rivero, P. Martinez Ruiz del Arbol, F. Matorras,J. Piedra Gomez, C. Prieels, F. Ricci-Tam, T. Rodrigo, A. Ruiz-Jimeno, L. Scodellaro, I. Vila,J.M. Vizan Garcia
University of Colombo, Colombo, Sri Lanka
MK Jayananda, B. Kailasapathy , D.U.J. Sonnadara, DDC Wickramarathna University of Ruhuna, Department of Physics, Matara, Sri Lanka
W.G.D. Dharmaratna, K. Liyanage, N. Perera, N. Wickramage
CERN, European Organization for Nuclear Research, Geneva, Switzerland
T.K. Aarrestad, D. Abbaneo, B. Akgun, E. Auffray, G. Auzinger, J. Baechler, P. Baillon, A.H. Ball,D. Barney, J. Bendavid, N. Beni, M. Bianco, A. Bocci, P. Bortignon, E. Bossini, E. Brondolin,T. Camporesi, G. Cerminara, L. Cristella, D. d’Enterria, A. Dabrowski, N. Daci, V. Daponte,A. David, A. De Roeck, M. Deile, R. Di Maria, M. Dobson, M. D ¨unser, N. Dupont, A. Elliott-Peisert, N. Emriskova, F. Fallavollita , D. Fasanella, S. Fiorendi, G. Franzoni, J. Fulcher,W. Funk, S. Giani, D. Gigi, K. Gill, F. Glege, L. Gouskos, M. Guilbaud, D. Gulhan, M. Haranko,J. Hegeman, Y. Iiyama, V. Innocente, T. James, P. Janot, J. Kaspar, J. Kieseler, M. Komm,N. Kratochwil, C. Lange, P. Lecoq, K. Long, C. Lourenc¸o, L. Malgeri, M. Mannelli, A. Massironi,F. Meijers, S. Mersi, E. Meschi, F. Moortgat, M. Mulders, J. Ngadiuba, J. Niedziela, S. Orfanelli,L. Orsini, F. Pantaleo , L. Pape, E. Perez, M. Peruzzi, A. Petrilli, G. Petrucciani, A. Pfeiffer,M. Pierini, D. Rabady, A. Racz, M. Rieger, M. Rovere, H. Sakulin, J. Salfeld-Nebgen, S. Scarfi,C. Sch¨afer, C. Schwick, M. Selvaggi, A. Sharma, P. Silva, W. Snoeys, P. Sphicas , J. Steggemann,S. Summers, V.R. Tavolaro, D. Treille, A. Tsirou, G.P. Van Onsem, A. Vartak, M. Verzetti,K.A. Wozniak, W.D. Zeuner Paul Scherrer Institut, Villigen, Switzerland
L. Caminada , W. Erdmann, R. Horisberger, Q. Ingram, H.C. Kaestli, D. Kotlinski,U. Langenegger, T. Rohe ETH Zurich - Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland
M. Backhaus, P. Berger, A. Calandri, N. Chernyavskaya, G. Dissertori, M. Dittmar, M. Doneg`a,C. Dorfer, T. Gadek, T.A. G ´omez Espinosa, C. Grab, D. Hits, W. Lustermann, A.-M. Lyon,R.A. Manzoni, M.T. Meinhard, F. Micheli, F. Nessi-Tedaldi, F. Pauss, V. Perovic, G. Perrin,L. Perrozzi, S. Pigazzini, M.G. Ratti, M. Reichmann, C. Reissel, T. Reitenspiess, B. Ristic,D. Ruini, D.A. Sanz Becerra, M. Sch ¨onenberger, V. Stampf, M.L. Vesterbacka Olsson, R. Wallny,D.H. Zhu
Universit¨at Z ¨urich, Zurich, Switzerland
C. Amsler , C. Botta, D. Brzhechko, M.F. Canelli, A. De Cosa, R. Del Burgo, J.K. Heikkil¨a,M. Huwiler, A. Jofrehei, B. Kilminster, S. Leontsinis, A. Macchiolo, P. Meiring, V.M. Mikuni,U. Molinatti, I. Neutelings, G. Rauco, A. Reimers, P. Robmann, K. Schweiger, Y. Takahashi,S. Wertz National Central University, Chung-Li, Taiwan
C. Adloff , C.M. Kuo, W. Lin, A. Roy, T. Sarkar , S.S. Yu National Taiwan University (NTU), Taipei, Taiwan
L. Ceard, P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.y. Li, R.-S. Lu, E. Paganis,A. Psallidas, A. Steen, E. Yazgan
Chulalongkorn University, Faculty of Science, Department of Physics, Bangkok, Thailand
B. Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas
C¸ ukurova University, Physics Department, Science and Art Faculty, Adana, Turkey
F. Boran, S. Damarseckin , Z.S. Demiroglu, F. Dolek, C. Dozen , I. Dumanoglu , E. Eskut,G. Gokbulut, Y. Guler, E. Gurpinar Guler , I. Hos , C. Isik, E.E. Kangal , O. Kara,A. Kayis Topaksu, U. Kiminsu, G. Onengut, K. Ozdemir , A. Polatoz, A.E. Simsek, B. Tali ,U.G. Tok, S. Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle East Technical University, Physics Department, Ankara, Turkey
B. Isildak , G. Karapinar , K. Ocalan , M. Yalvac Bogazici University, Istanbul, Turkey
I.O. Atakisi, E. G ¨ulmez, M. Kaya , O. Kaya , ¨O. ¨Ozc¸elik, S. Tekten , E.A. Yetkin Istanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak , Y. Komurcu, S. Sen Istanbul University, Istanbul, Turkey
F. Aydogmus Sen, S. Cerci , B. Kaynak, S. Ozkorucuklu, D. Sunar Cerci Institute for Scintillation Materials of National Academy of Science of Ukraine, Kharkov,Ukraine
B. Grynyov
National Scientific Center, Kharkov Institute of Physics and Technology, Kharkov, Ukraine
L. Levchuk
University of Bristol, Bristol, United Kingdom
E. Bhal, S. Bologna, J.J. Brooke, E. Clement, D. Cussans, H. Flacher, J. Goldstein, G.P. Heath,H.F. Heath, L. Kreczko, B. Krikler, S. Paramesvaran, T. Sakuma, S. Seif El Nasr-Storey, V.J. Smith,J. Taylor, A. Titterton
Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev , C. Brew, R.M. Brown, D.J.A. Cockerill, K.V. Ellis, K. Harder, S. Harper, J. Linacre, K. Manolopoulos, D.M. Newbold, E. Olaiya, D. Petyt, T. Reis, T. Schuh,C.H. Shepherd-Themistocleous, A. Thea, I.R. Tomalin, T. Williams
Imperial College, London, United Kingdom
R. Bainbridge, P. Bloch, S. Bonomally, J. Borg, S. Breeze, O. Buchmuller, A. Bundock, V. Cepaitis,G.S. Chahal , D. Colling, P. Dauncey, G. Davies, M. Della Negra, P. Everaerts, G. Fedi, G. Hall,G. Iles, J. Langford, L. Lyons, A.-M. Magnan, S. Malik, A. Martelli, V. Milosevic, J. Nash ,V. Palladino, M. Pesaresi, D.M. Raymond, A. Richards, A. Rose, E. Scott, C. Seez, A. Shtipliyski,M. Stoye, A. Tapper, K. Uchida, T. Virdee , N. Wardle, S.N. Webb, D. Winterbottom,A.G. Zecchinelli Brunel University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, C.K. Mackay, I.D. Reid, L. Teodorescu, S. Zahid
Baylor University, Waco, USA
A. Brinkerhoff, K. Call, B. Caraway, J. Dittmann, K. Hatakeyama, A.R. Kanuganti, C. Madrid,B. McMaster, N. Pastika, S. Sawant, C. Smith
Catholic University of America, Washington, DC, USA
R. Bartek, A. Dominguez, R. Uniyal, A.M. Vargas Hernandez
The University of Alabama, Tuscaloosa, USA
A. Buccilli, O. Charaf, S.I. Cooper, S.V. Gleyzer, C. Henderson, P. Rumerio, C. West
Boston University, Boston, USA
A. Akpinar, A. Albert, D. Arcaro, C. Cosby, Z. Demiragli, D. Gastler, C. Richardson, J. Rohlf,K. Salyer, D. Sperka, D. Spitzbart, I. Suarez, S. Yuan, D. Zou
Brown University, Providence, USA
G. Benelli, B. Burkle, X. Coubez , D. Cutts, Y.t. Duh, M. Hadley, U. Heintz, J.M. Hogan ,K.H.M. Kwok, E. Laird, G. Landsberg, K.T. Lau, J. Lee, M. Narain, S. Sagir , R. Syarif, E. Usai,W.Y. Wong, D. Yu, W. Zhang University of California, Davis, Davis, USA
R. Band, C. Brainerd, R. Breedon, M. Calderon De La Barca Sanchez, M. Chertok, J. Conway,R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk, F. Jensen, W. Ko † , O. Kukral, R. Lander,M. Mulhearn, D. Pellett, J. Pilot, M. Shi, D. Taylor, K. Tos, M. Tripathi, Y. Yao, F. Zhang University of California, Los Angeles, USA
M. Bachtis, R. Cousins, A. Dasgupta, A. Florent, D. Hamilton, J. Hauser, M. Ignatenko, T. Lam,N. Mccoll, W.A. Nash, S. Regnard, D. Saltzberg, C. Schnaible, B. Stone, V. Valuev
University of California, Riverside, Riverside, USA
K. Burt, Y. Chen, R. Clare, J.W. Gary, S.M.A. Ghiasi Shirazi, G. Hanson, G. Karapostoli,O.R. Long, N. Manganelli, M. Olmedo Negrete, M.I. Paneva, W. Si, S. Wimpenny, Y. Zhang
University of California, San Diego, La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, S. Cooperstein, N. Deelen, M. Derdzinski, J. Duarte,R. Gerosa, D. Gilbert, B. Hashemi, V. Krutelyov, J. Letts, M. Masciovecchio, S. May, S. Padhi,M. Pieri, V. Sharma, M. Tadel, F. W ¨urthwein, A. Yagil
University of California, Santa Barbara - Department of Physics, Santa Barbara, USA
N. Amin, C. Campagnari, M. Citron, A. Dorsett, V. Dutta, J. Incandela, B. Marsh, H. Mei,A. Ovcharova, H. Qu, M. Quinnan, J. Richman, U. Sarica, D. Stuart, S. Wang California Institute of Technology, Pasadena, USA
D. Anderson, A. Bornheim, O. Cerri, I. Dutta, J.M. Lawhorn, N. Lu, J. Mao, H.B. Newman,T.Q. Nguyen, J. Pata, M. Spiropulu, J.R. Vlimant, S. Xie, Z. Zhang, R.Y. Zhu
Carnegie Mellon University, Pittsburgh, USA
J. Alison, M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, M. Sun, I. Vorobiev
University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, E. MacDonald, T. Mulholland, R. Patel, A. Perloff, K. Stenson,K.A. Ulmer, S.R. Wagner
Cornell University, Ithaca, USA
J. Alexander, Y. Cheng, J. Chu, D.J. Cranshaw, A. Datta, A. Frankenthal, K. Mcdermott,J. Monroy, J.R. Patterson, D. Quach, A. Ryd, W. Sun, S.M. Tan, Z. Tao, J. Thom, P. Wittich,M. Zientek
Fermi National Accelerator Laboratory, Batavia, USA
S. Abdullin, M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S. Banerjee,L.A.T. Bauerdick, A. Beretvas, D. Berry, J. Berryhill, P.C. Bhat, K. Burkett, J.N. Butler, A. Canepa,G.B. Cerati, H.W.K. Cheung, F. Chlebana, M. Cremonesi, V.D. Elvira, J. Freeman, Z. Gecse,E. Gottschalk, L. Gray, D. Green, S. Gr ¨unendahl, O. Gutsche, R.M. Harris, S. Hasegawa,R. Heller, T.C. Herwig, J. Hirschauer, B. Jayatilaka, S. Jindariani, M. Johnson, U. Joshi,P. Klabbers, T. Klijnsma, B. Klima, M.J. Kortelainen, S. Lammel, D. Lincoln, R. Lipton, M. Liu,T. Liu, J. Lykken, K. Maeshima, D. Mason, P. McBride, P. Merkel, S. Mrenna, S. Nahn,V. O’Dell, V. Papadimitriou, K. Pedro, C. Pena , O. Prokofyev, F. Ravera, A. Reinsvold Hall,L. Ristori, B. Schneider, E. Sexton-Kennedy, N. Smith, A. Soha, W.J. Spalding, L. Spiegel,S. Stoynev, J. Strait, L. Taylor, S. Tkaczyk, N.V. Tran, L. Uplegger, E.W. Vaandering, H.A. Weber,A. Woodard University of Florida, Gainesville, USA
D. Acosta, P. Avery, D. Bourilkov, L. Cadamuro, V. Cherepanov, F. Errico, R.D. Field,D. Guerrero, B.M. Joshi, M. Kim, J. Konigsberg, A. Korytov, K.H. Lo, K. Matchev, N. Menendez,G. Mitselmakher, D. Rosenzweig, K. Shi, J. Wang, S. Wang, X. Zuo
Florida State University, Tallahassee, USA
T. Adams, A. Askew, D. Diaz, R. Habibullah, S. Hagopian, V. Hagopian, K.F. Johnson,R. Khurana, T. Kolberg, G. Martinez, H. Prosper, C. Schiber, R. Yohay, J. Zhang
Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, S. Butalla, T. Elkafrawy , M. Hohlmann, D. Noonan, M. Rahmani,M. Saunders, F. Yumiceva University of Illinois at Chicago (UIC), Chicago, USA
M.R. Adams, L. Apanasevich, H. Becerril Gonzalez, R. Cavanaugh, X. Chen, S. Dittmer,O. Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, C. Mills, G. Oh, T. Roy, M.B. Tonjes,N. Varelas, J. Viinikainen, X. Wang, Z. Wu
The University of Iowa, Iowa City, USA
M. Alhusseini, K. Dilsiz , S. Durgut, R.P. Gandrajula, M. Haytmyradov, V. Khristenko,O.K. K ¨oseyan, J.-P. Merlo, A. Mestvirishvili , A. Moeller, J. Nachtman, H. Ogul , Y. Onel,F. Ozok , A. Penzo, C. Snyder, E. Tiras, J. Wetzel, K. Yi Johns Hopkins University, Baltimore, USA O. Amram, B. Blumenfeld, L. Corcodilos, M. Eminizer, A.V. Gritsan, S. Kyriacou,P. Maksimovic, C. Mantilla, J. Roskes, M. Swartz, T. ´A. V´ami
The University of Kansas, Lawrence, USA
C. Baldenegro Barrera, P. Baringer, A. Bean, A. Bylinkin, T. Isidori, S. Khalil, J. King,G. Krintiras, A. Kropivnitskaya, C. Lindsey, N. Minafra, M. Murray, C. Rogan, C. Royon,S. Sanders, E. Schmitz, J.D. Tapia Takaki, Q. Wang, J. Williams, G. Wilson
Kansas State University, Manhattan, USA
S. Duric, A. Ivanov, K. Kaadze, D. Kim, Y. Maravin, T. Mitchell, A. Modak, A. Mohammadi
Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright
University of Maryland, College Park, USA
E. Adams, A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, N.J. Hadley, S. Jabeen, G.Y. Jeng,R.G. Kellogg, T. Koeth, A.C. Mignerey, S. Nabili, M. Seidel, A. Skuja, S.C. Tonwar, L. Wang,K. Wong
Massachusetts Institute of Technology, Cambridge, USA
D. Abercrombie, B. Allen, R. Bi, S. Brandt, W. Busza, I.A. Cali, Y. Chen, M. D’Alfonso,G. Gomez Ceballos, M. Goncharov, P. Harris, D. Hsu, M. Hu, M. Klute, D. Kovalskyi, J. Krupa,Y.-J. Lee, P.D. Luckey, B. Maier, A.C. Marini, C. Mcginn, C. Mironov, S. Narayanan, X. Niu,C. Paus, D. Rankin, C. Roland, G. Roland, Z. Shi, G.S.F. Stephans, K. Sumorok, K. Tatar,D. Velicanu, J. Wang, T.W. Wang, Z. Wang, B. Wyslouch
University of Minnesota, Minneapolis, USA
R.M. Chatterjee, A. Evans, S. Guts † , P. Hansen, J. Hiltbrand, Sh. Jain, M. Krohn, Y. Kubota,Z. Lesko, J. Mans, M. Revering, R. Rusack, R. Saradhy, N. Schroeder, N. Strobbe, M.A. Wadud University of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros
University of Nebraska-Lincoln, Lincoln, USA
K. Bloom, S. Chauhan, D.R. Claes, C. Fangmeier, L. Finco, F. Golf, J.R. Gonz´alez Fern´andez,I. Kravchenko, J.E. Siado, G.R. Snow † , B. Stieger, W. Tabb, F. Yan State University of New York at Buffalo, Buffalo, USA
G. Agarwal, C. Harrington, L. Hay, I. Iashvili, A. Kharchilava, C. McLean, D. Nguyen,A. Parker, J. Pekkanen, S. Rappoccio, B. Roozbahani
Northeastern University, Boston, USA
G. Alverson, E. Barberis, C. Freer, Y. Haddad, A. Hortiangtham, G. Madigan, B. Marzocchi,D.M. Morse, V. Nguyen, T. Orimoto, L. Skinnari, A. Tishelman-Charny, T. Wamorkar, B. Wang,A. Wisecarver, D. Wood
Northwestern University, Evanston, USA
S. Bhattacharya, J. Bueghly, Z. Chen, A. Gilbert, T. Gunter, K.A. Hahn, N. Odell, M.H. Schmitt,K. Sung, M. Velasco
University of Notre Dame, Notre Dame, USA
R. Bucci, N. Dev, R. Goldouzian, M. Hildreth, K. Hurtado Anampa, C. Jessop, D.J. Karmgard,K. Lannon, W. Li, N. Loukas, N. Marinelli, I. Mcalister, F. Meng, K. Mohrman, Y. Musienko ,R. Ruchti, P. Siddireddy, S. Taroni, M. Wayne, A. Wightman, M. Wolf, L. Zygala The Ohio State University, Columbus, USA
J. Alimena, B. Bylsma, B. Cardwell, L.S. Durkin, B. Francis, C. Hill, A. Lefeld, B.L. Winer,B.R. Yates
Princeton University, Princeton, USA
G. Dezoort, P. Elmer, B. Greenberg, N. Haubrich, S. Higginbotham, A. Kalogeropoulos,G. Kopp, S. Kwan, D. Lange, M.T. Lucchini, J. Luo, D. Marlow, K. Mei, I. Ojalvo, J. Olsen,C. Palmer, P. Pirou´e, D. Stickland, C. Tully
University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg
Purdue University, West Lafayette, USA
V.E. Barnes, R. Chawla, S. Das, L. Gutay, M. Jones, A.W. Jung, B. Mahakud, G. Negro,N. Neumeister, C.C. Peng, S. Piperov, H. Qiu, J.F. Schulte, N. Trevisani, F. Wang, R. Xiao, W. Xie
Purdue University Northwest, Hammond, USA
T. Cheng, J. Dolen, N. Parashar, M. Stojanovic
Rice University, Houston, USA
A. Baty, S. Dildick, K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Kilpatrick, A. Kumar, W. Li,B.P. Padley, R. Redjimi, J. Roberts † , J. Rorie, W. Shi, A.G. Stahl Leiton University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, J.L. Dulemba, C. Fallon, T. Ferbel, M. Galanti, A. Garcia-Bellido, O. Hindrichs, A. Khukhunaishvili, E. Ranken, R. Taus
Rutgers, The State University of New Jersey, Piscataway, USA
B. Chiarito, J.P. Chou, A. Gandrakota, Y. Gershtein, E. Halkiadakis, A. Hart, M. Heindl,E. Hughes, S. Kaplan, O. Karacheban , I. Laflotte, A. Lath, R. Montalvo, K. Nash, M. Osherson,S. Salur, S. Schnetzer, S. Somalwar, R. Stone, S.A. Thayil, S. Thomas, H. Wang University of Tennessee, Knoxville, USA
H. Acharya, A.G. Delannoy, S. Spanier
Texas A&M University, College Station, USA
O. Bouhali , M. Dalchenko, A. Delgado, R. Eusebi, J. Gilmore, T. Huang, T. Kamon , H. Kim,S. Luo, S. Malhotra, R. Mueller, D. Overton, L. Perni`e, D. Rathjens, A. Safonov, J. Sturdy Texas Tech University, Lubbock, USA
N. Akchurin, J. Damgov, V. Hegde, S. Kunori, K. Lamichhane, S.W. Lee, T. Mengke,S. Muthumuni, T. Peltola, S. Undleeb, I. Volobouev, Z. Wang, A. Whitbeck
Vanderbilt University, Nashville, USA
E. Appelt, S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H. Ni, K. Padeken,F. Romeo, P. Sheldon, S. Tuo, J. Velkovska, M. Verweij
University of Virginia, Charlottesville, USA
L. Ang, M.W. Arenton, B. Cox, G. Cummings, J. Hakala, R. Hirosky, M. Joyce, A. Ledovskoy,C. Neu, B. Tannenwald, Y. Wang, E. Wolfe, F. Xia
Wayne State University, Detroit, USA
P.E. Karchin, N. Poudyal, P. Thapa
University of Wisconsin - Madison, Madison, WI, USA
K. Black, T. Bose, J. Buchanan, C. Caillol, S. Dasu, I. De Bruyn, C. Galloni, H. He, M. Herndon, A. Herv´e, U. Hussain, A. Lanaro, A. Loeliger, R. Loveless, J. Madhusudanan Sreekala,A. Mallampalli, D. Pinna, T. Ruggles, A. Savin, V. Shang, V. Sharma, W.H. Smith, D. Teague,S. Trembath-reichert, W. Vetens†: Deceased1: Also at Vienna University of Technology, Vienna, Austria2: Also at Institute of Basic and Applied Sciences, Faculty of Engineering, Arab Academy forScience, Technology and Maritime Transport, Alexandria, Egypt, Alexandria, Egypt3: Also at Universit´e Libre de Bruxelles, Bruxelles, Belgium4: Also at IRFU, CEA, Universit´e Paris-Saclay, Gif-sur-Yvette, France5: Also at Universidade Estadual de Campinas, Campinas, Brazil6: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil7: Also at UFMS, Nova Andradina, Brazil8: Also at Universidade Federal de Pelotas, Pelotas, Brazil9: Also at University of Chinese Academy of Sciences, Beijing, China10: Also at Institute for Theoretical and Experimental Physics named by A.I. Alikhanov ofNRC ‘Kurchatov Institute’, Moscow, Russia11: Also at Joint Institute for Nuclear Research, Dubna, Russia12: Also at Ain Shams University, Cairo, Egypt13: Now at British University in Egypt, Cairo, Egypt14: Now at Cairo University, Cairo, Egypt15: Now at Fayoum University, El-Fayoum, Egypt16: Also at Purdue University, West Lafayette, USA17: Also at Universit´e de Haute Alsace, Mulhouse, France18: Also at Ilia State University, Tbilisi, Georgia19: Also at Erzincan Binali Yildirim University, Erzincan, Turkey20: Also at CERN, European Organization for Nuclear Research, Geneva, Switzerland21: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany22: Also at University of Hamburg, Hamburg, Germany23: Also at Department of Physics, Isfahan University of Technology, Isfahan, Iran, Isfahan,Iran24: Also at Brandenburg University of Technology, Cottbus, Germany25: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University,Moscow, Russia26: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary, Debrecen,Hungary27: Also at Physics Department, Faculty of Science, Assiut University, Assiut, Egypt28: Also at MTA-ELTE Lend ¨ulet CMS Particle and Nuclear Physics Group, E ¨otv ¨os Lor´andUniversity, Budapest, Hungary, Budapest, Hungary29: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary30: Also at IIT Bhubaneswar, Bhubaneswar, India, Bhubaneswar, India31: Also at Institute of Physics, Bhubaneswar, India32: Also at G.H.G. Khalsa College, Punjab, India33: Also at Shoolini University, Solan, India34: Also at University of Hyderabad, Hyderabad, India35: Also at University of Visva-Bharati, Santiniketan, India36: Also at Indian Institute of Technology (IIT), Mumbai, India37: Also at Deutsches Elektronen-Synchrotron, Hamburg, Germany38: Also at Department of Physics, University of Science and Technology of Mazandaran,Behshahr, Iran
39: Now at INFN Sezione di Bari a , Universit`a di Bari b , Politecnico di Bari c , Bari, Italy40: Also at Italian National Agency for New Technologies, Energy and Sustainable EconomicDevelopment, Bologna, Italy41: Also at Centro Siciliano di Fisica Nucleare e di Struttura Della Materia, Catania, Italy42: Also at Riga Technical University, Riga, Latvia, Riga, Latvia43: Also at Consejo Nacional de Ciencia y Tecnolog´ıa, Mexico City, Mexico44: Also at Warsaw University of Technology, Institute of Electronic Systems, Warsaw, Poland45: Also at Institute for Nuclear Research, Moscow, Russia46: Now at National Research Nuclear University ’Moscow Engineering Physics Institute’(MEPhI), Moscow, Russia47: Also at Institute of Nuclear Physics of the Uzbekistan Academy of Sciences, Tashkent,Uzbekistan48: Also at St. Petersburg State Polytechnical University, St. Petersburg, Russia49: Also at University of Florida, Gainesville, USA50: Also at Imperial College, London, United Kingdom51: Also at P.N. Lebedev Physical Institute, Moscow, Russia52: Also at California Institute of Technology, Pasadena, USA53: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia54: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia55: Also at Trincomalee Campus, Eastern University, Sri Lanka, Nilaveli, Sri Lanka56: Also at INFN Sezione di Pavia a , Universit`a di Pavia b , Pavia, Italy, Pavia, Italy57: Also at National and Kapodistrian University of Athens, Athens, Greece58: Also at Universit¨at Z ¨urich, Zurich, Switzerland59: Also at Stefan Meyer Institute for Subatomic Physics, Vienna, Austria, Vienna, Austria60: Also at Laboratoire d’Annecy-le-Vieux de Physique des Particules, IN2P3-CNRS, Annecy-le-Vieux, France61: Also at S¸ ırnak University, Sirnak, Turkey62: Also at Department of Physics, Tsinghua University, Beijing, China, Beijing, China63: Also at Near East University, Research Center of Experimental Health Science, Nicosia,Turkey64: Also at Beykent University, Istanbul, Turkey, Istanbul, Turkey65: Also at Istanbul Aydin University, Application and Research Center for Advanced Studies(App. & Res. Cent. for Advanced Studies), Istanbul, Turkey66: Also at Mersin University, Mersin, Turkey67: Also at Piri Reis University, Istanbul, Turkey68: Also at Adiyaman University, Adiyaman, Turkey69: Also at Ozyegin University, Istanbul, Turkey70: Also at Izmir Institute of Technology, Izmir, Turkey71: Also at Necmettin Erbakan University, Konya, Turkey72: Also at Bozok Universitetesi Rekt ¨orl ¨ug ¨u, Yozgat, Turkey, Yozgat, Turkey73: Also at Marmara University, Istanbul, Turkey74: Also at Milli Savunma University, Istanbul, Turkey75: Also at Kafkas University, Kars, Turkey76: Also at Istanbul Bilgi University, Istanbul, Turkey77: Also at Hacettepe University, Ankara, Turkey78: Also at School of Physics and Astronomy, University of Southampton, Southampton,United Kingdom79: Also at IPPP Durham University, Durham, United Kingdom80: Also at Monash University, Faculty of Science, Clayton, Australia8