An embedding technique to determine ττ backgrounds in proton-proton collision data
EEUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH (CERN)
CERN-EP-2019-0122019/07/02
CMS-TAU-18-001
An embedding technique to determine τ τ backgrounds inproton-proton collision data
The CMS Collaboration ∗ Abstract
An embedding technique is presented to estimate standard model τ τ backgroundsfrom data with minimal simulation input. In the data, the muons are removed fromreconstructed µµ events and replaced with simulated tau leptons with the same kine-matic properties. In this way, a set of hybrid events is obtained that does not rely onsimulation except for the decay of the tau leptons. The challenges in describing theunderlying event or the production of associated jets in the simulation are avoided.The technique described in this paper was developed for CMS. Its validation and theinherent uncertainties are also discussed. The demonstration of the performance ofthe technique is based on a sample of proton-proton collisions collected by CMS in2017 at √ s =
13 TeV corresponding to an integrated luminosity of 41.5 fb − . ”Published in the Journal of Instrumentation as doi:10.1088/1748-0221/14/06/P06032 .” c (cid:13) ∗ See Appendix B for the list of collaboration members a r X i v : . [ h e p - e x ] J un An important background for many measurements at the CERN LHC is the decay of Z bosonsinto pairs of tau leptons (Z → τ τ ). Among those measurements are studies of Higgs bosonevents in the τ τ [1–5] and WW [6, 7] decay channels, and searches for additional supersym-metric and charged Higgs bosons [3, 8–13]. This background can be estimated from observedevents, using selected Z boson events in the µµ final state (Z → µµ ). Initially, the method wasonly used to model events originating from Z → τ τ decays, which are the most prominentsource of τ τ background events at the LHC. However, all statements made throughout thispaper are equally true for other standard model (SM) background processes that decay intotwo tau leptons. The aim of this method is to model all such processes.In the embedding technique, all energy deposits of the recorded muons are removed from theZ → µµ events collected by CMS and replaced by the energy deposits of simulated tau leptondecays with the same kinematic properties for the tau leptons as for the removed muons. Inthis way, a hybrid event is created, comprised of information from both observed and sim-ulated events. The parts of an event that are challenging to describe in the simulation, suchas the underlying event or the production of additional jets, are taken directly from observeddata. Only the tau lepton decay, which is well understood, relies on the simulation. In Higgsboson analyses, the small coupling strength of the muon with respect to the tau lepton guar-antees a negligible contamination by signal events. The Z → µµ selection thus serves as asideband region for those analyses that rely on this technique, referred to as target analyses inthe following. In this picture, the simulation of the tau leptons in place of the removed muonscorresponds to the extrapolation into the signal region.The method itself can be studied by applying the embedding technique to a reference sampleof simulated Z → µµ events and comparing the result to an independent validation sample ofsimulated Z → (cid:96)(cid:96) events, where (cid:96) = e, µ , τ stands for the embedded lepton flavor. All leptonflavors are embedded for the validation of the technique. The corresponding application is re-ferred to as e-, µ -, or τ -embedding throughout the text. The µ -embedding holds the special roleof validating the technique itself. The e-embedding serves to validate the sophisticated electronidentification in CMS, which relies on many detector quantities. Reconstruction efficiencies aredetermined from each application, using the “tag-and-probe” method, as described in Ref. [14].This monitors the level of understanding of the reconstruction of each lepton flavor, and allowsus to derive residual correction factors for final use in the target analyses. Since these correctionfactors are derived for the simulated leptons that have been embedded into the event, they areexpected to be similar to the correction factors obtained without the embedding technique. Thebranching fractions for Z → ee, Z → µµ , and Z → τ τ are equal so the normalizations for allthe decays are equal.The embedding technique was implemented successfully for the first time by the CMS Collabo-ration in the search and analysis of Higgs boson events in the context of the SM and its minimalsupersymmetric extension (MSSM) based on the data set obtained during the first operationalrun of the LHC between 2009 and 2013 (Run-1) [3–6, 9, 10]. The technique has been upgradedsince then to cope with the new challenges of the most recent LHC data-taking periods that arerelated to the increased proton-proton ( pp ) collision rate. Further developments of the methodinclude (i) the inclusion of other processes than Z → τ τ ; (ii) the estimate of the normalizationof the corresponding background processes from data; (iii) and an improved description of theelectron identification. The upgraded embedding technique served as a cross-check of the esti-mate of the Z → τ τ background events from simulation in the first CMS search for additionalHiggs bosons in the τ τ final state at 13 TeV, in the context of the MSSM [15]. A similar tech- nique was used during the LHC Run 1 data-taking period by the ATLAS Collaboration [1, 2, 8]and is described in Ref. [16].In this paper, the methodology, validation, and application of the embedding technique devel-oped for the CMS experiment are described. The data sample used for the demonstration of thetechnique has been recorded in 2017 and corresponds to an integrated luminosity of 41.5 fb − .The validation of the method is based on event samples that have been simulated for the samerun period.In Sections 2 and 3 the CMS detector and event reconstruction are introduced. The produc-tion of simulated events used for the validation of the technique is described in Section 4. InSections 5 and 6 the technique itself and its validation are discussed. Section 7 contains ademonstration of the performance of the technique, when applied to data, for the selection andanalysis of Z or Higgs boson events in the τ τ final state. The paper is concluded with a briefsummary 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 sections. Forwardcalorimeters extend the pseudorapidity coverage provided by the barrel and endcap detectors.Muons are detected in gas-ionization chambers embedded in the steel flux-return yoke outsidethe solenoid.The silicon tracker measures charged particles within the pseudorapidity range | η | < < p T <
10 GeV and | η | < p T and 25–90 (45–150) µ m in the transverse (longitudinal) impact param-eter [17]. The electron momentum is estimated by combining the energy measurement in theECAL with the momentum measurement in the tracker. The momentum resolution for elec-trons with p T ≈
45 GeV from Z → ee decays ranges from 1.7% for nonshowering electronsin the barrel region to 4.5% for showering electrons in the endcaps [18]. Matching muons totracks measured in the silicon tracker results in a relative transverse momentum resolution, formuons with p T up to 100 GeV, of 1% in the barrel and 3% in the endcaps. The p T resolution inthe barrel is better than 7% for muons with p T up to 1 TeV [19]. In the barrel section of the ECAL,an energy resolution of about 1% is achieved for unconverted or late-converting photons in thetens of GeV energy range. The remaining barrel photons have a resolution of better than 2.5%for | η | ≤ µ s. The secondlevel, known as the high-level trigger, consists of a large array 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. [22].
The reconstruction of the pp collision products is based on the particle-flow (PF) algorithmdescribed in Ref. [23], which combines the available information from all CMS subdetectors toreconstruct an unambiguous set of individual particle candidates. The particle candidates arecategorized into electrons, photons, muons, and charged and neutral hadrons. A good under-standing of the CMS lepton reconstruction is an important prerequisite for the assessment ofthe embedding technique. Therefore the reconstruction of electrons, muons, and decays of tauleptons to hadrons ( τ h ) from charged and neutral PF candidates is discussed in more detail inthis section.In 2017, the CMS experiment operated with a varying instantaneous luminosity with, on av-erage, between 28 and 47 pp collisions per bunch crossing. Collision vertices are obtainedfrom reconstructed tracks using a deterministic annealing algorithm [24]. The reconstructedvertex with the largest value of summed physics-object p is the primary collision vertex (PV).The physics objects for this purpose are the jets, clustered using the anti- k T jet finding algo-rithm [25, 26], as described below, with the tracks assigned to the vertex as inputs, and theassociated missing transverse momentum calculated as the negative vector p T sum of thosejets. Any other collision vertices in the event are associated with additional soft inelastic ppcollisions called pileup (PU).Electrons are reconstructed by combining energy deposits in the ECAL with tracks obtainedfrom hits in the tracker [18]. Due to the strong curvature of the trajectory of charged particlesin the magnetic field and the significant amount of intervening material, an average fractionof 33% (at η ≈
0) to 86% (at | η | ≈ η and an extended window inthe azimuthal angle φ (measured in radians). The energy and position of the superclusters areobtained from the sum of the energies and the energy-weighted mean of the positions of thebuilding clusters. This way of clustering is complemented by an alternative clustering algo-rithm, based on the PF-reconstruction algorithm [23], resulting in an independent collection ofPF clusters.Hits in the tracker are combined into tracks, using an iterative tracking procedure as describedin Ref. [23]. To be efficient for the reconstruction of electrons, the track finding must includethe additional bending of the particle trajectory due to the bremsstrahlung emissions. This isachieved by a dedicated Gaussian-sum filter algorithm [27]. Since this method of track recon-struction can be time consuming, it is initiated only on a selected set of electron track seeds,which are likely to correspond to electron trajectories. Two approaches are followed to de-termine these seeds. In the first approach, starting from the ECAL, the energy and positionof the superclusters are used to extrapolate the electron trajectory to its origin. The intersec-tions of this extrapolation with the innermost tracker layers or discs are matched to hits inthe corresponding detectors. In the second approach, starting from the tracker, reconstructedtracks obtained from a less efficient, but also less CPU intensive, algorithm are extrapolated tothe ECAL surface and matched to PF clusters. The seeds of both approaches are combined toinitiate the final electron track finding with an efficiency of (cid:38)
95% for electrons from Z bosondecays.
The combination of the electron tracks with the ECAL clusters is achieved via a matching ofthe track extrapolated to the ECAL surface with the supercluster in η - φ space with an efficiencyof ≈
93% for electrons from Z boson decays. Alternatively, the electron track is matched to aPF cluster, while at each intersection with a layer or disc of the tracker a straight line is extrap-olated to the ECAL surface, tangent to the electron trajectory, to identify further PF clustersdue to bremsstrahlung emission. This approach improves the reconstruction for low p T elec-trons and electrons in jets. To increase their purity, the reconstructed electrons are required topass a multivariate electron identification discriminant [18], which combines information onthe quality of the differently reconstructed tracks, shower shape, and kinematic quantities. Inthe target analyses, for which the embedding technique is primarily foreseen, working pointsof this discriminant with an efficiency between 80 and 90% are used to identify electrons.Two main approaches are also pursued to reconstruct muons with the CMS detector [19]: inthe initial steps tracks are reconstructed independently in the inner silicon tracker and the outertrack detectors of the muon system. In the first approach inner and outer tracks are matchedby comparing their parameters propagated to a common surface. If a match is found, a global-muon track is fitted combining the hits from both tracks. In a second approach, tracks from theinner tracker are extrapolated to the muon system taking into account the magnetic field, theaverage expected energy losses, and multiple Coulomb scattering in the detector material. Ifat least one muon segment (i.e., a short track stub made of drift tube or cathode strip chamberhits) matches the extrapolation, the corresponding track is identified as a muon track. The sec-ond approach improves the reconstruction efficiency for muons with p T ≤ p T to reach the muon system, the reconstruction efficiency reaches up to99%. It is supplemented by specialized algorithms for muons with a p T of several hundreds ofGeV. The presence of hits in the muon chambers already leads to a strong suppression of parti-cles misidentified as muons. Additional identification requirements on the track fit quality andthe compatibility of individual track segments with the fitted track can reduce the misidentifi-cation rate further. In the analyses for which the embedding technique is primarily foreseen,muon identification requirements with an efficiency of about 99% are chosen.The contribution from nonprompt leptons to the electron (muon) selection is further reducedby requiring the selected leptons to be isolated from any hadronic activity in the detector. Thisproperty is quantified by a relative isolation variable I e ( µ ) rel = p e ( µ ) T (cid:104) ∑ p charged, PVT, i + max (cid:16) ∑ E neutralT, i − E neutral, PUT (cid:17)(cid:105) , (1)which uses the sum of the p T of all charged and transverse energy of all neutral particles in acone of radius ∆ R = (cid:112) ( ∆ η ) + ( ∆ φ ) around the lepton direction at the PV, where ∆ η and ∆ φ correspond to the angular distance of the particle to the lepton in the η and φ directions. Thechosen cone sizes are ∆ R = p T of charged particlesin the isolation cone whose tracks have been associated with PU vertices and multiplying thisquantity by a factor of 0.5 to account for the approximate ratio of neutral to charged hadronproduction, such that E neutral, PUT = ∑ p charged, PUT, i . For electrons, the F AST J ET technique [28,29] is applied as described in Ref. [18]. The energy of neutral particles from PU is estimated as E neutral,PUT = ρ A eff , where ρ is the median of the energy density distribution per area in the η - φ plane around any jet in the event and A eff is an effective area in η and φ . The value obtained is subtracted from the transverse energy sum, and the result set to zero in the case of negativevalues. Finally, the result is divided by the p T of the lepton to result in I e ( µ ) rel .For further characterization of the event, all reconstructed PF candidates are clustered intojets using the anti- k T jet clustering algorithm as implemented in F AST J ET [25, 26] with a dis-tance parameter of 0.4. To identify jets resulting from the hadronization of b quarks (b jets),a reoptimized version of the combined secondary vertex b tagging algorithm is used that ex-ploits information from the decay vertices of long-lived hadrons and the impact parameters ofcharged-particle tracks in a combined discriminant [30]. A typical working point for analysesfor which the embedding technique is foreseen corresponds to a b jet identification efficiencyof ≈
70% and a misidentification rate for jets induced by light quarks and gluons of 1%. Forthe validation of the embedding technique, jets with p T >
20 GeV and | η | < p T >
20 GeV and | η | < τ h candidates. The τ h reconstruction isperformed by further exploiting the substructure of the jets, using the hadrons-plus-strips al-gorithm described in Refs. [31, 32]. The decay into three charged hadrons, and the decay intoa single charged hadron, accompanied by up to two neutral pions with p T > strips , i.e., clusters of electronor photon constituents of the seeding jet with stretched energy deposits along the azimuthaldirection. The strip size varies as a function of the p T of the electron or photon candidate. The τ h decay mode is then obtained by combining the charged hadrons with the strips. High- p T tau leptons are expected to be isolated from any hadronic activity in the event, as are high- p T electrons and muons. Furthermore, in accordance with its finite lifetime, the charged decayproducts of the tau lepton are expected to be slightly displaced from the PV. To distinguish τ h decays from jets originating from the hadronization of quarks or gluons, a multivariate τ h identification discriminant is used [32]. It combines information on the hadronic activity inthe detector in the vicinity of the τ h candidate with the reconstructed properties related tothe lifetime of the tau lepton. Of the predefined working points given in Ref. [32], the tight,medium, and very loose working points are used in the target analyses. These have efficienciesbetween 27% (tight) and 71% (very loose) for genuine tau leptons, e.g., from Z → τ τ decays,for quark/gluon misidentification rates of less than 4.4 × − (tight), and 1.3 × − (veryloose). Finally, additional discriminants are imposed to reduce the misidentification probabil-ity for electrons and muons as τ h candidates, using predefined working points from Ref. [32].For the discrimination against electrons these working points have identification efficienciesfor genuine tau leptons ranging from 65% (tight) to 94% (very loose) for misidentification ratesbetween 6.2 × − (tight) and 2.4 × − (very loose). For the discrimination against muonsthe typical τ h identification efficiency is 99% for a misidentification rate of O ( − ) .The missing transverse momentum vector (cid:126) p missT , defined as the negative vector p T sum of allreconstructed PF objects, is also used to characterize the events. Its magnitude is referred toas p missT . It enters the target analyses via selection criteria and via the calculation of the finaldiscriminating variable used for the statistical analysis, which is usually correlated with theinvariant mass of the τ τ system. For the validation of the embedding technique and to demonstrate its performance, simulatedevents are used to model the most important processes contributing after the event selectionsdescribed in Sections 5 and 7. The Drell–Yan production in the ee, µµ , and τ τ final states, and the production of W bosons in association with jets (W + jets) are generated at leading or-der (LO) precision [33] in the strong coupling constant α S , using the M AD G RAPH MC @ NLO AD G RAPH MC @ NLO is used atnext-to-leading order (NLO) precision. For tt and single t quark production samples are gen-erated at NLO precision using
POWHEG v2 [35–41]. For the generation of all processes theNNPDF3.0 parton distribution functions [42] are used. The simulation of the underlying eventis parametrized according to the CUETP8M1 tune [43]. Hadronic showering and hadroniza-tion, as well as the τ decays, are modeled using PYTHIA
PYTHIA and adding them to the simulated events according to the expected PU distributionprofile in data. Differences between this expectation and the observed PU profile are mitigatedby reweighting the simulated events. All events generated are passed through a G
EANT
The embedding procedure can be split into four steps: • the selection of µµ events from data (Section 5.1), • the removal of tracks and energy deposits of the selected muons from the recon-structed event record (Section 5.2), • the simulation of two τ leptons with the same kinematic properties as the removedmuons in an otherwise empty detector (Section 5.3), and • the combination of the energy deposits of the simulated tau lepton decays with theoriginal reconstructed event record (Section 5.4).For validation purposes, electrons or muons can also be injected into the simulation to form anembedded ee or µµ event, referred to as an e- or µ -embedded event. A schematic view of theprocedure is given in Fig. 1. µµ events In the first step of the embedding procedure, µµ events are selected from data. Althoughthe selected muons might not necessarily originate from Z boson decays, Z → µµ events area natural target of this selection, which helps to identify genuine µµ events. The selectionshould be tight enough to ensure a high purity of genuine µµ events and at the same timeloose enough to minimize biases of the embedded event samples. The selection of the muonsdefines the minimal selection requirements to be used in the target analyses that are discussedin more detail in Section 5.3. Inefficiencies of the reconstruction and selection of the muons dueto the geometrical acceptance of the detector are estimated, giving correction factors which areapplied to the final distributions.While strict isolation requirements help to increase the purity of prompt muons, e.g., fromZ → µµ decays, in the selection, they introduce a bias towards less hadronic activity in thevicinities of the embedded leptons that will appear more isolated than expected in data. Tominimize this kind of bias, which cannot be corrected by a scale factor, isolation requirementsare omitted as much as possible. At the same time the selected phase space is desired to be as .1 Selection of µµ events Figure 1: Schematic view of the four main steps of the τ -embedding technique, as describedin Section 5. A Z → µµ candidate event is selected in data (“Z → µµ Selection”), all energydeposits associated with the muons are removed from the event record (“Z → µµ Cleaning”),and two tau lepton decays are simulated in an otherwise empty detector (“Z → τ τ Simula-tion”). Finally all energy deposits of the simulated tau lepton decays are combined with theoriginal reconstructed event record (“Z → τ τ Hybrid”). In the example, one of the simulatedtau leptons decays into a muon and the other one into hadrons. inclusive as possible for the embedded event samples to be applicable for a variety of targetanalyses. The loose selection in turn leads to an admixture of other processes in addition toZ → µµ . This admixture and the consequences for the embedded event samples are carefullychecked and assessed. At the trigger level, the events are required to be selected by at least one of a set of µµ triggerpaths, with a minimum requirement between 3.8 and 8.0 GeV on the invariant mass of the twomuons, m µµ . All trigger paths require p T > ( ) GeV for the leading (trailing) muon, veryloose isolation in the tracker, and a loose association of the muon track with the PV. Offline,the reconstructed muons are required to match the objects at the trigger level, their distanceextrapolated to the PV is required to be | d z | < | η | < p T > ( ) GeV forthe leading (trailing) muon to match the online selection requirements. No additional selectionrequirements are imposed on the isolation of the muons to minimize any bias of the embeddedevent samples in this respect.To form a Z boson candidate, each muon is required to originate from a global-muon track. Themuons are required to be of opposite charge with an invariant mass of m µµ >
20 GeV. If morethan one Z boson candidate is found in the event, the one with the value of m µµ closest to thenominal Z boson mass is chosen. This selection results in a total of more than 65 million events,with an average rate of about 1.5 million events per 1 fb − of collected data. The expected eventcomposition after these and several further selection requirements that will be specified in thefollowing discussion is given in Table 1. SM events composed exclusively of jets produced viathe strong interaction are referred to as quantum chromodynamics (QCD) multijet production.Throughout the paper this contribution is estimated from data using a background estimationmethod described in Ref. [15]. The distributions of m µµ and p T of the trailing muon for allselected events are shown in Fig. 2. Also shown are the contributing processes estimated bythe simulation, to illustrate their kinematic distributions.Table 1: Expected event composition after the selection of two muons, as described in Sec-tion 5.1. The label “QCD” refers to SM events composed exclusively of jets produced via thestrong interaction. The compositions after adding selections on m µµ >
70 GeV or on the num-ber of b jets in the event are shown in column 3 and 4 respectively. In the second column thefraction of events where the corresponding process has two genuine muons in the final stateis given in parentheses. For W + jets events the second muon originates from additional heavyflavor production. Fraction (%)Process Inclusive m µµ >
70 GeV N ( b jet ) > → µµ ( ) ( ) → τ τ ( ) ( ) + jets 0.08 ( ) Data-driven estimate, information not available. .1 Selection of µµ events (GeV) mm m
50 100 150 200 250 e v t s N
10 Observed mm fi Z QCD tt tt fi Z DibosonW + jets (2017, 13 TeV) -1 CMS (GeV) m T p
50 100 150 e v t s N
10 Observed mm fi Z QCD tt tt fi Z DibosonW + jets (2017, 13 TeV) -1 CMS
Figure 2: (Left) invariant mass, m µµ , of the selected dimuon Z boson candidates and (right) p T of the trailing muon after the event selection, as described in Section 5.1. In Table 1, a relaxed selection of two muons compatible with the properties of a Z boson can-didate already results in a sample of Z → µµ events with an expected purity of more than97%. Smaller contributions are expected from Z → τ τ events, mostly where both tau leptonssubsequently decay into muons, and from QCD multijet, tt, and diboson production.Without further correction, the presence of QCD multijet and Z → τ τ events in the selectedevent sample leads to an overestimate of the Z → µµ event yield and a bias of the m (cid:96)(cid:96) and p T distributions of the embedded leptons towards lower values. This can be inferred from Fig. 2,where the accumulation of these events is visible for m µµ <
70 GeV and p µ T <
20 GeV. Thefraction of QCD multijet and Z → τ τ events can be significantly suppressed by raising therequirement on m µµ to be higher than 70 GeV, at the cost of a loss of ≈
13% of selected Z → µµ events. However, because of the low transverse momentum of the selected muons, these eventshave a low probability to end up in the final sample of τ -embedded events, see Section 5.3.The contribution from tt and diboson events is distributed over the whole range of m µµ . Itsrelative contribution is larger at high values of m (cid:96)(cid:96) , where the overall event yield is small, andin event selections with b jets, as shown in the last column of Table 1. These conditions are met,e.g., in searches for additional Higgs bosons in models beyond the SM [15]. A large fractionof this contribution originates from events where the W bosons e.g., from both t quark decayssubsequently decay into a muon and neutrino (tt ( µµ ) ). The contribution from tt and dibosonproduction in all other modes is below the current accuracy requirements of the method. Thesubstitution of the muons by tau leptons provides an additional estimate for tt and dibosonproduction with two tau leptons in the final state from data. This class of events needs to beremoved from simulation in the target analyses to prevent double counting. For simplicity,all further discussion of the embedding technique will refer to the estimate of all genuine τ τ events from either Z → τ τ , tt, or diboson production, unless explicitly stated otherwise. As discussed above, inefficiencies in the reconstruction and selection of the µµ events lead tokinematic biases in the embedded event samples because of the limited detector acceptance. The global efficiency of the trigger selection in the kinematic regime where embedded eventsamples can be applied amounts to about 80%, the combined reconstruction and identificationefficiency lies well above 95%. Both efficiencies are estimated differentially in a fine grid inmuon η and p T , using the “tag-and-probe” method. They are then used to correct for theeffects of the detector acceptance.As a consequence, not only the kinematic distributions but also the yield of the estimated τ τ events can be obtained directly via the embedding technique, assuming the same branchingfraction of the Z boson into muons and tau leptons. This is achieved by correcting for the de-tector acceptance and selection efficiency of the µµ events and applying the reconstruction andselection efficiency from the τ -embedded event sample. Residual corrections of these efficien-cies with respect to the data, are discussed in Section 7.1. When applied to the data this estimaterenders uncertainties in the production cross sections and integrated luminosity irrelevant forthe involved processes, as will be further discussed in Section 7.2. µ energy deposits from the reconstructed event record In the second step, all energy deposits of the selected muons are removed from the recon-structed event record. This is done at the level of hits in the inner tracker and muon systems,and clusters in the calorimeters. Hits in the tracker are identified by their association to thefitted global-muon track. Clusters in the calorimeters are identified by the intercept of themuon trajectory interpolated through the calorimeters, as discussed in Section 3. If an interceptmatches with the position of a calorimeter cluster, an energy amount corresponding to a mini-mum ionizing particle is subtracted from the cluster. If the energy of the modified cluster dropsbelow the noise threshold defined for the event reconstruction, the cluster is removed from theevent record. By this procedure, all traces of the selected muons in the detector can be removedfrom the event reconstruction even in detector environments with additional hadronic activityin the vicinity of the selected muons.Effects of the removal of energy deposits in the calorimeters can arise in cases where the energydeposit of the muon is not completely removed or leads to the split of a geometrically extendedcluster into more than one piece. Such a removal may lead to the reconstruction of spuriousphoton or neutral hadron candidates. These additionally reconstructed objects are usually oflow energy and low reconstruction quality, and play a negligible role in the target analyses.The removal of the energy deposits of the muons from the detector is illustrated in Fig. 3. InFig. 3 (left), a selected Z → µµ candidate event in the data set is displayed in the η - φ plane ofthe calorimeters, with the intercepts of the reconstructed muons with the calorimeter surfaceand clusters in the ECAL (HCAL) shown. One muon (with p T =
32 GeV) in the upper andone muon (with p T =
59 GeV) in the lower parts of the figure are visible. Several clusters inthe calorimeters have been associated with the incident muon trajectories. In Fig. 3 (right) thesame detector area is shown after the hits and energy deposits associated with the muons havebeen removed from the reconstructed event record. The HCAL clusters associated with eachcorresponding muon have been completely removed, whereas the energy of the ECAL clusterassociated with the muon in the lower part of the figure has been reduced. The remainingECAL cluster is identified as low-energy photon in the subsequent reconstruction.
In the third step, the energy and momentum of the selected muons are either directly injectedas electrons or muons into the detector simulation, for validation purposes, or used to seed thesimulation of tau lepton decays via
PYTHIA , before entering the detector simulation. For thispurpose an event record is prepared that contains only the information related to the kinematic .3 Simulation of tau lepton decays CMS
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Figure 3: Display of a Z → µµ candidate event in the data set, in the η - φ plane at the surface ofthe calorimeters (left) before and (right) after the hits and energy deposits associated with themuons have been removed from the reconstructed event record. The red crosses indicate theintercepts of the reconstructed muon trajectories with the calorimeter surface. The red (blue)boxes correspond to clusters in the ECAL (HCAL).properties of the two selected muons in an otherwise empty detector that is free of any otherparticles from additional jet production, underlying event, or PU. The invariant mass of theselected muons is fixed to the reconstructed value, as shown in Fig. 2 (left). Polarization effectsare neglected in embedded events, since they are below the sensitivity of the target analyses.To account for the mass difference between the muon and the tau lepton or electron (referredto by (cid:96) = e, τ ), the four-momenta of the muons are boosted into the center-of-mass frameof the µµ pair, where the energy ( E ∗ (cid:96) ) and momentum ( (cid:126) p ∗ (cid:96) ) of each lepton, with mass m (cid:96) , aredetermined from E ∗ (cid:96) = m µµ | (cid:126) p ∗ (cid:96) | = (cid:113) E ∗ (cid:96) − m (cid:96) ; (cid:96) = e, τ . (2)The corrected values (cid:126) p ∗ (cid:96) and E ∗ (cid:96) are then boosted back into the laboratory frame and usedeither for the electrons or to seed the tau lepton decays. The event vertex for the simulationof the embedded leptons is set to the PV of the initially reconstructed µµ event. Four distinctsamples of τ -embedded events are produced from the same µµ event sample, for use in themost important final states of the target analyses, namely e µ , e τ h , µτ h , and τ h τ h . This isachieved by enforcing the subsequent decay of the injected τ lepton pair in the simulation,with a branching fraction of 100%. It has been checked that the overlap of the resulting τ -embedded event samples is small enough, such that even those distributions that are related tothe part of the event that originates from the observed data, e.g. like jet distributions, are fullyuncorrelated. A significant amount of the energy and momentum of the tau lepton is not transferred to thevisible decay products, but carried away by the neutrino(s) in the decay. As a consequence,the visible products of the tau lepton decays are usually significantly lower in p T than thatof the originally selected muons. A restricted phase space of the selected muons results from the finite detector acceptance. For each set of τ -embedded events, this translates into a final-state-dependent kinematic range, for later use in the target analyses. This range is furtherrestricted by the acceptance requirements that have to be imposed in the target analyses. Forexample, the ability to create τ -embedded events in the τ h τ h final state, with reconstructed τ h candidates with a p τ h T as low as 20 GeV each is useless for an analysis with a trigger thresholdof p τ h T >
30 GeV. To save computing time during the CPU-intensive detector simulation, akinematic filtering is applied to the visible decay products, after the simulation of the tau leptondecay and before the detector simulation. The final-state-dependent thresholds of this filteringon the p T of the visible decay products (prior to the detector simulation) define the kinematicrange of eligibility of the τ -embedded event samples for later use in the target analyses. Theyare given in Table 2.To increase the number of µµ events that can be used in the target analyses, the decay is re-peated 1000 times for each tau lepton pair. This is done to give the decay products a higherprobability to pass the eligibility requirements. Only the last trial that fulfills the kinematicrequirements for the given final state is saved for the subsequent detector simulation. If at leastone trial succeeds, the number of successful trials divided by 1000 times the branching fractionof the subsequent τ τ decay is saved as an additional weight factor to the event. These weightstake values below the corresponding branching fraction and can be as low as 10 − at the kine-matic thresholds of eligibility. Depending on the τ τ final state, the fraction of events that passthe kinematic filtering ranges between (cid:101) kin =
27% (in the τ h τ h final state) and 58% (in the e µ final state). In the τ h τ h final state this means that 73% of the τ -embedded events that could inprinciple be used, according to the acceptance restrictions of the originally selected µµ events,are usually not accessible due to the stricter acceptance requirements in the target analyses.Overall this procedure allows for the production of final-state-specific τ -embedded event sam-ples of approximately 5 to 60 times the size of the event sample of selected tau lepton pairsin the target analyses, independent of the integrated luminosity corresponding to this eventsample. The efficiency of the kinematic filtering and the size of each τ -embedded event sampleare given in Table 2.In Section 5.1.2, Z → τ τ events where both tau leptons subsequently decay into muons and thecorresponding neutrinos are discussed as a potential source of bias of the τ -embedded eventsamples. Of all Z → τ τ events in this final state a fraction of less than 0.25% is expected to endup in the τ -embedded event samples, in the given eligibility ranges. This corresponds to lessthan 2.8% of the events indicated by the Z → τ τ contribution in Fig. 2, and a fraction far belowthe 1% level in the initial event composition as given in Table 1.Table 2: Kinematic range of eligibility for each τ -embedded event sample in the e µ , e τ h , µτ h ,and τ h τ h final states. The expression “First/Second object” refers to the final state label usedin the first column. Also given are the probability of the simulated tau lepton pair to pass thekinematic filtering ( (cid:101) kin ), described in the text, and the equivalent of the integrated luminosity L int , of the corresponding τ -embedded event sample, in multiples of the data set, from whichthe embedded event sample has been created.Final state First object Second object (cid:101) kin L int /41.5 fb − e µ p eT > ( ) GeV p µ T > ( ) GeV 0.58 60e τ h p eT >
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33 GeV, | η τ h | < .3 Simulation of tau lepton decays Two more reconstruction effects arise in the discussion of the simulation step. First, the four-momenta of the selected muons correspond to already reconstructed objects, which are rein-jected into the simulation of the detector response, effects due to the finite momentum reso-lution of the detector lead to a broadening, especially of the p T and m (cid:96)(cid:96) distributions of theembedded leptons. The distributions are corrected for this effect by an m µµ -dependent rescal-ing of the energy and momentum of the selected muons on an event-by-event basis, beforeusing them to generate the simulated leptons for embedding. A simulated Z → µµ sample isused to derive this m µµ -dependent rescaling. Figure 4 (left) shows the m µµ distribution from asample of simulated Z → µµ events as well as the corresponding µ -embedded event samplebefore and after the correction. In the lower panel of the figure, the ratio is given with respect tothe simulated Z → µµ sample. The µ -embedded event sample without the correction reveals aslight broadening with respect to the simulated Z → µµ sample, which is compensated by thecorrection.A second effect can be attributed to the emission of photons from the initially selected muons,referred to as final-state radiation (FSR) in the following. When missed in the reconstruction,FSR leads to an additional broadening of the kinematic distributions and a systematic shift tolower values of the energy and momentum of the initially selected muons. This shift is sub-sequently transferred to the embedded leptons. Figure 4 (right) shows the m µµ distribution ofthe Z → µµ simulation sample for muons before and after FSR, to illustrate the effect. For thevalidation of µ -embedded events, this effect can be eliminated by executing the simulation stepof the embedding procedure without FSR. The Z → µµ simulation sample and the correspond-ing µ -embedded event data sample are then subjected to the same FSR effects during the initialsimulation. For e-embedded events the effects of FSR are underestimated; for τ -embeddedevents they are overestimated. / e v t s N (simulation) mm fi Z (embedded) mm fi Z (embedded, uncorr) mm fi Z mm (GeV) mm m
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Figure 4: Comparison of the reconstructed invariant mass, m µµ , of the selected muons froma simulated Z → µµ sample with the corresponding µ -embedded event sample. On the leftthe (red histogram) simulated Z → µµ sample and the µ -embedded event sample (blue dots)with and (green dots) without the correction for the effects of the finite detector resolution,as described in the text, are shown. On the right (green histogram) m µµ from the simulatedZ → µµ sample before FSR is shown in addition, to illustrate the effect. In the case of τ -embedding, both effects that were discussed in this section are negligible com-pared to the energy and momentum fluctuations introduced by the undetected neutrinos in thedecay, which already lead to a significant broadening of the related kinematic distributions. Amore detailed discussion is given in Section 6. In a fourth and final step of the procedure, all energy deposits of the simulated electrons,muons, or tau lepton decays are combined with the original reconstructed event record, fromwhich the energy deposits of the initially selected muons had been removed, to form a hybridevent that is mostly obtained from data and only relies on the simulation for the embeddedlepton pair. This is done at the earliest possible reconstruction step to guarantee that all sub-sequent quantities for the lepton identification are based on the full event information and notonly on parts of the event. The ideal way is to combine the reconstructed object collections atthe level of tracker hits and energy deposits in the calorimeter crystals. However, in practice,the information is combined at the level of reconstructed objects (tracks, calorimeter clusters,and muons) rather than at the level of individual hits. This is to avoid complications with resid-ual small differences between the simulation geometry and the real detector. The tracks of theembedded leptons are reconstructed based on the geometry used for the simulation, in the oth-erwise empty detector, of the simulation step. Since the detector in the simulation step is freefrom other particles, jet production, underlying event, or PU there may be a biased track recon-struction efficiency that must be checked and possibly corrected. Residual effects are discussedin Section 6.
Simulation-based closure tests are performed to test the validity of the embedding method.For this purpose, a validation sample for embedded events is created from simulated Z → µµ events, in which the embedding technique is applied in the same way as in the observed data:the selected muons are removed from the reconstructed event record and replaced with elec-trons, muons, or tau leptons. The embedded event data samples created in this way are com-pared to simulated events in the same final states. For e- and τ -embedded events, this com-parison is performed on statistically independent event samples. For µ -embedded events, thecomparison is performed on exactly the same simulated events, such that only the effects ofthe removal of energy deposits of the initially selected muons, and the reconstruction of thereinjected muons are tested.For e- and τ -embedded events, the normalization of the distributions is obtained from the yieldof selected Z → µµ events in the first step of the procedure, as described in Section 5.1. For the τ -embedded events, the yield of selected τ τ events matches the yield of the simulated Z → µµ sample within 1% with a statistical uncertainty of 0.5%. For the e-embedded events a similaragreement is achieved. µ -embedding technique The muon plays a special role in validating the embedding procedure itself. The broadeningof the kinematic distributions of the embedded muons, due to the repeated reconstruction andthe finite angular and p T resolution of the detector, and the effects of FSR, have already beendiscussed in Section 5.3. For the following discussion, the simulation of FSR is switched offin the simulation step of the embedding procedure. In this way FSR is simulated only once, .1 Validation using the µ -embedding technique / e v t s N (simulation) mm fi Z (embedded) mm fi Z mm m h - - s i m u l a t i on R a t i o t o
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Figure 5: Comparison of µ -embedded events with exactly the same Z → µµ events from sim-ulation. Shown are the (upper left) η and (upper right) p T distributions of the leading muon in p T , (middle left) p missT , (middle right) m jj , (lower left) jet and, (lower right) b jet multiplicities,as described in the text. from PV) – , h m R ( ( M e V ) æ ) – ( h T p DÆ mm fi Z (embedded) mm fi Z 13 TeV
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Figure 6: Comparison of µ -embedded events with exactly the same Z → µµ events from sim-ulation. Shown is the mean transverse momentum (energy) flux per muon, from all recon-structed particles with the distance R from the muon, split by (upper left) charged hadrons fromthe PV and (upper right) PU vertices, (lower left) photons, and (lower right) neutral hadrons.The distributions are shown for the µ − and for events with m µµ close to the nominal Z bosonmass. .1 Validation using the µ -embedding technique during the initial simulation of the validation sample, and all FSR effects are the same for thesimulated and the embedded event.Figure 5 shows the η and p T distributions of the leading muon in p T , the p missT , the invariantmass of the two leading jets in p T , m jj , the number of jets with p T >
30 GeV and | η | < p T >
20 GeV and | η | < µ -embedded event sample and the red histogram to the original simulation. The red-shadedbands represent the statistical uncertainty of the simulated event sample that is a referencefor the comparison. All distributions are based on exactly the same events, so that the ob-served differences can exclusively be attributed to the removal and repeated simulation andreconstruction of the embedded muons. The uncertainty bands are added to facilitate the as-sessment of the observed differences between the compared samples. These differences areconsidered acceptable if they are compatible with the statistical uncertainty of the validationsample, which is chosen with 10 times more events than the expected number of events in thetarget analyses.The kinematic distributions of the muons and jets, and the jet multiplicities are well repro-duced. The structure in the distributions of the muon η follows the geometry of the detector.The Jacobian peak corresponding to the Z boson decay is clearly visible in the p T distribu-tion of the muon. A 5% effect in the ratio is visible for low values of p missT , which is causedby the finite angular and p T resolution of the detector that can lead to small residual valuesof p missT for events with little or no p missT . Corrections due to the finite momentum resolutionof the detector, as described in Section 5.3, are not propagated to the p missT . For τ -embeddedevents this effect is negligible compared to the kinematic fluctuations related to the neutrinosinvolved in the decays, as will be discussed in Section 6.3. Another 5% effect in the ratio for p missT >
100 GeV is explained by rare reconstruction effects, where muons of high p T may createadditional track segments, e.g., due to multiple scattering in the outer tracker, which are notassociated with the initially reconstructed global muon track. After the cleaning step of theembedding procedure, such track segments may be picked up in a different way and thus leadto a different assignment of p missT . Since the validation is based on simulated Z → µµ events,without genuine p missT , it is clear that such events point to a poor reconstruction of the originalevent. The fact that this is a 5% effect only for a small fraction of events, and that the size of theeffect is small compared to the statistical uncertainty of the validation sample, indicates that itis subdominant to the effect at low p missT .Figure 6 shows the mean transverse momentum flux per muon, (cid:104) ∆ p T (cid:105) , from all reconstructedparticles within the distance R from the muon, split by charged hadrons originating from thePV and PU vertices, photons, and neutral hadrons. It is defined as the average sum of the p T (transverse energy in case of neutral particles) of all corresponding particles between two coneswith radii R and R + ∆ R in the distance R from the muon, where ∆ R corresponds to the widthsof the histogram bins. All distributions are shown for the µ − for events with m µµ close to thenominal Z boson mass.The figures indicate that in most cases no other particles are reconstructed in the spatial vicinityof the muon. For a uniform p T flux distribution, (cid:104) ∆ p T (cid:105) is expected to increase linearly, becauseof the increasing area of the ring segments. This trend is roughly observed for all reconstructedparticle types with a slope of 32 (550) MeV per unit of R for (cid:104) ∆ p T (cid:105) from charged hadrons orig-inating from the PV (PU vertices), 110 MeV for photons, and 66 MeV for neutral hadrons. Thelarger slope for charged hadrons from PU vertices, photons, and neutral hadrons is related tothe simulated PU profile and may vary in data. The displayed distributions are shown for thesimulated PU profile between 40 and 70 additional inelastic pp collisions. For charged hadrons h i h i s e v t s N
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Simulation ee Figure 7: Comparison of e-embedded events with a statistically independent sample of simu-lated Z → ee events. Shown are distributions of the energy-weighted standard deviations ofa 5 × η , σ i η i η , and (upper right) φ , σ i φ i φ , as described in the text,(lower left) the number N GSF of detector hits, used for the Gaussian Sum Filter algorithm [27]as described in Section 3, and (lower right) the multivariate discriminator for the identificationof electrons (electron-ID BDT). The black arrow, shown in addition to the electron-ID BDT dis-tribution, indicates the working point with 80% efficiency in the displayed electron η region.For better visibility, the statistical uncertainties of both samples, red-shaded band for simu-lated Z → ee events, and blue vertical bars for e-embedded events, are multiplied by 10 for thefigures. .2 Validation using the e-embedding technique and photons, the progression from the simulation is well reproduced, apart from small regionsclose to the muon, which show a small excess in (cid:104) ∆ p T (cid:105) for charged hadrons from the PV andphotons, and a small deficit in (cid:104) ∆ p T (cid:105) for charged hadrons from PU vertices. A larger differenceis observed for neutral hadrons, which is due to an incomplete removal of energy deposits ofthe muon in the HCAL, as discussed in Section 5.2. When integrated over R , and all recon-structed particle types, the additional hadronic energy in the predefined isolation cone addsup to less than 200 MeV. The identification of electrons in CMS is based on O ( ) closely related detector variables thatare combined into a multivariate discriminator [18]. As discussed in Sections 5.3 and 5.4 thesimulation of the embedded lepton pair takes place in an otherwise empty detector with noother particles from PU, underlying event, or additional jet production. The tight relation ofthe electron reconstruction and identification to closely related detector quantities poses anextra challenge to the embedding technique for this lepton flavor, which therefore requiresa unique validation procedure. To monitor the success in simulating the distribution of thisdiscriminator and its inputs, e-embedded events are created and compared to a statisticallyindependent sample of simulated Z → ee events. Figure 7 shows, for the leading electronin p T , the energy-weighted standard deviation of the position of a 5 × η ( σ i η i η ) and φ ( σ i φ i φ ), and N GSF , the number of detector hits used for the Gaussian SumFilter algorithm [27] that is introduced in Section 3. The quantities i η and i φ are measured ininteger crystal units, such that in a 5 × τ -embedded event samples, and are described in Section 7.1. InFig. 8, the distributions of m ee and the p T of the leading electron are shown. The observeddifferences are explained by differences in FSR, as discussed in Section 5.3. Also shown is theeffect of a variation of the electron energy scale by ± τ -embedding technique The main target of the embedding technique, the estimation of Z → τ τ events is validatedby comparing τ -embedded events to a statistically independent sample of simulated Z → τ τ events in each of the previously discussed τ τ final states. In Fig. 9 the p T and η distributions ofthe electron, muon, and τ h candidate are shown using the e µ , e τ h and, µτ h final states. To in-crease the statistical significance of the validation results, the distributions of the purely leptonrelated quantities are shown for the combination of multiple final states. Figure 10 shows thedistributions of the electron and muon isolation, I e ( µ ) rel , the multivariate τ h discriminant ( τ h -IDBDT), p missT , m jj , and the invariant mass of the visible decay products of the tau leptons, m vis in the µτ h final state. The τ -embedded event samples, by construction, have a larger size thanthe simulated validation sample and thus smaller statistical uncertainties, which becomes ap-parent from the smaller fluctuations, especially in the tails of the steeply falling distributions inthe upper panels of the subfigures. Figure 8: Comparison of the e-embedded events with a statistically independent sample ofsimulated Z → ee events. Shown are the distributions of (left) m ee and (right) p T of the lead-ing electron in p T . The blue vertical bars and red-shaded bands correspond to the statisticaluncertainty of each sample. The effect of a variation of the electron energy scale of ±
1% is alsoshown by the green lines.In general, a good agreement is observed, within the statistical precision. Effects of FSR in theselection of the µµ event are not visible in the muon p T and m vis distributions. This is true for all τ τ final states under investigation. Also shown for these distributions are the effects of a shiftof the electron energy scale by ±
1% and a shift of the tau lepton energy scale by ± η are covered by the additional uncertainties in the correction for the geometrical µµ detector acceptance. Potential differences in the electron p T are small compared to the electronenergy scale uncertainty usually applied to the target analyses, as discussed above. The effectof a corresponding shift in the electron energy scale is also shown in the corresponding subfig-ure. The same is true for the p T of the τ h candidate. More pronounced deviations are visiblein the I µ rel distribution. These are explained by an incomplete removal of the energy depositsof the initially selected muons. Integrated over the full isolation cone, the expected differencein p T amounts to less than 200 MeV, corresponding to the excess in (cid:104) ∆ p T (cid:105) , as observed in thecontext of the discussion of Fig. 6. The fact that similar effects are not visible in I erel can be ex-plained by the different reconstruction of electrons that may associate parts of the remainingenergy deposits of the initially selected muons in the calorimeters to the electron clusters, thusremoving them from the objects taken into account for the calculation of I erel . A 20% differencein the highest bin of the τ h -ID BDT distribution is explained by the reconstruction of tracks inthe otherwise empty detector in the simulation step, for τ h decays with one or three chargedand no additional neutral hadrons. The overall effect on the identification efficiency is smalland included in corresponding correction factors that are discussed in Section 7.1.In summary, in all investigated Drell–Yan final states, the agreement of the embedded eventsamples with the corresponding validation sample is observed to be compatible with the sim-ulation. Most of the observed differences are within the statistical precision of the validationsample and smaller than the statistical precision of the target analyses in the τ τ final state.Residual systematic trends have been checked to have negligible effects on the target analy-ses. No further measures are taken to improve the agreement of the embedded event sampleswith the simulation. Instead, correction factors for the reconstruction and identification of the .3 Validation using the τ -embedding technique e v t s N (simulation) tt fi Z (embedded) tt fi Z h t + e m e e h - - s i m u l a t i on R a t i o t o
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Figure 9: Comparison of τ -embedded events with a statistically independent sample of sim-ulated Z → τ τ events. Shown are the (left) η and (right) p T distributions of the (upper row)electron in the e µ + e τ h final states, (middle row) muon in e µ + µτ h final states, and (lower row) τ h candidate in the e τ h + µτ h final states. The blue vertical bars and red-shaded bands corre-spond to the statistical uncertainty of each sample. The effect of a variation of the electron ( τ h )energy scale of ± ± e v t s N (simulation) tt fi Z (embedded) tt fi Z h t + e m e erel I s i m u l a t i on R a t i o t o
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Figure 10: Comparison of τ -embedded events with a statistically independent sample of sim-ulated Z → τ τ events. Shown are distributions of (upper left) I erel , (upper right) p missT , (middleleft) I µ rel , (middle right) m jj , (lower left) τ h -ID BDT, and (lower right) m vis , as discussed in thetext. The black arrows indicate the working points usually used in the target analyses. Theblue vertical bars and red-shaded bands correspond to the statistical uncertainty of each sam-ple. The effect of a variation of the τ h energy scale of ± simulated electrons, muons and tau leptons are derived from e-, µ - and τ -embedded events,in analogy to the correction factors usually provided for fully simulated events, as will be dis-cussed in Section 7.1. τ -embedding technique to data The τ -embedded event samples used for the target analyses are obtained using the µµ dataevent selection. They replace the simulation of all Z → τ τ , tt ( τ τ ) and diboson ( τ τ ) events inthe τ τ final states. To prevent double counting, tt ( τ τ ) and diboson ( τ τ ) events are removedfrom background estimates that use simulation. Their selection must be performed on theundecayed tau leptons, at the stable particle level.The τ -embedded event sample, except for the τ decays, provides a data description betterthan the Z → τ τ simulation. The simulation can only reach an equivalent performance aftera significant amount of tuning. This is true for the time-dependent PU profile of the data,the production of additional jets, especially in exclusive kinematic corners, like for multijet,multi b jet, forward jet, or vector boson fusion topologies and the underlying event. Otherevent quantities which are typically difficult to model in the simulation are the number ofreconstructed primary interaction vertices, or p missT . All quantities referring to the part of theevent that is obtained from the data may be used in the target analyses without any furthercorrections. The time needed to produce the τ -embedded event sample is of the order of timenecessary to reprocess the collected µµ data set. The size of the τ -embedded event sample is 5to 60 times the size of the data sample used for the target analyses. These are advantages overthe simulation that will become even more important for the planned High-Luminosity LHCupgrade, where typically between 140 and 200 PU collisions are expected.The ability of the τ -embedded event samples to describe the data is demonstrated below us-ing a data set corresponding to an integrated luminosity of 41.5 fb − , collected with the CMSdetector in 2017. Residual differences between the τ -embedded event samples and the data in individual controldistributions, related to the simulated part of the event, can be adjusted by p T - and η -dependentcorrection factors for the efficiencies of the selection and isolation requirements on each corre-sponding lepton. These correction factors map the efficiencies observed in the embedded eventsamples to the efficiencies observed in data. For electrons and muons they are obtained froma comparison of ee ( µµ ) selected events on the e ( µ )-embedded event samples with the sameevent selection on data, using the “tag-and-probe” method [14]. They are provided as individ-ual correction factors for the lepton identification and isolation efficiency, and the correspond-ing leg of the triggers used in the target analyses. The estimate of the reconstruction efficiencyis included in the identification efficiency.For the identification efficiency of the τ h candidate, a global correction factor of 0.97 ± → τ τ events in the µτ h final state in a con-trol region. Figure 11 shows typical correction factors for the electron and muon identificationand isolation efficiencies in the central region of the detector, as function of the p T of the cor-responding lepton. Clear turn-on curves are visible for the muon isolation and the electronidentification and isolation efficiencies. In each case, a plateau is reached for each efficiencyabove a p T threshold of about 30 GeV, which is close to the 80% efficiency working point dis-cussed in Section 3 for the electron identification, and close to unity otherwise. In general, the correction factors differ from the efficiencies observed in data by less than 5% in the relevantkinematic regions, and they are smaller for the embedded event samples than for the simulatedones. I den t i f i c a t i on E ff i c i en cy Observed (simulation) mm fi Z (embedded) mm fi Z : [0,0.9] h Muon ID, (GeV) m T p
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20 30 100 200 1000 ob s e r v ed R a t i o t o (2017, 13 TeV) -1 CMS I s o l a t i on E ff i c i en cy Observed (simulation) mm fi Z (embedded) mm fi Z : [0,0.9] h < 0.15, m rel I (GeV) m T p
20 30 100 200 1000 ob s e r v ed R a t i o t o (2017, 13 TeV) -1 CMS I s o l a t i on E ff i c i en cy Observed ee (simulation) fi Z ee (embedded) fi Z : [0,1] h < 0.10, erel I (GeV) eT p
20 30 100 200 1000 ob s e r v ed R a t i o t o (2017, 13 TeV) -1 CMS
Figure 11: (Left column) muon and (right column) electron (upper row) identification and(lower row) isolation efficiencies as a function of the p T of the corresponding lepton in thecentral region of the detector. The black arrows indicate typical trigger thresholds of the targetanalyses. In the upper panel of each subfigure, the black dots correspond to the efficiencies ob-tained in data, the blue dots to the efficiencies obtained in the corresponding embedded eventsample, and the red dots to the efficiencies obtained from the simulation. The lower panelsshow the ratios of the (blue) embedded event sample and (red) simulation, to the efficiencyobserved in data, which corresponds to the correction factors. When applied to the target analyses, the following uncertainties, which are specific to themethod, should be applied: • For the normalization of the τ -embedded event samples, a global uncertainty of 2%should be assumed due to the insufficient knowledge of the unfolding corrections ofthe initially selected muons, as described in Section 5.1. The 2% is chosen in accor- .3 Comparison to data dance to the usual uncertainty in trigger leg efficiencies. This uncertainty should beapplied per muon, resulting in an overall uncertainty in the normalization of 4%. • For the simulated leptons, a variation of 1.2% in the τ h energy scale, split by decaymode, as described in Ref. [46], should be applied; a variation in the electron energyscale of 1% in the central detector and 2.5% in the endcaps of the ECAL should beapplied. • The uncertainty in the expected fraction of tt ( τ τ ) events in the embedded eventsamples is estimated from a 10% up and down variation of the expected fractionin simulation. The estimate is based on a study in a tt-enriched control region. Itincludes the uncertainty in the number of tt events that do not contain muons in thefinal state (as given in Table 1) and a general uncertainty in the tt event yield in theselected kinematic regime. • The uncertainties in the correction factors for the trigger leg, identification, and iso-lation efficiencies are usually of the order of 2% in the kinematic regions relevant forthe target analyses, which include the uncertainty in the removal of the energy de-posits of the selected muon that primarily affects the isolation efficiency for muons. • The effects of the finite angular and p T resolutions of the detector are checked andhave negligible influence on the τ -embedded events. They are covered by the varia-tion in the τ h and electron energy scale given above. This is also true for a variationof p missT within the observed discrepancies visible in Fig. 5 (middle left).These uncertainties are usually a part of a more complex uncertainty model such as describedin Ref. [46]. For the simulated processes that are replaced by the τ -embedded event sample,they replace uncertainties in the integrated luminosity, production cross sections, jet energyscale, p missT scale and resolution, as well as in the tagging and mistag rates of b jets. To demonstrate how the embedding technique can help in a physics analysis on data, an inclu-sive event selection is performed for the τ τ final states following typical selection requirements,as detailed in Ref. [46].The online selection for the e µ final state relies on a logical or of two lower-threshold triggersthat both require the presence of an electron and a muon in the event with p T >
23 GeV for thehigher- p T lepton and p T > ( ) GeV for the lower- p T electron (muon).In the offline selection of the e µ final state, an electron with p T >
13 GeV and | η | < p T > | η | < p T trigger object is required to have a p T >
24 GeV, whichguarantees a trigger acceptance well above the turn-on of at least one of the triggers used.Both leptons are required to pass identification criteria and to be isolated according to I e ( µ ) rel < ( ) . Events with additional electrons or muons fulfilling looser selection requirementsthan these are rejected.The e τ h ( µτ h ) final state is based on the presence of at least one electron (muon) with p T > ( ) GeV and | η | < p T > ( ) GeV and | η | < τ h candidate with p T >
30 GeV and | η | < τ h candidate must fulfill the identification requirements describedin Section 3. The τ h candidate is required to pass the tight working point of the τ h identificationdiscriminant, the tight (very loose) working point of the discriminant to suppress electrons andthe loose (tight) working point of the discriminant to suppress muons in the e τ h ( µτ h ) case. In addition, the electron (muon) is required to be isolated, according to I e ( µ ) rel < ( ) . Eventswith additional electrons or muons fulfilling looser selection requirements are rejected.In the τ h τ h final state, a trigger decision based on the presence of two hadronically decayingtau leptons with p T >
35 GeV and | η | < τ h candidates with p T >
40 GeV and | η | < τ h iden-tification discriminant, the very loose working point of the discriminant against electrons andthe loose working point of the discriminant against muons. Events with additional electrons ormuons fulfilling looser requirements on identification, isolation, and p T than described for thee τ h or µτ h final state above are rejected.In all cases, the decay products of the two tau leptons are required to be oppositely charged,separated by more than 0.5 units in ∆ R , and associated with the PV within a distance of0.045 cm in the transverse plane for electrons and muons and 0.2 cm along the beam axis forall final-state particles. The vetoing of additional electrons or muons ensures that no event isused for more than one τ τ final state. At most 0.8% of the selected events contain more τ h can-didates than required for the corresponding final state. In this case, the τ τ pair with the mostisolated final state products is chosen. In the e τ h and µτ h final state, the events are furtherselected according to the transverse mass, m e ( µ ) T = (cid:113) p e ( µ ) T p missT ( − cos ∆ φ ) , (3)where p e ( µ ) T refers to the p T of the electron (muon) and ∆ φ to the difference in the azimuthalangle between the electron (muon) momentum and (cid:126) p missT . In the e µ final state the events arefurther selected according to the event variable D ζ = p miss ζ − p vis ζ ; p miss ζ = (cid:126) p missT · ˆ ζ ; p vis ζ = (cid:0) (cid:126) p eT + (cid:126) p µ T (cid:1) · ˆ ζ , (4)where (cid:126) p e ( µ ) T corresponds to the transverse momentum vector of the electron (muon) and ˆ ζ to the bisectional direction between the electron and the muon momenta in the transverseplane [47]. Events with m e ( µ ) T <
40 GeV and − < D ζ <
30 GeV are used for further con-sideration in each corresponding final state. Both m e ( µ ) T and D ζ quantify the size of p missT andhow aligned it is with the momenta of the selected leptons. They are typical event variables todistinguish genuine τ τ events from W + jets and tt events.In Fig.12, the distributions of p missT , D ζ , m eT , and m µ T are shown. In addition to the expectationusing the τ -embedded event samples, the overall expectation when using the simulation ofZ → τ τ , tt ( τ τ ) , and diboson( τ τ ) events is shown by an open histogram in the upper panel ofthe subfigures. For this comparison a series of corrections have been applied to the simulation,including a correction to match the pileup distribution in data, a reweighting of the Z boson p T distribution of the LO simulation to that in Z → µµ events observed in data, corrections forthe electron and muon legs of the corresponding trigger paths, and for the electron and muonidentification and isolation, and corrections of the Z boson recoil, to mitigate differences in de-tector resolution, between the simulation and data, for the calculation of p missT . For τ -embeddedevents the corrections related to simulated leptons are applied, discussed in Section 7.1. A gen-erally good agreement between the expectation and the data is observed, within the applieduncertainty model. A better agreement is found when using the τ -embedded event samplesinstead of the simulation. Fluctuations in the distributions of m eT and m µ T originate mostly fromthe limited size of the sample of simulated W + jets events. In the target analyses, a large frac-tion of W + jets and QCD multijet events are usually estimated from data, which implies that .3 Comparison to data Figure 12: Distributions of (upper left) p missT in the µτ h final state, (upper right) D ζ in the e µ final state, (lower left) m eT in the e τ h final state, and (lower right) m µ T in the µτ h final state. Thedistributions are shown prior to the maximum likelihood fit described in the text. For thesefigures, no uncertainties that affect the shape of the distributions have been included in theuncertainty model. The background estimation purely from the CMS simulation is shown asan additional red line.a fraction of up to 90% of the typical background expectation for the target analyses can beestimated from data.In the target analyses the distributions of a variable related to the invariant mass of the τ τ sys-tem are usually used as input for a maximum likelihood fit to extract the actual signal. Thissignal can be an SM process, such as the SM Z or Higgs boson production in the τ τ final state,or any other process of physics beyond the SM. In Fig. 13 the distributions of m vis , as such a vari-able, in the e µ , e τ h , µτ h , and τ h τ h final states are shown, after the event selections, as describedabove, and after applying a maximum likelihood fit to the observation with the τ -embeddedevent sample as signal. For this purpose, a likelihood model has been adapted from Ref. [46]. Itincorporates O ( ) uncertainties in form of nuisance parameters that may be correlated acrossthe processes contributing to the input distributions, and across final states. Within a single in-put distribution the nuisance parameters may allow for correlated shifts across bins, such as process normalization or energy scale uncertainties, and for shifts of individual bins, within thestatistical precision of the template distributions used in the model. The ability of the model todescribe the data can be quantified using a goodness-of-fit test, based on a saturated likelihoodmodel (SAT) described in Ref. [48], which corresponds to a generalization of a χ test includ-ing all systematic uncertainties of the model and their correlations. The SAT test indicates theoverall statistical compatibility of the model with the observation, treating each bin of the in-put distributions as an independent measurement. Goodness-of-fit tests based on the empiricaldistribution function are usually more sensitive than a χ -like test to small deviations that arecorrelated across several bins of a single histogram. A classical test of this kind that is mostlysensitive to deviations correlated across bins in the center of a given binned distribution is theKolmogorov–Smirnov (KS) test [49, 50]. A variant of this test that gives more emphasis to theedges of the given input distribution is the Anderson–Darling (AD) test [51]. The p -values foreach of these tests, split by final state, are shown in Table 3. They have a one-to-one correspon-Figure 13: Invariant mass distribution of the visible τ τ decay products, m vis , in the (upperleft) e µ , (upper right) e τ h , (lower left) µτ h , and (lower right) τ h τ h final states, after a fit to thedata exploiting a typical uncertainty model as discussed in Ref. [46]. In the e τ h final state asignificantly larger contribution of Z → (cid:96)(cid:96) events is visible compared to the µτ h final state.The reason for this is that high- p T electrons have a higher probability to be misidentified as τ h decays than muons. dence to the distributions shown in Fig. 13, with the small difference that these distributionsare shown for a fit to the observation in all final states combined. The p -values are obtainedfrom the comparison of the observed value for the corresponding test statistic with the out-come of pseudo-experiments based on the expectation. Their statistical precision is better than0.5%. The actual values range from 17%, for the p -value of the AD test in the e τ h final state,to 82%, for the p -value of the AD test in the µτ h final state. All tests reveal good compatibilityof the statistical model with the observation, which implies a successful description of the datawith the given template distributions, especially with the τ -embedded event samples. The fitto the observation in all final states combined reveals a p -value of 51% and a normalizationof 1.00 ± for the τ -embedded event samples, which is in good agreement with the observa-tions of Ref. [46] that have been made on an independent data set. Also a good compatibilityof the normalization across all final states is observed. The normalization of the τ -embeddedsamples is obtained from the data. Figures showing distributions of more quantities relevantfor the analysis of τ τ events are given in Appendix A.Table 3: Normalization of the τ -embedded event samples and p -values of the saturated model(SAT), Kolmogorov–Smirnov (KS) and Anderson–Darling (AD) test, as discussed in the text,separated by τ τ final state, as introduced in Section 5 and (where applicable) for all channelscombined. The p -values have a statistical precision better than 0.5%. p -valuesFinal state Normalization SAT KS ADe µ ± τ h ± µτ h ± τ h τ h ± ± The τ -embedding technique developed for the CMS experiment is described and its valida-tion and relevant uncertainties are discussed. The 13 TeV proton-proton collisions collected byCMS in 2017 are used to demonstrate the performance of the technique with the data samplecorresponding to an integrated luminosity of 41.5 fb − .The main goal of the procedure is to estimate the background from Z → τ τ events usingrecorded Z → µµ events. The estimate also includes events from tt and diboson productionwith two tau leptons in the final state. Recorded µµ events are selected, the muons are removedfrom the reconstructed event record, and replaced with simulated tau leptons with the samekinematic properties as the removed muons. In that way hybrid events are obtained, which relyon the simulation only for the decay of the tau leptons. Challenges in describing the underly-ing event or the production of associated jets in the simulation, as well as the costly simulationof PU events thus are avoided. The embedding technique decreases the uncertainties inherentin a typical simulation process, such as the uncertainties in the missing transverse momentum,jet energy scale and resolution, b tagging efficiency, and misidentification probability.A number of validation tests for µ -, e- , and τ -embedding, as well as several goodness-of-fittests, show good agreement of embedded distributions with those obtained using simulatedand recorded data events. The embedding technique avoids time-consuming simulations of events that becomes critical for the planned High-Luminosity LHC upgrade, where typicalpileup of 140–200 collisions per bunch crossing is expected. 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, weacknowledge the enduring support for the construction and operation of the LHC and the CMSdetector provided by the following funding agencies: the Austrian Federal Ministry of Educa-tion, Science and Research and the Austrian Science Fund; the Belgian Fonds de la RechercheScientifique, and Fonds voor Wetenschappelijk Onderzoek; the Brazilian Funding Agencies(CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP); the Bulgarian Ministry of Education andScience; CERN; the Chinese Academy of Sciences, Ministry of Science and Technology, and Na-tional Natural Science Foundation of China; the Colombian Funding Agency (COLCIENCIAS);the Croatian Ministry of Science, Education and Sport, and the Croatian Science Foundation;the Research Promotion Foundation, Cyprus; the Secretariat for Higher Education, Science,Technology and Innovation, Ecuador; the Ministry of Education and Research, Estonian Re-search Council via IUT23-4 and IUT23-6 and European Regional Development Fund, Estonia;the Academy of Finland, Finnish Ministry of Education and Culture, and Helsinki Institute ofPhysics; the Institut National de Physique Nucl´eaire et de Physique des Particules / CNRS, andCommissariat `a l’ ´Energie Atomique et aux ´Energies Alternatives / CEA, France; the Bundes-ministerium f ¨ur Bildung und Forschung, Deutsche Forschungsgemeinschaft, and Helmholtz-Gemeinschaft Deutscher Forschungszentren, Germany; the General Secretariat for Researchand Technology, Greece; the National Research, Development and Innovation Fund, Hungary;the Department of Atomic Energy and the Department of Science and Technology, India; theInstitute for Studies in Theoretical Physics and Mathematics, Iran; the Science Foundation, Ire-land; the Istituto Nazionale di Fisica Nucleare, Italy; the Ministry of Science, ICT and FuturePlanning, and National Research Foundation (NRF), Republic of Korea; the Ministry of Educa-tion and Science of the Republic of Latvia; the Lithuanian Academy of Sciences; the Ministryof Education, and University of Malaya (Malaysia); the Ministry of Science of Montenegro; theMexican Funding Agencies (BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI); theMinistry of Business, Innovation and Employment, New Zealand; the Pakistan Atomic EnergyCommission; the Ministry of Science and Higher Education and the National Science Center,Poland; the Fundac¸ ˜ao para a Ciˆencia e a Tecnologia, Portugal; JINR, Dubna; the Ministry ofEducation and Science of the Russian Federation, the Federal Agency of Atomic Energy of theRussian Federation, Russian Academy of Sciences, the Russian Foundation for Basic Research,and the National Research Center “Kurchatov Institute”; the Ministry of Education, Scienceand Technological Development of Serbia; the Secretar´ıa de Estado de Investigaci ´on, Desar-rollo e Innovaci ´on, Programa Consolider-Ingenio 2010, Plan Estatal de Investigaci ´on Cient´ıficay T´ecnica y de Innovaci ´on 2013-2016, Plan de Ciencia, Tecnolog´ıa e Innovaci ´on 2013-2017 delPrincipado de Asturias, and Fondo Europeo de Desarrollo Regional, Spain; the Ministry ofScience, Technology and Research, Sri Lanka; the Swiss Funding Agencies (ETH Board, ETHZurich, PSI, SNF, UniZH, Canton Zurich, and SER); the Ministry of Science and Technology,Taipei; the Thailand Center of Excellence in Physics, the Institute for the Promotion of Teach-ing Science and Technology of Thailand, Special Task Force for Activating Research and theNational Science and Technology Development Agency of Thailand; the Scientific and Techni- cal Research Council of Turkey, and Turkish Atomic Energy Authority; the National Academyof Sciences of Ukraine, and State Fund for Fundamental Researches, Ukraine; the Science andTechnology Facilities Council, UK; the US Department of Energy, and the US National ScienceFoundation.Individuals have received support from the Marie-Curie program and the European ResearchCouncil and Horizon 2020 Grant, contract No. 675440 (European Union); the Leventis Foun-dation; the A. P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Fed-eral Science Policy Office; the Fonds pour la Formation `a la Recherche dans l’Industrie et dansl’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technolo-gie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the “Excellence of Science -EOS” - be.h project n. 30820817; the Ministry of Education, Youth and Sports (MEYS) of theCzech Republic; the Lend ¨ulet (“Momentum”) Program and the J´anos Bolyai Research Schol-arship of the Hungarian Academy of Sciences, the New National Excellence Program ´UNKP,the NKFIA research grants 123842, 123959, 124845, 124850 and 125105 (Hungary); the Councilof Scientific and Industrial Research, India; the HOMING PLUS program of the Foundationfor Polish Science, cofinanced from European Union, Regional Development Fund, the Mo-bility Plus program of the Ministry of Science and Higher Education, the National ScienceCenter (Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543,2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406; theNational Priorities Research Program by Qatar National Research Fund; the Programa de Ex-celencia Mar´ıa de Maeztu, and the Programa Severo Ochoa del Principado de Asturias; theThalis and Aristeia programs cofinanced by EU-ESF, and the Greek NSRF; the RachadapisekSompot Fund for Postdoctoral Fellowship, Chulalongkorn University, and the ChulalongkornAcademic into Its 2nd Century Project Advancement Project (Thailand); the Welch Foundation,contract C-1845; and the Weston Havens Foundation (USA). 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Figure 15: Distributions of the (left) jet and (right) b jet multiplicity, as described in the text, inthe µτ h final state. Figure 16: Distributions of the p T of the (left) leading and (right) trailing jet for events withmore than one jet in the µτ h final state. B The CMS Collaboration
Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan, A. Tumasyan
Institut f ¨ur Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, E. Asilar, T. Bergauer, J. Brandstetter, M. Dragicevic, J. Er ¨o,A. Escalante Del Valle, M. Flechl, R. Fr ¨uhwirth , V.M. Ghete, J. Hrubec, M. Jeitler , N. Krammer,I. Kr¨atschmer, D. Liko, T. Madlener, I. Mikulec, N. Rad, H. Rohringer, J. Schieck , R. Sch ¨ofbeck,M. Spanring, D. Spitzbart, W. Waltenberger, J. Wittmann, C.-E. Wulz , M. Zarucki Institute for Nuclear Problems, Minsk, Belarus
V. Chekhovsky, V. Mossolov, J. Suarez Gonzalez
Universiteit Antwerpen, Antwerpen, Belgium
E.A. De Wolf, D. Di Croce, X. Janssen, J. Lauwers, A. Lelek, M. Pieters, H. Van Haevermaet,P. Van Mechelen, N. Van Remortel
Vrije Universiteit Brussel, Brussel, Belgium
F. Blekman, J. D’Hondt, J. De Clercq, K. Deroover, G. Flouris, D. Lontkovskyi, S. Lowette,I. Marchesini, S. Moortgat, L. Moreels, Q. Python, K. Skovpen, S. Tavernier, W. Van Doninck,P. Van Mulders, I. Van Parijs
Universit´e Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, B. Dorney,G. Fasanella, L. Favart, A. Grebenyuk, A.K. Kalsi, J. Luetic, A. Popov , N. Postiau, E. Starling,L. Thomas, C. Vander Velde, P. Vanlaer, D. Vannerom, Q. Wang Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, A. Fagot, M. Gul, I. Khvastunov , C. Roskas, D. Trocino, M. Tytgat,W. Verbeke, B. Vermassen, M. Vit, N. Zaganidis Universit´e Catholique de Louvain, Louvain-la-Neuve, Belgium
H. Bakhshiansohi, O. Bondu, G. Bruno, C. Caputo, P. David, C. Delaere, M. Delcourt,A. Giammanco, G. Krintiras, V. Lemaitre, A. Magitteri, K. Piotrzkowski, A. Saggio,M. Vidal Marono, P. Vischia, J. Zobec
Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
F.L. Alves, G.A. Alves, G. Correia Silva, C. Hensel, A. Moraes, M.E. Pol, P. Rebello Teles
Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato , E. Coelho, E.M. Da Costa,G.G. Da Silveira , D. De Jesus Damiao, C. De Oliveira Martins, S. Fonseca De Souza,L.M. Huertas Guativa, H. Malbouisson, D. Matos Figueiredo, M. Melo De Almeida,C. Mora Herrera, L. Mundim, H. Nogima, W.L. Prado Da Silva, L.J. Sanchez Rosas, A. Santoro,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 S. Ahuja a , C.A. Bernardes a , L. Calligaris a , T.R. Fernandez Perez Tomei a , E.M. Gregores b ,P.G. Mercadante b , S.F. Novaes a , SandraS. Padula a Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia,Bulgaria
A. Aleksandrov, R. Hadjiiska, P. Iaydjiev, A. Marinov, M. Misheva, M. Rodozov, M. Shopova,G. Sultanov University of Sofia, Sofia, Bulgaria
A. Dimitrov, L. Litov, B. Pavlov, P. Petkov
Beihang University, Beijing, China
W. Fang , X. Gao , L. Yuan Institute of High Energy Physics, Beijing, China
M. Ahmad, J.G. Bian, G.M. Chen, H.S. Chen, M. Chen, Y. Chen, C.H. Jiang, D. Leggat, H. Liao,Z. Liu, S.M. Shaheen , A. Spiezia, J. Tao, E. Yazgan, H. Zhang, S. Zhang , J. Zhao State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing, China
Y. Ban, G. Chen, A. Levin, J. Li, L. Li, Q. Li, Y. Mao, S.J. Qian, D. Wang
Tsinghua University, Beijing, China
Y. Wang
Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C.A. Carrillo Montoya, L.F. Chaparro Sierra, C. Florez,C.F. Gonz´alez Hern´andez, M.A. Segura Delgado
University of Split, Faculty of Electrical Engineering, Mechanical Engineering and NavalArchitecture, Split, Croatia
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, K. Kadija, B. Mesic, M. Roguljic, A. Starodumov , T. Susa University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, M. Kolosova, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos,P.A. Razis, H. Rykaczewski
Charles University, Prague, Czech Republic
M. Finger , M. Finger Jr. 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
A. Ellithi Kamel , M.A. Mahmoud , E. Salama 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, H. Kirschenmann, J. Pekkanen, M. Voutilainen Helsinki Institute of Physics, Helsinki, Finland
J. Havukainen, J.K. Heikkil¨a, T. J¨arvinen, V. Karim¨aki, R. Kinnunen, T. Lamp´en, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lind´en, P. Luukka, T. M¨aenp¨a¨a, H. Siikonen, E. Tuominen,J. Tuominiemi
Lappeenranta University of Technology, Lappeenranta, Finland
T. Tuuva
IRFU, CEA, Universit´e Paris-Saclay, Gif-sur-Yvette, France
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, C. Leloup, E. Locci, J. Malcles, G. Negro, J. Rander,A. Rosowsky, M. ¨O. Sahin, M. Titov
Laboratoire Leprince-Ringuet, Ecole polytechnique, CNRS/IN2P3, Universit´e Paris-Saclay,Palaiseau, France
A. Abdulsalam , C. Amendola, I. Antropov, F. Beaudette, P. Busson, C. Charlot, B. Diab,R. Granier de Cassagnac, I. Kucher, A. Lobanov, J. Martin Blanco, C. Martin Perez,M. Nguyen, C. Ochando, G. Ortona, P. Paganini, J. Rembser, R. Salerno, J.B. Sauvan, Y. Sirois,A.G. Stahl Leiton, 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, V. Cherepanov,C. Collard, E. Conte , J.-C. Fontaine , D. Gel´e, U. Goerlach, M. Jansov´a, A.-C. Le Bihan,N. Tonon, P. Van Hove Centre de Calcul de l’Institut National de Physique Nucleaire et de Physique des Particules,CNRS/IN2P3, Villeurbanne, France
S. Gadrat
Universit´e de Lyon, Universit´e Claude Bernard Lyon 1, CNRS-IN2P3, Institut de PhysiqueNucl´eaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, N. Chanon, R. Chierici, D. Contardo, P. Depasse,H. El Mamouni, J. Fay, S. Gascon, M. Gouzevitch, G. Grenier, B. Ille, F. Lagarde, I.B. Laktineh,H. Lattaud, M. Lethuillier, L. Mirabito, S. Perries, V. Sordini, G. Touquet, M. Vander Donckt,S. Viret
Georgian Technical University, Tbilisi, Georgia
A. Khvedelidze Tbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze RWTH Aachen University, I. Physikalisches Institut, Aachen, Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, M. Preuten, M.P. Rauch,C. Schomakers, J. Schulz, M. Teroerde, B. Wittmer
RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany
A. Albert, M. Erdmann, S. Erdweg, T. Esch, R. Fischer, S. Ghosh, T. Hebbeker, C. Heidemann,K. Hoepfner, H. Keller, L. Mastrolorenzo, M. Merschmeyer, A. Meyer, P. Millet, S. Mukherjee,A. Novak, T. Pook, A. Pozdnyakov, M. Radziej, H. Reithler, M. Rieger, A. Schmidt, D. Teyssier,S. Th ¨uer
RWTH Aachen University, III. Physikalisches Institut B, Aachen, Germany
G. Fl ¨ugge, O. Hlushchenko, T. Kress, T. M ¨uller, A. Nehrkorn, A. Nowack, C. Pistone, O. Pooth,D. Roy, H. Sert, A. Stahl Deutsches Elektronen-Synchrotron, Hamburg, Germany
M. Aldaya Martin, T. Arndt, C. Asawatangtrakuldee, I. Babounikau, K. Beernaert, O. Behnke,U. Behrens, A. Berm ´udez Mart´ınez, D. Bertsche, A.A. Bin Anuar, K. Borras , V. Botta,A. Campbell, P. Connor, C. Contreras-Campana, V. Danilov, A. De Wit, M.M. Defranchis,C. Diez Pardos, D. Dom´ınguez Damiani, G. Eckerlin, T. Eichhorn, A. Elwood, E. Eren,E. Gallo , A. Geiser, J.M. Grados Luyando, A. Grohsjean, M. Guthoff, M. Haranko, A. Harb,H. Jung, M. Kasemann, J. Keaveney, C. Kleinwort, J. Knolle, D. Kr ¨ucker, W. Lange, T. Lenz,J. Leonard, K. Lipka, W. Lohmann , R. Mankel, I.-A. Melzer-Pellmann, A.B. Meyer, M. Meyer,M. Missiroli, G. Mittag, J. Mnich, V. Myronenko, S.K. Pflitsch, D. Pitzl, A. Raspereza, A. Saibel,M. Savitskyi, P. Saxena, P. Sch ¨utze, C. Schwanenberger, R. Shevchenko, A. Singh, H. Tholen,O. Turkot, A. Vagnerini, M. Van De Klundert, G.P. Van Onsem, R. Walsh, Y. Wen, K. Wichmann,C. Wissing, O. Zenaiev University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, V. Blobel, T. Dreyer, A. Ebrahimi, E. Garutti,D. Gonzalez, P. Gunnellini, J. Haller, A. Hinzmann, A. Karavdina, G. Kasieczka, R. Klanner,R. Kogler, N. Kovalchuk, S. Kurz, V. Kutzner, J. Lange, D. Marconi, J. Multhaup, M. Niedziela,C.E.N. Niemeyer, D. Nowatschin, A. Perieanu, A. Reimers, O. Rieger, C. Scharf, P. Schleper,S. Schumann, J. Schwandt, J. Sonneveld, H. Stadie, G. Steinbr ¨uck, F.M. Stober, M. St ¨over,B. Vormwald, I. Zoi
Karlsruher Institut fuer Technologie, Karlsruhe, Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, J. Bechtel, S. Brommer, E. Butz, R. Caspart,T. Chwalek, F. Colombo, W. De Boer, A. Dierlamm, K. El Morabit, N. Faltermann, B. Freund,M. Giffels, A. Gottmann, M.A. Harrendorf, F. Hartmann , S.M. Heindl, U. Husemann,I. Katkov , S. Kudella, S. Mitra, M.U. Mozer, Th. M ¨uller, M. Musich, M. Plagge, G. Quast,K. Rabbertz, M. Schr ¨oder, I. Shvetsov, H.J. Simonis, R. Ulrich, S. Wayand, M. Weber, T. Weiler,C. W ¨ohrmann, R. Wolf Institute of Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia Paraskevi,Greece
G. Anagnostou, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas, G. Paspalaki
National and Kapodistrian University of Athens, Athens, Greece
A. Agapitos, G. Karathanasis, P. Kontaxakis, A. Panagiotou, I. Papavergou, N. Saoulidou,K. Vellidis
National Technical University of Athens, Athens, Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis
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, E. Paradas, J. Strologas, F.A. Triantis, D. Tsitsonis
MTA-ELTE Lend ¨ulet CMS Particle and Nuclear Physics Group, E ¨otv ¨os Lor´and University,Budapest, Hungary
M. Bart ´ok , M. Csanad, N. Filipovic, P. Major, K. Mandal, A. Mehta, M.I. Nagy, G. Pasztor,O. Sur´anyi, G.I. Veres Wigner Research Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath , ´A. Hunyadi, F. Sikler, T. ´A. V´ami, V. Veszpremi,G. Vesztergombi † Institute of Nuclear Research ATOMKI, Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi , A. Makovec, J. Molnar, Z. Szillasi Institute of Physics, University of Debrecen, Debrecen, Hungary
P. Raics, Z.L. Trocsanyi, B. Ujvari
Indian Institute of Science (IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri, P.C. Tiwari
National Institute of Science Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati , C. Kar, P. Mal, A. Nayak , S. Roy Chowdhury, D.K. Sahoo , S.K. Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, R. Chawla, N. Dhingra, R. Gupta, A. Kaur,M. Kaur, S. Kaur, P. Kumari, M. Lohan, M. Meena, K. Sandeep, S. Sharma, J.B. Singh, A.K. Virdi,G. Walia
University of Delhi, Delhi, India
A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, Ashok Kumar, S. Malhotra,M. Naimuddin, P. Priyanka, K. Ranjan, Aashaq Shah, R. Sharma
Saha Institute of Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj , M. Bharti , R. Bhattacharya, S. Bhattacharya, U. Bhawandeep , D. Bhowmik,S. Dey, S. Dutt , S. Dutta, S. Ghosh, M. Maity , K. Mondal, S. Nandan, A. Purohit, P.K. Rout,A. Roy, G. Saha, S. Sarkar, T. Sarkar , M. Sharan, B. Singh , S. Thakur Indian Institute of Technology Madras, Madras, India
P.K. Behera, A. Muhammad
Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, D.K. Mishra, P.K. Netrakanti, L.M. Pant, P. Shukla,P. Suggisetti
Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, G.B. Mohanty, N. Sur, RavindraKumar Verma
Tata Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, Sa. Jain, S. Karmakar, S. Kumar,G. Majumder, K. Mazumdar, N. Sahoo
Indian Institute of Science Education and Research (IISER), Pune, India
S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, A. Rastogi,S. Sharma
Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
S. Chenarani , E. Eskandari Tadavani, S.M. Etesami , M. Khakzad, M. Mohammadi Na-jafabadi, M. Naseri, F. Rezaei Hosseinabadi, B. Safarzadeh , M. Zeinali 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 , C. Calabria a , b , A. Colaleo a , D. Creanza a , c , L. Cristella a , b , N. De Filippis a , c ,M. De Palma a , b , A. Di Florio a , b , F. Errico a , b , L. Fiore a , A. Gelmi a , b , G. Iaselli a , c , M. Ince a , b ,S. Lezki a , b , G. Maggi a , c , M. Maggi a , G. Miniello a , b , S. My a , b , S. Nuzzo a , b , A. Pompili a , b , G. Pugliese a , c , R. Radogna a , A. Ranieri a , G. Selvaggi a , b , A. Sharma a , L. Silvestris a , 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 , S.S. Chhibra a , b , G. Codispoti a , b ,M. Cuffiani a , b , G.M. Dallavalle a , F. Fabbri a , A. Fanfani a , b , E. Fontanesi, P. Giacomelli a ,C. Grandi a , L. Guiducci a , b , F. Iemmi a , b , S. Lo Meo a ,29 , S. Marcellini a , G. Masetti a , A. Montanari 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 ,30 , A. Di Mattia a , R. Potenza a , b , A. Tricomi a , b ,30 , C. Tuve a , b INFN Sezione di Firenze a , Universit`a di Firenze b , Firenze, Italy G. Barbagli a , K. Chatterjee a , b , V. Ciulli a , b , C. Civinini a , R. D’Alessandro a , b , E. Focardi a , b ,G. Latino, P. Lenzi a , b , M. Meschini a , S. Paoletti a , L. Russo a ,31 , G. Sguazzoni a , D. Strom a ,L. Viliani a INFN Laboratori Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, F. Fabbri, D. Piccolo
INFN Sezione di Genova a , Universit`a di Genova b , Genova, Italy 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 b , F. Brivio a , b , V. Ciriolo a , b ,16 , S. Di Guida a , b ,16 , M.E. Dinardo a , b ,S. Fiorendi a , b , S. Gennai a , A. Ghezzi a , b , P. Govoni a , b , M. Malberti a , b , S. Malvezzi a , D. Menasce a ,F. Monti, L. Moroni a , M. Paganoni a , b , D. Pedrini a , S. Ragazzi a , b , T. Tabarelli de Fatis a , b ,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 , A. Di Crescenzo a , b , F. Fabozzi a , c , F. Fienga a ,G. Galati a , A.O.M. Iorio a , b , L. Lista a , S. Meola a , d ,16 , P. Paolucci a ,16 , 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 , D. Bisello a , b , A. Boletti a , b , A. Bragagnolo, R. Carlin a , b , P. Checchia a ,M. Dall’Osso a , b , P. De Castro Manzano a , T. Dorigo a , U. Dosselli a , F. Gasparini a , b ,U. Gasparini a , b , A. Gozzelino a , S.Y. Hoh, S. Lacaprara a , P. Lujan, M. Margoni a , b ,A.T. Meneguzzo a , b , J. Pazzini a , b , M. Presilla b , P. Ronchese a , b , R. Rossin a , b , F. Simonetto a , b ,A. Tiko, E. Torassa a , M. Tosi a , b , M. Zanetti a , b , P. Zotto a , b , G. Zumerle a , b INFN Sezione di Pavia a , Universit`a di Pavia b , Pavia, Italy A. Braghieri a , A. Magnani a , 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 , b , P. Vitulo a , b INFN Sezione di Perugia a , Universit`a di Perugia b , Perugia, Italy M. Biasini a , b , G.M. Bilei a , C. Cecchi a , b , D. Ciangottini a , b , L. Fan `o a , b , P. Lariccia a , b , R. Leonardi a , b ,E. Manoni a , G. Mantovani a , b , V. Mariani a , b , M. Menichelli a , A. Rossi a , b , A. Santocchia a , b ,D. Spiga a INFN Sezione di Pisa a , Universit`a di Pisa b , Scuola Normale Superiore di Pisa c , Pisa, Italy K. Androsov a , P. Azzurri a , G. Bagliesi a , L. Bianchini a , T. Boccali a , L. Borrello, R. Castaldi a ,M.A. Ciocci a , b , R. Dell’Orso a , G. Fedi a , F. Fiori a , c , L. Giannini a , c , A. Giassi a , M.T. Grippo a ,5
INFN Sezione di Genova a , Universit`a di Genova b , Genova, Italy 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 b , F. Brivio a , b , V. Ciriolo a , b ,16 , S. Di Guida a , b ,16 , M.E. Dinardo a , b ,S. Fiorendi a , b , S. Gennai a , A. Ghezzi a , b , P. Govoni a , b , M. Malberti a , b , S. Malvezzi a , D. Menasce a ,F. Monti, L. Moroni a , M. Paganoni a , b , D. Pedrini a , S. Ragazzi a , b , T. Tabarelli de Fatis a , b ,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 , A. Di Crescenzo a , b , F. Fabozzi a , c , F. Fienga a ,G. Galati a , A.O.M. Iorio a , b , L. Lista a , S. Meola a , d ,16 , P. Paolucci a ,16 , 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 , D. Bisello a , b , A. Boletti a , b , A. Bragagnolo, R. Carlin a , b , P. Checchia a ,M. Dall’Osso a , b , P. De Castro Manzano a , T. Dorigo a , U. Dosselli a , F. Gasparini a , b ,U. Gasparini a , b , A. Gozzelino a , S.Y. Hoh, S. Lacaprara a , P. Lujan, M. Margoni a , b ,A.T. Meneguzzo a , b , J. Pazzini a , b , M. Presilla b , P. Ronchese a , b , R. Rossin a , b , F. Simonetto a , b ,A. Tiko, E. Torassa a , M. Tosi a , b , M. Zanetti a , b , P. Zotto a , b , G. Zumerle a , b INFN Sezione di Pavia a , Universit`a di Pavia b , Pavia, Italy A. Braghieri a , A. Magnani a , 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 , b , P. Vitulo a , b INFN Sezione di Perugia a , Universit`a di Perugia b , Perugia, Italy M. Biasini a , b , G.M. Bilei a , C. Cecchi a , b , D. Ciangottini a , b , L. Fan `o a , b , P. Lariccia a , b , R. Leonardi a , b ,E. Manoni a , G. Mantovani a , b , V. Mariani a , b , M. Menichelli a , A. Rossi a , b , A. Santocchia a , b ,D. Spiga a INFN Sezione di Pisa a , Universit`a di Pisa b , Scuola Normale Superiore di Pisa c , Pisa, Italy K. Androsov a , P. Azzurri a , G. Bagliesi a , L. Bianchini a , T. Boccali a , L. Borrello, R. Castaldi a ,M.A. Ciocci a , b , R. Dell’Orso a , G. Fedi a , F. Fiori a , c , L. Giannini a , c , A. Giassi a , M.T. Grippo a ,5 F. Ligabue a , c , E. Manca a , c , G. Mandorli a , c , A. Messineo a , b , F. Palla a , A. Rizzi a , b , G. Rolandi ,P. Spagnolo a , R. Tenchini a , G. Tonelli a , b , A. Venturi a , P.G. Verdini a INFN Sezione di Roma a , Sapienza Universit`a di Roma b , Rome, Italy L. Barone a , b , F. Cavallari a , M. Cipriani a , b , D. Del Re a , b , E. Di Marco a , b , M. Diemoz a , S. Gelli a , b ,E. Longo a , b , B. Marzocchi a , b , P. Meridiani a , G. Organtini a , b , F. Pandolfi a , R. Paramatti a , b ,F. Preiato a , b , S. Rahatlou a , b , C. Rovelli a , F. Santanastasio 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 ,C. Biino a , A. Cappati a , b , N. Cartiglia a , F. Cenna a , b , S. Cometti a , M. Costa a , b , R. Covarelli a , b ,N. Demaria a , B. Kiani a , b , 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 , L. Pacher a , b , N. Pastrone a , M. Pelliccioni a ,G.L. Pinna Angioni a , b , A. Romero a , b , M. Ruspa a , c , R. Sacchi a , b , R. Salvatico a , b , K. Shchelina a , b ,V. Sola a , A. Solano a , b , D. Soldi a , b , A. Staiano a 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 , A. Zanetti a Kyungpook National University, Daegu, Korea
D.H. Kim, G.N. Kim, M.S. Kim, J. Lee, S.W. Lee, C.S. Moon, Y.D. Oh, S.I. Pak, S. Sekmen,D.C. Son, Y.C. Yang
Chonnam National University, Institute for Universe and Elementary Particles, Kwangju,Korea
H. Kim, D.H. Moon, G. Oh
Hanyang University, Seoul, Korea
B. Francois, J. Goh , T.J. Kim Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, Y. Jo, K. Lee, K.S. Lee, S. Lee, J. Lim, S.K. Park,Y. Roh
Sejong University, Seoul, Korea
H.S. Kim
Seoul National University, Seoul, Korea
J. Almond, J. Kim, J.S. Kim, H. Lee, K. Lee, S. Lee, K. Nam, S.B. Oh, B.C. Radburn-Smith,S.h. Seo, U.K. Yang, H.D. Yoo, G.B. Yu
University of Seoul, Seoul, Korea
D. Jeon, H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park
Sungkyunkwan University, Suwon, Korea
Y. Choi, C. Hwang, J. Lee, I. Yu
Riga Technical University, Riga, Latvia
V. Veckalns Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, J. Vaitkus National Centre for Particle Physics, Universiti Malaya, Kuala Lumpur, Malaysia
Z.A. Ibrahim, M.A.B. Md Ali , F. Mohamad Idris , 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
Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
H. Castilla-Valdez, E. De La Cruz-Burelo, M.C. Duran-Osuna, I. Heredia-De La Cruz ,R. Lopez-Fernandez, J. Mejia Guisao, R.I. Rabadan-Trejo, G. Ramirez-Sanchez, R. Reyes-Almanza, 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 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. Ahmad, M.I. Asghar, Q. Hassan, H.R. Hoorani, W.A. Khan, M.A. Shah,M. Shoaib, M. Waqas
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. Misiura,M. Olszewski, A. Pyskir, M. Walczak Laborat ´orio de Instrumenta¸c˜ao e F´ısica Experimental de Part´ıculas, Lisboa, Portugal
M. Araujo, P. Bargassa, C. Beir˜ao Da Cruz E Silva, A. Di Francesco, P. Faccioli, B. Galinhas,M. Gallinaro, J. Hollar, N. Leonardo, J. Seixas, G. Strong, O. Toldaiev, J. Varela
Joint Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov, A. Kamenev, V. Karjavine,A. Lanev, A. Malakhov, V. Matveev , P. Moisenz, V. Palichik, V. Perelygin, S. Shmatov,S. Shulha, N. Skatchkov, V. Smirnov, N. Voytishin, A. Zarubin
Petersburg Nuclear Physics Institute, Gatchina (St. Petersburg), Russia
V. Golovtsov, Y. Ivanov, V. Kim , E. Kuznetsova , P. Levchenko, V. Murzin, V. Oreshkin,I. Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Vavilov, A. Vorobyev Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov, N. Krasnikov,A. Pashenkov, A. Shabanov, D. Tlisov, A. Toropin Institute for Theoretical and Experimental Physics, Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, V. Popov, I. Pozdnyakov, G. Safronov,A. Spiridonov, A. Stepennov, V. Stolin, 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
M. Chadeeva , P. Parygin, 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. Belyaev, E. Boos, V. Bunichev, M. Dubinin , L. Dudko, A. Ershov, A. Gribushin, V. Klyukhin,O. Kodolova, I. Lokhtin, S. Obraztsov, M. Perfilov, V. Savrin Novosibirsk State University (NSU), Novosibirsk, Russia
A. Barnyakov , V. Blinov , T. Dimova , L. Kardapoltsev , Y. Skovpen Institute for High Energy Physics of National Research Centre ’Kurchatov Institute’,Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, V. Kachanov, A. Kalinin, D. Konstantinov, P. Mandrik,V. Petrov, R. Ryutin, S. Slabospitskii, A. Sobol, S. Troshin, N. Tyurin, A. Uzunian, A. Volkov
National Research Tomsk Polytechnic University, Tomsk, Russia
A. Babaev, S. Baidali, V. Okhotnikov
University of Belgrade: Faculty of Physics and VINCA Institute of Nuclear Sciences
P. Adzic , P. Cirkovic, D. Devetak, M. Dordevic, P. Milenovic , J. Milosevic Centro de Investigaciones Energ´eticas Medioambientales y Tecnol ´ogicas (CIEMAT),Madrid, Spain
J. Alcaraz Maestre, A. ´Alvarez Fern´andez, I. Bachiller, M. Barrio Luna, J.A. Brochero Cifuentes,M. Cerrada, N. Colino, B. De La Cruz, A. Delgado Peris, C. Fernandez Bedoya,J.P. Fern´andez Ramos, J. Flix, M.C. Fouz, O. Gonzalez Lopez, S. Goy Lopez, J.M. Hernandez,M.I. Josa, D. Moran, A. P´erez-Calero Yzquierdo, J. Puerta Pelayo, I. Redondo, L. Romero,S. S´anchez Navas, M.S. Soares, A. Triossi
Universidad Aut ´onoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Troc ´oniz
Universidad de Oviedo, Oviedo, Spain
J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras, I. Gonzalez Caballero,J.R. Gonz´alez Fern´andez, E. Palencia Cortezon, V. Rodr´ıguez Bouza, S. Sanchez Cruz,J.M. Vizan Garcia
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, A. Garc´ıa Alonso, J. Garcia-Ferrero, G. Gomez, A. Lopez Virto,J. Marco, C. Martinez Rivero, P. Martinez Ruiz del Arbol, F. Matorras, J. Piedra Gomez,C. Prieels, T. Rodrigo, A. Ruiz-Jimeno, L. Scodellaro, N. Trevisani, I. Vila, R. Vilar Cortabitarte University of Ruhuna, Department of Physics, Matara, Sri Lanka
N. Wickramage
CERN, European Organization for Nuclear Research, Geneva, Switzerland
D. Abbaneo, B. Akgun, E. Auffray, G. Auzinger, P. Baillon, A.H. Ball, D. Barney, J. Bendavid,M. Bianco, A. Bocci, C. Botta, E. Brondolin, T. Camporesi, M. Cepeda, G. Cerminara, E. Chapon,Y. Chen, G. Cucciati, D. d’Enterria, A. Dabrowski, N. Daci, V. Daponte, A. David, A. De Roeck,N. Deelen, M. Dobson, M. D ¨unser, N. Dupont, A. Elliott-Peisert, F. Fallavollita , D. Fasanella,G. Franzoni, J. Fulcher, W. Funk, D. Gigi, A. Gilbert, K. Gill, F. Glege, M. Gruchala, M. Guilbaud,D. Gulhan, J. Hegeman, C. Heidegger, Y. Iiyama, V. Innocente, G.M. Innocenti, A. Jafari,P. Janot, O. Karacheban , J. Kieseler, A. Kornmayer, M. Krammer , C. Lange, P. Lecoq,C. Lourenc¸o, L. Malgeri, M. Mannelli, A. Massironi, F. Meijers, J.A. Merlin, S. Mersi, E. Meschi,F. Moortgat, M. Mulders, J. Ngadiuba, S. Nourbakhsh, S. Orfanelli, L. Orsini, F. Pantaleo ,L. Pape, E. Perez, M. Peruzzi, A. Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, F.M. Pitters,D. Rabady, A. Racz, M. Rovere, H. Sakulin, C. Sch¨afer, C. Schwick, M. Selvaggi, A. Sharma,P. Silva, P. Sphicas , A. Stakia, J. Steggemann, D. Treille, A. Tsirou, A. Vartak, M. Verzetti,W.D. Zeuner Paul Scherrer Institut, Villigen, Switzerland
L. Caminada , K. Deiters, W. Erdmann, R. Horisberger, Q. Ingram, H.C. Kaestli, D. Kotlinski,U. Langenegger, T. Rohe, S.A. Wiederkehr ETH Zurich - Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland
M. Backhaus, L. B¨ani, P. Berger, N. Chernyavskaya, G. Dissertori, M. Dittmar, M. Doneg`a,C. Dorfer, T.A. G ´omez Espinosa, C. Grab, D. Hits, T. Klijnsma, W. Lustermann, R.A. Manzoni,M. Marionneau, M.T. Meinhard, F. Micheli, P. Musella, F. Nessi-Tedaldi, F. Pauss,G. Perrin, L. Perrozzi, S. Pigazzini, M. Reichmann, C. Reissel, D. Ruini, D.A. Sanz Becerra,M. Sch ¨onenberger, L. Shchutska, V.R. Tavolaro, K. Theofilatos, M.L. Vesterbacka Olsson,R. Wallny, D.H. Zhu
Universit¨at Z ¨urich, Zurich, Switzerland
T.K. Aarrestad, C. Amsler , D. Brzhechko, M.F. Canelli, A. De Cosa, R. Del Burgo, S. Donato,C. Galloni, T. Hreus, B. Kilminster, S. Leontsinis, V.M. Mikuni, I. Neutelings, G. Rauco,P. Robmann, D. Salerno, K. Schweiger, C. Seitz, Y. Takahashi, S. Wertz, A. Zucchetta National Central University, Chung-Li, Taiwan
T.H. Doan, C.M. Kuo, W. Lin, S.S. Yu
National Taiwan University (NTU), Taipei, Taiwan
P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.F. Liu, R.-S. Lu, E. Paganis, A. Psallidas,A. Steen
Chulalongkorn University, Faculty of Science, Department of Physics, Bangkok, Thailand
B. Asavapibhop, N. Srimanobhas, N. Suwonjandee
C¸ ukurova University, Physics Department, Science and Art Faculty, Adana, Turkey
M.N. Bakirci , A. Bat, F. Boran, S. Damarseckin, Z.S. Demiroglu, F. Dolek, C. Dozen,I. Dumanoglu, G. Gokbulut, EmineGurpinar Guler , Y. Guler, I. Hos , C. Isik, E.E. Kangal ,O. Kara, A. Kayis Topaksu, U. Kiminsu, M. Oglakci, G. Onengut, K. Ozdemir , A. Polatoz,B. Tali , U.G. Tok, H. Topakli , S. Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle East Technical University, Physics Department, Ankara, Turkey
B. Isildak , G. Karapinar , M. Yalvac, M. Zeyrek Bogazici University, Istanbul, Turkey
I.O. Atakisi, E. G ¨ulmez, M. Kaya , O. Kaya , ¨O. ¨Ozc¸elik, S. Ozkorucuklu , S. Tekten,E.A. Yetkin Istanbul Technical University, Istanbul, Turkey
M.N. Agaras, A. Cakir, K. Cankocak, Y. Komurcu, S. Sen 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
F. Ball, J.J. Brooke, D. Burns, E. Clement, D. Cussans, O. Davignon, H. Flacher, J. Goldstein,G.P. Heath, H.F. Heath, L. Kreczko, D.M. Newbold , S. Paramesvaran, B. Penning, T. Sakuma,D. Smith, V.J. Smith, J. Taylor, A. Titterton Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev , C. Brew, R.M. Brown, D. Cieri, D.J.A. Cockerill, J.A. Coughlan,K. Harder, S. Harper, J. Linacre, K. Manolopoulos, E. Olaiya, D. Petyt, T. Reis, T. Schuh,C.H. Shepherd-Themistocleous, A. Thea, I.R. Tomalin, T. Williams, W.J. Womersley Imperial College, London, United Kingdom
R. Bainbridge, P. Bloch, J. Borg, S. Breeze, O. Buchmuller, A. Bundock, D. Colling, P. Dauncey,G. Davies, M. Della Negra, R. Di Maria, P. Everaerts, G. Hall, G. Iles, T. James, M. Komm,C. Laner, L. Lyons, A.-M. Magnan, S. Malik, A. Martelli, V. Milosevic, J. Nash , A. Nikitenko ,V. Palladino, M. Pesaresi, D.M. Raymond, A. Richards, A. Rose, E. Scott, C. Seez, A. Shtipliyski,G. Singh, M. Stoye, T. Strebler, S. Summers, A. Tapper, K. Uchida, T. Virdee , N. Wardle,D. Winterbottom, J. Wright, S.C. Zenz Brunel University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, C.K. Mackay, A. Morton, I.D. Reid, L. Teodorescu,S. Zahid
Baylor University, Waco, USA
K. Call, J. Dittmann, K. Hatakeyama, H. Liu, C. Madrid, B. McMaster, N. Pastika, C. Smith
Catholic University of America, Washington, DC, USA
R. Bartek, A. Dominguez
The University of Alabama, Tuscaloosa, USA
A. Buccilli, O. Charaf, S.I. Cooper, C. Henderson, P. Rumerio, C. West
Boston University, Boston, USA
D. Arcaro, T. Bose, Z. Demiragli, D. Gastler, S. Girgis, D. Pinna, C. Richardson, J. Rohlf,D. Sperka, I. Suarez, L. Sulak, D. Zou
Brown University, Providence, USA
G. Benelli, B. Burkle, X. Coubez, D. Cutts, M. Hadley, J. Hakala, U. Heintz, J.M. Hogan ,K.H.M. Kwok, E. Laird, G. Landsberg, J. Lee, Z. Mao, M. Narain, S. Sagir , R. Syarif, E. Usai,D. Yu University of California, Davis, Davis, USA
R. Band, C. Brainerd, R. Breedon, D. Burns, M. Calderon De La Barca Sanchez, M. Chertok, J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk, W. Ko, O. Kukral, R. Lander,M. Mulhearn, D. Pellett, J. Pilot, S. Shalhout, M. Shi, D. Stolp, D. Taylor, K. Tos, M. Tripathi,Z. Wang, F. Zhang
University of California, Los Angeles, USA
M. Bachtis, C. Bravo, R. Cousins, A. Dasgupta, A. Florent, J. Hauser, M. Ignatenko, N. Mccoll,S. Regnard, D. Saltzberg, C. Schnaible, V. Valuev
University of California, Riverside, Riverside, USA
E. Bouvier, K. Burt, R. Clare, J.W. Gary, S.M.A. Ghiasi Shirazi, G. Hanson, G. Karapostoli,E. Kennedy, F. Lacroix, O.R. Long, M. Olmedo Negrete, M.I. Paneva, W. Si, L. Wang, H. Wei,S. Wimpenny, B.R. Yates
University of California, San Diego, La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, M. Derdzinski, R. Gerosa, D. Gilbert, B. Hashemi,A. Holzner, D. Klein, G. Kole, V. Krutelyov, J. Letts, M. Masciovecchio, S. May, D. Olivito,S. Padhi, M. Pieri, V. Sharma, M. Tadel, J. Wood, F. W ¨urthwein, A. Yagil, G. Zevi Della Porta
University of California, Santa Barbara - Department of Physics, Santa Barbara, USA
N. Amin, R. Bhandari, C. Campagnari, M. Citron, V. Dutta, M. Franco Sevilla, L. Gouskos,R. Heller, J. Incandela, H. Mei, A. Ovcharova, H. Qu, J. Richman, D. Stuart, S. Wang, J. Yoo
California Institute of Technology, Pasadena, USA
D. Anderson, A. Bornheim, J.M. Lawhorn, N. Lu, H.B. Newman, T.Q. Nguyen, J. Pata,M. Spiropulu, J.R. Vlimant, R. Wilkinson, S. Xie, Z. Zhang, R.Y. Zhu
Carnegie Mellon University, Pittsburgh, USA
M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, M. Sun, I. Vorobiev, M. Weinberg
University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, F. Jensen, A. Johnson, E. MacDonald, T. Mulholland, R. Patel, A. Perloff,K. Stenson, K.A. Ulmer, S.R. Wagner
Cornell University, Ithaca, USA
J. Alexander, J. Chaves, Y. Cheng, J. Chu, A. Datta, K. Mcdermott, N. Mirman, J. Monroy,J.R. Patterson, D. Quach, A. Rinkevicius, A. Ryd, L. Skinnari, L. Soffi, S.M. Tan, Z. Tao, J. Thom,J. Tucker, 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, J. Berryhill, P.C. Bhat, K. Burkett, J.N. Butler, A. Canepa,G.B. Cerati, H.W.K. Cheung, F. Chlebana, M. Cremonesi, J. Duarte, V.D. Elvira, J. Freeman,Z. Gecse, E. Gottschalk, L. Gray, D. Green, S. Gr ¨unendahl, O. Gutsche, J. Hanlon, R.M. Harris,S. Hasegawa, J. Hirschauer, Z. Hu, B. Jayatilaka, S. Jindariani, M. Johnson, U. Joshi, B. Klima,M.J. Kortelainen, B. Kreis, S. Lammel, D. Lincoln, R. Lipton, M. Liu, T. Liu, J. Lykken,K. Maeshima, J.M. Marraffino, D. Mason, P. McBride, P. Merkel, S. Mrenna, S. Nahn, V. O’Dell,K. Pedro, C. Pena, O. Prokofyev, G. Rakness, F. Ravera, A. Reinsvold, L. Ristori, A. Savoy-Navarro , B. Schneider, E. Sexton-Kennedy, A. Soha, W.J. Spalding, L. Spiegel, S. Stoynev,J. Strait, N. Strobbe, L. Taylor, S. Tkaczyk, N.V. Tran, L. Uplegger, E.W. Vaandering, C. Vernieri,M. Verzocchi, R. Vidal, M. Wang, H.A. Weber University of Florida, Gainesville, USA
D. Acosta, P. Avery, P. Bortignon, D. Bourilkov, A. Brinkerhoff, L. Cadamuro, A. Carnes, D. Curry, R.D. Field, S.V. Gleyzer, B.M. Joshi, J. Konigsberg, A. Korytov, K.H. Lo, P. Ma,K. Matchev, N. Menendez, G. Mitselmakher, D. Rosenzweig, K. Shi, J. Wang, S. Wang, X. Zuo
Florida International University, Miami, USA
Y.R. Joshi, S. Linn
Florida State University, Tallahassee, USA
A. Ackert, T. Adams, A. Askew, S. Hagopian, V. Hagopian, K.F. Johnson, R. Khurana,T. Kolberg, G. Martinez, T. Perry, H. Prosper, A. Saha, C. Schiber, R. Yohay
Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, V. Bhopatkar, S. Colafranceschi, M. Hohlmann, D. Noonan, M. Rahmani,T. Roy, M. Saunders, F. Yumiceva
University of Illinois at Chicago (UIC), Chicago, USA
M.R. Adams, L. Apanasevich, D. Berry, R.R. Betts, R. Cavanaugh, X. Chen, S. Dittmer,O. Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, K. Jung, J. Kamin, C. Mills, M.B. Tonjes,N. Varelas, H. Wang, X. Wang, Z. Wu, J. Zhang
The University of Iowa, Iowa City, USA
M. Alhusseini, B. Bilki , W. Clarida, 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 Johns Hopkins University, Baltimore, USA
B. Blumenfeld, A. Cocoros, N. Eminizer, D. Fehling, L. Feng, A.V. Gritsan, W.T. Hung,P. Maksimovic, J. Roskes, U. Sarica, M. Swartz, M. Xiao
The University of Kansas, Lawrence, USA
A. Al-bataineh, P. Baringer, A. Bean, S. Boren, J. Bowen, A. Bylinkin, J. Castle, S. Khalil,A. Kropivnitskaya, D. Majumder, W. Mcbrayer, M. Murray, C. Rogan, S. Sanders, E. Schmitz,J.D. Tapia Takaki, Q. Wang
Kansas State University, Manhattan, USA
S. Duric, A. Ivanov, K. Kaadze, D. Kim, Y. Maravin, D.R. Mendis, T. Mitchell, A. Modak,A. Mohammadi
Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright
University of Maryland, College Park, USA
A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, C. Ferraioli, N.J. Hadley, S. Jabeen, G.Y. Jeng,R.G. Kellogg, J. Kunkle, A.C. Mignerey, S. Nabili, F. Ricci-Tam, M. Seidel, Y.H. Shin, A. Skuja,S.C. Tonwar, K. Wong
Massachusetts Institute of Technology, Cambridge, USA
D. Abercrombie, B. Allen, V. Azzolini, A. Baty, R. Bi, S. Brandt, W. Busza, I.A. Cali,M. D’Alfonso, G. Gomez Ceballos, M. Goncharov, P. Harris, D. Hsu, M. Hu, M. Klute,D. Kovalskyi, 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, B. Wyslouch
University of Minnesota, Minneapolis, USA
A.C. Benvenuti † , R.M. Chatterjee, A. Evans, P. Hansen, J. Hiltbrand, Sh. Jain, S. Kalafut,M. Krohn, Y. Kubota, Z. Lesko, J. Mans, R. Rusack, M.A. Wadud University of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros
University of Nebraska-Lincoln, Lincoln, USA
E. Avdeeva, K. Bloom, D.R. Claes, C. Fangmeier, L. Finco, F. Golf, R. Gonzalez Suarez,R. Kamalieddin, I. Kravchenko, J.E. Siado, G.R. Snow, B. Stieger
State University of New York at Buffalo, Buffalo, USA
A. Godshalk, C. Harrington, I. Iashvili, A. Kharchilava, C. Mclean, D. Nguyen, A. Parker,S. Rappoccio, B. Roozbahani
Northeastern University, Boston, USA
G. Alverson, E. Barberis, C. Freer, Y. Haddad, A. Hortiangtham, G. Madigan, D.M. Morse,T. Orimoto, A. Tishelman-charny, T. Wamorkar, B. Wang, A. Wisecarver, D. Wood
Northwestern University, Evanston, USA
S. Bhattacharya, J. Bueghly, T. Gunter, K.A. Hahn, N. Odell, M.H. Schmitt, K. Sung, M. Trovato,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, F. Meng, C. Mueller, Y. Musienko , M. Planer,R. Ruchti, P. Siddireddy, G. Smith, S. Taroni, M. Wayne, A. Wightman, M. Wolf, A. Woodard The Ohio State University, Columbus, USA
J. Alimena, L. Antonelli, B. Bylsma, L.S. Durkin, S. Flowers, B. Francis, C. Hill, W. Ji, A. Lefeld,T.Y. Ling, W. Luo, B.L. Winer
Princeton University, Princeton, USA
S. Cooperstein, G. Dezoort, P. Elmer, J. Hardenbrook, N. Haubrich, S. Higginbotham,A. Kalogeropoulos, S. Kwan, D. Lange, M.T. Lucchini, J. Luo, D. Marlow, K. Mei, I. Ojalvo,J. Olsen, C. Palmer, P. Pirou´e, J. Salfeld-Nebgen, D. Stickland, C. Tully
University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg
Purdue University, West Lafayette, USA
A. Barker, V.E. Barnes, S. Das, L. Gutay, M. Jones, A.W. Jung, A. Khatiwada, B. Mahakud,D.H. Miller, N. Neumeister, C.C. Peng, S. Piperov, H. Qiu, J.F. Schulte, J. Sun, F. Wang, R. Xiao,W. Xie
Purdue University Northwest, Hammond, USA
T. Cheng, J. Dolen, N. Parashar
Rice University, Houston, USA
Z. Chen, K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Kilpatrick, Arun Kumar, W. Li, B.P. Padley,R. Redjimi, J. Roberts, J. Rorie, W. Shi, Z. Tu, A. Zhang
University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, Y.t. Duh, J.L. Dulemba, C. Fallon, T. Ferbel, M. Galanti,A. Garcia-Bellido, J. Han, O. Hindrichs, A. Khukhunaishvili, E. Ranken, P. Tan, R. Taus
Rutgers, The State University of New Jersey, Piscataway, USA
B. Chiarito, J.P. Chou, Y. Gershtein, E. Halkiadakis, A. Hart, M. Heindl, E. Hughes, S. Kaplan,R. Kunnawalkam Elayavalli, S. Kyriacou, I. Laflotte, A. Lath, R. Montalvo, K. Nash, M. Osherson, H. Saka, S. Salur, S. Schnetzer, D. Sheffield, S. Somalwar, R. Stone, S. Thomas,P. Thomassen
University of Tennessee, Knoxville, USA
H. Acharya, A.G. Delannoy, J. Heideman, G. Riley, S. Spanier
Texas A&M University, College Station, USA
O. Bouhali , A. Celik, M. Dalchenko, M. De Mattia, A. Delgado, S. Dildick, R. Eusebi,J. Gilmore, T. Huang, T. Kamon , S. Luo, D. Marley, R. Mueller, D. Overton, L. Perni`e,D. Rathjens, A. Safonov Texas Tech University, Lubbock, USA
N. Akchurin, J. Damgov, F. De Guio, P.R. Dudero, 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
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, Q. Xu
University of Virginia, Charlottesville, USA
M.W. Arenton, P. Barria, B. Cox, R. Hirosky, M. Joyce, A. Ledovskoy, H. Li, C. Neu, Y. Wang,E. Wolfe, F. Xia
Wayne State University, Detroit, USA
R. Harr, P.E. Karchin, N. Poudyal, J. Sturdy, P. Thapa, S. Zaleski
University of Wisconsin - Madison, Madison, WI, USA
J. Buchanan, C. Caillol, D. Carlsmith, S. Dasu, I. De Bruyn, L. Dodd, B. Gomber , M. Grothe,M. Herndon, A. Herv´e, U. Hussain, P. Klabbers, A. Lanaro, K. Long, R. Loveless, T. Ruggles,A. Savin, V. Sharma, N. Smith, W.H. Smith, N. Woods † : Deceased1: Also at Vienna University of Technology, Vienna, Austria2: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University,Moscow, Russia3: Also at IRFU, CEA, Universit´e Paris-Saclay, Gif-sur-Yvette, France4: Also at Universidade Estadual de Campinas, Campinas, Brazil5: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil6: Also at Universit´e Libre de Bruxelles, Bruxelles, Belgium7: Also at University of Chinese Academy of Sciences, Beijing, China8: Also at Institute for Theoretical and Experimental Physics, Moscow, Russia9: Also at Joint Institute for Nuclear Research, Dubna, Russia10: Now at Cairo University, Cairo, Egypt11: Also at Fayoum University, El-Fayoum, Egypt12: Now at British University in Egypt, Cairo, Egypt13: Now at Ain Shams University, Cairo, Egypt14: Also at Department of Physics, King Abdulaziz University, Jeddah, Saudi Arabia15: Also at Universit´e de Haute Alsace, Mulhouse, France16: Also at CERN, European Organization for Nuclear Research, Geneva, Switzerland17: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany18: Also at University of Hamburg, Hamburg, Germany19: Also at Brandenburg University of Technology, Cottbus, Germany20: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary
21: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary22: Also at MTA-ELTE Lend ¨ulet CMS Particle and Nuclear Physics Group, E ¨otv ¨os Lor´andUniversity, Budapest, Hungary23: Also at Indian Institute of Technology Bhubaneswar, Bhubaneswar, India24: Also at Institute of Physics, Bhubaneswar, India25: Also at Shoolini University, Solan, India26: Also at University of Visva-Bharati, Santiniketan, India27: Also at Isfahan University of Technology, Isfahan, Iran28: Also at Plasma Physics Research Center, Science and Research Branch, Islamic AzadUniversity, Tehran, Iran29: Also at ITALIAN NATIONAL AGENCY FOR NEW TECHNOLOGIES, ENERGY ANDSUSTAINABLE ECONOMIC DEVELOPMENT, Bologna, Italy30: Also at CENTRO SICILIANO DI FISICA NUCLEARE E DI STRUTTURA DELLAMATERIA, Catania, Italy31: Also at Universit`a degli Studi di Siena, Siena, Italy32: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy33: Also at Kyung Hee University, Department of Physics, Seoul, Korea34: Also at Riga Technical University, Riga, Latvia35: Also at International Islamic University of Malaysia, Kuala Lumpur, Malaysia36: Also at Malaysian Nuclear Agency, MOSTI, Kajang, Malaysia37: Also at Consejo Nacional de Ciencia y Tecnolog´ıa, Mexico City, Mexico38: Also at Warsaw University of Technology, Institute of Electronic Systems, Warsaw, Poland39: Also at Institute for Nuclear Research, Moscow, Russia40: Now at National Research Nuclear University ’Moscow Engineering Physics Institute’(MEPhI), Moscow, Russia41: Also at St. Petersburg State Polytechnical University, St. Petersburg, Russia42: Also at University of Florida, Gainesville, USA43: Also at P.N. Lebedev Physical Institute, Moscow, Russia44: Also at California Institute of Technology, Pasadena, USA45: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia46: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia47: Also at University of Belgrade, Belgrade, Serbia48: Also at INFN Sezione di Pavia a , Universit`a di Pavia b , Pavia, Italy49: Also at National and Kapodistrian University of Athens, Athens, Greece50: Also at Universit¨at Z ¨urich, Zurich, Switzerland51: Also at Stefan Meyer Institute for Subatomic Physics (SMI), Vienna, Austria52: Also at Gaziosmanpasa University, Tokat, Turkey53: Also at Beykent University, Istanbul, Turkey54: Also at Istanbul Aydin University, Istanbul, Turkey55: Also at Mersin University, Mersin, Turkey56: Also at Piri Reis University, Istanbul, Turkey57: Also at Adiyaman University, Adiyaman, Turkey58: Also at Ozyegin University, Istanbul, Turkey59: Also at Izmir Institute of Technology, Izmir, Turkey60: Also at Marmara University, Istanbul, Turkey61: Also at Kafkas University, Kars, Turkey62: Also at Istanbul University, Faculty of Science, Istanbul, Turkey63: Also at Istanbul Bilgi University, Istanbul, Turkey64: Also at Hacettepe University, Ankara, Turkey5