Evidence for Higgs boson decay to a pair of muons
EEUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH (CERN)
CERN-EP-2020-1642020/09/10
CMS-HIG-19-006
Evidence for Higgs boson decay to a pair of muons
The CMS Collaboration ∗ Abstract
Evidence for Higgs boson decay to a pair of muons is presented. This result combinessearches in four exclusive categories targeting the production of the Higgs boson viagluon fusion, via vector boson fusion, in association with a vector boson, and in as-sociation with a top quark-antiquark pair. The analysis is performed using proton-proton collision data at √ s =
13 TeV, corresponding to an integrated luminosity of137 fb − , recorded by the CMS experiment at the CERN LHC. An excess of eventsover the background expectation is observed in data with a significance of 3.0 stan-dard deviations, where the expectation for the standard model (SM) Higgs boson withmass of 125.38 GeV is 2.5. The combination of this result with that from data recordedat √ s = − ,respectively, increases both the expected and observed significances by 1%. The mea-sured signal strength, relative to the SM prediction, is 1.19 + − (stat) + − (syst). Thisresult constitutes the first evidence for the decay of the Higgs boson to second gener-ation fermions and is the most precise measurement of the Higgs boson coupling tomuons reported to date. Submitted to the Journal of High Energy Physics c (cid:13) ∗ See Appendix A for the list of collaboration members a r X i v : . [ h e p - e x ] S e p Since the discovery of the Higgs boson at the CERN LHC in 2012 [1–3], various measurementsof its interactions with standard model (SM) particles have been performed. The interactionsof the Higgs boson with the electroweak gauge bosons and charged fermions belonging to thethird generation of the SM have been observed, with coupling strengths consistent with theSM predictions [4–17]. The Yukawa couplings of the Higgs boson to fermions of the first andsecond generation, however, have yet to be established experimentally. The SM predicts thatthe strengths of the couplings of the Higgs boson to fermions are proportional to the fermionmasses [18–21]. Consequently, the branching fractions of the Higgs boson to fermions of thefirst and second generation are expected to be small, and their measurement at hadron collidersis challenging. The expected branching fraction for the decay of the Higgs boson with massof 125 GeV to a pair of muons is B ( H → µ + µ − ) = × − [22]. The study of H → µ + µ − decays is of particular importance since it is the most experimentally sensitive probe of theHiggs boson couplings to second-generation fermions at the LHC.The CMS Collaboration performed a search for H → µ + µ − decays using a combination ofproton-proton (pp) collision data collected at centre-of-mass energies of 7, 8, and 13 TeV, cor-responding to integrated luminosities of 5.0, 19.7, and 35.9 fb − , respectively. An observed(expected in absence of H → µ + µ − decays) upper limit of 2.9 (2.2) times the SM prediction wasset at the 95% confidence level (CL) on the product of the Higgs boson production cross sectionand B ( H → µ + µ − ) [23]. The corresponding signal strength, relative to the SM expectation,was µ = ± → µ + µ − decaysusing 13 TeV pp collision data, corresponding to an integrated luminosity of 139 fb − , resultingin an observed (expected for µ =
0) upper limit at 95% CL of 2.2 (1.1) times the SM predictionand a signal strength µ = ± → µ + µ − decays, obtained using pp collision datacollected by the CMS experiment at √ s =
13 TeV and corresponding to a total integratedluminosity of 137 fb − . The final states considered contain two prompt, isolated, and oppo-sitely charged muons from the Higgs boson decay, with a narrow resonant invariant masspeak around the Higgs boson mass for signal events. The dimuon mass serves as a powerfuldiscriminant against SM background processes. Events are separated into mutually exclusivecategories targeting the main production modes of the Higgs boson at hadron colliders, namelygluon fusion (ggH), vector boson fusion (VBF), associated production with a vector boson (VH,where V = W or Z), and associated production with a top quark-antiquark pair (ttH). Resultsare given for m H = ± ∆ η jj ) and largedijet invariant mass ( m jj ). These characteristic features allow a significant suppression of the DYbackground, providing an expected sensitivity to H → µ + µ − decays that is better than that ofthe ggH category, despite the smaller VBF production cross section. The VH signal events tar-geted by this analysis contain leptonic decays of the W or Z boson. This results in a final statewith three or more charged leptons, with the dominant background from WZ and ZZ events. Finally, the ttH category contains the decays of a top quark-antiquark pair. Events in this cate-gory are therefore characterized by the presence of one or more b quark jets, and may containadditional charged leptons. The dominant backgrounds in the ttH category are the tt and ttZprocesses.This paper is organized as follows: after a brief description of the CMS detector in Section 2,the event reconstruction, simulation, and selection are discussed in Sections 3, 4, and 5, re-spectively. Sections 6, 7, 8, and 9 are dedicated to the description of the four exclusive eventcategories designed to target the VBF, ggH, ttH, and VH production modes, respectively. Fi-nally, Section 10 describes the main results and their combination which are then summarizedin Section 11.
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 out-side the solenoid. Events of interest are selected using a two-tiered trigger system [26]. Thefirst level (L1) is composed of custom hardware processors, which use information from thecalorimeters and muon detectors to select events at a rate of about 100 kHz. The second level,known as high-level trigger (HLT), is a software-based system which runs a version of the CMSfull event reconstruction optimized for fast processing, reducing the event rate to about 1 kHz.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. [27].
The particle-flow (PF) algorithm [28] aims to reconstruct and identify each individual particle(PF candidate) in an event, with an optimized combination of information from the various el-ements of the CMS detector. The energy of photons is obtained from the ECAL measurement.The energy of electrons is determined from a combination of the electron momentum at the pri-mary interaction vertex as determined by the tracker, the energy of the corresponding ECALcluster, and the energy sum of all bremsstrahlung photons spatially compatible with originat-ing from the electron track. The energy of charged hadrons is determined from a combinationof their momentum measured in the tracker and the matching ECAL and HCAL energy de-posits, corrected for the response function of the calorimeters to hadronic showers. The energyof neutral hadrons is obtained from the corresponding corrected ECAL and HCAL energies.Finally, the momentum of muons is obtained from the curvature of the corresponding trackreconstructed in the silicon tracker as well as in the muon system.For each event, hadronic jets are clustered from these reconstructed particles using the infraredand collinear-safe anti- k T algorithm [29, 30] with a distance parameter of R = p T spectrum and detector acceptance. Additional pp interactions within thesame or nearby bunch crossings (pileup) can contribute additional tracks and calorimetric en-ergy depositions to the jet momentum. To mitigate this effect, charged particles identified as originating from pileup vertices are discarded and an offset correction is applied to subtract theremaining contributions from neutral particles. Jet energy corrections are derived from simu-lation to bring, on average, the measured response of jets to that of particle-level jets. In situmeasurements of the momentum balance in dijet, γ +jets, Z+jets, and multijet events are usedto account for any residual differences in jet energy scale between data and simulation. The jetenergy resolution amounts typically to 15–20% at 30 GeV, 10% at 100 GeV, and 5% at 1 TeV [31].Additional selection criteria are applied to each jet to remove those potentially dominated byanomalous contributions from various subdetector components or reconstruction failures [32].The missing transverse momentum vector (cid:126) p missT is computed as the negative vector p T sumof all the PF candidates in an event, and its magnitude is denoted as p missT [33]. The (cid:126) p missT is modified to account for corrections to the energy scale of the reconstructed jets in the event.Events with anomalously high- p missT can arise from a variety of reconstruction failures, detectormalfunctions, or noncollision backgrounds. Such events are rejected by event filters that aredesigned to identify more than 85–90% of the spurious high- p missT events with a mistaggingrate smaller than 0.1% [33].Primary vertices are reconstructed from charged-particle tracks in the event. The candidatevertex with the largest value of the sum of the p of all associated physics objects is taken to bethe primary pp interaction vertex. In this sum, the physics objects are the jets, clustered usingthe jet finding algorithm [29, 30] with the tracks assigned to candidate vertices as inputs, andthe associated p missT , taken as the negative vector p T sum of those jets.Jets originating from b quarks are identified using a deep neural network (DeepCSV) that takesas input tracks displaced from the primary interaction vertex, identified secondary vertices,jet kinematic variables, and information related to the presence of soft leptons in the jet [34].Working points (WPs) that yield either a 1% (medium WP) or a 10% (loose WP) probability ofmisidentifying a light-flavour (udsg) jet with p T >
30 GeV as a b quark jet are used. The corre-sponding average efficiencies for the identification of the hadronization products of a bottomquark as a b quark jet are about 70 and 85%, respectively.Muon candidates, within the geometrical acceptance of the muon detectors ( | η | < | η | < < | η | < p T of all the PF candidatesin a cone of radius R = √ ( ∆ η ) + ( ∆ φ ) = ( ) around the muon (electron) track, where φ is the azimuthal angle in radians, and is corrected for the contribution of neutral particles frompileup interactions [35, 36]. Simulated events from Monte Carlo (MC) event generators for the signal and dominant back-ground processes are used to optimize the analysis strategy, evaluate the acceptance, and assesssystematic uncertainties. The generated events are processed through a detailed simulation ofthe CMS detector based on G
EANT AD G RAPH MC @ NLO v2.4.2 [38] and
POWHEG v2.0 [39–42] MC event generators. In the M AD G RAPH MC @ NLO event generation,up to two additional partons in the final state are included in the matrix element (ME) calcu-lation. The p T distribution of the Higgs boson produced via gluon fusion is then reweightedto match the POWHEG NNLOPS predictions [43, 44]. The VBF, qq → VH, and ttH processes aresimulated with
POWHEG v2.0 [45–47] at NLO precision in QCD. In addition to the four mainproduction modes, the contributions due to Higgs boson production in association with a pairof b quarks (bbH), with a Z boson through gluon fusion (gg → ZH), and with a single topquark and either a W boson (tHW) or a quark (tHq) are also considered. The bbH process issimulated at NLO precision in QCD with
POWHEG , while tHq and tHW (gg → ZH) events aregenerated at leading order (LO) with the M AD G RAPH MC @ NLO ( POWHEG ) generator. Sim-ulated signal events are generated, for each production mode, at m H values of 120, 125, and130 GeV.Expected signal yields are normalized to the production cross sections and B ( H → µ + µ − ) values taken from the recommendations of Ref. [22]. The ggH production cross section iscomputed at next-to-next-to-NLO (N LO) precision in QCD, and at NLO in electroweak (EW)theory [48]. The cross section of Higgs boson production in the VBF [49] and qq → VH [50]modes is calculated at next-to-NLO (NNLO) in QCD, including NLO EW corrections, whilethe ttH cross section is computed at NLO in QCD and EW theory [51, 52]. The bbH, tHq, andtHW cross sections are computed at NLO in QCD without including higher-order EW correc-tions [22, 53, 54]. The H → µ + µ − partial width is computed with HDECAY [55, 56] at NLO inQCD and EW theory.The DY process, which is the main background in the ggH and VBF categories, is simulated atNLO in QCD using the M AD G RAPH MC @ NLO generator with up to two partons in the finalstate at the ME level. The corresponding cross section is calculated with
FEWZ v3.1b2 [57] atNNLO in QCD and NLO accuracy in EW theory. The EW production of a Z boson in associationwith two jets (Zjj-EW) is an important background in the VBF category. This process is simu-lated at LO using the M AD G RAPH MC @ NLO v2.6.5 generator. The WZ, qq → ZZ, and WWprocesses, which constitute the main backgrounds in the VH category, are simulated at NLO inQCD using either the
POWHEG or M AD G RAPH MC @ NLO generators. Their production crosssections are corrected with the NNLO/NLO K factors taken from Refs. [58], [59], and [60]. Thegluon-initiated loop-induced ZZ process (gg → ZZ) is simulated with the
MCFM v7.0 gener-ator [61] at LO and the corresponding production cross section is corrected to match higher-order QCD predictions, following the strategy detailed in Ref. [9]. Minor contributions fromtriboson processes (WWW, WWZ, WZZ, and ZZZ) are also taken into account and are simu-lated at NLO in QCD using the M AD G RAPH MC @ NLO generator. The main backgrounds inthe ttH category involve the production of top quarks. The tt background is simulated withNLO precision in QCD using the
POWHEG generator, and its cross section is obtained from the
TOP ++ v2.0 [62] prediction that includes NNLO corrections in QCD and resummation ofNNLL soft gluon terms. The single top quark processes are simulated at NLO in QCD via ei-ther
POWHEG or M AD G RAPH MC @ NLO and their cross sections are computed, at the sameorder of precision, using
HATHOR [63]. Finally, contributions from the ttZ, ttW, ttWW, tttt,and tZq processes are also considered and are simulated using the M AD G RAPH MC @ NLO generator at NLO precision in QCD. For the simulated samples corresponding to the 2016(2017–2018) data-taking periods, the NNPDF v3.0 (v3.1) NLO (NNLO) parton distributionfunctions (PDFs) are used [64, 65]. For processes simulated at NLO (LO) in QCD with theM AD G RAPH MC @ NLO generator, events from the ME characterized by different parton mul-tiplicities are merged via the FxFx (MLM) prescription [66, 67].The simulated events at the ME level for both signal and background processes, except forZjj-EW production, are interfaced with
PYTHIA v8.2.2 or higher [68] to simulate the showerand hadronization of partons in the initial and final states, along with the underlying event de-scription. The CUETP8M1 tune [69] is used for simulated samples corresponding to the 2016data-taking period, while the CP5 tune [70] is used for the 2017 and 2018 simulated data. Sim-ulated VBF signal events are interfaced with
PYTHIA but, rather than the standard p T -orderedparton shower, the dipole shower is chosen to model the ISR and FSR [71]. The dipole showercorrectly takes into account the structure of the colour flow between incoming and outgoingquark lines, and its predictions are found to be in good agreement with NNLO QCD calcula-tions, as reported in Ref. [72]. In contrast, the parton shower (PS), hadronization, and simula-tion of the underlying event for the Zjj-EW process are performed with the HERWIG ++ (2016simulation) and
HERWIG p T -ordered PYTHIA predictions in the description of the ad-ditional hadronic activity in the rapidity range between the two leading jets [74]. The EE5C [69]and CH3 tunes [75] are used in the
HERWIG ++ and
HERWIG
The analysis is performed using √ s =
13 TeV pp collision data collected by the CMS experi-ment from 2016 to 2018, corresponding to an integrated luminosity of 137 fb − . Signal eventsconsidered in this analysis are expected to contain two prompt isolated muons, regardless ofthe targeted Higgs boson production mode. Events are initially selected by the L1 trigger, re-quiring at least one muon candidate reconstructed in the muon chambers with p T >
22 GeV.Events of interest are selected by the HLT using single muon triggers that have a p T thresholdof 27 (24) GeV for data recorded in 2017 (2016, 2018).After passing the trigger selections, each event is required to contain at least two oppositelycharged muons with p T >
20 GeV, | η | < p T . Muons from the Higgs boson decay satisfy theseidentification and isolation requirements with an average selection efficiency of about 95%. Inaddition, at least one of the two muons is required to have p T > ( ) GeV for data collectedin 2017 (2016, 2018), ensuring nearly 100% trigger efficiency.The sensitivity of this analysis depends primarily on the resolution of the m µµ peak in the signalevents. This resolution depends on the precision with which the muon p T is measured, whichworsens with increasing muon | η | . The relative p T resolution of muons with p T >
20 GeV pass- ing through the barrel region of the detector ( | η | < p T resolution of muons passing through the endcaps of the muon system ( | η | > p T and η using thedecay products of known dilepton resonances, following the method described in Ref. [76]. Insignal events, the Higgs boson decays into a muon pair at the interaction point. Therefore, theprecision of the muon p T measurement can be improved by including the interaction point asan additional constraint in the muon track fit. However, instead of requiring the muon track tocome from the interaction point, an equivalent analytical correction to the muon p T is derivedin simulated Z → µµ events. The corresponding adjustment is proportional (on average) tothe product of the muon p and the minimum distance in the transverse plane between themuon track and the beam position. The resulting improvement in the expected m µµ resolutionin signal events ranges from 3 to 10%, depending on muon p T , η , and the data-taking period.In a nonnegligible fraction of signal events, a muon from the Higgs boson decay radiates aphoton that carries away a significant fraction of the muon momentum. If not taken into ac-count, this worsens the resolution of the dimuon invariant mass ( m µµ ) peak in signal events.Furthermore, if the FSR photon falls in the isolation cone of the corresponding muon candi-date, it can significantly increase the value of the isolation sum, thereby creating an inefficiencyin selecting signal events. Therefore, a procedure is implemented to identify and recover thecontribution of FSR photons similar to that described in Ref. [9]. The FSR recovery is appliedonly to muons with p T >
20 GeV and | η | < p T > | η | < R = ( Σ i p i T ( ∆ R ( γ , i ) < )) / p T ( γ ) < ∆ R ( µ , γ ) / p ( γ ) < p T ( γ ) is the p T of the FSR photon candidate and the index i refers to the PF candidates other than the muon within a cone of R = → Z ( µµ ) γ decays, the ratio between the p T of the FSR photon and that of the associated muon is required to be smaller than 0.4. In thecase of multiple FSR candidates associated with a muon, the candidate with the smallest valueof ∆ R ( µ , γ ) / p ( γ ) is chosen. The momentum of the photon is added to that of the muon andits contribution to the muon isolation sum is ignored. The FSR recovery increases the signalefficiency by about 2% and improves the m µµ resolution by about 3%.In order to maximize the analysis sensitivity, event candidates selected with the requirementsdescribed above are separated into independent and nonoverlapping classes based on the fea-tures of the final state expected from each production mode. Events with b-tagged jets areassigned to the ttH production category, which is further split into the hadronic and leptonicsubclasses by the presence of additional charged leptons ( µ or e) in the final state. Dimuonevents with one (two) additional charged lepton(s) and no b-tagged jets are assigned to theWH (ZH) category. Events with neither additional charged leptons nor b-tagged jets belongto the VBF category if a pair of jets is present with large m jj and ∆ η jj . The remaining untaggedevents, which constitute about 96% of the total sample of dimuon candidate events, belongto the ggH-enriched category. In each production category, multivariate techniques are usedto enhance the discrimination between the expected signal and background contributions byfurther dividing events into several subcategories with different signal-to-background ratios.The measured H → µ + µ − signal is then extracted via a simultaneous maximum-likelihood fitacross all event categories to observables chosen for each category to maximize the overall mea-surement precision. In the following Sections, each production category is presented in orderof decreasing sensitivity. A dimuon event passing the baseline selection detailed in Section 5 is considered in the VBFproduction category if it contains two or more jets, with the p T of the leading jet ( p T ( j ) ) largerthan 35 GeV, the p T of the second-highest p T jet ( p T ( j ) ) greater than 25 GeV, and the | η | ofboth jets less than 4.7. Hadronic jets containing the two identified muons are removed fromthe event. In addition, the two highest p T jets in the event are required to have m jj >
400 GeVand | ∆ η jj | > | η | < p T >
25 GeV and identified as a b quark jet by themedium (loose) WP of the DeepCSV b-tagging algorithm. These requirements suppress the ttand single top quark backgrounds and ensure mutual exclusivity between the VBF and ttHcategories. Moreover, events containing an additional muon (electron) with p T >
20 GeV and | η | < ( ) passing the selection criteria described in Section 9 are discarded. This require-ment ensures no overlap between the analyses targeting VBF and VH production. Selectedevents are further grouped into two independent classes. Events in which the two muons forman invariant mass between 115 and 135 GeV belong to the signal region (VBF-SR), which is en-riched in signal-like events. Events with 110 < m µµ <
115 GeV or 135 < m µµ <
150 GeV belongto the mass sideband region (VBF-SB), which is used as a control region to estimate the back-ground. The VBF-SR is defined to be 20 GeV wide in order to be sensitive to Higgs boson masshypotheses in the range of 120–130 GeV. A summary of the selection criteria used to define theVBF-SB and VBF-SR regions is reported in Table 1.Table 1: Summary of the kinematic selections used to define the VBF-SB and VBF-SR regions.
Observable VBF-SB VBF-SRNumber of loose (medium) b-tagged jets ≤ = = p T >
25 GeV, | η | < ≥ p T ≥
35 GeVDijet mass ( m jj ) ≥
400 GeVPseudorapidity separation ( | ∆ η jj | ) ≥ < m µµ <
115 GeV 115 < m µµ <
135 GeVor 135 < m µµ <
150 GeV
A deep neural network (DNN) multivariate discriminant is trained to distinguish the expectedsignal from background events using kinematic input variables that characterize the signal andthe main background processes in the VBF-SR. The DNN is implemented using
KERAS [77]with
TENSORFLOW [78] as backend. The DNN inputs include six variables associated with theproduction and decay of the dimuon system, namely the m µµ , the per-event uncertainty in themeasured dimuon mass σ ( m µµ ) , the dimuon transverse momentum ( p µµ T ), the dimuon rapidity( y µµ ), and the azimuthal angle ( φ CS ) and the cosine of the polar angle (cos θ CS ) computed in thedimuon Collins–Soper rest frame [79]. The DNN also takes as input a set of variables describingthe properties of the dijet system, namely the full momentum vector of the two highest p T jetsin the event ( p T ( j ) , p T ( j ) , η ( j ) , η ( j ) , φ ( j ) , and φ ( j ) ), m jj , and ∆ η jj . In addition, observablessensitive to angular and p T correlations between muons and jets are also included, namely theminimum ∆ η between the dimuon system and each of the two leading jets, the Zeppenfeldvariable ( z ∗ ) [80] constructed from y µµ and the rapidities of the two jets as z ∗ = y µµ − ( y j + y j ) /2 | y j − y j | , (1) and the p T -balance ratio R ( p T ) = | (cid:126) p T µµ + (cid:126) p Tjj | p µµ T + p T ( j ) + p T ( j ) . (2)The VBF signal events are expected to have suppressed hadronic activity in the rapidity regionbetween the two leading jets. This feature is exploited by considering “soft track-jets” in theevent that are defined by clustering, via the anti- k T algorithm with a distance parameter of 0.4,charged particles from the primary interaction vertex, excluding the two identified muons andthose associated with the two VBF jets. The use of soft track-jet observables is a robust andvalidated method to reconstruct the hadronization products of partons with energy as low as afew GeV [81]. The soft track-jets are required to have p T > p T , are used as additional input variables. Finally,since jets in signal events are expected to originate from quarks, whereas in the DY processthey can also be initiated by gluons, the quark-gluon likelihood [82] of the two leading jets isalso used as input to the DNN.The DNN is trained using simulated events from signal (VBF) and background (DY, Zjj-EW, tt,and diboson) processes selected in the VBF-SR. Signal events generated with m H =
125 GeV areused in the DNN training. The last hidden layers of four intermediate networks are combinedto form a single binary classifier: two networks exploit the full set of variables described abovein order to optimize the separation between the VBF signal and the Zjj-EW or DY background,while the other two optimize the separation between the VBF signal and the total expectedbackground. The first of the two networks discriminating against the total background usesall the inputs except for m µµ , while the second uses only the dimuon mass and its resolution.Every network contains three or four hidden layers, each with a few tens of nodes. All trainingsare performed using a four-fold strategy [83], where 50% of the events are used for training,25% for validation, and 25% for testing. The validation sample is used to optimize the DNNhyper-parameters, while the test sample is used to evaluate the DNN performance and for theexpected distributions in the signal extraction fit. The selected training epoch maximizes theexpected significance, determined using the Asimov data set [84], defined as the minimumbetween the significances computed from the training and validation samples.Events belonging to the VBF-SR are divided into nonoverlapping bins based on the DNN value,independently for each data-taking period. These bins are defined to achieve optimal sensitiv-ity, while minimizing the total number of bins. From this optimization procedure, thirteenbins are obtained in each data-taking period characterized by different bin boundaries. Giventhe negligible correlation between the m µµ and other input variables, the m µµ variable can bemarginalized from the DNN by replacing the m µµ with a fixed value of 125 GeV during theDNN evaluation. The resulting DNN score is not significantly correlated with the m µµ . Thismass-decorrelated DNN is used for events in the VBF-SB region and captures the main fea-tures of the DNN distribution in the VBF-SR. The signal is extracted from a binned maximum-likelihood fit to the output of the DNN discriminator performed simultaneously over the VBF-SR and VBF-SB regions. Because of significant variations in the detector response to forwardjets during different data-taking periods, the fit is performed separately for data collected in2016, 2017, and 2018. The contributions of the various background processes are estimated fromsimulation. This follows the strategy that we employed in the measurement of the Zjj-EW crosssection with 13 TeV data [74]. It yields an improvement in sensitivity of about 20% comparedto an alternative approach in which a multivariate classifier is used to divide events in subcat-egories, characterized by different signal purities, then the signal is extracted by fitting the m µµ distribution in each subcategory to parametric functions [23]. While the background determi-nation in this alternative strategy is entirely based on data, the precision of the background estimate depends on the number of observed events in the mass sidebands, thereby limitingthe performance in the high purity subcategories that contain a small number of events. Incontrast, the approach presented here relies on the precision with which the simulation is ableto predict the different background components. The uncertainty in this prediction is validatedand constrained using the signal-depleted sideband regions.Theoretical uncertainties affect both the expected rate and the shape of signal and backgroundhistograms (templates) used in the fit. The Higgs boson production cross section for the var-ious modes, and their corresponding uncertainties, are taken from Ref. [22]. These includeuncertainties in the choice of the PDF, as well as the QCD renormalization ( µ R ) and factoriza-tion ( µ F ) scales. The uncertainty in the prediction of B ( H → µ + µ − ) is also considered. For theVBF process, uncertainties in the modelling of the p T ( H ) , p T ( Hjj ) , jet multiplicity, and m jj dis-tributions are considered. Their total uncertainty on the VBF signal prediction is about 2–4%.Similarly, for the ggH process, seven independent additional sources are included to accountfor the uncertainty in the modelling of the p T ( H ) distribution, the number of jets in the event,and its contamination in the VBF selected region, as described in Ref. [22]. The magnitude ofthese uncertainties for ggH events in the VBF category varies from about 15 to 25%. The theo-retical uncertainties described so far affect also the signal prediction in the ggH, ttH, and VHproduction categories reported in the next Sections. For each background process, templatevariations are built by changing the values of µ R and µ F by factors of 2 and 0.5 from the defaultvalues used in the ME calculation, excluding the combinations for which µ R / µ F = PYTHIA . The Zjj-EW and VBF signal simulations are very sen-sitive to the PS model, as shown in Refs. [72, 74]. A conservative PS uncertainty is assignedto the Zjj-EW background and VBF signal, defined as the full symmetrized difference between
PYTHIA (dipole shower) and
HERWIG (angular-ordered shower) predictions in each DNN bin,which is larger than that obtained by varying the PS ISR and FSR parameters.Several sources of experimental uncertainty are taken into account for both signal and back-ground processes. These include the uncertainty in the measurement of the integrated lumi-nosity, in the modelling of the pileup conditions during data taking, in the measurement ofthe muon selection and trigger efficiencies, in the muon momentum scale and resolution, inthe efficiency of vetoing b quark jets, and in the jet energy scale and resolution. If not explic-itly mentioned, experimental uncertainties are considered correlated across event categoriesand data-taking periods. Most of the sources of uncertainty affecting the jet energy scale arecorrelated across processes and years, while those affecting the jet energy resolution are onlycorrelated across processes but not across years. The uncertainty in the measurement of the in-tegrated luminosity is partially correlated across years. The integrated luminosities of the 2016,2017, and 2018 data-taking periods are individually known with uncertainties in the 2.3–2.5%range [85–87], while the total integrated luminosity has an uncertainty of 1.8%. The improve-ment in precision reflects the (uncorrelated) time evolution of some systematic effects. Duringthe 2016 and 2017 data-taking periods, a gradual shift in the timing of the inputs of the ECALL1 trigger in the forward endcap region ( | η | > p T jets with 2.4 < | η | < m jj =
400 GeV in the2016 (2017) data-taking period and it increases to about 6 (9)% for m jj >0
400 GeV in the2016 (2017) data-taking period and it increases to about 6 (9)% for m jj >0 (about 30–35%) of the DY background populating bins with low DNN score is comprised ofevents in which either the leading or subleading jet are in the forward region of the detector( | η | > m H = ∆ B), and the S/(S+B) and S/ √ B ratios obtained by summing the post-fit estimates from eachof the three data-taking periods.Table 2: Event yields in each bin or in group of bins defined along the DNN output in the VBF-SR for various processes. The expected signal contribution for m H = B ( H → µ + µ − ) , is shown. Thebackground yields (B) and the corresponding uncertainties ( ∆ B) are obtained after performinga combined S+B fit across the VBF-SR and VBF-SB regions and each data-taking period. Theobserved event yields, S/(S+B) ratios and S/ √ B ratios are also reported.DNN bin Total signal VBF (%) ggH (%) Bkg. ± ∆ B Data S/(S+B) (%) S/ √ B1–3 19.5 30 70 8890 ±
67 8815 0.22 0.214–6 11.6 57 43 394 ± ± ± ± ± ± - -
10 110 E v en t s Data mmfi
HZjj-EW DYTop quark DibosonVBF ggH (13 TeV) -1 CMS
Post-fitVBF-SR 2016 = 125.38 GeV H m D a t a / B k g . Pre-fit
VBF DNN output D a t a / B k g . Post-fit - -
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VBF DNN output D a t a / B k g . Post-fit
Figure 1: The observed DNN output distribution in the VBF-SR region for data collected in2016 (first row, left), 2017 (first row, right), and 2018 (second row) compared to the post-fitbackground estimate for the contributing SM processes. The post-fit distributions for the Higgsboson signal produced via ggH (solid red) and VBF (solid black) modes with m H = m H = -
10 110 E v en t s Data Zjj-EWDY Top quarkDiboson (13 TeV) -1 CMS
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VBF DNN output D a t a / B k g . Post-fit -
10 110 E v en t s Data Zjj-EWDY Top quarkDiboson (13 TeV) -1 CMS
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VBF DNN output D a t a / B k g . Post-fit -
10 110 E v en t s Data Zjj-EWDY Top quarkDiboson (13 TeV) -1 CMS
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VBF DNN output D a t a / B k g . Post-fit
Figure 2: The observed DNN output distribution for data collected in 2016 (first row, left),2017 (first row, right), and 2018 (second row) in the VBF-SB region compared to the post-fitbackground estimate from SM processes. The predicted backgrounds are obtained from a S+Bfit performed across analysis regions and years. The description of the three panels is the sameas in Fig. 1. -
10 110 E v en t s Data Zjj-EWDY Top quarkDiboson (13 TeV) -1
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Post-fitVBF-SR Run2 = 125.38 GeV H m VBF DNN bin D a t a / B k g . Figure 3: The observed DNN output distribution in the VBF-SB (left) and VBF-SR (right) re-gions for the combination of 2016, 2017, and 2018 data, compared to the post-fit predictionfrom SM processes. The post-fit distributions for the Higgs boson signal produced via ggH(solid red) and VBF (solid black) modes with m H = → µ + µ − signal contribution for m H = An event is considered in the ggH category if it contains exactly two muons passing the base-line selection requirements detailed in Section 5. Events with additional muons or electronsare rejected to avoid overlap with the VH category. Any jets considered in the event mustbe spatially separated ( ∆ R > p T >
25 GeV areonly considered if the leading jet has p T <
35 GeV, the invariant mass of the two highest p T jets is smaller than 400 GeV, or the | ∆ η jj | < p T >
25 GeV and | η | < Observable SelectionNumber of loose (medium) b-tagged jets ≤ = = jets ≥ m jj <
400 GeV or | ∆ η jj | < p T ( j ) <
35 GeV
A multivariate discriminant based on boosted decision trees (BDTs) is employed to discrimi-nate between signal and background events. To account for the evolution in the detector re-sponse during data-taking periods, the BDT discriminant is trained separately for the 2016, TMVA package [90], resulting in three independentBDT outputs. The input variables are chosen such that the BDT discriminants are effectivelyuncorrelated with m µµ . This is required by the chosen analysis strategy, in which events arefirst divided into independent subcategories based on the BDT output, then a potential sig-nal is extracted from each subcategory by searching for a narrow peak over a smoothly fallingbackground in the m µµ distribution. In this category, given the prior knowledge of the expectedDY background shape and the large number of data events in the mass sideband around thepeak that can be used to constrain the background, this strategy provides a robust backgroundestimate from data while maximizing the analysis sensitivity.The BDT discriminants include input variables that describe the production and decay of thedimuon system, namely p µµ T , y µµ , φ CS , and cos θ CS . In addition, the η of each of the two muonsand the ratio of each muon’s p T to m µµ are also included. In order to increase the signal-to-background separation for events in which the ggH signal is produced in association withjets, the BDT discriminants also take into account the p T and η of the leading jet in the eventwith p T >
25 GeV and the absolute distance in η and φ between the jet and the muon pair.For events with two or more jets with p T >
25 GeV in the final state, additional inputs areincluded: the m jj , ∆ η jj , and ∆ φ jj of the two highest p T jets. The m jj , as well as the other dijetvariables, is sensitive to the residual contribution from VBF and VH modes, in which the vectorboson decays hadronically. Furthermore, the Zeppenfeld variable defined in Eq. (1) and theangular separation ( ∆ η , ∆ φ ) between the dimuon system and each of the two leading jets arealso included, which target residual VBF signal events in the ggH selected region. Lastly, thetotal number of jets in the event with p T >
25 GeV and | η | < POWHEG since it provides positively weighted events at NLO in QCD. In later stages of theanalysis, the prediction from M AD G RAPH MC @ NLO is used instead since it provides a moreaccurate description of gluon fusion events accompanied by more than one jet, as detailed inSection 4. The background simulation consists of DY, tt, single top quark, diboson, and Zjj-EWprocesses. Only events with m µµ in the range 115–135 GeV are included in the training. Signaland background events both contain two prompt muons in the final state, and the correspond-ing dimuon mass resolution ( σ µµ / m µµ ) does not discriminate between them. For this reason, σ µµ / m µµ is not added as an input to the BDT. Instead, signal events in the BDT training areassigned a weight inversely proportional to the expected mass resolution, derived from theuncertainties in the p T measurements of the individual muon tracks. This weighting improvesthe average signal σ µµ / m µµ in the high-score BDT region by assigning increased importance tothe high-resolution signal events. Apart from m µµ , the p µµ T is one of the most discriminating ob-servables in the ggH category. Discrepancies between data and simulation in the p µµ T spectrumfor the DY background, similar to those reported in Ref. [91], are also observed in this analysis.In order to correctly model the p µµ T spectrum of the DY background during the training of theBDT discriminants, corrections are derived for each data-taking period by reweighting the p µµ T distribution of the DY simulation to reproduce the observation in data for dimuon events with70 < m µµ <
110 GeV. These corrections are obtained separately for events containing zero, one,and two or more jets with p T >
25 GeV and | η | < < m µµ <
150 GeV, where the outputs of the individual BDTs obtainedin each year are combined into a single distribution. The distributions for various signal pro- -
10 110 E v en t s / . un i t s Data DYTop quark Zjj-EWDiboson Other bkg.ggH VBFOther sig. (13 TeV) -1
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116 118 120 122 124 126 128 130 132 134 (GeV) mm m a . u . Category: Category: ggH-cat1 ggH-cat4
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Simulation
Figure 4: Left: the observed BDT output distribution compared to the prediction from thesimulation of various SM background processes. Dimuon events passing the event selectionrequirements of the ggH category, with m µµ between 110–150 GeV, are considered. The ex-pected distributions for ggH, VBF, and other signal processes are overlaid. The grey verticalbands indicate the range between the minimum and maximum BDT output values used to de-fine the boundaries for the optimized event categories for different data-taking periods. In thelower panel, the ratio between data and the expected background is shown. The grey bandindicates the uncertainty due to the limited size of the simulated samples. The azure bandcorresponds to the sum in quadrature between the statistical and experimental systematic un-certainties, while the orange band additionally includes the theoretical uncertainties affectingthe background prediction. Right: the signal shape model for the simulated H → µ + µ − samplewith m H =
125 GeV in the best (red) and the worst (blue) resolution categories.cesses (ggH, VBF, and VH+ttH) are also shown. Five event subcategories are defined basedon the output of these BDT discriminants. The subcategory boundaries are determined via aniterative process that aims to maximize the expected sensitivity of this analysis to H → µ + µ − decays of the SM Higgs boson. The expected sensitivity is estimated from S+B fits to the m µµ distribution in simulated events with 110 < m µµ <
150 GeV. In these fits, the Higgs boson sig-nal is modelled using a parametric shape, the double-sided Crystal Ball function (DCB) [92]DCB ( m µµ ) = e − ( m µµ − ˆ m ) /2 σ , − α L < m µµ − ˆ m σ < α R (cid:16) n L | α L | (cid:17) n L e − α /2 (cid:16) n L | α L | − | α L | − m µµ − ˆ m σ (cid:17) − n L , m µµ − ˆ m σ ≤ − α L (cid:16) n R | α R | (cid:17) n R e − α /2 (cid:16) n R | α R | − | α R | − m µµ − ˆ m σ (cid:17) − n R , m µµ − ˆ m σ ≥ α R . (3)The core of the DCB function consists of a Gaussian distribution of mean ˆ m and standard de-viation σ , while the tails on either side are modelled by a power-law function with parameters α L and n L (low-mass tail), and α R and n R (high-mass tail). The total expected background ismodelled with a modified form of the Breit–Wigner function (mBW) [23],mBW ( m µµ ; m Z , Γ Z , a , a , a ) = e a m µµ + a m µµ ( m µµ − m Z ) a + ( Γ Z /2 ) a , (4)where the parameters m Z and Γ Z are fixed to the measured Z boson mass of 91.19 GeV andwidth 2.49 GeV [93], and the parameters a , a , and a are free to float. A first boundary is selected by optimizing the total expected significance against all possible boundaries de-fined in quantiles of signal efficiency. This strategy accounts for the slight differences in theBDT shapes among data-taking periods for both signal and background processes. This pro-cess is repeated recursively to define additional subcategory boundaries until the further gainin the expected significance is less than 1%. The optimized event categories are labelled as“ggH-cat1 (cid:48)(cid:48) , “ggH-cat2 (cid:48)(cid:48) , “ggH-cat3 (cid:48)(cid:48) , “ggH-cat4 (cid:48)(cid:48) , and “ggH-cat5 (cid:48)(cid:48) corresponding to signalefficiency quantiles of 0–30, 30–60, 60–80, 80–95, and > m µµ distributions is performedover the mass range 110–150 GeV to extract the H → µ + µ − signal. A bin size of 50 MeV is cho-sen for the m µµ distributions, which is about one order of magnitude smaller than the expectedresolution of the signal peak. In each event category, simulated signal distributions from thedifferent production modes (ggH, VBF, WH, ZH, and ttH) are modelled independently withDCB functions, and the best fit values of the DCB tail parameters are treated as constants in thefinal fit to the data. The ˆ m and σ parameters of the DCB function represent the peak positionand resolution of the Higgs boson resonance, respectively. These are the only signal shape pa-rameters allowed to vary in the fit. Their predicted values from simulation are constrained byGaussian priors with widths corresponding to the muon momentum scale (up to 0.2%) and res-olution uncertainties (up to 10%) in each event category. Figure 4 (right) shows the total signalmodel for m H =
125 GeV obtained by summing the contributions from the different produc-tion modes in the best and the worst resolution subcategories of the ggH category, ggH-cat4and ggH-cat1, where HWHM represents the half-width at half maximum of the signal peak.The category with the highest signal purity (ggH-cat5) uses particular kinematic features ( p µµ T , ∆ η and ∆ φ between the dimuon system and jets) to isolate the signal, while ggH-cat4 reliesmore heavily on the m µµ resolution itself. Therefore, the mass resolution for signal events inggH-cat4 is expected to be about 2% better than in ggH-cat5.The theoretical and experimental sources of systematic uncertainties affecting the expected sig-nal rate in each event category are similar to those described in the VBF analysis. Experimentaluncertainties in the measurement of the muon selection efficiencies (0.5–1% per event cate-gory), jet energy scale (1–4% per event category) and resolution (1–6% per event category),the modelling of the pileup conditions (0.3–0.8% per event category), the integrated luminos-ity, and the efficiency for vetoing b quark jets (0.1–0.5% per event category) are considered.Theoretical uncertainties in the prediction of the Higgs boson production cross section, decayrate, and acceptance are also included, corresponding to a total uncertainty in the ggH yieldranging from 6–12% depending on the event category. Rate uncertainties are modelled in thesignal extraction as nuisance parameters acting on the relative signal yield with log-normalconstraints.The background contribution in each subcategory is modelled with parametric functions. Noprior knowledge of the shape parameters of these functions or the yield of the total backgroundis assumed. These parameters are therefore constrained directly by the observed data in theS+B fit. Since the background composition expected from simulation is very similar across sub-categories and largely dominated by the DY process, the background shape in m µµ is similarin all event categories. There are, however, variations in the overall slope of the m µµ spectrumacross the BDT score categories. The function describing the background in each event categoryis therefore defined as the product of a “core” shape that is common among all event categories,with parameters correlated across categories, and a Chebyshev polynomial term (shape modi-fier) specific to each event category that modulates the core shape. This background modelling approach is referred to as the “core-pdf method”. The core background shape is obtained froman envelope of three distinct functions: the mBW defined in Eq. (4), a sum of two exponen-tials, and the product of a nonanalytical shape derived from the FEWZ v3.1 generator [57] and athird-order Bernstein polynomial. Each of these functions contains three freely floating shapeparameters. The nonanalytical shape derived from the
FEWZ generator is obtained by simulat-ing DY events at NNLO precision in QCD and NLO accuracy in EW theory and interpolatingthe resulting m µµ distribution using a spline function [94, 95]. In a given subcategory, each ofthe three core functions is modulated by either a third- (ggH-cat1 and ggH-cat2) or a second-order polynomial, with parameters uncorrelated across event categories. A discrete profilingmethod [96] is employed, which treats the choice of the core function used to model the back-ground as a discrete nuisance parameter in the signal extraction.The following strategy is adopted to estimate the uncertainty in the measured signal due to thechoice of parametric function for the background model. In each event category, background-only fits to the data are performed using different types of functions: the mBW, a sum of twoexponentials, a sum of two power-law functions, a Bernstein polynomial, the product betweenthe nonanalytical shape described above and a Bernstein polynomial, the product between the“BWZ” function, defined asBWZ ( m µµ ; a , m Z , Γ Z ) = Γ Z e am µµ ( m µµ − m Z ) + ( Γ Z /2 ) , (5)and a Bernstein polynomial, and the “BWZ γ ” function [97]BWZ γ ( m µµ ; a , f , m Z , Γ Z ) = f BWZ ( m µµ ; a , m Z , Γ Z ) + ( − f ) e am µµ m µµ . (6)The BWZ γ function is the sum of a Breit–Wigner function and a 1/ m µµ term, which are usedto model the Z boson and the photon contributions to the m µµ spectrum in DY events, respec-tively. Both terms are multiplied by an exponential function to approximate the effect of thePDFs. The BWZ function is a Breit–Wigner distribution with an exponential tail. For the func-tions including Bernstein polynomials, a Fisher test [98] is used to determine the maximumdegree of the polynomials to be considered in the fit. The chosen functional forms fit the datawith a χ probability larger than 5% in all event categories.Pseudodata sets are generated across all event categories from the post-fit background shapesobtained for each type of function in each subcategory, taking into account the uncertainties inthe fit parameters as well as their correlations, and injecting a given number of signal events.Signal-plus-background fits are performed on the pseudodata sets using the core-pdf method.The median difference between the measured and injected signal yields, relative to the post-fituncertainty in the signal yields, gives an estimate of the bias due to the choice of the back-ground model. The bias measured in each BDT category, as well as from pseudodata sets inwhich the signal is injected simultaneously in all event categories, is smaller than 20% of thepost-fit uncertainty on the signal yield. Including these observed deviations as spurious sig-nals leads to a change in the overall uncertainty in the measured signal rate of less than 1% andis therefore neglected. The core-pdf method employed in this analysis yields an improvementin sensitivity of about 10% with respect to the background functions used in the previous re-sult [23]. It also ensures a negligible bias in the measured signal with significantly fewer totaldegrees of freedom in the signal extraction fit.Figure 5 shows the m µµ distributions in each of the ggH subcategories, in which the signalis extracted by performing a binned maximum-likelihood fit using a DCB function to model the signal contribution, while the background is estimated with the core-pdf method. Table 4reports the total number of expected signal events (S), the signal composition in each ggHcategory, and the HWHM of the expected signal shape. In addition, the estimated number ofbackground events (B), the observation in data, the S/(S+B), and the S/ √ B ratios computedwithin the HWHM range around the signal peak are listed.Table 4: The total expected number of signal events with m H = ± HWHM,and the S/(S+B) and the S/ √ B ratios within ± HWHM, for each of the optimized ggH eventcategories.
Event Total ggH VBF Other HWHM Bkg. Data S/(S+B) (%) S/ √ Bcategory signal (%) (%) (%) (GeV) @HWHM @HWHM @HWHM @HWHMggH-cat1 268 93.7 2.9 3.4 2.12 86 360 86 632 0.20 0.60ggH-cat2 312 93.5 3.4 3.1 1.75 46 350 46 393 0.46 0.98ggH-cat3 131 93.2 4.0 2.8 1.60 12 660 12 738 0.70 0.80ggH-cat4 126 91.5 5.5 3.0 1.47 8260 8377 1.03 0.96ggH-cat5 53.8 83.5 14.3 2.2 1.50 1680 1711 2.16 0.91
The ttH process has the smallest cross section among the targeted Higgs boson productionmodes at the LHC. However, the presence of a top quark-antiquark pair in addition to theHiggs boson helps to reduce the background to a level that is comparable to the expected sig-nal rate. The top quark decays predominantly into a b quark and a W boson [93], therefore asample of events enriched in ttH production is selected by requiring the presence of at leasttwo jets passing the loose WP of the DeepCSV b-tagging algorithm, or at least one b-taggedjet passing the medium WP. This requirement suppresses background processes in which jetsoriginate mainly from the hadronization of light-flavour quarks, such as DY and diboson pro-duction. This selection also ensures mutual exclusivity between the ttH category and the otherproduction categories considered in this analysis.In order to increase the signal selection efficiency in events with large hadronic activity, as ex-pected for the ttH signal process, the isolation requirement on all muons described in Section 5is relaxed to be less than 40% of the muon p T . In addition, the isolation cone size decreasesdynamically with the muon p T ( R = p T <
50 GeV, R = p T for 50 < p T <
200 GeV,and R = p T >
200 GeV), following the approach used in Ref. [99]. Electron candidatesare required to have p T >
20 GeV, | η | < p T >
15 GeV that is nearest to the lepton in ∆ R separation is b-tagged according to theDeepCSV medium WP. Furthermore, all muons and electrons in the ttH category are requiredto pass the medium WP of a multivariate lepton identification discriminant specifically de-signed to reject nonprompt leptons [100], resulting in a selection efficiency of about 95 (92)%per prompt muon (electron). · E v en t s / G e V (13 TeV) -1
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110 115 120 125 130 135 140 145 150 (GeV) mm m - D a t a - B k g . · E v en t s / G e V (13 TeV) -1
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CMS ggH-cat5 = 125.38 GeV H m DataS+B fitBkg. component s – s –
110 115 120 125 130 135 140 145 150 (GeV) mm m - - D a t a - B k g . Figure 5: Comparison between the data and the total background extracted from a S+B fitperformed across the various ggH subcategories. The one (green) and two (yellow) standarddeviation bands include the uncertainties in the background component of the fit. The lowerpanel shows the residuals after background subtraction and the red line indicates the signalwith m H =0
110 115 120 125 130 135 140 145 150 (GeV) mm m - - D a t a - B k g . Figure 5: Comparison between the data and the total background extracted from a S+B fitperformed across the various ggH subcategories. The one (green) and two (yellow) standarddeviation bands include the uncertainties in the background component of the fit. The lowerpanel shows the residuals after background subtraction and the red line indicates the signalwith m H =0 The ttH signal events may contain additional charged leptons, depending on the decay of thetop quarks. Events with one or two additional charged leptons in the final state are groupedin the ttH leptonic category. An event in the ttH leptonic category containing three (four)charged leptons is further required to have the net sum of the lepton electric charges equalto one (zero). In the case of events with more than one pair of oppositely charged muonswith 110 < m µµ <
150 GeV, the pair with the largest dimuon p T is chosen as the Higgs bosoncandidate. The invariant mass of each pair of same-flavour, opposite-sign leptons is required tobe greater than 12 GeV to suppress backgrounds arising from quarkonium decays. An event isvetoed if it contains a pair of oppositely charged electrons or muons with an invariant mass inthe range 81–101 GeV, consistent with the decay of an on-shell Z boson. In contrast, events withexactly two oppositely charged muons with 110 < m µµ <
150 GeV, no identified electrons, andat least one combination of three jets in the final state with invariant mass ( m jjj ) between 100 and300 GeV belong to the ttH hadronic category. Each jet must have p T >
25 GeV and | η | < Observable ttH hadronic ttH leptonicNumber of b quark jets > > ( (cid:96) = µ , e ) ) = = q ( (cid:96) ) ) ∑ q ( (cid:96) ) = ( (cid:96) ) = ( ) → ∑ q ( (cid:96) ) = ± ( ) Jet multiplicity ( p T >
25 GeV, | η | < ≥ ≥ p T >
50 GeV >
35 GeVZ boson veto — | m (cid:96)(cid:96) − m Z | >
10 GeVLow-mass resonance veto — m (cid:96)(cid:96) >
12 GeVJet triplet mass 100 < m jjj <
300 GeV —
The dominant background in the ttH hadronic category comes from fully leptonic tt decays,while the main backgrounds in the ttH leptonic category are the ttZ and tt processes. In or-der to obtain an optimal discrimination between the ttH signal and the expected backgrounds,BDT-based multivariate discriminants are trained in both the hadronic and leptonic categories.The input variables are chosen to account for both the kinematic properties of the dimuonsystem and the properties of the top quark decay products, while ensuring that the BDT out-puts remain uncorrelated with m µµ . A common set of observables is used as input to the twoBDT discriminants. These include variables that characterize the production and decay of theHiggs boson candidate, namely the p µµ T , y µµ , φ CS , and cos θ CS . In addition, the η of each ofthe two muons and the ratio of each muon’s p T to m µµ are also considered. To account forthe large hadronic activity in ttH signal events, the p T and η of the three leading jets, themaximum DeepCSV value of jets not overlapping with charged leptons ( ∆ R ( (cid:96) , j ) > p T sum H T ( | (cid:126) H missT | ) of all identified leptons and jets( p T >
25 GeV, | η | < p missT is also considered along with the ∆ ζ vari-able [101], which is defined as the projection of the (cid:126) p missT on the bisector of the dimuon systemin the transverse plane. Signal events are weighted during the BDT training with the inverseof the per-event mass resolution, following the same approach used in the ggH categories.In the ttH leptonic category, several additional variables are used in the BDT discriminant thattarget the kinematic properties of a leptonic top quark decay. These include the azimuthal sep-aration between the Higgs boson candidate and the highest p T additional charged lepton, theinvariant mass formed by the leading additional lepton and the jet with the highest DeepCSVscore, and the transverse mass formed by the additional lepton and (cid:126) p missT in the event. In the ttH hadronic category, the resolved hadronic top tagger (RHTT), which combines a kinematicfit and a BDT-based multivariate discriminant, is used to identify top quark decays to threeresolved jets following a similar approach to the one reported in Ref. [102]. The jet triplet with100 < m jjj <
300 GeV and the highest RHTT score is selected as a hadronic top quark candidate.The corresponding RHTT score is used as input to the BDT discriminant. Furthermore, the p T of the top quark candidate and the p T balance of the top quark and the muon pair are alsoconsidered.Figure 6 shows the output of the BDT discriminant in the ttH hadronic (left) and leptonic (right)categories. The high BDT score region of the ttH hadronic category is enriched in events withlarge jet multiplicity, where the tt and DY background predictions rely on a significant num-ber of jets from the PS and are known to not entirely reproduce the data [103]. The signalprediction, however, relies largely on jets derived from the ME calculation. Since the back-ground prediction is extracted from the data, the observed differences between data and back-ground simulation do not affect the fit result. Based on the BDT output, events in the ttHleptonic category are further divided into two subcategories, labelled as “ttHlep-cat1 (cid:48)(cid:48) and“ttHlep-cat2 (cid:48)(cid:48) , corresponding to signal efficiency quantiles of 0–52 and > (cid:48)(cid:48) , “ttHhad-cat2 (cid:48)(cid:48) , and “ttHhad-cat3 (cid:48)(cid:48) , corresponding to signal efficiency quan-tiles of 0–70, 70–86, and > - -
10 110 E v en t s Data Top quarkDY ttZttW(W) Other bkg.Htt tHOther sig. (13 TeV) -1
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H hadronic BDT outputtt D a t a / P r ed . - - -
10 110 E v en t s / . un i t s Data ttZTop quark ttW(W)DY Other bkg.Htt tHOther sig. (13 TeV) -1
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CMS - - - - - H leptonic BDT outputtt D a t a / P r ed . Figure 6: The observed BDT output distribution in the ttH hadronic (left) and leptonic (right)categories compared to the prediction from the simulation of various SM background pro-cesses. Signal distributions expected from different production modes of the Higgs boson with m H =
125 GeV are overlaid. The dashed vertical lines indicate the boundaries of the optimizedevent categories. The description of the ratio panels is the same as in Fig. 4.Figure 7 shows the m µµ distributions in the ttH hadronic and leptonic event categories. The sig-nal is extracted by performing a binned maximum-likelihood fit to these m µµ distributions (binsize of 50 MeV), where signal is modelled using the DCB function and the background is mod-elled using a second-order Bernstein polynomial (Bern(2)) in “ttHhad-cat1 (cid:48)(cid:48) and “ttHhad-cat2 (cid:48)(cid:48) ,a sum of two exponentials (S-Exp) in “ttHhad-cat3 (cid:48)(cid:48) , and a single exponential (Exp) in the ttH leptonic event categories. Table 6 reports the expected signal composition of each ttH subcate-gory, along with the HWHM of the expected signal shape. In addition, the estimated numberof background events, the observation in data, and the S/(S+B) and S/ √ B ratios within theHWHM of the signal shape are shown.Table 6: The total expected number of signal events with m H = ± HWHM, and the S/(S+B) and S/ √ B ratioscomputed within the HWHM of the signal peak, for each of the optimized event categoriesdefined along the ttH hadronic and leptonic BDT outputs.
Event Total ttH ggH VH Other HWHM Bkg. fit Bkg. Data S/(S+B) (%) S/ √ Bcategory signal (%) (%) (%) (%) (GeV) function @HWHM @HWHM @HWHM @HWHMttHhad-cat1 6.87 32.3 40.3 17.2 10.2 1.85 Bern(2) 4298 4251 1.07 0.07ttHhad-cat2 1.62 84.3 3.8 5.6 6.2 1.81 Bern(2) 82.0 89 1.32 0.12ttHhad-cat3 1.33 94.0 0.3 1.3 4.4 1.80 S-Exp 12.3 12 6.87 0.26ttHlep-cat1 1.06 85.8 — 4.7 9.5 1.92 Exp 9.00 13 7.09 0.22ttHlep-cat2 0.99 94.7 — 1.0 4.3 1.75 Exp 2.08 4 24.5 0.47
The systematic uncertainties considered account for possible mismodelling of the signal shapeand rate. Uncertainties in the calibration of the muon momentum scale and resolution arepropagated to the shape of the signal m µµ distribution, yielding variations of up to 0.1% in thepeak position and up to 10% in width. Experimental uncertainties from the measurement ofthe electron and muon selection efficiencies (0.5–1.5% per event category), muon momentumscale and resolution (0.1–0.8% per event category), jet energy scale and resolution (2–6% perevent category), efficiency of identifying b quark jets (1–3% per event category), integratedluminosity, and modelling of the pileup conditions (0.2–1% per event category) affect the pre-dicted signal rate. Furthermore, theoretical uncertainties in the prediction of the Higgs bosonproduction cross sections, decay rate, and acceptance are also included, as already describedfor the ggH, VBF, and VH analyses. Rate uncertainties are included in the signal extraction asnuisance parameters acting on the relative signal yield with log-normal constraints.In order to estimate the potential bias arising from the choice of the parametric function usedto model the background, alternative functions able to fit the data with a χ p -value larger than5% are considered. These include Bernstein polynomials, sum of exponentials, and sum ofpower laws. In each event category, background-only fits to the data are performed with eachfunction listed above. From each of these fits, pseudodata sets are generated taking into accountthe uncertainties in the fit parameters and their correlations, and injecting a certain number ofsignal events. A S+B fit is then performed on these pseudodata sets using, in each category, theparametric functions listed above. The corresponding bias is observed to be smaller than 20%of the post-fit uncertainty on the signal yield and is therefore neglected in the signal extraction.The chosen functions maximize the expected sensitivity to the 125 GeV Higgs boson. Events considered in the VH category contain at least two muons passing the selection require-ments listed in Section 5. In order to ensure no overlap with the ttH category, events containingat least two b-tagged jets with p T >
25 GeV and | η | < · E v en t s / G e V (13 TeV) -1
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Hlep-cat2tt = 125.38 GeV H m DataS+B fitBkg. component s – s –
110 115 120 125 130 135 140 145 150 (GeV) mm m - - D a t a - B k g . Figure 7: Comparison between the data and the total background extracted from a S+B fit per-formed across the various ttH hadronic and leptonic event subcategories. The one (green) andtwo (yellow) standard deviation bands include the uncertainties in the background componentof the fit. The lower panel shows the residuals after the background subtraction, where the redline indicates the signal with m H = have p T >
20 GeV, | η | < ( ) , and pass certain isolation and identification requirementswith an average efficiency of 95 (90)%. Furthermore, all muons and electrons in this categoryare required to pass the medium WP of a multivariate discriminant developed in Ref. [100]to identify and suppress nonprompt leptons, with a selection efficiency of about 95 (92)% perprompt muon (electron).Events containing exactly one additional charged lepton belong to the WH category, whichtargets signal events where the Higgs boson is produced in association with a leptonicallydecaying ¶@ boson. If the additional lepton is a muon, the two pairs of oppositely chargedmuons are required to have m µµ >
12 GeV to suppress background events from quarkoniumdecays. Moreover, neither of the two oppositely charged muon pairs can have an invariantmass consistent with m Z within 10 GeV. Finally, at least one of these two muon pairs musthave m µµ in the range 110–150 GeV. If both m µµ pairs satisfy this criterion, the pair with thehighest p µµ T is considered as the Higgs boson candidate. If the additional lepton is an electron,the only requirement imposed is that 110 < m µµ <
150 GeV.The ZH category targets signal events where the Higgs boson is produced in association witha Z boson that decays to a pair of electrons or muons. Events in the ZH category are thereforerequired to contain four charged leptons, with a combined lepton number and electric chargeof zero. As in the WH category, the invariant mass of each pair of same-flavour, opposite-signleptons is required to be greater than 12 GeV. An event is rejected if it does not contain exactlyone pair of same-flavour, opposite-sign leptons with invariant mass compatible with the Zboson within 10 (20) GeV for muon (electron) pairs. In addition, each event must contain oneoppositely charged muon pair satisfying 110 < m µµ <
150 GeV. For events with four muons,the muon pair with m µµ closer to m Z is chosen as the Z boson candidate, while the other muonpair is selected as the Higgs boson candidate. A summary of the selection criteria applied inthe WH and ZH production categories is reported in Table 7.Table 7: Summary of the kinematic selection used to define the WH and ZH production cate-gories. Observable WH leptonic ZH leptonic µµµ µµ e 4 µ µ ≤ ≤ ≤ ≤ = = = = = = = = q ( (cid:96) ) ) ∑ q ( (cid:96) ) = ± ∑ q ( (cid:96) ) = m (cid:96)(cid:96) >
12 GeVN ( µ + µ − ) pairs with 110 < m µµ <
150 GeV ≥ = ≥ = ( µ + µ − ) pairs with | m µµ − m Z | <
10 GeV = = = = ( e + e − ) pairs with | m ee − m Z | <
20 GeV = = = = m µµ of the Higgs boson candidate. This is required bythe chosen analysis strategy, which is analogous to that adopted for the signal extraction inthe ggH category. The impact of the m µµ resolution, which evolves as a function of muon p T and η , is taken into account during the BDT training by applying weights to the simulatedsignal events that are inversely proportional to the per-event mass resolution, estimated fromthe uncertainty in the measured m µµ following the same strategy described in Section 7 and 8.The BDT discriminant used in the WH category takes as inputs several variables that exploit the kinematic features of the three charged leptons in the event, as well as the p missT . Thesevariables include the full kinematic information, apart from the invariant mass, of the dimuonsystem corresponding to the Higgs boson candidate. In addition, the ∆ φ and ∆ η separations be-tween the additional lepton ( (cid:96) W ) and the Higgs boson candidate, between (cid:96) W and both muonsfrom the Higgs boson candidate, and between (cid:96) W and (cid:126) H missT are considered. The (cid:126) H missT is de-fined as the negative vector p T sum of all jets in the event with p T >
30 GeV and | η | < (cid:96) W and (cid:126) H missT system, the flavour of (cid:96) W , and the p T of (cid:96) W are added as inputs to the BDT. The particular kinematic properties in signal eventsof the (cid:96) W and H missT enable a large suppression of the residual DY background. The BDT dis-criminant trained in the ZH category considers several input observables constructed from thelepton pair associated with the Z boson decay ( (cid:96)(cid:96) Z ) and the muon pair considered as the Higgsboson candidate ( µµ H ). These include the p T and η of both Z and Higgs boson candidates, the ∆ φ ( ∆ R ) between the muons (charged leptons) of the µµ H ( (cid:96)(cid:96) Z ) system, m (cid:96)(cid:96) Z , ∆ η ( µµ H , (cid:96)(cid:96) Z ) ,and the cosine of the polar angle between the µµ H and (cid:96)(cid:96) Z candidates. The flavour of thelepton pair associated with the Z boson decay is also included as an input variable.Figure 8 shows the output of the BDT classifiers in the WH (left) and ZH (right) categories.Based on these outputs, events in the WH category are further divided into three subcategoriestermed “WH-cat1 (cid:48)(cid:48) , “WH-cat2 (cid:48)(cid:48) , and “WH-cat3 (cid:48)(cid:48) corresponding to signal efficiency quantilesof 0–22, 22–70, > (cid:48)(cid:48) and “ZH-cat2 (cid:48)(cid:48) corresponding to signal efficiency quantilesof 0–52 and > - -
10 110 E v en t s / . un i t s Data WZZZ DYTop quark Other bkg.WH ZHOther sig. (13 TeV) -1
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CMS - - - - ZH BDT output D a t a / P r ed . Figure 8: The observed BDT output distribution in the WH (left) and ZH (right) categoriescompared to the prediction from the simulation of various SM background processes. Signaldistributions expected from different production modes of the Higgs boson with m H =
125 GeVare overlaid. The description of the ratio panel is the same as in Fig. 4. The dashed vertical linesindicate the boundaries of the optimized event categories.Figure 9 shows the m µµ distributions in the WH and ZH event categories. The signal isextracted via a binned maximum-likelihood fit in each event category, where the signal is modelled with a DCB function and the background is modelled with the BWZ γ function inWH-cat1, as defined in Eq. (6) and the BWZ function in the remaining subcategories, as de-fined in Eq. (5). Table 8 reports the signal composition in the WH and ZH subcategories, alongwith the HWHM of the expected signal shape. In addition, the estimated number of back-ground events, the S/(S+B) and S/ √ B ratios, and the observation in data within the HWHMof the signal peak are also listed.Table 8: The total expected number of signal events with m H = ± HWHM, and theS/(S+B) and the S/ √ B ratios computed within the HWHM of the signal peak for each of theoptimized event categories defined along the WH and ZH BDT outputs.
Event Total WH qqZH ggZH ttH+tH HWHM Bkg. fit Bkg. Data S/(S+B) (%) S/ √ Bcategory signal (%) (%) (%) (%) (GeV) function @HWHM @HWHM @HWHM @HWHMWH-cat1 0.82 76.2 9.6 1.6 12.6 2.00 BWZ γ The systematic uncertainties considered in this category account for possible mismodelling inthe signal shape and rate. The shape of the reconstructed Higgs boson resonance, modelledusing the DCB function defined in Eq. (3), is affected by the uncertainty in the muon momen-tum scale and resolution. Uncertainties in the calibration of these values are propagated to theshape of the m µµ distribution, yielding variations of up to 0.2% in the peak position and up to10% in the width. Experimental systematic uncertainties from the measurement of the electronand muon selection efficiencies (1–3% per event category), jet energy scale and resolution (0.5–2% per event category), the efficiency of vetoing b quark jets (1–3% per event category), theintegrated luminosity, and the pileup model (0.5–2% per event category) affect the predictedsignal rate. Furthermore, theoretical uncertainties in the prediction of the Higgs boson produc-tion cross section, decay rate, and acceptance are also considered. Rate uncertainties are takeninto account in the signal extraction as nuisance parameters acting on the relative signal yieldwith log-normal constraints.The potential bias due to the choice of the parametric function used to model the backgroundis estimated using the same procedure employed in the ttH analysis, detailed in Section 8. Theset of parametric functional forms considered in the bias studies includes BWZ, BWZ γ , sumof exponentials, Bernstein polynomials, and sum of power laws. The chosen parametrizationmaximizes the expected sensitivity without introducing a significant bias in the measured sig-nal yield. The corresponding bias is found to be smaller than 20% and is therefore neglectedin the signal extraction. The chosen functions maximize the expected sensitivity to the 125 GeVHiggs boson.
10 Results
A simultaneous fit is performed across all event categories, with a single overall signal strengthmodifier ( µ ) free to float in the fit. The signal strength modifier is defined as the ratio be-tween the observed Higgs boson rate in the H → µ + µ − decay channel and the SM expectation, µ = ( σ B ( H → µ + µ − )) obs / ( σ B ( H → µ + µ − )) SM . The relative contributions from the differentHiggs boson production modes are fixed to the SM prediction within uncertainties. Confidence E v en t s / G e V (13 TeV) -1
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ZH-cat2 = 125.38 GeV H m DataS+B fitBkg. component s – s –
110 115 120 125 130 135 140 145 150 (GeV) mm m - - D a t a - B k g . Figure 9: Comparison between the data and the total background extracted from a S+B fit per-formed across the various WH and ZH event subcategories. The one (green) and two (yellow)standard deviation bands include the uncertainties in the background component of the fit. Thelower panel shows the residuals after the background subtraction, where the red line indicatesthe signal with m H = intervals on the signal strength are estimated using a profile likelihood ratio test statistic [84],in which systematic uncertainties are modelled as nuisance parameters following a modifiedfrequentist approach [104]. The profile likelihood ratio is defined as q µ = − ∆ ln L = ln L ( data | µ , ˆ θ µ ) |L ( data ˆ µ , ˆ θ ) ,where ˆ µ represents the value of the signal strength that maximizes the likelihood L for thedata, while ˆ θ and ˆ θ µ denote the best fit estimate for the nuisance parameters and the estimatefor a given fixed value of µ , respectively. Theoretical uncertainties affecting the signal predic-tion are correlated among all the event categories included in the fit. Similarly, experimentaluncertainties in the measurement of the integrated luminosity in each year, jet energy scaleand resolution, b quark jet identification, modelling of the pileup conditions, and selection ef-ficiencies of muons and electrons are also correlated across categories. Because of the differentanalysis strategy employed in the VBF category, the acceptance uncertainties from the muonenergy scale and resolution are correlated only among the ggH, WH, ZH, and ttH categories.Furthermore, their effect on the position and width of the signal peak are assumed to be uncor-related across event categories.The local p -value quantifies the probability for the background to produce a fluctuation largerthan the apparent signal observed in the search region. Figure 10 (left) shows the observedlocal p -value for the combined fit, and for each individual production category, as a functionof m H in a 5 GeV window around the expected Higgs boson mass. The solid markers indicatethe mass points for which the observed p -values are computed. Figure 10 (right) shows theexpected p -values computed for the combined fit, and for each production category, on an Asi-mov data set [84] generated from the background expectation obtained from the S+B fit witha m H = m H are compatible,within the statistical variation, with the expectation for the Higgs boson with m H = p -values for masses different from125 GeV, signal models are derived using alternative H → µ + µ − signal samples generatedwith m H fixed to 120 and 130 GeV. Signal shape parameters and the expected rate for eachproduction mode in each event category are then interpolated using a spline function within120 < m H <
130 GeV, providing a signal model for any mass value in the m H = ± m µµ is a DNN input variable.As described in Section 6, the DNN output can be decorrelated from the m µµ information by fix-ing its value to 125 GeV. Therefore, a potential signal with mass m (cid:48) different from 125 GeV canbe extracted by fitting the data with an alternative set of signal and background templates, ob-tained by shifting the mass value used as input to the DNN evaluation by ∆ m =
125 GeV − m (cid:48) and adjusting the expected signal yields by the corresponding differences in the productioncross section and decay rate. Variations in the acceptance per DNN bin as a function of ∆ m arefound to be negligible in the mass range of interest. This procedure is also applied to the data,yielding for each tested mass hypothesis a different observed DNN distribution to fit. Through-out the explored mass range, 120 < m H <
130 GeV, the VBF category has the highest expectedsensitivity to H → µ + µ − decays, followed by the ggH, ttH, and VH categories, respectively.The observed (expected for µ =
1) significance at m H = s criterion [84, 105, 106], is also de-rived from the combined fit performed across all event categories. The observed (expected for µ =
0) UL on µ at 95% CL for m H = p -value for the VBF category and the combined fit arise from event migrations in data between neighbouring bins when reevaluating the VBF category DNN for different mass hypotheses,following the procedure described above.
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Expected = 125.38 GeV H m Figure 10: Left: observed local p -values as a function of m H , extracted from the combined fit aswell as from each individual production category, are shown. The solid markers indicate themass points for which the observed p -values are computed. Right: the expected p -values arecalculated using the background expectation obtained from the S+B fit and injecting a signalwith m H = µ = µ = + − (stat) + − (syst). Assuming SM production cross sections forthe various modes, the H → µ + µ − branching fraction is constrained at 95% CL to be within0.8 × − < B ( H → µ + µ − ) < × − . The statistical component of the post-fit uncertaintyis separated by performing a likelihood scan as a function of µ in which nuisance parametersassociated with systematic uncertainties are fixed to their best fit values. The systematic un-certainty component is then taken as the difference in quadrature between the total and thestatistical uncertainties. The individual contributions to the uncertainty in the measured sig-nal strength from experimental uncertainties, the limited size of the simulated samples, andtheoretical uncertainties are also evaluated following a similar procedure. The individual un-certainty components are summarized in Table 9. The uncertainty in the measured signal rateis dominated by the limited number of events in data.Figure 11 (left) reports a summary of the best fit values for the signal strength and the corre-sponding 68% CL intervals obtained from a profile likelihood scan in each production category.The best fit signal strengths in each production category are consistent with the combined fitresult as well as the SM expectation. A likelihood scan is performed in which the four mainHiggs boson production mechanisms are associated to either fermion (ggH and ttH) or vec-tor boson (VBF and VH) couplings. Two signal strength modifiers, denoted as µ ggH,ttH and µ VBF,VH , are varied independently as unconstrained parameters in the fit. Figure 11 (right)shows the 1 σ and 2 σ contours, computed as variations around the minimum of − ∆ ln ( L ) for m H = µ ggH,ttH and µ VBF,VH . The best fit valuesfor these parameters are ˆ µ ggH,ttH = + − and ˆ µ VBF,VH = + − , consistent with the SMexpectation.An unbiased mass distribution representative of the fit result in the VBF category is obtained byweighting both simulated and data events from the VBF-SR and VBF-SB regions by the S/(S+B)0
Expected = 125.38 GeV H m Figure 10: Left: observed local p -values as a function of m H , extracted from the combined fit aswell as from each individual production category, are shown. The solid markers indicate themass points for which the observed p -values are computed. Right: the expected p -values arecalculated using the background expectation obtained from the S+B fit and injecting a signalwith m H = µ = µ = + − (stat) + − (syst). Assuming SM production cross sections forthe various modes, the H → µ + µ − branching fraction is constrained at 95% CL to be within0.8 × − < B ( H → µ + µ − ) < × − . The statistical component of the post-fit uncertaintyis separated by performing a likelihood scan as a function of µ in which nuisance parametersassociated with systematic uncertainties are fixed to their best fit values. The systematic un-certainty component is then taken as the difference in quadrature between the total and thestatistical uncertainties. The individual contributions to the uncertainty in the measured sig-nal strength from experimental uncertainties, the limited size of the simulated samples, andtheoretical uncertainties are also evaluated following a similar procedure. The individual un-certainty components are summarized in Table 9. The uncertainty in the measured signal rateis dominated by the limited number of events in data.Figure 11 (left) reports a summary of the best fit values for the signal strength and the corre-sponding 68% CL intervals obtained from a profile likelihood scan in each production category.The best fit signal strengths in each production category are consistent with the combined fitresult as well as the SM expectation. A likelihood scan is performed in which the four mainHiggs boson production mechanisms are associated to either fermion (ggH and ttH) or vec-tor boson (VBF and VH) couplings. Two signal strength modifiers, denoted as µ ggH,ttH and µ VBF,VH , are varied independently as unconstrained parameters in the fit. Figure 11 (right)shows the 1 σ and 2 σ contours, computed as variations around the minimum of − ∆ ln ( L ) for m H = µ ggH,ttH and µ VBF,VH . The best fit valuesfor these parameters are ˆ µ ggH,ttH = + − and ˆ µ VBF,VH = + − , consistent with the SMexpectation.An unbiased mass distribution representative of the fit result in the VBF category is obtained byweighting both simulated and data events from the VBF-SR and VBF-SB regions by the S/(S+B)0 Table 9: Major sources of uncertainty in the measurement of the signal strength µ and theirimpact. The total post-fit uncertainty on µ is divided into the statistical and systematic compo-nents. The systematic component is further separated into three parts depending on the originof the different sources of uncertainty: experimental, theoretical, and size of the simulated sam-ples. The uncertainty due to the limited statistics of the simulated samples only affects the VBFcategory results. Uncertainty source ∆ µ Post-fit uncertainty + − + − + − + − + − + − - - m Best-fit
VH-cat.H-cat.ttggH-cat.VBF-cat. -0.61+0.69 = 1.36 m -0.64+0.65 = 0.63 m -1.95+2.27 = 2.32 m -2.83+3.10 = 5.48 m m Combined best fit SM expectation68% CL95% CL -0.42 +0.44 = 1.19 m Combined = 125.38 GeV H m (13 TeV) -1
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Best fit68% CL95% CLSM
Figure 11: Left: signal strength modifiers measured for m H = µ ggH,ttH and µ VBF,VH with the corresponding 1 σ and 2 σ uncertainty contours. The black crossindicates the best fit values ( ˆ µ ggH,ttH , ˆ µ VBF,VH ) = ( ) , while the red circle representsthe SM expectation.ratio. The S/(S+B) weights are computed as a function of the mass-decorrelated DNN output,defined in Section 6, for events within m µµ = ± HWHM and using the same binboundaries as displayed in Fig. 1. The HWHM of the signal peak in the VBF category is about2 GeV. The best fit estimates for the nuisance parameters and signal strength are propagatedto the m µµ distribution. This distribution is not used for any of the measurements presented inthis paper, but only to visualize the fit result. Figure 12 (left) shows the observed and predictedweighted m µµ distributions for events in the VBF-SB and VBF-SR regions, combining 2016,2017, and 2018 data. The lower panel shows the residuals between the data and the post-fitbackground prediction, along with the post-fit uncertainty obtained from the background-onlyfit. The best fit signal contribution with m H = m H near 125 GeV and compatible with the excess observed at high DNN score in Fig. 3. The signal and background distributions are then inter-polated with a spline function in order to obtain a continuous spectrum that can be summedwith the parametric fit results in the ggH, WH, ZH, and ttH categories. Figure 12 (right) showsthe m µµ distribution for the weighted combination of all event categories. The ggH, VH, andttH categories are weighted proportionally to the corresponding S/(S+B) ratio, where S andB are the number of expected signal and background events with mass within ± HWHM ofthe expected signal peak with m H = m H = m µµ =
125 GeV. S / ( S + B ) W e i gh t ed E v en t s / G e V Data mmfi
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S/(S+B) weightedAll categories = 125.38 GeV H m Data =1.19) m S+B (Bkg. component s – s –
110 115 120 125 130 135 140 145 150 (GeV) mm m - D a t a - B k g . Figure 12: Left: the m µµ distribution for the weighted combination of VBF-SB and VBF-SRevents. Each event is weighted proportionally to the S/(S+B) ratio, calculated as a function ofthe mass-decorrelated DNN output. The lower panel shows the residuals after subtracting thebackground prediction from the S+B fit. The best fit H → µ + µ − signal contribution is indicatedby the blue line and histogram, while the grey band indicates the total background uncertaintyfrom the background-only fit. Right: the m µµ distribution for the weighted combination of allevent categories. The lower panel shows the residuals after background subtraction, with thebest fit SM H → µ + µ − signal contribution for m H = B ( H → µ + µ − ) arecorrelated across the 7, 8, and 13 TeV analyses. Experimental uncertainties affecting the mea-sured properties of the various physics objects (muons, electrons, jets, and b quark jets), themeasurement of the integrated luminosity, and the modelling of the pileup conditions are as-sumed to be uncorrelated between the 7+8 and 13 TeV analyses. Table 10 reports the observedand expected significances over the background-only expectation at m H = µ in each production category, as well as for the 13 TeV and the 7+8+13 TeVcombined fits. The combination improves, relative to the 13 TeV-only result, both the expectedand the observed significance at m H = p -values derived from the 7+8+13 TeV com- bined fit as a function of m H in a 5 GeV window around the expected Higgs boson mass. Theexpected p -value is computed on an Asimov data set generated from the background expec-tation obtained from the S+B fit with a m H = p -values are computed. Thebest fit signal strength, and the corresponding 68% CL interval, obtained from the 7+8+13 TeVcombination for the Higgs boson with mass of 125.38 GeV is 1.19 + − (stat) + − (syst).Table 10: Observed and expected significances for the incompatibility with the background-only hypothesis for m H = µ (in theabsence of H → µ + µ − decays) for each production category, as well as for the 13 TeV and the7+8+13 TeV combined fits.Production category Observed (expected) signif. Observed (expected) UL on µ VBF 2.40 (1.77) 2.57 (1.22)ggH 0.99 (1.56) 1.77 (1.28)ttH 1.20 (0.54) 6.48 (4.20)VH 2.02 (0.42) 10.8 (5.13)Combined √ s =
13 TeV 2.95 (2.46) 1.94 (0.82)Combined √ s =
7, 8, 13 TeV 2.98 (2.48) 1.93 (0.81)
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10 1 Lo c a l p - v a l ue Observed = 125.38 GeV H Expected m s s s (13 TeV) -1 (8 TeV) +137 fb -1 (7 TeV) + 19.7 fb -1 CMS
Figure 13: Observed (solid black) and expected (dashed black) local p -values as a function of m H , extracted from the combined fit performed on data recorded at √ s =
7, 8, and 13 TeV, areshown. The expected p -values are calculated using the background expectation obtained fromthe S+B fit and injecting a signal with m H = µ = → µ + µ − de-cay rate reported to date, and provide the best constraint of the coupling between the Higgsboson and the muon. The signal strength measured in the H → µ + µ − analysis cannot be trans-lated directly into a measurement of the Higgs boson coupling to muons because it is alsosensitive to the interactions between the Higgs boson and several SM particles involved in theproduction processes considered, primarily the top quark and vector boson couplings. TheseHiggs boson couplings to other particles are constrained by combining the result of this anal-ysis with those presented in Ref. [10], based on pp collision data recorded by the CMS exper-iment at √ s =
13 TeV in 2016 corresponding to an integrated luminosity of 35.9 fb − . Under the assumption that there are no new particles contributing to the Higgs boson total width,Higgs boson production and decay rates in each category are expressed in terms of couplingmodifiers within the κ -framework [107]. Six free coupling parameters are introduced in thelikelihood function ( κ W , κ Z , κ t , κ τ , κ b , and κ µ ) and are extracted from a simultaneous fit acrossall event categories. In the combined fit, the event categories of the √ s =
13 TeV H → µ + µ − analysis described in this paper supersede those considered in Ref. [10]. Figure 14 (left) showsthe observed profile likelihood ratio as a function of κ µ for m H = κ µ ( κ µ = κ µ parameter are 0.85 < κ µ < < κ µ < λ F ) is proportionalto the fermion mass ( m F ), while the coupling to weak bosons ( g V ) is proportional to the squareof the vector boson masses ( m V ). The results from the κ -framework fit can therefore be trans-lated in terms of reduced coupling strength modifiers, defined as y V = √ κ V m V / ν for weakbosons and y F = κ F m F / ν for fermions, where ν is the vacuum expectation value of the Higgsfield of 246.22 GeV [93]. Figure 14 (right) shows the best fit estimates for the six reduced cou-pling strength modifiers as a function of particle mass, where lepton, vector boson, and quarkmasses are taken from Ref. [93]. The compatibility between the measured coupling strengthmodifiers and their SM expectation is derived from the − ∆ ln ( L ) separation between the bestfit and an alternative one, performed by fixing the six coupling modifiers to the SM prediction( κ W = κ Z = κ t = κ τ = κ b = κ µ = p -value of 44%. m k Coupling strength l n ( L ) D -
68% CL95% CL (13 TeV) -1 CMS = 125.38 GeV H m at 68% CL - +0.22 = 1.07 m k - - - -
10 1 n V m V k o r n F m F k Vector bosons generation fermions rd m t b W Z t (13 TeV) -1 CMS = 125.38 GeV H mp-value = 44% -
10 1 10 Particle mass (GeV) R a t i o t o S M Figure 14: Left: the observed profile likelihood ratio as a function of κ µ for m H = κ -framework. The best fit value for κ µ is1.07 and the corresponding observed 68% CL interval is 0.85 < κ µ < κ -framework compared to their corresponding prediction from the SM. The error barsrepresent 68% CL intervals for the measured parameters. In the lower panel, the ratios of themeasured coupling modifiers values to their SM predictions are shown.
11 Summary
Evidence for Higgs boson decay to a pair of muons is presented. This result combines searchesin four exclusive categories targeting the production of the Higgs boson via gluon fusion, viavector boson fusion, in association with a vector boson, and in association with a top quark-antiquark pair. The analysis is performed using proton-proton collision data at √ s =
13 TeV,corresponding to an integrated luminosity of 137 fb − , recorded by the CMS experiment at theCERN LHC. An excess of events over the background expectation is observed in data witha significance of 3.0 standard deviations, where the expectation for the standard model (SM)Higgs boson with mass of 125.38 GeV is 2.5. The combination of this result with that from datarecorded at √ s = − ,respectively, increases both the expected and observed significances by 1%. The measuredsignal strength, relative to the SM prediction, is 1.19 + − (stat) + − (syst). This result constitutesthe first evidence for the decay of the Higgs boson to second generation fermions and is themost precise measurement of the Higgs boson coupling to muons reported to date. 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 centres and personnel of the Worldwide LHC Computing Gridfor delivering so effectively the computing infrastructure essential to our analyses. Finally,we acknowledge the enduring support for the construction and operation of the LHC and theCMS detector provided by the following funding agencies: BMBWF and FWF (Austria); FNRSand FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria);CERN; CAS, MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia);RIF (Cyprus); SENESCYT (Ecuador); MoER, ERC IUT, PUT and ERDF (Estonia); Academy ofFinland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF(Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland);INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LAS (Lithuania); MOE and UM(Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Mon-tenegro); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal);JINR (Dubna); MON, RosAtom, RAS, RFBR, and NRC KI (Russia); MESTD (Serbia); SEIDI,CPAN, PCTI, and FEDER (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland);MST (Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK (Turkey);NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA).Individuals have received support from the Marie-Curie programme and the European Re-search Council and Horizon 2020 Grant, contract Nos. 675440, 752730, and 765710 (Euro-pean Union); the Leventis Foundation; the A.P. Sloan Foundation; the Alexander von Hum-boldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation `a laRecherche dans l’Industrie et dans l’Agriculture (FRIA-Belgium); the Agentschap voor Inno-vatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium)under the “Excellence of Science – EOS” – be.h project n. 30820817; the Beijing Municipal Sci-ence & Technology Commission, No. Z191100007219010; the Ministry of Education, Youth andSports (MEYS) of the Czech Republic; the Deutsche Forschungsgemeinschaft (DFG) under Ger-many’s Excellence Strategy – EXC 2121 “Quantum Universe” – 390833306; the Lend ¨ulet (“Mo-mentum”) Programme and the J´anos Bolyai Research Scholarship of the Hungarian Academyof Sciences, the New National Excellence Program ´UNKP, the NKFIA research grants 123842, eferences References [1] ATLAS Collaboration, “Observation of a new particle in the search for the StandardModel Higgs boson with the ATLAS detector at the LHC”,
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Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan † , A. Tumasyan Institut f ¨ur Hochenergiephysik, Wien, Austria
W. Adam, T. Bergauer, M. Dragicevic, J. Er ¨o, A. Escalante Del Valle, R. Fr ¨uhwirth , M. Jeitler ,N. Krammer, L. Lechner, D. Liko, I. Mikulec, F.M. Pitters, N. Rad, J. Schieck , R. Sch ¨ofbeck,M. Spanring, S. Templ, W. Waltenberger, C.-E. Wulz , M. Zarucki Institute for Nuclear Problems, Minsk, Belarus
V. Chekhovsky, A. Litomin, V. Makarenko, J. Suarez Gonzalez
Universiteit Antwerpen, Antwerpen, Belgium
M.R. Darwish , E.A. De Wolf, D. Di Croce, X. Janssen, T. Kello , A. Lelek, M. Pieters,H. Rejeb Sfar, H. Van Haevermaet, P. Van Mechelen, S. Van Putte, N. Van Remortel Vrije Universiteit Brussel, Brussel, Belgium
F. Blekman, E.S. Bols, S.S. Chhibra, J. D’Hondt, J. De Clercq, D. Lontkovskyi, S. Lowette,I. Marchesini, S. Moortgat, A. Morton, D. M ¨uller, Q. Python, S. Tavernier, W. Van Doninck,P. Van Mulders
Universit´e Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, B. Clerbaux, G. De Lentdecker, B. Dorney, L. Favart, A. Grebenyuk,A.K. Kalsi, I. Makarenko, L. Moureaux, L. P´etr´e, A. Popov, N. Postiau, E. Starling, L. Thomas,C. Vander Velde, P. Vanlaer, D. Vannerom, L. Wezenbeek
Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, M. Gruchala, I. Khvastunov , M. Niedziela, C. Roskas, K. Skovpen,M. Tytgat, W. Verbeke, B. Vermassen, M. Vit Universit´e Catholique de Louvain, Louvain-la-Neuve, Belgium
G. Bruno, F. Bury, C. Caputo, P. David, C. Delaere, M. Delcourt, I.S. Donertas, A. Giammanco,V. Lemaitre, K. Mondal, J. Prisciandaro, A. Taliercio, M. Teklishyn, P. Vischia, S. Wertz,S. Wuyckens
Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
G.A. Alves, C. Hensel, A. Moraes
Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
W.L. Ald´a J ´unior, E. Belchior Batista Das Chagas, H. BRANDAO MALBOUISSON,W. Carvalho, J. Chinellato , E. Coelho, E.M. Da Costa, G.G. Da Silveira , D. De Jesus Damiao,S. Fonseca De Souza, J. Martins , D. Matos Figueiredo, M. Medina Jaime , C. Mora Herrera,L. Mundim, H. Nogima, P. Rebello Teles, L.J. Sanchez Rosas, A. Santoro, S.M. Silva Do Amaral,A. Sznajder, M. Thiel, F. Torres Da Silva De Araujo, A. Vilela Pereira Universidade Estadual Paulista a , Universidade Federal do ABC b , S˜ao Paulo, Brazil C.A. Bernardes a , a , L. Calligaris a , T.R. Fernandez Perez Tomei a , E.M. Gregores a , b , D.S. Lemos a ,P.G. Mercadante a , b , S.F. Novaes a , Sandra S. Padula a Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia,Bulgaria
A. Aleksandrov, G. Antchev, I. Atanasov, R. Hadjiiska, P. Iaydjiev, M. Misheva, M. Rodozov,M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov, A. Petrov
Beihang University, Beijing, China
T. Cheng, W. Fang , Q. Guo, H. Wang, L. Yuan Department of Physics, Tsinghua University, Beijing, China
M. Ahmad, G. Bauer, Z. Hu, Y. Wang, K. Yi
Institute of High Energy Physics, Beijing, China
E. Chapon, G.M. Chen , H.S. Chen , M. Chen, T. Javaid , A. Kapoor, D. Leggat, H. Liao,Z.-A. LIU , R. Sharma, A. Spiezia, J. Tao, J. Thomas-wilsker, J. Wang, H. Zhang, S. Zhang ,J. Zhao State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing, China
A. Agapitos, Y. Ban, C. Chen, Q. Huang, A. Levin, Q. Li, M. Lu, X. Lyu, Y. Mao, S.J. Qian,D. Wang, Q. Wang, J. Xiao
Sun Yat-Sen University, Guangzhou, China
Z. You
Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beamApplication (MOE) - Fudan University, Shanghai, China
X. Gao Zhejiang University, Hangzhou, China
M. Xiao
Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C. Florez, J. Fraga, A. Sarkar, M.A. Segura Delgado
Universidad de Antioquia, Medellin, Colombia
J. Jaramillo, J. Mejia Guisao, F. Ramirez, J.D. Ruiz Alvarez, C.A. Salazar Gonz´alez,N. Vanegas Arbelaez
University of Split, Faculty of Electrical Engineering, Mechanical Engineering and NavalArchitecture, Split, Croatia
D. Giljanovic, N. Godinovic, D. Lelas, I. Puljak
University of Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac, T. Sculac
Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, D. Majumder, M. Roguljic, A. Starodumov , T. Susa University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, E. Erodotou, A. Ioannou, G. Kole, M. Kolosova, S. Konstantinou,J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H. Rykaczewski, H. Saka, D. Tsiakkouri
Charles University, Prague, Czech Republic
M. Finger , M. Finger Jr. , A. Kveton, J. Tomsa Escuela Politecnica Nacional, Quito, Ecuador
E. Ayala
Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab Republic of Egypt, EgyptianNetwork of High Energy Physics, Cairo, Egypt
S. Abu Zeid , S. Khalil , E. Salama Center for High Energy Physics (CHEP-FU), Fayoum University, El-Fayoum, Egypt
M.A. Mahmoud, Y. Mohammed National Institute of Chemical Physics and Biophysics, Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M. Kadastik,M. Raidal, C. Veelken
Department of Physics, University of Helsinki, Helsinki, Finland
P. Eerola, L. Forthomme, H. Kirschenmann, K. Osterberg, M. Voutilainen
Helsinki Institute of Physics, Helsinki, Finland
E. Br ¨ucken, F. Garcia, J. Havukainen, V. Karim¨aki, M.S. Kim, R. Kinnunen, T. Lamp´en,K. Lassila-Perini, S. Lehti, T. Lind´en, H. Siikonen, E. Tuominen, J. Tuominiemi
Lappeenranta University of Technology, Lappeenranta, Finland
P. Luukka, T. Tuuva
IRFU, CEA, Universit´e Paris-Saclay, Gif-sur-Yvette, France
C. Amendola, M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F. Ferri, S. Ganjour,A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry, B. Lenzi, E. Locci, J. Malcles,J. Rander, A. Rosowsky, M. ¨O. Sahin, A. Savoy-Navarro , M. Titov, G.B. Yu Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut Polytechniquede Paris, Palaiseau, France
S. Ahuja, F. Beaudette, M. Bonanomi, A. Buchot Perraguin, P. Busson, C. Charlot, O. Davignon,B. Diab, G. Falmagne, R. Granier de Cassagnac, A. Hakimi, I. Kucher, A. Lobanov,C. Martin Perez, M. Nguyen, C. Ochando, P. Paganini, J. Rembser, R. Salerno, J.B. Sauvan,Y. Sirois, A. Zabi, A. Zghiche
Universit´e de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France
J.-L. Agram , J. Andrea, D. Bloch, G. Bourgatte, J.-M. Brom, E.C. Chabert, C. Collard, J.-C. Fontaine , D. Gel´e, U. Goerlach, C. Grimault, A.-C. Le Bihan, P. Van Hove Universit´e de Lyon, Universit´e Claude Bernard Lyon 1, CNRS-IN2P3, Institut de PhysiqueNucl´eaire de Lyon, Villeurbanne, France
E. Asilar, S. Beauceron, C. Bernet, G. Boudoul, C. Camen, A. Carle, N. Chanon, D. Contardo,P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M. Gouzevitch, B. Ille, Sa. Jain, I.B. Laktineh,H. Lattaud, A. Lesauvage, M. Lethuillier, L. Mirabito, K. Shchablo, L. Torterotot, G. Touquet,M. Vander Donckt, S. Viret
Georgian Technical University, Tbilisi, Georgia
A. Khvedelidze , Z. Tsamalaidze RWTH Aachen University, I. Physikalisches Institut, Aachen, Germany
L. Feld, K. Klein, M. Lipinski, D. Meuser, A. Pauls, M. Preuten, M.P. Rauch, J. Schulz,M. Teroerde
RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany
D. Eliseev, M. Erdmann, P. Fackeldey, B. Fischer, S. Ghosh, T. Hebbeker, K. Hoepfner, H. Keller,L. Mastrolorenzo, M. Merschmeyer, A. Meyer, G. Mocellin, S. Mondal, S. Mukherjee, D. Noll, A. Novak, T. Pook, A. Pozdnyakov, Y. Rath, H. Reithler, J. Roemer, A. Schmidt, S.C. Schuler,A. Sharma, S. Wiedenbeck, S. Zaleski
RWTH Aachen University, III. Physikalisches Institut B, Aachen, Germany
C. Dziwok, G. Fl ¨ugge, W. Haj Ahmad , O. Hlushchenko, T. Kress, A. Nowack, C. Pistone,O. Pooth, D. Roy, H. Sert, A. Stahl , T. Ziemons Deutsches Elektronen-Synchrotron, Hamburg, Germany
H. Aarup Petersen, M. Aldaya Martin, P. Asmuss, I. Babounikau, S. Baxter, O. Behnke,A. Berm ´udez Mart´ınez, A.A. Bin Anuar, K. Borras , V. Botta, D. Brunner, A. Campbell,A. Cardini, P. Connor, S. Consuegra Rodr´ıguez, V. Danilov, A. De Wit, M.M. Defranchis,L. Didukh, D. Dom´ınguez Damiani, G. Eckerlin, D. Eckstein, T. Eichhorn, L.I. Estevez Banos,E. Gallo , A. Geiser, A. Giraldi, A. Grohsjean, M. Guthoff, A. Harb, A. Jafari , N.Z. Jomhari,H. Jung, A. Kasem , M. Kasemann, H. Kaveh, C. Kleinwort, J. Knolle, D. Kr ¨ucker, W. Lange,T. Lenz, J. Lidrych, K. Lipka, W. Lohmann , T. Madlener, R. Mankel, I.-A. Melzer-Pellmann,J. Metwally, A.B. Meyer, M. Meyer, M. Missiroli, J. Mnich, A. Mussgiller, V. Myronenko,Y. Otarid, D. P´erez Ad´an, S.K. Pflitsch, D. Pitzl, A. Raspereza, A. Saggio, A. Saibel, M. Savitskyi,V. Scheurer, C. Schwanenberger, A. Singh, R.E. Sosa Ricardo, N. Tonon, O. Turkot, A. Vagnerini,M. Van De Klundert, R. Walsh, D. Walter, Y. Wen, K. Wichmann, C. Wissing, S. Wuchterl,O. Zenaiev, R. Zlebcik University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, K. De Leo, T. Dreyer, A. Ebrahimi, M. Eich, F. Feindt,A. Fr ¨ohlich, C. Garbers, E. Garutti, P. Gunnellini, J. Haller, A. Hinzmann, A. Karavdina,G. Kasieczka, R. Klanner, R. Kogler, T. Kramer, V. Kutzner, J. Lange, T. Lange, A. Malara,C.E.N. Niemeyer, A. Nigamova, K.J. Pena Rodriguez, O. Rieger, P. Schleper, S. Schumann,J. Schwandt, D. Schwarz, J. Sonneveld, H. Stadie, G. Steinbr ¨uck, B. Vormwald, I. Zoi
Karlsruher Institut fuer Technologie, Karlsruhe, Germany
J. Bechtel, T. Berger, E. Butz, R. Caspart, T. Chwalek, W. De Boer, A. Dierlamm, A. Droll,K. El Morabit, N. Faltermann, K. Fl ¨oh, M. Giffels, A. Gottmann, F. Hartmann , C. Heidecker,U. Husemann, I. Katkov , P. Keicher, R. Koppenh ¨ofer, S. Maier, M. Metzler, S. Mitra,Th. M ¨uller, M. Musich, G. Quast, K. Rabbertz, J. Rauser, D. Savoiu, D. Sch¨afer, M. Schnepf,M. Schr ¨oder, D. Seith, I. Shvetsov, H.J. Simonis, R. Ulrich, M. Wassmer, M. Weber, R. Wolf,S. Wozniewski Institute of Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia Paraskevi,Greece
G. Anagnostou, P. Asenov, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas, G. Paspalaki,A. Stakia
National and Kapodistrian University of Athens, Athens, Greece
M. Diamantopoulou, D. Karasavvas, G. Karathanasis, P. Kontaxakis, C.K. Koraka,A. Manousakis-katsikakis, A. Panagiotou, I. Papavergou, N. Saoulidou, K. Theofilatos,E. Tziaferi, K. Vellidis, E. Vourliotis
National Technical University of Athens, Athens, Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis, A. Zacharopoulou
University of Io´annina, Io´annina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, K. Manitara, N. Manthos,I. Papadopoulos, J. Strologas MTA-ELTE Lend ¨ulet CMS Particle and Nuclear Physics Group, E ¨otv ¨os Lor´and University,Budapest, Hungary
M. Bart ´ok , M. Csanad, M.M.A. Gadallah , S. L ¨ok ¨os , P. Major, K. Mandal, A. Mehta,G. Pasztor, O. Sur´anyi, G.I. Veres Wigner Research Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath , F. Sikler, V. Veszpremi, G. Vesztergombi † Institute of Nuclear Research ATOMKI, Debrecen, Hungary
S. Czellar, J. Karancsi , J. Molnar, Z. Szillasi, D. Teyssier Institute of Physics, University of Debrecen, Debrecen, Hungary
P. Raics, Z.L. Trocsanyi, B. Ujvari
Eszterhazy Karoly University, Karoly Robert Campus, Gyongyos, Hungary
T. Csorgo , F. Nemes , T. Novak Indian Institute of Science (IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri, D. Kumar, L. Panwar, P.C. Tiwari
National Institute of Science Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati , D. Dash, C. Kar, P. Mal, T. Mishra, V.K. Muraleedharan Nair Bindhu,A. Nayak , D.K. Sahoo , N. Sur, S.K. Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, G. Chaudhary, S. Chauhan, N. Dhingra , R. Gupta, A. Kaur,S. Kaur, P. Kumari, M. Meena, K. Sandeep, S. Sharma, J.B. Singh, A.K. Virdi University of Delhi, Delhi, India
A. Ahmed, A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, A. Kumar,M. Naimuddin, P. Priyanka, K. Ranjan, A. Shah
Saha Institute of Nuclear Physics, HBNI, Kolkata, India
M. Bharti , R. Bhattacharya, S. Bhattacharya, D. Bhowmik, S. Dutta, S. Ghosh, B. Gomber ,M. Maity , S. Nandan, P. Palit, P.K. Rout, G. Saha, B. Sahu, S. Sarkar, M. Sharan, B. Singh ,S. Thakur Indian Institute of Technology Madras, Madras, India
P.K. Behera, S.C. Behera, P. Kalbhor, A. Muhammad, R. Pradhan, P.R. Pujahari, A. Sharma,A.K. Sikdar
Bhabha Atomic Research Centre, Mumbai, India
D. Dutta, V. Kumar, K. Naskar , P.K. Netrakanti, L.M. Pant, P. Shukla Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, R. Kumar Verma, G.B. Mohanty, U. Sarkar
Tata Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, R. Chudasama, M. Guchait, S. Karmakar, S. Kumar,G. Majumder, K. Mazumdar, S. Mukherjee, D. Roy
Indian Institute of Science Education and Research (IISER), Pune, India
S. Dube, B. Kansal, S. Pandey, A. Rane, A. Rastogi, S. Sharma
Department of Physics, Isfahan University of Technology, Isfahan, Iran
H. Bakhshiansohi , M. Zeinali Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
S. Chenarani , S.M. Etesami, M. Khakzad, M. Mohammadi Najafabadi University College Dublin, Dublin, Ireland
M. Felcini, M. Grunewald
INFN Sezione di Bari a , Universit`a di Bari b , Politecnico di Bari c , Bari, Italy M. Abbrescia a , b , R. Aly a , b ,43 , C. Aruta a , b , A. Colaleo a , D. Creanza a , c , N. De Filippis a , c ,M. De Palma a , b , A. Di Florio a , b , A. Di Pilato a , b , W. Elmetenawee a , b , L. Fiore a , A. Gelmi a , b ,M. Gul a , G. Iaselli a , c , M. Ince a , b , S. Lezki a , b , G. Maggi a , c , M. Maggi a , I. Margjeka a , b ,V. Mastrapasqua a , b , J.A. Merlin a , S. My a , b , S. Nuzzo a , b , A. Pompili a , b , G. Pugliese a , c , A. Ranieri a ,G. Selvaggi a , b , L. Silvestris a , F.M. Simone a , b , R. Venditti a , P. Verwilligen a INFN Sezione di Bologna a , Universit`a di Bologna b , Bologna, Italy G. Abbiendi a , C. Battilana a , b , D. Bonacorsi a , b , L. Borgonovi a , S. Braibant-Giacomelli a , b ,R. Campanini a , b , P. Capiluppi a , b , A. Castro a , b , F.R. Cavallo a , C. Ciocca a , M. Cuffiani a , b ,G.M. Dallavalle a , T. Diotalevi a , b , F. Fabbri a , A. Fanfani a , b , E. Fontanesi a , b , P. Giacomelli a ,L. Giommi a , b , C. Grandi a , L. Guiducci a , b , F. Iemmi a , b , S. Lo Meo a ,44 , S. Marcellini a , G. Masetti a ,F.L. Navarria a , b , A. Perrotta a , F. Primavera a , b , A.M. Rossi a , b , T. Rovelli a , b , G.P. Siroli a , b , N. Tosi a INFN Sezione di Catania a , Universit`a di Catania b , Catania, Italy S. Albergo a , b ,45 , S. Costa a , b , A. Di Mattia a , R. Potenza a , b , A. Tricomi a , b ,45 , C. Tuve a , b INFN Sezione di Firenze a , Universit`a di Firenze b , Firenze, Italy G. Barbagli a , A. Cassese a , R. Ceccarelli a , b , V. Ciulli a , b , C. Civinini a , R. D’Alessandro a , b , F. Fiori a ,E. Focardi a , b , G. Latino a , b , P. Lenzi a , b , M. Lizzo a , b , M. Meschini a , S. Paoletti a , R. Seidita a , b ,G. Sguazzoni a , L. Viliani a INFN Laboratori Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, D. Piccolo
INFN Sezione di Genova a , Universit`a di Genova b , Genova, Italy M. Bozzo a , b , F. Ferro a , R. Mulargia a , b , E. Robutti a , S. Tosi a , b INFN Sezione di Milano-Bicocca a , Universit`a di Milano-Bicocca b , Milano, Italy A. Benaglia a , A. Beschi a , b , F. Brivio a , b , F. Cetorelli a , b , V. Ciriolo a , b ,21 , F. De Guio a , b ,M.E. Dinardo a , b , P. Dini a , S. Gennai a , A. Ghezzi a , b , P. Govoni a , b , L. Guzzi a , b , M. Malberti a ,S. Malvezzi a , A. Massironi a , D. Menasce a , F. Monti a , b , L. Moroni a , M. Paganoni a , b , D. Pedrini a ,S. Ragazzi a , b , T. Tabarelli de Fatis a , b , D. Valsecchi a , b ,21 , D. Zuolo a , b INFN Sezione di Napoli a , Universit`a di Napoli ’Federico II’ b , Napoli, Italy, Universit`a dellaBasilicata c , Potenza, Italy, Universit`a G. Marconi d , Roma, Italy S. Buontempo a , N. Cavallo a , c , A. De Iorio a , b , F. Fabozzi a , c , F. Fienga a , A.O.M. Iorio a , b , L. Lista a , b ,S. Meola a , d ,21 , P. Paolucci a ,21 , B. Rossi a , C. Sciacca a , b , E. Voevodina a , b INFN Sezione di Padova a , Universit`a di Padova b , Padova, Italy, Universit`a di Trento c ,Trento, Italy P. Azzi a , N. Bacchetta a , D. Bisello a , b , P. Bortignon a , A. Bragagnolo a , b , R. Carlin a , b , P. Checchia a ,P. De Castro Manzano a , T. Dorigo a , F. Gasparini a , b , U. Gasparini a , b , S.Y. Hoh a , b , L. Layer a ,46 ,M. Margoni a , b , A.T. Meneguzzo a , b , M. Presilla a , b , P. Ronchese a , b , R. Rossin a , b , F. Simonetto a , b ,G. Strong a , M. Tosi a , b , H. YARAR a , b , M. Zanetti a , b , P. Zotto a , b , A. Zucchetta a , b , G. Zumerle a , b INFN Sezione di Pavia a , Universit`a di Pavia b , Pavia, Italy C. Aime‘ a , b , A. Braghieri a , S. Calzaferri a , b , D. Fiorina a , b , P. Montagna a , b , S.P. Ratti a , b , V. Re a ,M. Ressegotti a , b , C. Riccardi a , b , P. Salvini a , I. Vai a , P. Vitulo a , b INFN Sezione di Perugia a , Universit`a di Perugia b , Perugia, Italy M. Biasini a , b , G.M. Bilei a , D. Ciangottini a , b , L. Fan `o a , b , P. Lariccia a , b , G. Mantovani a , b ,V. Mariani a , b , M. Menichelli a , F. Moscatelli a , A. Piccinelli a , b , A. Rossi a , b , A. Santocchia a , b ,D. Spiga a , T. Tedeschi a , b INFN Sezione di Pisa a , Universit`a di Pisa b , Scuola Normale Superiore di Pisa c , Pisa, Italy K. Androsov a , P. Azzurri a , G. Bagliesi a , V. Bertacchi a , c , L. Bianchini a , T. Boccali a , R. Castaldi a ,M.A. Ciocci a , b , R. Dell’Orso a , M.R. Di Domenico a , b , S. Donato a , L. Giannini a , c , A. Giassi a ,M.T. Grippo a , F. Ligabue a , c , E. Manca a , c , G. Mandorli a , c , A. Messineo a , b , F. Palla a , G. Ramirez-Sanchez a , c , A. Rizzi a , b , G. Rolandi a , c , S. Roy Chowdhury a , c , A. Scribano a , N. Shafiei a , b ,P. Spagnolo a , R. Tenchini a , G. Tonelli a , b , N. Turini a , A. Venturi a , P.G. Verdini a INFN Sezione di Roma a , Sapienza Universit`a di Roma b , Rome, Italy F. Cavallari a , M. Cipriani a , b , D. Del Re a , b , E. Di Marco a , M. Diemoz a , E. Longo a , b , P. Meridiani a ,G. Organtini a , b , F. Pandolfi a , R. Paramatti a , b , C. Quaranta a , b , S. Rahatlou a , b , C. Rovelli a ,F. Santanastasio a , b , L. Soffi a , b , R. Tramontano a , b INFN Sezione di Torino a , Universit`a di Torino b , Torino, Italy, Universit`a del PiemonteOrientale c , Novara, Italy N. Amapane a , b , R. Arcidiacono a , c , S. Argiro a , b , M. Arneodo a , c , N. Bartosik a , R. Bellan a , b ,A. Bellora a , b , J. Berenguer Antequera a , b , C. Biino a , A. Cappati a , b , N. Cartiglia a , S. Cometti a ,M. Costa a , b , R. Covarelli a , b , N. Demaria a , B. Kiani a , b , F. Legger a , C. Mariotti a , S. Maselli a ,E. Migliore a , b , V. Monaco a , b , E. Monteil a , b , M. Monteno a , M.M. Obertino a , b , G. Ortona a ,L. Pacher a , b , N. Pastrone a , M. Pelliccioni a , G.L. Pinna Angioni a , b , M. Ruspa a , c , R. Salvatico a , b ,F. Siviero a , b , V. Sola a , A. Solano a , b , D. Soldi a , b , A. Staiano a , M. Tornago a , b , D. Trocino a , b INFN Sezione di Trieste a , Universit`a di Trieste b , Trieste, Italy S. Belforte a , V. Candelise a , b , M. Casarsa a , F. Cossutti a , A. Da Rold a , b , G. Della Ricca a , b ,F. Vazzoler a , b Kyungpook National University, Daegu, Korea
S. Dogra, C. Huh, B. Kim, D.H. Kim, G.N. Kim, J. Lee, S.W. Lee, C.S. Moon, Y.D. Oh, S.I. Pak,B.C. Radburn-Smith, S. Sekmen, Y.C. Yang
Chonnam National University, Institute for Universe and Elementary Particles, Kwangju,Korea
H. Kim, D.H. Moon
Hanyang University, Seoul, Korea
B. Francois, T.J. Kim, J. Park
Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, S. Ha, B. Hong, K. Lee, K.S. Lee, J. Lim, J. Park, S.K. Park, J. Yoo
Kyung Hee University, Department of Physics, Seoul, Republic of Korea
J. Goh, A. Gurtu
Sejong University, Seoul, Korea
H.S. Kim, Y. Kim
Seoul National University, Seoul, Korea
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, S. Ko, H. Kwon, H. Lee, K. Lee, S. Lee,K. Nam, B.H. Oh, M. Oh, S.B. Oh, H. Seo, U.K. Yang, I. Yoon0
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, S. Ko, H. Kwon, H. Lee, K. Lee, S. Lee,K. Nam, B.H. Oh, M. Oh, S.B. Oh, H. Seo, U.K. Yang, I. Yoon0 University of Seoul, Seoul, Korea
D. Jeon, J.H. Kim, B. Ko, J.S.H. Lee, I.C. Park, Y. Roh, D. Song, I.J. Watson
Yonsei University, Department of Physics, Seoul, Korea
H.D. Yoo
Sungkyunkwan University, Suwon, Korea
Y. Choi, C. Hwang, Y. Jeong, H. Lee, Y. Lee, I. Yu
College of Engineering and Technology, American University of the Middle East (AUM),Kuwait
Y. Maghrbi
Riga Technical University, Riga, Latvia
V. Veckalns Vilnius University, Vilnius, Lithuania
A. Juodagalvis, A. Rinkevicius, G. Tamulaitis, A. Vaitkevicius
National Centre for Particle Physics, Universiti Malaya, Kuala Lumpur, Malaysia
W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli
Universidad de Sonora (UNISON), Hermosillo, Mexico
J.F. Benitez, A. Castaneda Hernandez, J.A. Murillo Quijada, L. Valencia Palomo
Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
G. Ayala, H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz , R. Lopez-Fernandez, C.A. Mondragon Herrera, D.A. Perez Navarro, A. Sanchez-Hernandez Universidad Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, M. Ramirez-Garcia, F. Vazquez Valencia
Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada
Universidad Aut ´onoma de San Luis Potos´ı, San Luis Potos´ı, Mexico
A. Morelos Pineda
University of Montenegro, Podgorica, Montenegro
J. Mijuskovic , N. Raicevic University of Auckland, Auckland, New Zealand
D. Krofcheck
University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler
National Centre for Physics, Quaid-I-Azam University, Islamabad, Pakistan
A. Ahmad, M.I. Asghar, A. Awais, M.I.M. Awan, H.R. Hoorani, W.A. Khan, M.A. Shah,M. Shoaib, M. Waqas
AGH University of Science and Technology Faculty of Computer Science, Electronics andTelecommunications, Krakow, Poland
V. Avati, L. Grzanka, M. Malawski
National Centre for Nuclear Research, Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. G ´orski, M. Kazana, M. Szleper, P. Traczyk,P. Zalewski Institute of Experimental Physics, Faculty of Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, K. Doroba, A. Kalinowski, M. Konecki, J. Krolikowski, M. Walczak
Laborat ´orio de Instrumenta¸c˜ao e F´ısica Experimental de Part´ıculas, Lisboa, Portugal
M. Araujo, P. Bargassa, D. Bastos, A. Boletti, P. Faccioli, M. Gallinaro, J. Hollar, N. Leonardo,T. Niknejad, J. Seixas, K. Shchelina, O. Toldaiev, J. Varela
Joint Institute for Nuclear Research, Dubna, Russia
V. Alexakhin, P. Bunin, A. Golunov, I. Golutvin, N. Gorbounov, I. Gorbunov, V. Karjavine,V. Korenkov, A. Lanev, A. Malakhov, V. Matveev , V. Palichik, V. Perelygin, M. Savina,V. Shalaev, S. Shmatov, S. Shulha, O. Teryaev, N. Voytishin, B.S. Yuldashev , A. Zarubin,I. Zhizhin Petersburg Nuclear Physics Institute, Gatchina (St. Petersburg), Russia
G. Gavrilov, V. Golovtcov, Y. Ivanov, V. Kim , E. Kuznetsova , V. Murzin, V. Oreshkin,I. Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Volkov, A. Vorobyev Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov, N. Krasnikov,A. Pashenkov, G. Pivovarov, D. Tlisov † , A. Toropin Institute for Theoretical and Experimental Physics named by A.I. Alikhanov of NRC‘Kurchatov Institute’, Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, A. Nikitenko , V. Popov, G. Safronov,A. Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A. Zhokin Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev
National Research Nuclear University ’Moscow Engineering Physics Institute’ (MEPhI),Moscow, Russia
R. Chistov , M. Danilov , A. Oskin, P. Parygin, S. Polikarpov 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, S. Petrushanko, V. Savrin Novosibirsk State University (NSU), Novosibirsk, Russia
V. Blinov , T. Dimova , L. Kardapoltsev , I. Ovtin , Y. Skovpen Institute for High Energy Physics of National Research Centre ‘Kurchatov Institute’,Protvino, Russia
I. Azhgirey, I. Bayshev, V. Kachanov, A. Kalinin, D. Konstantinov, V. Petrov, R. Ryutin, A. Sobol,S. Troshin, N. Tyurin, A. Uzunian, A. Volkov
National Research Tomsk Polytechnic University, Tomsk, Russia
A. Babaev, A. Iuzhakov, V. Okhotnikov, L. Sukhikh
Tomsk State University, Tomsk, Russia
V. Borchsh, V. Ivanchenko, E. Tcherniaev University of Belgrade: Faculty of Physics and VINCA Institute of Nuclear Sciences,Belgrade, Serbia
P. Adzic , P. Cirkovic, M. Dordevic, P. Milenovic, J. Milosevic Centro de Investigaciones Energ´eticas Medioambientales y Tecnol ´ogicas (CIEMAT),Madrid, Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, A. ´Alvarez Fern´andez, I. Bachiller, M. Barrio Luna,Cristina F. Bedoya, C.A. Carrillo Montoya, M. Cepeda, M. Cerrada, N. Colino, B. De La Cruz,A. Delgado Peris, J.P. Fern´andez Ramos, J. Flix, M.C. Fouz, O. Gonzalez Lopez, S. Goy Lopez,J.M. Hernandez, M.I. Josa, J. Le ´on Holgado, D. Moran, ´A. Navarro Tobar, A. P´erez-Calero Yzquierdo, J. Puerta Pelayo, I. Redondo, L. Romero, S. S´anchez Navas, M.S. Soares,A. Triossi, L. Urda G ´omez, C. Willmott
Universidad Aut ´onoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Troc ´oniz, R. Reyes-Almanza
Universidad de Oviedo, Instituto Universitario de Ciencias y Tecnolog´ıas Espaciales deAsturias (ICTEA), Oviedo, Spain
B. Alvarez Gonzalez, J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras, I. Gonza-lez Caballero, E. Palencia Cortezon, C. Ram ´on ´Alvarez, J. Ripoll Sau, V. Rodr´ıguez Bouza,S. Sanchez Cruz, A. Trapote
Instituto de F´ısica de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, Spain
J.A. Brochero Cifuentes, I.J. Cabrillo, A. Calderon, B. Chazin Quero, J. Duarte Campderros,M. Fernandez, P.J. Fern´andez Manteca, A. Garc´ıa Alonso, G. Gomez, C. Martinez Rivero,P. Martinez Ruiz del Arbol, F. Matorras, J. Piedra Gomez, C. Prieels, F. Ricci-Tam, T. Rodrigo,A. Ruiz-Jimeno, L. Scodellaro, I. Vila, J.M. Vizan Garcia
University of Colombo, Colombo, Sri Lanka
MK Jayananda, B. Kailasapathy , D.U.J. Sonnadara, DDC Wickramarathna University of Ruhuna, Department of Physics, Matara, Sri Lanka
W.G.D. Dharmaratna, K. Liyanage, N. Perera, N. Wickramage
CERN, European Organization for Nuclear Research, Geneva, Switzerland
T.K. Aarrestad, D. Abbaneo, E. Auffray, G. Auzinger, J. Baechler, P. Baillon, A.H. Ball, D. Barney,J. Bendavid, N. Beni, M. Bianco, A. Bocci, E. Bossini, E. Brondolin, T. Camporesi, G. Cerminara,L. Cristella, D. d’Enterria, A. Dabrowski, N. Daci, V. Daponte, A. David, A. De Roeck,M. Deile, R. Di Maria, M. Dobson, M. D ¨unser, N. Dupont, A. Elliott-Peisert, N. Emriskova,F. Fallavollita , D. Fasanella, S. Fiorendi, A. Florent, G. Franzoni, J. Fulcher, W. Funk, S. Giani,D. Gigi, K. Gill, F. Glege, L. Gouskos, M. Guilbaud, D. Gulhan, M. Haranko, J. Hegeman,Y. Iiyama, V. Innocente, T. James, P. Janot, J. Kaspar, J. Kieseler, M. Komm, N. Kratochwil,C. Lange, S. Laurila, P. Lecoq, K. Long, C. Lourenc¸o, L. Malgeri, S. Mallios, M. Mannelli,F. Meijers, S. Mersi, E. Meschi, F. Moortgat, M. Mulders, S. Orfanelli, L. Orsini, F. Pantaleo ,L. Pape, E. Perez, M. Peruzzi, A. Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, T. Quast,D. Rabady, A. Racz, M. Rieger, M. Rovere, H. Sakulin, J. Salfeld-Nebgen, S. Scarfi, C. Sch¨afer,C. Schwick, M. Selvaggi, A. Sharma, P. Silva, W. Snoeys, P. Sphicas , S. Summers, V.R. Tavolaro,D. Treille, A. Tsirou, G.P. Van Onsem, A. Vartak, M. Verzetti, K.A. Wozniak, W.D. Zeuner Paul Scherrer Institut, Villigen, Switzerland
L. Caminada , W. Erdmann, R. Horisberger, Q. Ingram, H.C. Kaestli, D. Kotlinski,U. Langenegger, T. Rohe ETH Zurich - Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland
M. Backhaus, P. Berger, A. Calandri, N. Chernyavskaya, A. De Cosa, G. Dissertori, M. Dittmar,M. Doneg`a, C. Dorfer, T. Gadek, T.A. G ´omez Espinosa, C. Grab, D. Hits, W. Lustermann, A.-M. Lyon, R.A. Manzoni, M.T. Meinhard, F. Micheli, F. Nessi-Tedaldi, J. Niedziela, F. Pauss,V. Perovic, G. Perrin, S. Pigazzini, M.G. Ratti, M. Reichmann, C. Reissel, T. Reitenspiess,B. Ristic, D. Ruini, D.A. Sanz Becerra, M. Sch ¨onenberger, V. Stampf, J. Steggemann ,M.L. Vesterbacka Olsson, R. Wallny, D.H. Zhu Universit¨at Z ¨urich, Zurich, Switzerland
C. Amsler , C. Botta, D. Brzhechko, M.F. Canelli, R. Del Burgo, J.K. Heikkil¨a, M. Huwiler,A. Jofrehei, B. Kilminster, S. Leontsinis, A. Macchiolo, P. Meiring, V.M. Mikuni, U. Molinatti,I. Neutelings, G. Rauco, A. Reimers, P. Robmann, K. Schweiger, Y. Takahashi National Central University, Chung-Li, Taiwan
C. Adloff , C.M. Kuo, W. Lin, A. Roy, T. Sarkar , S.S. Yu National Taiwan University (NTU), Taipei, Taiwan
L. Ceard, P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.y. Li, R.-S. Lu, E. Paganis,A. Psallidas, A. Steen, E. Yazgan
Chulalongkorn University, Faculty of Science, Department of Physics, Bangkok, Thailand
B. Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas
C¸ ukurova University, Physics Department, Science and Art Faculty, Adana, Turkey
F. Boran, S. Damarseckin , Z.S. Demiroglu, F. Dolek, C. Dozen , I. Dumanoglu , E. Eskut,G. Gokbulut, Y. Guler, E. Gurpinar Guler , I. Hos , C. Isik, E.E. Kangal , O. Kara,A. Kayis Topaksu, U. Kiminsu, G. Onengut, K. Ozdemir , A. Polatoz, A.E. Simsek, B. Tali ,U.G. Tok, S. Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle East Technical University, Physics Department, Ankara, Turkey
B. Isildak , G. Karapinar , K. Ocalan , M. Yalvac Bogazici University, Istanbul, Turkey
B. Akgun, I.O. Atakisi, E. G ¨ulmez, M. Kaya , O. Kaya , ¨O. ¨Ozc¸elik, S. Tekten , E.A. Yetkin Istanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak , Y. Komurcu, S. Sen Istanbul University, Istanbul, Turkey
F. Aydogmus Sen, S. Cerci , B. Kaynak, S. Ozkorucuklu, D. Sunar Cerci Institute for Scintillation Materials of National Academy of Science of Ukraine, Kharkov,Ukraine
B. Grynyov
National Scientific Center, Kharkov Institute of Physics and Technology, Kharkov, Ukraine
L. Levchuk
University of Bristol, Bristol, United Kingdom
E. Bhal, S. Bologna, J.J. Brooke, E. Clement, D. Cussans, H. Flacher, J. Goldstein, G.P. Heath,H.F. Heath, L. Kreczko, B. Krikler, S. Paramesvaran, T. Sakuma, S. Seif El Nasr-Storey, V.J. Smith,N. Stylianou , J. Taylor, A. Titterton Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev , C. Brew, R.M. Brown, D.J.A. Cockerill, K.V. Ellis, K. Harder, S. Harper, J. Linacre, K. Manolopoulos, D.M. Newbold, E. Olaiya, D. Petyt, T. Reis, T. Schuh,C.H. Shepherd-Themistocleous, A. Thea, I.R. Tomalin, T. Williams
Imperial College, London, United Kingdom
R. Bainbridge, P. Bloch, S. Bonomally, J. Borg, S. Breeze, O. Buchmuller, A. Bundock, V. Cepaitis,G.S. Chahal , D. Colling, P. Dauncey, G. Davies, M. Della Negra, G. Fedi, G. Hall, G. Iles,J. Langford, L. Lyons, A.-M. Magnan, S. Malik, A. Martelli, V. Milosevic, J. Nash , V. Palladino,M. Pesaresi, D.M. Raymond, A. Richards, A. Rose, E. Scott, C. Seez, A. Shtipliyski, M. Stoye,A. Tapper, K. Uchida, T. Virdee , N. Wardle, S.N. Webb, D. Winterbottom, A.G. Zecchinelli Brunel University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, C.K. Mackay, I.D. Reid, L. Teodorescu, S. Zahid
Baylor University, Waco, USA
S. Abdullin, A. Brinkerhoff, K. Call, B. Caraway, J. Dittmann, K. Hatakeyama, A.R. Kanuganti,C. Madrid, B. McMaster, N. Pastika, S. Sawant, C. Smith, J. Wilson
Catholic University of America, Washington, DC, USA
R. Bartek, A. Dominguez, R. Uniyal, A.M. Vargas Hernandez
The University of Alabama, Tuscaloosa, USA
A. Buccilli, O. Charaf, S.I. Cooper, S.V. Gleyzer, C. Henderson, C.U. Perez, P. Rumerio, C. West
Boston University, Boston, USA
A. Akpinar, A. Albert, D. Arcaro, C. Cosby, Z. Demiragli, D. Gastler, J. Rohlf, K. Salyer,D. Sperka, D. Spitzbart, I. Suarez, S. Yuan, D. Zou
Brown University, Providence, USA
G. Benelli, B. Burkle, X. Coubez , D. Cutts, Y.t. Duh, M. Hadley, U. Heintz, J.M. Hogan ,K.H.M. Kwok, E. Laird, G. Landsberg, K.T. Lau, J. Lee, M. Narain, S. Sagir , R. Syarif, E. Usai,W.Y. Wong, D. Yu, W. Zhang University of California, Davis, Davis, USA
R. Band, C. Brainerd, R. Breedon, M. Calderon De La Barca Sanchez, M. Chertok, J. Conway,R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk, F. Jensen, W. Ko † , O. Kukral, R. Lander,M. Mulhearn, D. Pellett, J. Pilot, M. Shi, D. Taylor, K. Tos, M. Tripathi, Y. Yao, F. Zhang University of California, Los Angeles, USA
M. Bachtis, R. Cousins, A. Dasgupta, D. Hamilton, J. Hauser, M. Ignatenko, M.A. Iqbal, T. Lam,N. Mccoll, W.A. Nash, S. Regnard, D. Saltzberg, C. Schnaible, B. Stone, V. Valuev
University of California, Riverside, Riverside, USA
K. Burt, Y. Chen, R. Clare, J.W. Gary, G. Hanson, G. Karapostoli, O.R. Long, N. Manganelli,M. Olmedo Negrete, M.I. Paneva, W. Si, S. Wimpenny, Y. Zhang
University of California, San Diego, La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, S. Cooperstein, N. Deelen, J. Duarte, R. Gerosa, D. Gilbert,V. Krutelyov, J. Letts, M. Masciovecchio, S. May, S. Padhi, M. Pieri, V. Sharma, M. Tadel,F. W ¨urthwein, A. Yagil
University of California, Santa Barbara - Department of Physics, Santa Barbara, USA
N. Amin, C. Campagnari, M. Citron, A. Dorsett, V. Dutta, J. Incandela, B. Marsh, H. Mei,A. Ovcharova, H. Qu, M. Quinnan, J. Richman, U. Sarica, D. Stuart, S. Wang California Institute of Technology, Pasadena, USA
A. Bornheim, O. Cerri, I. Dutta, J.M. Lawhorn, N. Lu, J. Mao, H.B. Newman, J. Ngadiuba,T.Q. Nguyen, J. Pata, M. Spiropulu, J.R. Vlimant, C. Wang, S. Xie, Z. Zhang, R.Y. Zhu
Carnegie Mellon University, Pittsburgh, USA
J. Alison, M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, M. Sun, I. Vorobiev
University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, E. MacDonald, T. Mulholland, R. Patel, A. Perloff, K. Stenson,K.A. Ulmer, S.R. Wagner
Cornell University, Ithaca, USA
J. Alexander, Y. Cheng, J. Chu, D.J. Cranshaw, A. Datta, A. Frankenthal, K. Mcdermott,J. Monroy, J.R. Patterson, D. Quach, A. Ryd, W. Sun, S.M. Tan, Z. Tao, J. Thom, P. Wittich,M. Zientek
Fermi National Accelerator Laboratory, Batavia, USA
M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S. Banerjee, L.A.T. Bauerdick,A. Beretvas, D. Berry, J. Berryhill, P.C. Bhat, K. Burkett, J.N. Butler, A. Canepa, G.B. Cerati,H.W.K. Cheung, F. Chlebana, M. Cremonesi, V.D. Elvira, J. Freeman, Z. Gecse, E. Gottschalk,L. Gray, D. Green, S. Gr ¨unendahl, O. Gutsche, R.M. Harris, S. Hasegawa, R. Heller, T.C. Herwig,J. Hirschauer, B. Jayatilaka, S. Jindariani, M. Johnson, U. Joshi, P. Klabbers, T. Klijnsma,B. Klima, M.J. Kortelainen, S. Lammel, D. Lincoln, R. Lipton, M. Liu, T. Liu, J. Lykken,K. Maeshima, D. Mason, P. McBride, P. Merkel, S. Mrenna, S. Nahn, V. O’Dell, V. Papadimitriou,K. Pedro, C. Pena , O. Prokofyev, F. Ravera, A. Reinsvold Hall, L. Ristori, B. Schneider,E. Sexton-Kennedy, N. Smith, A. Soha, W.J. Spalding, L. Spiegel, S. Stoynev, J. Strait, L. Taylor,S. Tkaczyk, N.V. Tran, L. Uplegger, E.W. Vaandering, H.A. Weber, A. Woodard University of Florida, Gainesville, USA
D. Acosta, P. Avery, D. Bourilkov, L. Cadamuro, V. Cherepanov, F. Errico, R.D. Field,D. Guerrero, B.M. Joshi, M. Kim, J. Konigsberg, A. Korytov, K.H. Lo, K. Matchev, N. Menendez,G. Mitselmakher, D. Rosenzweig, K. Shi, J. Sturdy, J. Wang, S. Wang, E. Yigitbasi, X. Zuo
Florida State University, Tallahassee, USA
T. Adams, A. Askew, D. Diaz, R. Habibullah, S. Hagopian, V. Hagopian, K.F. Johnson,R. Khurana, T. Kolberg, G. Martinez, H. Prosper, C. Schiber, R. Yohay, J. Zhang
Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, S. Butalla, T. Elkafrawy , M. Hohlmann, D. Noonan, M. Rahmani,M. Saunders, F. Yumiceva University of Illinois at Chicago (UIC), Chicago, USA
M.R. Adams, L. Apanasevich, H. Becerril Gonzalez, R. Cavanaugh, X. Chen, S. Dittmer,O. Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, C. Mills, G. Oh, T. Roy, M.B. Tonjes,N. Varelas, J. Viinikainen, X. Wang, Z. Wu, Z. Ye
The University of Iowa, Iowa City, USA
M. Alhusseini, K. Dilsiz , S. Durgut, R.P. Gandrajula, M. Haytmyradov, V. Khristenko,O.K. K ¨oseyan, J.-P. Merlo, A. Mestvirishvili , A. Moeller, J. Nachtman, H. Ogul , Y. Onel,F. Ozok , A. Penzo, C. Snyder, E. Tiras , J. Wetzel Johns Hopkins University, Baltimore, USA
O. Amram, B. Blumenfeld, L. Corcodilos, M. Eminizer, A.V. Gritsan, S. Kyriacou,P. Maksimovic, C. Mantilla, J. Roskes, M. Swartz, T. ´A. V´ami The University of Kansas, Lawrence, USA
C. Baldenegro Barrera, P. Baringer, A. Bean, A. Bylinkin, T. Isidori, S. Khalil, J. King,G. Krintiras, A. Kropivnitskaya, C. Lindsey, N. Minafra, M. Murray, C. Rogan, C. Royon,S. Sanders, E. Schmitz, J.D. Tapia Takaki, Q. Wang, J. Williams, G. Wilson
Kansas State University, Manhattan, USA
S. Duric, A. Ivanov, K. Kaadze, D. Kim, Y. Maravin, T. Mitchell, A. Modak, A. Mohammadi
Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright
University of Maryland, College Park, USA
E. Adams, A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, N.J. Hadley, S. Jabeen, G.Y. Jeng,R.G. Kellogg, T. Koeth, A.C. Mignerey, S. Nabili, M. Seidel, A. Skuja, S.C. Tonwar, L. Wang,K. Wong
Massachusetts Institute of Technology, Cambridge, USA
D. Abercrombie, B. Allen, R. Bi, S. Brandt, W. Busza, I.A. Cali, Y. Chen, M. D’Alfonso,G. Gomez Ceballos, M. Goncharov, P. Harris, D. Hsu, M. Hu, M. Klute, D. Kovalskyi, J. Krupa,Y.-J. Lee, P.D. Luckey, B. Maier, A.C. Marini, C. Mcginn, C. Mironov, S. Narayanan, X. Niu,C. Paus, D. Rankin, C. Roland, G. Roland, Z. Shi, G.S.F. Stephans, K. Sumorok, K. Tatar,D. Velicanu, J. Wang, T.W. Wang, Z. Wang, B. Wyslouch
University of Minnesota, Minneapolis, USA
R.M. Chatterjee, A. Evans, P. Hansen, J. Hiltbrand, Sh. Jain, M. Krohn, Y. Kubota, Z. Lesko,J. Mans, M. Revering, R. Rusack, R. Saradhy, N. Schroeder, N. Strobbe, M.A. Wadud
University of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros
University of Nebraska-Lincoln, Lincoln, USA
K. Bloom, S. Chauhan, D.R. Claes, C. Fangmeier, L. Finco, F. Golf, J.R. Gonz´alez Fern´andez,C. Joo, I. Kravchenko, J.E. Siado, G.R. Snow † , W. Tabb, F. Yan State University of New York at Buffalo, Buffalo, USA
G. Agarwal, H. Bandyopadhyay, C. Harrington, L. Hay, I. Iashvili, A. Kharchilava, C. McLean,D. Nguyen, J. Pekkanen, S. Rappoccio, B. Roozbahani
Northeastern University, Boston, USA
G. Alverson, E. Barberis, C. Freer, Y. Haddad, A. Hortiangtham, J. Li, G. Madigan, B. Marzocchi,D.M. Morse, V. Nguyen, T. Orimoto, A. Parker, L. Skinnari, A. Tishelman-Charny, T. Wamorkar,B. Wang, A. Wisecarver, D. Wood
Northwestern University, Evanston, USA
S. Bhattacharya, J. Bueghly, Z. Chen, A. Gilbert, T. Gunter, K.A. Hahn, N. Odell, M.H. Schmitt,K. Sung, M. Velasco
University of Notre Dame, Notre Dame, USA
R. Bucci, N. Dev, R. Goldouzian, M. Hildreth, K. Hurtado Anampa, C. Jessop, D.J. Karmgard,K. Lannon, N. Loukas, N. Marinelli, I. Mcalister, F. Meng, K. Mohrman, Y. Musienko ,R. Ruchti, P. Siddireddy, S. Taroni, M. Wayne, A. Wightman, M. Wolf, L. Zygala The Ohio State University, Columbus, USA
J. Alimena, B. Bylsma, B. Cardwell, L.S. Durkin, B. Francis, C. Hill, A. Lefeld, B.L. Winer,B.R. Yates Princeton University, Princeton, USA
B. Bonham, P. Das, G. Dezoort, A. Dropulic, P. Elmer, B. Greenberg, N. Haubrich,S. Higginbotham, A. Kalogeropoulos, G. Kopp, S. Kwan, D. Lange, M.T. Lucchini, J. Luo,D. Marlow, K. Mei, I. Ojalvo, J. Olsen, C. Palmer, P. Pirou´e, D. Stickland, C. Tully
University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg
Purdue University, West Lafayette, USA
V.E. Barnes, R. Chawla, S. Das, L. Gutay, M. Jones, A.W. Jung, D. Kondratyev, G. Negro,N. Neumeister, C.C. Peng, S. Piperov, A. Purohit, H. Qiu, J.F. Schulte, M. Stojanovic ,N. Trevisani, F. Wang, A. Wildridge, R. Xiao, W. Xie Purdue University Northwest, Hammond, USA
J. Dolen, N. Parashar
Rice University, Houston, USA
A. Baty, S. Dildick, K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Kilpatrick, A. Kumar, W. Li,B.P. Padley, R. Redjimi, J. Roberts † , J. Rorie, W. Shi, A.G. Stahl Leiton University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, J.L. Dulemba, C. Fallon, T. Ferbel, M. Galanti, A. Garcia-Bellido, O. Hindrichs, A. Khukhunaishvili, E. Ranken, R. Taus
Rutgers, The State University of New Jersey, Piscataway, USA
B. Chiarito, J.P. Chou, A. Gandrakota, Y. Gershtein, E. Halkiadakis, A. Hart, M. Heindl,E. Hughes, S. Kaplan, O. Karacheban , I. Laflotte, A. Lath, R. Montalvo, K. Nash, M. Osherson,S. Salur, S. Schnetzer, S. Somalwar, R. Stone, S.A. Thayil, S. Thomas, H. Wang University of Tennessee, Knoxville, USA
H. Acharya, A.G. Delannoy, S. Spanier
Texas A&M University, College Station, USA
O. Bouhali , M. Dalchenko, A. Delgado, R. Eusebi, J. Gilmore, T. Huang, T. Kamon , H. Kim,S. Luo, S. Malhotra, R. Mueller, D. Overton, L. Perni`e, D. Rathjens, A. Safonov Texas Tech University, Lubbock, USA
N. Akchurin, J. Damgov, V. Hegde, S. Kunori, K. Lamichhane, S.W. Lee, T. Mengke,S. Muthumuni, T. Peltola, S. Undleeb, I. Volobouev, Z. Wang, A. Whitbeck
Vanderbilt University, Nashville, USA
E. Appelt, S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H. Ni, K. Padeken,F. Romeo, P. Sheldon, S. Tuo, J. Velkovska
University of Virginia, Charlottesville, USA
M.W. Arenton, B. Cox, G. Cummings, J. Hakala, R. Hirosky, M. Joyce, A. Ledovskoy, A. Li,C. Neu, B. Tannenwald, Y. Wang, E. Wolfe, F. Xia
Wayne State University, Detroit, USA
P.E. Karchin, N. Poudyal, P. Thapa
University of Wisconsin - Madison, Madison, WI, USA
K. Black, T. Bose, J. Buchanan, C. Caillol, S. Dasu, I. De Bruyn, P. Everaerts, C. Galloni,H. He, M. Herndon, A. Herv´e, U. Hussain, A. Lanaro, A. Loeliger, R. Loveless,J. Madhusudanan Sreekala, A. Mallampalli, D. Pinna, A. Savin, V. Shang, V. Sharma,W.H. Smith, D. Teague, S. Trembath-reichert, W. Vetens † : Deceased1: Also at Vienna University of Technology, Vienna, Austria2: Also at Institute of Basic and Applied Sciences, Faculty of Engineering, Arab Academy forScience, Technology and Maritime Transport, Alexandria, Egypt3: Also at Universit´e Libre de Bruxelles, Bruxelles, Belgium4: Also at IRFU, CEA, Universit´e Paris-Saclay, Gif-sur-Yvette, France5: Also at Universidade Estadual de Campinas, Campinas, Brazil6: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil7: Also at UFMS, Nova Andradina, Brazil8: Also at Universidade Federal de Pelotas, Pelotas, Brazil9: Also at Nanjing Normal University Department of Physics, Nanjing, China10: Now at The University of Iowa, Iowa City, USA11: Also at University of Chinese Academy of Sciences, Beijing, China12: Also at Institute for Theoretical and Experimental Physics named by A.I. Alikhanov ofNRC ‘Kurchatov Institute’, Moscow, Russia13: Also at Joint Institute for Nuclear Research, Dubna, Russia14: Also at Ain Shams University, Cairo, Egypt15: Also at Zewail City of Science and Technology, Zewail, Egypt16: Also at British University in Egypt, Cairo, Egypt17: Now at Fayoum University, El-Fayoum, Egypt18: Also at Purdue University, West Lafayette, USA19: Also at Universit´e de Haute Alsace, Mulhouse, France20: Also at Erzincan Binali Yildirim University, Erzincan, Turkey21: Also at CERN, European Organization for Nuclear Research, Geneva, Switzerland22: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany23: Also at University of Hamburg, Hamburg, Germany24: Also at Department of Physics, Isfahan University of Technology, Isfahan, Iran, Isfahan,Iran25: Also at Brandenburg University of Technology, Cottbus, Germany26: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University,Moscow, Russia27: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary, Debrecen,Hungary28: Also at Physics Department, Faculty of Science, Assiut University, Assiut, Egypt29: Also at Eszterhazy Karoly University, Karoly Robert Campus, Gyongyos, Hungary30: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary31: Also at MTA-ELTE Lend ¨ulet CMS Particle and Nuclear Physics Group, E ¨otv ¨os Lor´andUniversity, Budapest, Hungary, Budapest, Hungary32: Also at Wigner Research Centre for Physics, Budapest, Hungary33: Also at IIT Bhubaneswar, Bhubaneswar, India, Bhubaneswar, India34: Also at Institute of Physics, Bhubaneswar, India35: Also at G.H.G. Khalsa College, Punjab, India36: Also at Shoolini University, Solan, India37: Also at University of Hyderabad, Hyderabad, India38: Also at University of Visva-Bharati, Santiniketan, India39: Also at Indian Institute of Technology (IIT), Mumbai, India40: Also at Deutsches Elektronen-Synchrotron, Hamburg, Germany41: Also at Sharif University of Technology, Tehran, Iran42: Also at Department of Physics, University of Science and Technology of Mazandaran, Behshahr, Iran43: Now at INFN Sezione di Bari a , Universit`a di Bari b , Politecnico di Bari c , Bari, Italy44: Also at Italian National Agency for New Technologies, Energy and Sustainable EconomicDevelopment, Bologna, Italy45: Also at Centro Siciliano di Fisica Nucleare e di Struttura Della Materia, Catania, Italy46: Also at Universit`a di Napoli ’Federico II’, NAPOLI, Italy47: Also at Riga Technical University, Riga, Latvia, Riga, Latvia48: Also at Consejo Nacional de Ciencia y Tecnolog´ıa, Mexico City, Mexico49: Also at Institute for Nuclear Research, Moscow, Russia50: Now at National Research Nuclear University ’Moscow Engineering Physics Institute’(MEPhI), Moscow, Russia51: Also at Institute of Nuclear Physics of the Uzbekistan Academy of Sciences, Tashkent,Uzbekistan52: Also at St. Petersburg State Polytechnical University, St. Petersburg, Russia53: Also at University of Florida, Gainesville, USA54: Also at Imperial College, London, United Kingdom55: Also at Moscow Institute of Physics and Technology, Moscow, Russia, Moscow, Russia56: Also at P.N. Lebedev Physical Institute, Moscow, Russia57: Also at California Institute of Technology, Pasadena, USA58: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia59: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia60: Also at Trincomalee Campus, Eastern University, Sri Lanka, Nilaveli, Sri Lanka61: Also at INFN Sezione di Pavia a , Universit`a di Pavia b , Pavia, Italy, Pavia, Italy62: Also at National and Kapodistrian University of Athens, Athens, Greece63: Also at Universit¨at Z ¨urich, Zurich, Switzerland64: Also at Ecole Polytechnique F´ed´erale Lausanne, Lausanne, Switzerland65: Also at Stefan Meyer Institute for Subatomic Physics, Vienna, Austria, Vienna, Austria66: Also at Laboratoire d’Annecy-le-Vieux de Physique des Particules, IN2P3-CNRS, Annecy-le-Vieux, France67: Also at S¸ ırnak University, Sirnak, Turkey68: Also at Department of Physics, Tsinghua University, Beijing, China, Beijing, China69: Also at Near East University, Research Center of Experimental Health Science, Nicosia,Turkey70: Also at Beykent University, Istanbul, Turkey, Istanbul, Turkey71: Also at Istanbul Aydin University, Application and Research Center for Advanced Studies(App. & Res. Cent. for Advanced Studies), Istanbul, Turkey72: Also at Mersin University, Mersin, Turkey73: Also at Piri Reis University, Istanbul, Turkey74: Also at Adiyaman University, Adiyaman, Turkey75: Also at Ozyegin University, Istanbul, Turkey76: Also at Izmir Institute of Technology, Izmir, Turkey77: Also at Necmettin Erbakan University, Konya, Turkey78: Also at Bozok Universitetesi Rekt ¨orl ¨ug ¨u, Yozgat, Turkey79: Also at Marmara University, Istanbul, Turkey80: Also at Milli Savunma University, Istanbul, Turkey81: Also at Kafkas University, Kars, Turkey82: Also at Istanbul Bilgi University, Istanbul, Turkey83: Also at Hacettepe University, Ankara, Turkey84: Also at Vrije Universiteit Brussel, Brussel, Belgium0