Search for nonresonant Higgs boson pair production in final states with two bottom quarks and two photons in proton-proton collisions at s √ = 13 TeV
EEUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH (CERN)
CERN-EP-2020-2222020/11/26
CMS-HIG-19-018
Search for nonresonant Higgs boson pair production infinal states with two bottom quarks and two photons inproton-proton collisions at √ s =
13 TeV
The CMS Collaboration * Abstract
A search for nonresonant production of Higgs boson pairs via gluon-gluon and vec-tor boson fusion processes in final states with two bottom quarks and two photons ispresented. The search uses data from proton-proton collisions at a center-of-mass en-ergy of √ s =
13 TeV recorded with the CMS detector at the LHC, corresponding to anintegrated luminosity of 137 fb − . No significant deviation from the background-onlyhypothesis is observed. An upper limit at 95% confidence level is set on the productof the Higgs boson pair production cross section and branching fraction into γγ bb.The observed (expected) upper limit is determined to be 0.67 (0.45) fb, which corre-sponds to 7.7 (5.2) times the standard model prediction. This search has the highestsensitivity to Higgs boson pair production to date. Assuming all other Higgs bo-son couplings are equal to their values in the standard model, the observed couplingmodifiers of the trilinear Higgs boson self-coupling κ λ and the coupling between apair of Higgs bosons and a pair of vector bosons c are constrained within the ranges − < κ λ < − < c < κ λ are also set by combining this analysis with a search for single Higgs bosons decay-ing to two photons, produced in association with top quark-antiquark pairs, and byperforming a simultaneous fit of κ λ and the top quark Yukawa coupling modifier κ t . Submitted to the Journal of High Energy Physics © 2020 CERN for the benefit of the CMS Collaboration. CC-BY-4.0 license * See Appendix A for the list of collaboration members a r X i v : . [ h e p - e x ] N ov Following the discovery of the Higgs boson (H) by the ATLAS and CMS Collaborations [1–3],there has been significant interest in thoroughly understanding the Brout–Englert–Higgs mech-anism [4, 5]. With the last remaining free parameter, the mass of the Higgs boson ( m H ), nowmeasured to be around 125 GeV, the Higgs boson self-coupling and the structure of the scalarHiggs field potential are precisely predicted in the standard model (SM). Therefore, measuringthe Higgs boson’s trilinear self-coupling λ HHH is of particular importance because it providesvaluable information for reconstructing the shape of the scalar potential.At the CERN LHC, the trilinear self-coupling of the Higgs boson is only directly accessiblevia Higgs boson pair (HH) production. This rare process dominantly occurs via gluon-gluonfusion (ggF). Vector boson fusion (VBF) is the second largest production mode. In the SM, theggF production cross section in proton-proton (pp) collisions at √ s =
13 TeV is 31.05 + − fb [6–12], calculated at next-to-next-to-leading order (NNLO) with the resummation at next-to-next-to-leading-logarithm accuracy and including top-quark mass effects at next-to-leading order(NLO). For VBF, the production cross section is calculated to be 1.726 ± α S ). The cross sections arecalculated for m H =
125 GeV.Contributions from physics beyond the SM (BSM) can significantly enhance the HH produc-tion cross section, as well as change the kinematical properties of the produced Higgs bosonpair, and consequently those of the decay products. The modification of the properties of non-resonant HH production via ggF from BSM effects can be parametrized through an effectiveLagrangian that extends the SM one with dimension-6 operators [16]. This parametrizationresults in five couplings: λ HHH , the coupling between the Higgs boson and the top quark ( y t ),and three additional couplings not present in the SM. Those three couplings represent contactinteractions between two Higgs bosons and two gluons ( c ), between one Higgs boson andtwo gluons ( c g ), and between two Higgs bosons and two top quarks ( c ). The Feynman dia-grams contributing to ggF HH production at leading order (LO) are shown in Fig. 1. All five ofthese couplings are investigated in this analysis.The VBF HH production mode gives access to λ HHH , as well as to the coupling between twovector bosons and the Higgs boson (HVV) and the coupling between a pair of Higgs bosonsand a pair of vector bosons (HHVV). The Feynman diagrams contributing to this productionmode at LO are shown in Fig. 2. While λ HHH is mainly constrained from measurements of HHproduction via ggF, and the HVV coupling modifier ( c V ) is constrained by measurements ofvector boson associated production of a single Higgs boson and the decay of the Higgs bosonto a pair of bosons [17], the HHVV coupling modifier ( c ) is only directly measurable viaVBF HH production. Anomalous values of c are investigated to establish the presence of theHHVV-mediated process as a probe of BSM physics.Previous searches for nonresonant production of a Higgs boson pair via ggF were performedby both the ATLAS and CMS Collaborations using the LHC data collected at √ s = γγ bb channel performed by the ATLAS [24] and CMS [28] Col-laborations using up to 36.1 fb − of pp collision data at √ s =
13 TeV set upper limits at 95%confidence level (CL) on the product of the HH cross section and the branching fraction into γγ bb. The observed upper limits are found to be 24 (30 expected) and 26 (20 expected) timesthe SM expectation for the ATLAS and CMS searches, respectively. Statistical combinations ofsearch results in various decay channels were also performed by the two experiments [22, 29]. Hgg HH y t λ HHH gg HH y t y t gg HH c gg HH c g Hgg HH c g λ HHH
Figure 1: Feynman diagrams of the processes contributing to the production of Higgs bo-son pairs via ggF at LO. The upper diagrams correspond to SM processes, involving the topYukawa coupling y t and the trilinear Higgs boson self-coupling λ HHH , respectively. The lowerdiagrams correspond to BSM processes: the diagram on the left involves the contact interactionof two Higgs bosons with two top quarks ( c ), the middle diagram shows the quartic couplingbetween the Higgs bosons and two gluons ( c ), and the diagram on the right describes thecontact interactions between the Higgs boson and gluons ( c g ). qq VV HH c qq VV HH qq VV HH c V c V λ HHH c V V H qq VV HH c qq VV HH qq VV HH c V c V λ HHH c V V H qq VV HH c qq VV HH qq VV HH c V c V λ HHH c V V H
Figure 2: Feynman diagrams that contribute to the production of Higgs boson pairs via VBFat LO. On the left the diagram involving the HHH vertex ( λ HHH ), in the middle the diagramwith two HVV vertices ( c V ), and on the right the diagram with the HHVV vertex ( c ).Recently, the first search for HH production via VBF was carried out by the ATLAS Collabora-tion in the bbbb channel [30].This paper describes a search for nonresonant production of pairs of Higgs bosons decaying to γγ bb using a data sample of 137 fb − collected by the CMS experiment from 2016 to 2018. The γγ bb final state has a combined branching fraction of 2.633 × − [16] for a Higgs boson massof 125 GeV. This channel is one of the most sensitive to HH production because of the large SMbranching fraction of Higgs boson decays to bottom quarks, the good mass resolution of theH → γγ channel, and relatively low background rates.The analysis targets the main HH production modes: ggF and VBF. Both modes are analyzedfollowing similar strategies. After reducing the nonresonant γγ bb background and the back-ground coming from single Higgs boson production in association with a top quark-antiquarkpair (ttH), the events are categorized into ggF- and VBF-enriched signal regions using a mul-tivariate technique. The signal is extracted from a simultaneous fit to the invariant masses of the Higgs boson candidates in the bb and γγ final states. The analysis described in this paperadvances the previous pp → HH → γγ bb search [28] by a factor of four, benefiting equallyfrom the larger collected data sets, and the innovative analysis techniques. The enhanced sen-sitivity of the present analysis was achieved by improving the b jet energy resolution with adedicated energy regression, introducing new multivariate methods for background rejection,optimizing the event categorization, and adding dedicated VBF categories.Finally, the search for Higgs boson pair production is combined with an independent analysisthat targets ttH production, where the Higgs boson decays to a diphoton pair [31]. The ttHproduction cross section depends on y t , and also includes a trilinear Higgs boson self-couplingcontribution from NLO electroweak corrections [32, 33]. The combination enables λ HHH and y t to be measured simultaneously and provides constraints applicable to a wide range of theo-retical models, where both couplings have anomalous values.This paper is organized as follows: after a brief description of the CMS detector in Section 2, theproduction of Higgs boson pairs is described in Section 3. The data samples and simulation,event reconstruction, and analysis strategy are discussed in Sections 4, 5, and 6, respectively.Sections 7 and 8 are dedicated to the description of the background rejection methods. Theevent categorization is described in Section 9. Section 10 and 11 describe the modeling of thesignal and background, respectively. The systematic uncertainties are discussed in Section 12.Finally, the results are presented in Section 13. The analysis and its results are then summarizedin Section 14. 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 ( η ) coverage provided by the barrel and endcap detec-tors. Muons are detected in gas-ionization chambers embedded in the steel flux-return yokeoutside the solenoid.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. [34].Events of interest are selected using a two-tiered trigger system [35]. The first level (L1), com-posed of custom hardware processors, uses information from the calorimeters and muon de-tectors to select events at a rate of around 100 kHz within a time interval of less than 4 µ s. Thesecond level, known as the high-level trigger, consists of a farm of processors running a versionof the full event reconstruction software optimised for fast processing, and reduces the eventrate to around 1 kHz before data storage [36].The particle-flow algorithm [37] (PF) aims to reconstruct and identify each individual particlein an event (PF candidate), with an optimised combination of information from the variouselements of the CMS detector. The energy of photons is obtained from the ECAL measure-ment. The energy of electrons is determined from a combination of the track momentum atthe main interaction vertex, the corresponding ECAL cluster energy, and the energy sum ofall bremsstrahlung photons attached to the track. The momentum of muons is obtained fromthe curvature of the corresponding track. The energy of charged hadrons is determined from acombination of their momentum measured in the tracker and the matching ECAL and HCAL energy deposits, corrected for zero-suppression effects and for the response function of thecalorimeters to hadronic showers. Finally, the energy of neutral hadrons is obtained from thecorresponding corrected ECAL and HCAL energies.For each event, hadronic jets are clustered from these reconstructed particles using the infraredand collinear safe anti- k T algorithm [38, 39] with a distance parameter of 0.4. Jet momentum isdetermined as the vectorial sum of all particle momenta in the jet, and is found from simula-tion to be, on average, within 5 to 10% of the true momentum over the whole p T spectrum anddetector acceptance. Additional proton-proton interactions within the same or nearby bunchcrossings can contribute additional tracks and calorimetric energy depositions, increasing theapparent jet momentum. To mitigate this effect, tracks identified to be originating from pileupvertices are discarded and an offset correction is applied to correct for remaining contributions.Jet energy corrections are derived from simulation studies so that the average measured energyof jets becomes identical to that of particle level jets. In situ measurements of the momentumbalance in dijet, photon+jet, Z+jet, and multijet events are used to determine any residual dif-ferences between the jet energy scale in data and in simulation, and appropriate corrections aremade [40]. Additional selection criteria are applied to each jet to remove jets potentially dom-inated by instrumental effects or reconstruction failures. The jet energy resolution amountstypically to 15–20% at 30 GeV, 10% at 100 GeV, and 5% at 1 TeV [40].The missing transverse momentum vector (cid:126) p missT is computed as the negative vector sum ofthe transverse momenta of all the PF candidates in an event, and its magnitude is denoted as p missT [41]. The (cid:126) p missT is modified to account for corrections to the energy scale of the recon-structed jets in the event. Nonresonant ggF HH production at the LHC can be described using an effective field theory(EFT) approach [16]. Considering operators up to dimension 6, the tree-level interactions of theHiggs boson are modeled by five parameters. Deviations from the SM values of λ HHH and y t are parametrized as κ λ ≡ λ HHH / λ SMHHH and κ t ≡ y t / y SMt , where the SM values of the couplingsare defined as λ SMHHH ≡ m / ( v ) = y SMt = m t / v ≈ v =
246 GeV is thevacuum expectation value of the Higgs field, and m t ≈
173 GeV is the top quark mass. Theanomalous couplings c , c , and c g are not present in the SM. The corresponding part of theLagrangian can be written as [42]: L HH = κ λ λ SMHHH v H − m t v (cid:0) κ t H + c v H (cid:1) (cid:0) t L t R + h.c. (cid:1) + α S π v (cid:0) c g H − c v H (cid:1) G µν G µν , (1)where t L and t R are the top quark fields with left and right chiralities, respectively. The Higgsboson field is denoted as H, G µν is the gluon field strength tensor, and h . c . denotes the Hermi-tian conjugate.At LO the full cross section of ggF Higgs boson pair production can be expressed by a poly-nomial with 15 terms corresponding to five individual diagrams, shown in Fig. 1, and theirinterference. It has been observed in Ref. [43] that twelve benchmark hypotheses, described byvarious combinations of the five parameters ( κ λ , κ t , c , c g , c ), are able to represent the distri-butions of the main kinematic observables of the HH processes over the full phase space. Theparameter values for these benchmark hypotheses are summarized in Table 1. The simulatedsamples generated with the EFT parameters that describe the twelve benchmark hypotheses are combined to cover all possible kinematic configurations of the EFT parameter space. The spe-cific kinematic configurations at any point in the full 5D parameter space are obtained througha corresponding reweighting procedure [43] that parametrizes the changes in the differentialggF HH cross section.The reweighting procedure described in Ref. [43] to obtain the distributions of the kinematicobservables is implemented for LO only, and cannot be applied to the higher-order simulationbecause of the presence of additional partons at the matrix element level. Therefore, the 12BSM signal benchmark hypotheses summarized in Table 1 are investigated using an LO MonteCarlo (MC) simulation, and only anomalous values of κ λ and κ t are studied with the NLOsimulation, as described in Section 4.Table 1: Coupling parameter values in the SM and in twelve BSM benchmark hypotheses iden-tified using the method described in Ref. [43]. κ λ − κ t c − − − − c g − − − c − − − − − In the SM, three different couplings are involved in HH production via VBF: λ HHH , HVV, andHHVV. The Lagrangians corresponding to the left, middle, and right diagrams in Fig. 2 scalewith c V κ λ , c , and c , respectively, where c and c V are the HHVV and HVV couplingmodifiers, normalized to the SM values. The analyzed data correspond to a total integrated luminosity of 137 fb − and were collectedover a data-taking period spanning three years: 35.9 fb − in 2016, 41.5 fb − in 2017, and 59.4 fb − in 2018. Events are selected using double-photon triggers with asymmetric thresholds on thephoton transverse momenta of p γ >
30 GeV and p γ > ( ) GeV for the data collected dur-ing 2016 (2017 and 2018). In addition, loose calorimetric identification requirements [44], basedon the shape of the electromagnetic shower, the isolation of the photon candidate, and the ratiobetween the hadronic and electromagnetic energy deposit of the shower, are imposed on thephoton candidates at the trigger level.The ggF HH signal samples are simulated at NLO [45–48] including the full top quark massdependence [49] using
POWHEG κ λ . Asshown in Ref. [47] the dependence of the ggF HH cross section on κ λ and κ t can be recon-structed from three terms corresponding to the diagrams involving κ λ , κ t and the interference.Therefore, samples corresponding to any point in the ( κ λ , κ t ) parameter space can be obtainedfrom the linear combination of any three of the generated MC samples with different values of κ λ . A global k-factor is applied to scale the cross section to NNLO accuracy.In addition, LO signal samples are generated for the BSM benchmark hypotheses described inSection 3 using M AD G RAPH MC @ NLO v2.2.2 (2016) or v2.4.2 (2017 and 2018) [50–52]. Thesimulated LO signal samples, corresponding to the 12 BSM benchmark hypotheses, are addedtogether to increase the number of events, and then reweighted to any coupling configuration( κ λ , κ t , c , c g , c ) using generator-level information on the HH system. We apply a globalk-factor to scale the cross section to NNLO accuracy. The VBF HH signal samples are generated at LO [50] using M AD G RAPH MC @ NLO v2.4.2.The simulated samples are generated for different combinations of the coupling modifier val-ues ( κ λ , c V , c ). Similarly to what is done for the ggF HH samples generated at NLO, samplescorresponding to any point in the ( κ λ , c V , c ) parameter space can be obtained from the lin-ear combination of any six of the generated samples. The cross section at LO is then scaled tonext-to-NNLO accuracy.The dominant backgrounds in this search are irreducible prompt diphoton production ( γγ + jets) and the reducible background from γ + jets events, where the jets are misidentified asisolated photons and b jets. Although these backgrounds are estimated using data-drivenmethods, simulated samples are used for the training of multivariate discriminants and theoptimization of the analysis categories. The γγ + jets background is modeled with SHERPA v.2.2.1 [53] at LO and includes up to three additional partons at the matrix element level. Inaddition, a b-enriched diphoton background is generated with
SHERPA at LO requiring up totwo b jets. The γ + jets background is modeled with PYTHIA
POWHEG AD G RAPH MC @ NLO v2.2.2 (2016) / v2.4.2 (2017 and 2018) for ttH, vector boson associated production (VH), andproduction associated with a single top quark. The cross sections and decay branching fractionsare taken from Ref. [16]. The contribution from the other single H decay modes is negligible.All simulated samples are interfaced with
PYTHIA for parton showering and fragmentationwith the standard p T -ordered parton shower (PS) scheme. The underlying event is mod-eled with PYTHIA , using the CUETP8M1 tune for 2016 and the CP5 tune for 2017–2018 [58,59]. PDFs are taken from the NNPDF3.0 [60] NLO (2016) or NNPDF3.1 [61] NNLO (2017and 2018) set for all simulated samples except for the signal simulated at LO, for which thePDF4LHC15 NLO MC set at NLO [60, 62–65] is used. The response of the CMS detector ismodeled using the G
EANT
PYTHIA dipoleshower scheme to model initial-state radiation (ISR) and final-state radiation (FSR) [67]. Thedipole shower scheme correctly takes into account the structure of the color flow between in-coming and outgoing quark lines, and its predictions are found to be in good agreement withthe NNLO QCD calculations, as reported in Ref. [68]. These simulated samples are used toderive the uncertainties associated with the
PYTHIA
PS ISR and FSR parameters.
The photon candidates are reconstructed from energy clusters in the ECAL not linked to charged-particle tracks (with the exception of converted photons). The photon energies measured by theECAL are corrected with a multivariate regression technique based on simulation that accountsfor radiation lost in material upstream of the ECAL and imperfect shower containment [44].The ECAL energy scale in data is corrected using simulated Z → ee events, while the photonenergy in simulated events is smeared to reproduce the resolution measured in data.Photons are identified using a boosted decision tree (BDT)-based multivariate analysis (MVA)technique trained to separate photons from jets (photon ID) [44]. The photon ID is trainedusing variables that describe the shape of the photon electromagnetic shower and the isolation criteria, defined using sums of the transverse momenta of photons, and of charged hadrons,inside a cone of radius ∆ R = √ ( ∆ η ) + ( ∆ φ ) = φ is the azimuthal angle in radians. The imperfect MC simulation modeling of the inputvariables is corrected to match the data using a chained quantile regression method [69] basedon studies of Z → ee events. In this method, a set of BDTs is trained to predict the cumulativedistribution function for a given input. Its prediction is conditional upon the three kinematicvariables ( p T , | η | , φ ) and the global event energy density [44], which are the input variablesto the BDTs. The corrections are then applied to the simulated photons such that the predictedcumulative distribution function of the simulated variables is morphed onto the one observedin data.Events are required to have at least two identified photon candidates that are within the ECALand tracker fiducial region ( | η | < < | η | < < m γγ <
180 GeV, p γ / m γγ > p γ / m γγ > m γγ is the invariant mass of the photon candidates.When more than two photon candidates are found, the photon pair with the highest transversemomentum p γγ T is chosen to construct the Higgs boson candidate.The primary pp interaction vertex in the event is identified using a multivariate techniquebased on a BDT following the same approach described in Ref. [70]. The BDT is trained onsimulated ggH events and has observables related to tracks recoiling against the identifieddiphoton system as inputs. The efficiency of the correct vertex assignment is greater than 99.9%,thanks to the requirement of at least two jets in the γγ bb final state.Jet candidates are required to have p T >
25 GeV and | η | < ∆ R γ j ≡ √ ( ∆ η γ j ) + ( ∆ φ γ j ) > η range is extended for the 2017 and 2018 data-taking years because of the new CMSpixel detector installed during the Phase-1 upgrade [71]. In addition, identification criteria areapplied to remove spurious jets associated with calorimeter noise [72]. Jets from the hadron-ization of b quarks are tagged by a secondary vertex algorithm, D EEP J ET , based on the scorefrom a deep neural network (DNN) [73, 74]. We will refer to the output of this DNN as the btagging score.In addition to standard CMS jet energy corrections [75], a b jet energy regression [76] is used toimprove the energy resolution of b jets and, therefore, the m jj resolution. The energy correctionand resolution estimator are computed for each of the Higgs boson candidate jets through aregression implemented in a DNN and trained on jet properties. The regression simultaneouslyprovides a b jet energy correction and a resolution estimator.In events with more than two jets, the Higgs boson candidate is reconstructed from the two jetswith the highest b tagging scores. The dijet invariant mass is required to be 70 < m jj <
190 GeV.An additional regression was developed specifically for the γγ bb final states to further im-prove the dijet invariant mass resolution. This regression exploits the fact that there is no gen-uine missing transverse momentum from the hard-scattering process in the γγ bb final state,and follows a similar approach as used in Ref. [28]. The regression targets the dijet invariantmass at the generator level, and is trained using the kinematic properties of the event and p missT .The regression is trained on a simulated sample of b-enriched γγ + jets events.The two regression techniques were validated on data collected by the CMS experiment. Thetwo-step regression technique improves the dijet invariant mass resolution of the SM HH sig- nal by about 20%, and the m jj peak position is shifted by 5.5 GeV (5%) closer to the expectedHiggs boson mass.To select events corresponding to HH production via VBF, additional requirements are im-posed. The VBF process is characterized by the presence of two additional energetic jets, cor-responding to two quarks from each of the colliding protons scattered away from the beamline. These “VBF-tagged” jets are expected to have a large pseudorapidity separation, | ∆ η VBFjj | ,and a large dijet invariant mass, m VBFjj . VBF-tagged jets are required to have p T > ( ) GeVfor the leading (subleading) jet, | η | < ∆ R γ j > ∆ R bj > m VBFjj is selected as the two VBF-tagged jets. We will refer tothese requirements as “VBF selection criteria”.
To improve the sensitivity of the search, MVA techniques are used to distinguish the ggF andVBF HH signal from the dominant nonresonant background. The output of the MVA classi-fiers is then used to define mutually exclusive analysis categories targeting VBF and ggF HHproduction. The HH signal is extracted from a fit to the invariant masses of the two Higgsboson candidates in the ( m γγ , m jj ) plane simultaneously in all categories.We study the properties of the HH system, built from the reconstructed diphoton and dijetcandidates, to identify observables that can help us distinguish between the signal and back-ground. The invariant mass distributions are shown in Fig. 3 for diphoton and dijet pairs indata and in signal and background simulation after imposing the selection criteria describedin Section 5. The signal has a peaking distribution in m γγ and m jj . The data distribution, dom-inated by the γγ + jets and γ + jets backgrounds, exhibits a falling spectrum because of thenonresonant nature of these processes. In this analysis, these characteristics are used to extractthe signal via a simultaneous fit to m γγ and m jj .The distribution of (cid:101) M X , defined as: (cid:101) M X = m γγ jj − ( m jj − m H ) − ( m γγ − m H ) , (2)where m γγ jj is the invariant mass of the two Higgs boson candidates, is particularly sensitive todifferent values of the couplings described in Section 3. The (cid:101) M X distribution is less dependenton the dijet and diphoton energy resolutions than m γγ jj if the dijet and diphoton pairs originatefrom a Higgs boson decay [77]. In Fig. 4, the distribution of (cid:101) M X is shown for several BSMbenchmark hypotheses affecting ggF HH production (described in Table 1) and for differentvalues of c affecting the VBF HH production mode. The SM HH process exhibits a broadstructure in (cid:101) M X , induced by the interference between different processes contributing to HHproduction and shaped by the analysis selection. The signals with c = c = Single Higgs boson production is an important resonant background in the γγ bb final state,with ttH production being dominant in high purity signal regions. To reduce ttH background
100 110 120 130 140 150 160 170 180 (GeV) gg m E v en t s / G e V Data ggH VBF H x 10bb ggfi SM HH VH HttData ggH VBF H x 10bb ggfi SM HH VH Htt (13 TeV) -1
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Figure 3: The invariant mass distributions of the reconstructed Higgs boson candidates m γγ (left) and m jj (right) in data and simulated events. Data, dominated by the γγ + jets and γ + jetsbackgrounds, are compared to the SM ggF HH signal samples and single H samples (ttH, ggH,VBF H, VH) after imposing the selection criteria described in Section 5. The error bars on thedata points indicate statistical uncertainties. The HH signal has been scaled by a factor of 10 for display purposes.
300 400 500 600 700 800 900 1000 (GeV) X M~ - - - N o r m a li z ed t o U n i t y bb ggfi SM ggF HH BSM 8BSM 4 BSM 10bb ggfi
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400 600 800 100012001400160018002000 (GeV) X M~ - - - N o r m a li z ed t o U n i t y bb ggfi SM VBF HH = 2 c = 0 c bb ggfi SM VBF HH = 2 c = 0 c
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Figure 4: Distributions of (cid:101) M X . The SM ggF HH signal is compared with several BSM hypothe-ses listed in Table 1 (left), and the SM VBF HH signal is compared with two different anomalousvalues of c (right). All distributions are normalized to unity.contamination, a dedicated classifier (ttHScore) was developed. The classifier is trained on amixture of SM HH events and events generated for the twelve BSM benchmark hypotheses(described in Table 1) as signal, and ttH events as background. The discriminant uses a com-bination of low-level information from the individual PF candidates and high-level featuresdescribing kinematic properties of the event. The kinematic variables used in the training canbe classified in three groups: angular variables, variables to distinguish semileptonic decays ofW bosons produced in the top quark decay, and variables to distinguish hadronic decays of Wbosons. The ttHScore discriminant is implemented with a DNN combining feed-forward andlong short-term memory neural networks [78], based on the topology-classifier architecture in-troduced in Ref. [79]. The network is implemented in K ERAS [80] using the T
ENSOR F LOW [81]backend, and the hyperparameters are chosen through Bayesian optimization. The ttHScoreoutput is shown in Fig. 5 (left) for data and simulated events. The events entering the analy-sis are required to pass a selection based on this classifier, which is optimized as described inSection 9. An MVA discriminant implemented with a BDT is used to separate the ggF HH signal and thedominant nonresonant γγ + jets and γ + jets backgrounds. We select several discriminatingobservables to be used in the training. They can be classified in three groups: kinematic vari-ables, object identification variables, and object resolution variables. The first group exploitsthe kinematic properties of the HH system, the second helps to separate the signal from thereducible γ + jets background, and the third takes into account the resonant nature of the γγ and bb final states for signal. The following discriminating variables were chosen: • The H candidate kinematic variables: p γ T / m γγ , p jT / m jj for leading and subleadingphotons and jets, where p γ T and p jT are the transverse momenta of the selected photonand jet candidates. • The HH transverse balance: p γγ T / m γγ jj and p jjT / m γγ jj , where p γγ T and p jjT are the trans-verse momenta of the diphoton and dijet candidates. • Helicity angles: | cos θ CSHH | , | cos θ jj | , | cos θ γγ | , where | cos θ CSHH | is the Collins-Soperangle [82] between the direction of the H → γγ candidate and the average beamdirection in the HH center-of-mass frame, while | cos θ jj | and | cos θ γγ | are the anglesbetween one of the Higgs boson decay products and the direction defined by theHiggs boson candidate. • Angular distance: minimum ∆ R γ j between a photon and a jet, ∆ R min γ j , consideringall combinations between objects passing the selection criteria, and ∆ R γ j betweenthe other photon-jet pair not used in the ∆ R min γ j calculation. • b tagging: the b tagging score of each jet in the dijet candidate. • photon ID: photon identification variables for leading and subleading photons. • Object resolution: energy resolution for the leading and subleading photons and jetsobtained from the photon [44] and b jet [76] energy regressions, the mass resolutionestimators for the diphoton and dijet candidates.The BDT is trained using the
XGBOOST [83] software package using a gradient boosting algo-rithm. The γγ + jets and γ + jets MC samples are used as background, while an ensemble ofSM HH and the 12 BSM HH benchmark hypotheses listed in Table 1 is used as signal. Train-ing on an ensemble of BSM and SM HH signals makes the BDT sensitive to a broad spectrumof theoretical scenarios. During the training, signal events are weighted with the product ofthe inverse mass resolution of the diphoton and dijet systems. These resolutions are obtainedusing the per-object resolution estimators provided by the energy regressions developed forphotons and b jets. In the training, the mass dependence of the classifier is removed by us-ing only dimensionless kinematic variables. The inverse resolution weighting at training timeimproves the performance by bringing back the information about the resonant nature of thesignal. Independent training and testing samples are created by splitting the signal and back-ground samples. The classifier hyperparameters are optimized using a randomized grid searchand a 5-fold cross-validation technique [84]. The BDT is trained separately for the 2016, 2017,and 2018 data-taking years. The BDT output distribution is very similar among the three years,leading to the same definitions of optimal signal regions based on the BDT output. There-fore, during the event categorization, a single set of analysis categories is defined using datafrom 2016–2018. The distributions of the BDT output for signal and background are very wellseparated. In order to avoid problems of numerical precision when defining optimal signal- .2 Background reduction in the VBF HH signal region enriched regions, the BDT output is transformed such that the signal distribution is uniform.This transformation is applied to all events, both in simulation and data. The distribution ofthe MVA output for data and simulated events is shown in Fig. 5 (right). ttHScore -
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Figure 5: The distribution of the ttHScore (left) and MVA output (right) in data and simulatedevents. Data, dominated by γγ + jets and γ + jets background, are compared to the SM ggFHH signal samples and single H samples (ttH, ggH, VBF H, VH) after imposing the selectioncriteria described in Section 5. The error bars on the data points indicate statistical uncertain-ties. The HH signal has been scaled by a factor of 10 for display purposes. Similarly to the ggF HH analysis strategy, an MVA discriminant is employed to separate theVBF HH signal from the background. As for the ggF case, the γγ + jets and γ + jets processesare the dominant sources of background. For the VBF production mode, the ggF HH eventsare considered as background. About a third of the ggF HH events passing the selection re-quirements described in Section 5 also pass the dedicated VBF selection criteria. The distinc-tive topology of the VBF HH process is used to separate the VBF HH signal from the varioussources of background. In addition to the discriminating features of the HH signal describedin Sections 6 and 8.1, the following set of VBF-discriminating features were identified: • VBF-tagged jet kinematic variables: p VBFT / m VBFjj , η VBF for VBF-tagged jets. • VBF-tagged jet invariant mass: invariant mass m VBFjj of the VBF-tagged jets. • Rapidity gap: product of and difference in the pseudorapidity of the two VBF-tagged jets. • Quark-gluon likelihood [85, 86] of the two VBF-tagged jets. A likelihood discrimi-nator used to distinguish between jets originating from quarks and from gluons. • Kinematic variables related to the HH system: (cid:101) M X and the transverse momentumof the pair of reconstructed Higgs bosons. • Angular distance: minimum ∆ R between a photon and a VBF-tagged jet, and be-tween a b jet and a VBF-tagged jet. • Centrality variables for the reconstructed Higgs boson candidates: C H = exp (cid:34) − ( η VBF1 − η VBF2 ) (cid:18) η H − η VBF1 + η VBF2 (cid:19) (cid:35) , (3)2
Figure 5: The distribution of the ttHScore (left) and MVA output (right) in data and simulatedevents. Data, dominated by γγ + jets and γ + jets background, are compared to the SM ggFHH signal samples and single H samples (ttH, ggH, VBF H, VH) after imposing the selectioncriteria described in Section 5. The error bars on the data points indicate statistical uncertain-ties. The HH signal has been scaled by a factor of 10 for display purposes. Similarly to the ggF HH analysis strategy, an MVA discriminant is employed to separate theVBF HH signal from the background. As for the ggF case, the γγ + jets and γ + jets processesare the dominant sources of background. For the VBF production mode, the ggF HH eventsare considered as background. About a third of the ggF HH events passing the selection re-quirements described in Section 5 also pass the dedicated VBF selection criteria. The distinc-tive topology of the VBF HH process is used to separate the VBF HH signal from the varioussources of background. In addition to the discriminating features of the HH signal describedin Sections 6 and 8.1, the following set of VBF-discriminating features were identified: • VBF-tagged jet kinematic variables: p VBFT / m VBFjj , η VBF for VBF-tagged jets. • VBF-tagged jet invariant mass: invariant mass m VBFjj of the VBF-tagged jets. • Rapidity gap: product of and difference in the pseudorapidity of the two VBF-tagged jets. • Quark-gluon likelihood [85, 86] of the two VBF-tagged jets. A likelihood discrimi-nator used to distinguish between jets originating from quarks and from gluons. • Kinematic variables related to the HH system: (cid:101) M X and the transverse momentumof the pair of reconstructed Higgs bosons. • Angular distance: minimum ∆ R between a photon and a VBF-tagged jet, and be-tween a b jet and a VBF-tagged jet. • Centrality variables for the reconstructed Higgs boson candidates: C H = exp (cid:34) − ( η VBF1 − η VBF2 ) (cid:18) η H − η VBF1 + η VBF2 (cid:19) (cid:35) , (3)2 where H is the Higgs boson candidate reconstructed either from diphoton or dijetpairs, and η VBF1 and η VBF2 are the pseudorapidities of the two VBF-tagged jets.We split events into two regions: (cid:101) M X <
500 GeV and (cid:101) M X >
500 GeV. While the region of (cid:101) M X >
500 GeV is sensitive to anomalous values of c , the (cid:101) M X <
500 GeV region retains thesensitivity to SM VBF HH production.A multi-class BDT, using a gradient boosting algorithm and implemented in the
XGBOOST [83]framework, is trained to separate the VBF HH signal from the γγ + jets, γ + jets, and SM ggFHH background. A mix of VBF HH samples with the SM couplings and quartic coupling c = c are similar, the cross section of the signal with c = (cid:101) M X regions. As it is done for the ggF MVA output, data from2016–2018 are merged to create a single set of analysis categories based on the BDT output.The BDT output is transformed such that the distribution of the mix of the VBF HH signalswith SM couplings and quartic coupling c = (cid:101) M X regions. The distribution of the MVA outputs for data and simulatedevents is shown in Fig. 6. ) X M~VBF MVA (High -
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10 110 E v en t s / . Data ggH VBF H SM ggF HH x 10 VH HttData ggH VBF H SM ggF HH x 10 VH Htt (13 TeV) -1
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CMS < 500 GeV X M~ SM VBF HH x 10 = 0 VBF HH x 10 c Figure 6: The distribution of the two MVA outputs is shown in data and simulated events inthe two VBF (cid:101) M X regions: (cid:101) M X >
500 GeV (left) and (cid:101) M X <
500 GeV (right). Data, dominatedby the γγ + jets and γ + jets backgrounds, are compared to the VBF HH signal samples withSM couplings and c =
0, SM ggF HH and single H samples (ttH, ggH, VBF H, VH) afterimposing the VBF selection criteria described in Section 5. The error bars on the data pointsindicate statistical uncertainties. The HH signal has been scaled by a factor of 10 for displaypurposes. In order to maximize the sensitivity of the search, events are split into different categories ac-cording to the output of the MVA classifier and the mass of the Higgs boson pair system (cid:101) M X .The (cid:101) M X distribution changes significantly for different BSM hypotheses, as shown in Fig. 4. .1 Combination of the HH and ttH signals to constrain κ λ and κ t Table 2: Summary of the analysis categories. Two VBF- and twelve ggF-enriched categories aredefined based on the output of the MVA classifier and the mass of the Higgs boson pair system (cid:101) M X . The VBF and ggF categories are mutually exclusive.Category MVA (cid:101) M X (GeV)VBF CAT 0 0.52–1.00 > > > > (cid:101) M X creates signal regions sensitive to multipletheoretical scenarios. In the search for VBF HH production, the categories in (cid:101) M X are definedbefore the MVA is trained, as described in Section 8.2. For the categories that target ggF HHproduction, categories in (cid:101) M X are defined after the MVA is trained.The categorization is optimized by maximizing the expected significance estimated as the sumin quadrature of S/ √ B over all categories in a window centered on m H : 115 < m γγ <
135 GeV.Here, S and B are the numbers of expected signal and background events, respectively. Simu-lated events are used for this optimization. The SM HH process is considered as signal, whilethe background consists of the γγ + jets, γ + jets, and ttH processes. The MVA categories areoptimized simultaneously with a threshold on the value of ttHScore. Two VBF and three ggFcategories are optimized based on the MVA output. For ggF HH in each MVA category a setof (cid:101) M X categories is then optimized. The optimization procedure leads to 12 ggF analysis cate-gories: four categories in (cid:101) M X in each of the three categories in the MVA score. The optimizedselection on ttHScore > (cid:101) M X >
500 ( (cid:101) M X < κ λ and κ t As discussed in Section 3, the HH production cross section depends on κ λ and κ t . The produc-tion cross section of the single H processes also depends on κ λ , as a result of NLO electroweakcorrections [32]. The ggH and ttH production cross sections additionally depend on κ t . There-fore, the HH → γγ bb signal can be combined with the single H production modes to providean improved constraint on the κ λ and κ t parameters. In the case of anomalous values of κ λ , thesingle H process with the largest modification of the cross section is ttH. For this reason, ad- ditional orthogonal categories targeting the ttH process are included in the analysis: the “ttHleptonic” and the “ttH hadronic” categories, developed and optimized for the measurementof the ttH production cross section in the diphoton decay channel [31]. The events that do notpass the selections for the HH categories defined in Table 2 are tested for the ttH categories.This ensures the orthogonality between the events selected by the HH and ttH categories.The H → γγ candidate selection is the same as described in Section 5. The ttH leptonic cate-gories target ttH events where at least one W boson, originating from the top or antitop quark,decays leptonically. At least one isolated electron (muon) with | η | < p T >
10 (5) GeV,and at least one jet with p T >
25 GeV are required. The ttH hadronic categories target hadronicdecays of W bosons. In these categories at least three jets are required, one of which must be btagged, and a lepton veto is imposed. In order to maximize the sensitivity, an MVA approachis used to separate the ttH events from the background, dominated by γγ + jets, γ + jets, tt+ jets, tt + γ , and tt + γγ events. A BDT classifier is trained for each of the two channelsusing simulated events. The variables used for the training include kinematic properties ofthe reconstructed objects, object identification variables, and global event properties such as jetand lepton multiplicities. The BDT input variables also include the outputs of other machinelearning algorithms trained specifically to target different backgrounds. These include DNNclassifiers trained to reduce the tt + γγ and γγ + jets background, and a top quark taggerbased on a BDT [87]. The output scores of the BDTs are used to reject background-like eventsand to classify the remaining events in four subcategories for each of the two channels. Theboundaries of the categories are optimized by maximizing the expected significance of the ttHsignal.
10 Signal model
In each of the HH categories, a parametric fit in the ( m γγ , m jj ) plane is performed. In the ttHcategories, the m γγ distribution is fitted to extract the signal. When the HH and ttH categoriesare combined, both the HH and ttH production modes are considered as signals.The shape templates of the diphoton and dijet invariant mass distributions are constructedfrom simulation. In each HH and ttH analysis category, the m γγ distribution is fitted using asum of, at most, five Gaussian functions. Figure 7 (left) shows the signal model for m γγ in theVBF and ggF CAT0 categories, which are the categories with the best resolution.For the HH categories, the m jj distributions are modeled with a double-sided Crystal Ball (CB)function, a modified version of the standard CB function [88] with two independent exponen-tial tails. Figure 7 (right) shows the signal model for m jj in the VBF and ggF categories with thebest resolution.For the HH signal, the final two-dimensional (2D) signal probability distribution function is aproduct of the independent m γγ and m jj models. The possible correlations are investigated bycomparing the 2D m γγ - m jj distributions in the simulated signal samples with the 2D probabilitydistributions built as a product of the one-dimensional (1D) ones. With the statistical precisionavailable in this analysis, the correlations have been found to be negligible. (GeV) gg m
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Figure 7: Parametrized signal shape for m γγ (left) and m jj (right) in the best resolution ggF (up-per) and VBF (lower) categories. The open squares represent simulated events and the bluelines are the corresponding models. Also shown are the σ eff value (half the width of the nar-rowest interval containing 68.3% of the invariant mass distribution) and the corresponding in-terval as a gray band, and the full width at half the maximum (FWHM) and the correspondinginterval as a double arrow.
11 Background model
The SM single H background shape is constructed from the simulation following the samemethodology as used for the signal model described in Section 10. For each analysis categoryand single H production mode, the m γγ distributions are fitted using a sum of, at most, fiveGaussian functions. The m jj modeling in the HH categories depends on the production mech-anism, and a parametrisation is obtained from the simulated distributions: for the ggH andVBF H processes, the m jj distribution is modeled with a Bernstein polynomial; for VH produc-tion, a CB function is used to model the distribution of the hadronic decays of vector bosons;for ttH, where the two b jets are produced from a top quark decay, a Gaussian function with amean around 120 GeV is used. Like for the signal modeling, the final 2D SM single H model isa product of the independent models of the m γγ and m jj distributions. The model used to describe the nonresonant background is extracted from data using the dis-crete profiling method [89] as described in Ref. [70]. This technique was designed as a way toestimate the systematic uncertainty associated with choosing a particular analytic function tofit the background m γγ and m jj distributions. The method treats the choice of the backgroundfunction as a discrete nuisance parameter in the likelihood fit to the data. This method is usedto model m γγ distribution of the nonresonant background in the ttH categories. For the HHcategories, the method is generalized to the 2D model case as a product of two 1D models for m γγ and m jj .A set of MC pseudo-experiments was generated with positive and negative correlations be-tween m γγ and m jj injected and then fitted with the factorized 2D model. A negligible bias hasbeen observed, and the correlations have been found to be within the statistical precision of theanalysis.
12 Systematic uncertainties
The systematic uncertainties only affect the signal model and the resonant single H back-ground, since the nonresonant background model is constructed in a data-driven way with theuncertainties associated with the choice of a background fit function taken into account by thediscrete profiling method described in Section 11.2. The systematic uncertainties can affect theoverall normalization, or a variation in category yields, representing event migration betweenthe categories. Theoretical uncertainties have been applied to the HH and single H normaliza-tions. The following sources of theoretical uncertainty are considered: the uncertainty in thesignal cross section arising from scale variations, uncertainties on α S , PDFs and in the predictionof the branching fraction B ( HH → γγ bb ) . The dominant theoretical uncertainties arise fromthe prediction of the SM HH and ttH production cross sections. In addition, a conservativePS uncertainty is assigned to the VBF HH signal, defined as the full symmetrized difference inyields in each category obtained with simulated samples of VBF HH events interfaced with thestandard p T -ordered and dipole shower PS schemes. The dominant experimental uncertaintiesare: • Photon identification BDT score : the uncertainty arising from the imperfect MC sim-ulation of the input variables to the photon ID is estimated by rederiving the cor-rections with equally sized subsets of the Z → ee events used to train the quantile regression BDTs. Its magnitude corresponds to the standard deviation of the event-by-event differences in the photon ID evaluated on the two different sets of correctedinput variables. This uncertainty reflects the limited capacity of the BDTs arisingfrom the finite size of the training set. It is seen to cover the residual discrepanciesbetween data and simulation. The uncertainty in the signal yields is estimated bypropagating this uncertainty through the full category selection procedure. • Photon energy scale and resolution : the uncertainties associated with the correctionsapplied to the photon energy scale in data and the resolution in simulation are eval-uated using Z → ee events [90]. • Per-photon energy resolution estimate : the uncertainty in the per-photon resolution isparametrized as a rescaling of the resolution by ±
5% around its nominal value. Thisis designed to cover all differences between data and simulation in the distribution,which is an output of the energy regression. • Jet energy scale and resolution corrections : the energy scale of jets is measured usingthe p T balance of jets with Z bosons and photons in Z → ee, Z → µµ , and γ + jets events, as well as using the p T balance between jets in dijet and multijet events[40, 86]. The uncertainty in the jet energy scale and resolution is a few percent anddepends on p T and η . The impact of uncertainties on the event yields is evaluatedby varying the jet energy corrections within their uncertainties and propagating theeffect to the final result. Some sources of the jet energy scale uncertainty are fully(anti-)correlated, while others are considered uncorrelated. • Jet b tagging : uncertainties in the b tagging efficiency are evaluated by comparingdata and simulated distributions for the b tagging discriminator [91]. These includethe statistical uncertainty in the estimate of the fraction of heavy- and light-flavorjets in data and simulation. • Trigger efficiency : the efficiency of the trigger selection is measured with Z → eeevents using a tag-and-probe technique [92]. An additional uncertainty is intro-duced to account for a gradual shift in the timing of the inputs of the ECAL L1trigger in the region | η | > • Photon preselection : the uncertainty in the preselection efficiency is computed as theratio between the efficiency measured in data and in simulation. The preselection ef-ficiency in data is measured with the tag-and-probe technique in Z → ee events [92]. • Integrated luminosity : uncertainties are determined by the CMS luminosity monitor-ing for the 2016–2018 data-taking years [93–95] and are in the range of 2.3–2.5%. Toaccount for common sources of uncertainty in the luminosity measurement schemes,some sources are fully (anti-)correlated across the different data-taking years, whileothers are considered uncorrelated. The total 2016–2018 integrated luminosity hasan uncertainty of 1.8%. • Pileup jet identification : the uncertainty in the pileup jet classification output score isestimated by comparing the score of jets in events with a Z boson and one balancedjet in data and simulation. The assigned uncertainty depends on p T and η , and isdesigned to cover all differences between data and simulation in the distribution.Most of the experimental uncertainties are uncorrelated among the three data-taking years.Some sources of uncertainty in the measured luminosity and jet energy corrections are fully(anti-)correlated, while others are considered uncorrelated. This search is statistically limited, and the total impact of systematic uncertainties on the result is about 2%.
13 Results
A simultaneous unbinned maximum likelihood fit to the m γγ and m jj distributions is performedin the 14 HH categories to extract the HH signal. A likelihood function is defined for eachanalysis category using analytic models to describe the m γγ and m jj distributions of signal andbackground events, with nuisance parameters to account for the experimental and theoreti-cal systematic uncertainties described in Section 12 The fit is performed in the mass ranges100 < m γγ <
180 GeV and 70 < m jj <
190 GeV for all categories apart from ggF CAT10 andCAT11. In those two categories, a small but nonnegligible shoulder was observed in the m jj distribution. Therefore, the m jj fit range is reduced to 90 < m jj <
190 GeV to avoid a possiblebias with minimal impact on the analysis sensitivity.In order to determine κ λ and κ t , the HH and ttH categories are used together in a simultaneousmaximum likelihood fit. In the ttH categories, a binned maximum likelihood fit is performedto m γγ in the mass range 100 < m γγ <
180 GeV.The data and the signal-plus-background model fit to m γγ and m jj are shown in Fig. 8 for thebest resolution ggF and VBF categories. The distribution of events weighted by S/(S+B) fromall HH categories is shown in Fig. 9 for m γγ and m jj . In this expression, S (B) is the number ofsignal (background) events extracted from the signal-plus-background fit.No significant deviation from the background-only hypothesis is observed. We set upperlimits at 95% CL on the product of the production cross section of a pair of Higgs bosonsand the branching fraction into γγ bb, σ HH B ( HH → γγ bb ) , using the modified frequentistapproach for confidence levels (CL s ), taking the LHC profile likelihood ratio as a test statis-tic [96–99] in the asymptotic approximation. The observed (expected) 95% CL upper limit on σ HH B ( HH → γγ bb ) amounts to 0.67 (0.45) fb. The observed (expected) limit corresponds to7.7 (5.2) times the SM prediction. All results were extracted assuming m H =
125 GeV. We ob-serve a variation smaller than 1% in both the expected and observed upper limits when using m H = ± κ λ , assuming that the top quark Yukawa coupling isSM-like ( κ t = κ λ is directly related to changes in the kinematical properties of HH production.At 95% CL, κ λ is constrained to values in the interval [ − ] , while the expected constrainton κ λ is in the interval [ − ] . This is the most sensitive search to date.Assuming instead that an HH signal exists with the properties predicted by the SM, constraintson λ HHH can be set. The results are obtained both with the HH categories only, and with theHH categories combined with the ttH categories in a simultaneous maximum likelihood fit.The HH signal is considered together with the single H processes (ttH, ggH, VBF H,VH,and Higgs boson production in association with a single top quark). The cross sections andbranching fractions of the HH and single H processes are scaled as a function of κ λ , whilethe top quark Yukawa coupling is assumed to be SM-like, κ t =
1. One-dimensional negativelog-likelihood scans for κ λ are shown in Fig. 11 for an Asimov data set [98] generated with theSM signal-plus-background hypothesis, κ λ =
1, and for the observed data. When combiningthe HH analysis categories with the ttH categories, we obtain κ λ = + − (1.0 + − expected).Values of κ λ outside the interval [ − ] are excluded at 95% CL. The expected exclusion at E v en t s / ( G e V ) DataHH + H + B fitH + B componentB component s – s – CMS (13 TeV) -1
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Figure 8: Invariant mass distributions m γγ (upper) and m jj (lower) for the selected events indata (black points) in the best resolution ggF (CAT0) and VBF (CAT0) categories. The solid redline shows the sum of the fitted signal and background (HH+H+B), the solid blue line showsthe background component from the single Higgs boson and the nonresonant processes (H+B),and the dashed black line shows the nonresonant background component (B). The normaliza-tion of each component (HH, H, B) is extracted from the combined fit to the data in all analysiscategories. The one (green) and two (yellow) standard deviation bands include the uncertain-ties in the background component of the fit. The lower panel in each plot shows the residualsignal yield after the background (H+B) subtraction. S / ( S + B ) W e i gh t ed E v en t s / ( G e V ) DataHH + H + B fitH + B componentB component s – s – CMS (13 TeV) -1
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Figure 9: Invariant mass distributions m γγ (left) and m jj (right) for the selected events in data(black points) weighted by S/(S+B), where S (B) is the number of signal (background) eventsextracted from the signal-plus-background fit. The solid red line shows the sum of the fittedsignal and background (HH+H+B), the solid blue line shows the background component fromthe single Higgs boson and the nonresonant processes (H+B), and the dashed black line showsthe nonresonant background component (B). The normalization of each component (HH, H, B)is extracted from the combined fit to the data in all analysis categories. The one (green) andtwo (yellow) standard deviation bands include the uncertainties in the background componentof the fit. The lower panel in each plot shows the residual signal yield after the background(H+B) subtraction. l k - - - ) ( f b ) bb gg fi B ( HH HH s CMS (13 TeV) -1
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95% CL upper limitsObservedMedian expected68% CL expected95% CL expectedTheoretical prediction bb gg fi HH Figure 10: Expected and observed 95% CL upper limits on the product of the HH productioncross section and B ( HH → γγ bb ) obtained for different values of κ λ assuming κ t = 1. Thegreen and yellow bands represent, respectively, the one and two standard deviation extensionsbeyond the expected limit. The long-dashed red line shows the theoretical prediction.95% CL corresponds to the region outside the interval [ − ] . The shape of the likelihood asfunction of κ λ in Fig. 11 is characterized by 2 minima. This is related to an interplay between thecross section dependence on κ λ and differences in acceptance between the analysis categories. - - l k l n ( L ) D - -2.6+5.9 = 1.0 l k HH cat., -2.5+5.7 = 1.0 l k H cat., tHH+t
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SM expected - - l k l n ( L ) D - -1.7+2.5 = -0.2 l k HH cat., -1.8+6.3 = 0.6 l k H cat., tHH+t
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Figure 11: Negative log-likelihood, as a function of κ λ , evaluated with an Asimov data setassuming the SM hypothesis (left) and the observed data (right). The 68 and 95% CL intervalsare shown with the dashed gray lines. The two curves are shown for the HH (blue) and HH+ttH (orange) analysis categories. All other couplings are set to their SM values.The HH and single Higgs boson production cross sections depend not only on κ λ , but also on κ t . To better constrain the κ λ and κ t coupling modifiers, a 2D negative log-likelihood scan inthe ( κ λ , κ t ) plane is performed, taking into account the modification of the production crosssections and B ( H → bb ) , B ( H → γγ ) for anomalous ( κ λ , κ t ) values. The modification of thesingle H production cross section for anomalous κ λ is modeled at NLO, while the dependenceon κ t is parametrized at LO only, neglecting NLO effects [32]. This approximation holds as longas the value of | κ t | is close to unity, roughly in the range 0.7 < κ t < κ λ versus κ t for an Asimov data set assuming the SM hypothesis and for the observed data. The regions ofthe 2D scan where the κ t parametrization for anomalous values of κ λ at LO is not reliable areshown with a gray band.The inclusion of the ttH categories significantly improves the constraint on κ t . The 1D negativelog-likelihood scan, as a function of κ t with κ λ fixed at κ λ = 1, is shown in Fig. 13 for an Asimovdata set generated assuming the SM hypothesis, κ t = 1, as well as for the observed data. Themeasured value of κ t is κ t = + − (1.0 + − expected). Values of κ t outside the interval [ ] are excluded at 95% CL. The constraint on κ t is comparable to the one recently set in Ref. [101],where anomalous values of c V were also considered.Upper limits at 95% CL are also set on the product of the HH VBF production cross section andbranching fraction, σ VBF HH B ( HH → γγ bb ) , with the yield of the ggF HH signal constrainedwithin uncertainties to the one predicted in the SM. The observed (expected) 95% CL upperlimit on σ VBF HH B ( HH → γγ bb ) amounts to 1.02 (0.94) fb. The limit corresponds to 225 (208)times the SM prediction. This is the most stringent constraint on σ VBF HH B ( HH → γγ bb ) todate.Limits are also set, as a function of c , as presented in Fig. 14. The observed excluded regioncorresponds to c < − c > c < − c > c than to the region around the SM prediction. This is related to the fact that, for anomalousvalues of c , the total cross section is enhanced and the (cid:101) M X spectrum is harder as shown in2
Figure 11: Negative log-likelihood, as a function of κ λ , evaluated with an Asimov data setassuming the SM hypothesis (left) and the observed data (right). The 68 and 95% CL intervalsare shown with the dashed gray lines. The two curves are shown for the HH (blue) and HH+ttH (orange) analysis categories. All other couplings are set to their SM values.The HH and single Higgs boson production cross sections depend not only on κ λ , but also on κ t . To better constrain the κ λ and κ t coupling modifiers, a 2D negative log-likelihood scan inthe ( κ λ , κ t ) plane is performed, taking into account the modification of the production crosssections and B ( H → bb ) , B ( H → γγ ) for anomalous ( κ λ , κ t ) values. The modification of thesingle H production cross section for anomalous κ λ is modeled at NLO, while the dependenceon κ t is parametrized at LO only, neglecting NLO effects [32]. This approximation holds as longas the value of | κ t | is close to unity, roughly in the range 0.7 < κ t < κ λ versus κ t for an Asimov data set assuming the SM hypothesis and for the observed data. The regions ofthe 2D scan where the κ t parametrization for anomalous values of κ λ at LO is not reliable areshown with a gray band.The inclusion of the ttH categories significantly improves the constraint on κ t . The 1D negativelog-likelihood scan, as a function of κ t with κ λ fixed at κ λ = 1, is shown in Fig. 13 for an Asimovdata set generated assuming the SM hypothesis, κ t = 1, as well as for the observed data. Themeasured value of κ t is κ t = + − (1.0 + − expected). Values of κ t outside the interval [ ] are excluded at 95% CL. The constraint on κ t is comparable to the one recently set in Ref. [101],where anomalous values of c V were also considered.Upper limits at 95% CL are also set on the product of the HH VBF production cross section andbranching fraction, σ VBF HH B ( HH → γγ bb ) , with the yield of the ggF HH signal constrainedwithin uncertainties to the one predicted in the SM. The observed (expected) 95% CL upperlimit on σ VBF HH B ( HH → γγ bb ) amounts to 1.02 (0.94) fb. The limit corresponds to 225 (208)times the SM prediction. This is the most stringent constraint on σ VBF HH B ( HH → γγ bb ) todate.Limits are also set, as a function of c , as presented in Fig. 14. The observed excluded regioncorresponds to c < − c > c < − c > c than to the region around the SM prediction. This is related to the fact that, for anomalousvalues of c , the total cross section is enhanced and the (cid:101) M X spectrum is harder as shown in2 - - - - l k - - - - t k SMHH cat. Best fitHH cat. 68% CLHH cat. 95% CLH cat. Best fittHH+t H cat. 68% CLtHH+t H cat. 95% CLtHH+t
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Figure 12: Negative log-likelihood contours at 68 and 95% CL in the ( κ λ , κ t ) plane evaluatedwith an Asimov data set assuming the SM hypothesis (left) and the observed data (right). Thecontours obtained using the HH analysis categories only are shown in blue, and in orangewhen combined with the ttH categories. The best fit value for the HH categories only ( κ λ =0.6, κ t = 1.2) is indicated by a blue circle, for the HH + ttH categories ( κ λ = 1.4, κ t = 1.3) by anorange diamond, and the SM prediction ( κ λ = 1.0, κ t = 1.0) by a black star. The regions of the 2Dscan where the κ t parametrization for anomalous values of κ λ at LO is not reliable are shownwith a gray band. - - - - - t k l n ( L ) D - -1.2+0.4 = 1.0 t k HH cat., -0.2+0.2 = 1.0 t k H cat., tHH+t
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Figure 13: Negative log-likelihood scan, as a function of κ t , evaluated with an Asimov data setassuming the SM hypothesis (left) and the observed data (right). The 68 and 95% CL intervalsare shown with the dashed gray lines. The two curves are shown for the HH (blue) and theHH +ttH (orange) analysis categories. All other couplings are fixed to their SM values.Fig. 4 (right). This leads to an increase in the product of signal acceptance and efficiency as wellas a more distinct signal topology.Assuming HH production occurs via the VBF and ggF modes, we set constraints on the κ λ and c coupling modifiers simultaneously. A 2D negative log-likelihood scan in the ( κ λ , c ) planeis performed using the 14 HH analysis categories. Figure 15 shows 2D likelihood scans for theobserved data and for an Asimov data set assuming all couplings are at their SM values. c - - - ) ( f b ) bb gg fi B ( HH VB F HH s - - -
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95% CL upper limitsObservedMedian expected68% CL expected95% CL expectedTheoretical prediction bb gg fi HH Figure 14: Expected and observed 95% CL upper limits on the product of the VBF HH pro-duction cross section and B ( HH → γγ bb ) obtained for different values of c . The green andyellow bands represent, respectively, the one and two standard deviation extensions beyondthe expected limit. The long-dashed red line shows the theoretical prediction. - - - - l k - - V c SMHH cat. Best fitHH cat. 68% CLHH cat. 95% CL
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SM expected - - - - l k - - V c SMHH cat. Best fitHH cat. 68% CLHH cat. 95% CL
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Figure 15: Negative log-likelihood contours at 68 and 95% CL in the ( κ λ , c ) plane evaluatedwith an Asimov data set assuming the SM hypothesis (left) and with the observed data (right).The contours are obtained using the HH analysis categories only. The best fit value ( κ λ = 0.0, c = 0.3) is indicated by a blue circle, and the SM prediction ( κ λ = 1.0, c = 1.0) by a black star.We also set upper limits at 95% CL for the twelve BSM benchmark hypotheses defined in Ta-ble 1. In this fit, the yield of the VBF HH signal is constrained within uncertainties to the onepredicted in the SM. The limits for different BSM hypotheses are shown in Fig. 16 (upper).In addition, limits are also calculated as a function of the BSM coupling between two Higgsbosons and two top quarks, c , as presented in Fig. 16 (lower). The observed excluded regioncorresponds to c < − c > c < − c > Shape benchmark ) ( f b ) bb gg fi B ( HH gg F HH s -
10 110
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95% CL upper limitsObservedMedian expected68% CL expected95% CL expected - - c ) ( f b ) bb gg fi B ( HH gg F HH s CMS (13 TeV) -1
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95% CL upper limitsObservedMedian expected68% CL expected95% CL expectedTheoretical prediction bb gg fi HH Figure 16: Expected and observed 95% CL upper limits on the product of the ggF HH produc-tion cross section and B ( HH → γγ bb ) obtained for different nonresonant benchmark models(defined in Table 1) (upper) and BSM coupling c (lower). In this fit, the yield of the VBF HHsignal is constrained within uncertainties to the one predicted in the SM. The green and yel-low bands represent, respectively, the one and two standard deviation extensions beyond theexpected limit. On the lower plot the long-dashed red line shows the theoretical prediction.
14 Summary
A search for nonresonant Higgs boson pair production (HH) has been presented, where oneof the Higgs bosons decays to a pair of bottom quarks and the other to a pair of photons. Thissearch uses proton-proton collision data collected at √ s =
13 TeV by the CMS experiment atthe LHC, corresponding to a total integrated luminosity of 137 fb − . No significant deviationfrom the background-only hypothesis is observed. Upper limits at 95% confidence level (CL)on the product of the HH production cross section and the branching fraction into γγ bb are ex- tracted for production in the standard model (SM) and in several scenarios beyond the SM. Theexpected upper limit at 95% CL on σ HH B ( HH → γγ bb ) is 0.45 fb, corresponding to about 5.2times the SM prediction, while the observed upper limit is 0.67 fb, corresponding to 7.7 timesthe expected value for the SM process. The presented search has the highest sensitivity to theSM HH production to date. Upper limits at 95% CL on the SM HH production cross sectionare also derived as a function of the Higgs boson self-coupling modifier κ λ ≡ λ HHH / λ SMHHH as-suming that the top quark Yukawa coupling is SM-like. The coupling modifier κ λ is constrainedwithin a range − < κ λ < − < κ λ < κ λ has been constrained. In ad-dition, a simultaneous constraint on κ λ and the modifier of the coupling between the Higgsboson and the top quark κ t is presented when both the HH and single Higgs boson processesare considered as signals.Limits are also set on the cross section of nonresonant HH production via vector boson fusion(VBF). The most stringent limit to date is set on the product of the HH VBF production crosssection and the branching fraction into γγ bb. The observed (expected) upper limit at 95% CLamounts to 1.02 (0.94) fb, corresponding to 225 (208) times the SM prediction. Limits are alsoset as a function of the modifier of the coupling between two vector bosons and two Higgsbosons, c . The observed excluded region corresponds to c < − c > c < − c > Acknowledgments
We congratulate our colleagues in the CERN accelerator departments for the excellent perfor-mance of the LHC and thank the technical and administrative staffs at CERN and at other CMSinstitutes for their contributions to the success of the CMS effort. In addition, we gratefullyacknowledge the computing centers and personnel of the Worldwide LHC Computing Gridfor delivering so effectively the computing infrastructure essential to our analyses. Finally,we acknowledge the enduring support for the construction and operation of the LHC and theCMS detector provided by the following funding agencies: BMBWF and FWF (Austria); FNRSand FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria);CERN; CAS, MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croa-tia); RIF (Cyprus); SENESCYT (Ecuador); MoER, ERC PUT and ERDF (Estonia); Academy ofFinland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and 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 program and the European ResearchCouncil and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, and 765710 (Euro-pean Union); the Leventis Foundation; the A.P. Sloan Foundation; the Alexander von Hum-boldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation `a laRecherche dans l’Industrie et dans l’Agriculture (FRIA-Belgium); the Agentschap voor Inno-vatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) un-der the “Excellence of Science – EOS” – be.h project n. 30820817; the Beijing Municipal Science &Technology Commission, No. Z191100007219010; the Ministry of Education, Youth and Sports(MEYS) of the Czech Republic; the Deutsche Forschungsgemeinschaft (DFG) under Germany’sExcellence Strategy – EXC 2121 “Quantum Universe” – 390833306; the Lend ¨ulet (“Momen-tum”) Program and the J´anos Bolyai Research Scholarship of the Hungarian Academy of Sci-ences, the New National Excellence Program ´UNKP, the NKFIA research grants 123842, 123959,124845, 124850, 125105, 128713, 128786, and 129058 (Hungary); the Council of Science and In-dustrial Research, India; the HOMING PLUS program of the Foundation for Polish Science,cofinanced from European Union, Regional Development Fund, the Mobility Plus program ofthe Ministry of Science and Higher Education, the National Science Center (Poland), contractsHarmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406; the National Priorities ResearchProgram by Qatar National Research Fund; the Ministry of Science and Higher Education,project no. 0723-2020-0041 (Russia); the Tomsk Polytechnic University Competitiveness En-hancement Program; the Programa Estatal de Fomento de la Investigaci ´on Cient´ıfica y T´ecnicade Excelencia Mar´ıa de Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa delPrincipado de Asturias; the Thalis and Aristeia programs cofinanced by EU-ESF and the GreekNSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn Universityand the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thai-land); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the WelchFoundation, contract C-1845; and the Weston Havens Foundation (USA).
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Eur. Phys. J. C . A The CMS Collaboration
Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan † , A. Tumasyan Institut f ¨ur Hochenergiephysik, Wien, Austria
W. Adam, T. Bergauer, M. Dragicevic, A. Escalante Del Valle, R. Fr ¨uhwirth , M. Jeitler ,N. Krammer, L. Lechner, D. Liko, I. Mikulec, F.M. Pitters, 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
Universiteit Antwerpen, Antwerpen, Belgium
M.R. Darwish , E.A. De Wolf, X. Janssen, T. Kello , A. Lelek, H. Rejeb Sfar, P. Van Mechelen,S. Van Putte, N. Van Remortel Vrije Universiteit Brussel, Brussel, Belgium
F. Blekman, E.S. Bols, J. D’Hondt, J. De Clercq, S. Lowette, S. Moortgat, A. Morton, D. M ¨uller,A.R. Sahasransu, 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, K. Lee, 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 , G. Mestdach, M. Niedziela, C. Roskas,K. Skovpen, M. Tytgat, W. Verbeke, B. Vermassen, M. Vit Universit´e Catholique de Louvain, Louvain-la-Neuve, Belgium
A. Bethani, 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, 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, M. Mittal, 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 , H. Okawa 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
H. Abdalla , A.A. Abdelalim , Y. Assran Center for High Energy Physics (CHEP-FU), Fayoum University, El-Fayoum, Egypt
A. Lotfy, M.A. Mahmoud
National Institute of Chemical Physics and Biophysics, Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M. Kadastik, J. Pata,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. Apparu, D. Bloch, G. Bourgatte, J.-M. Brom, E.C. Chabert,C. Collard, D. Darej, J.-C. Fontaine , 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
I. Bagaturia , Z. Tsamalaidze RWTH Aachen University, I. Physikalisches Institut, Aachen, Germany
L. Feld, K. Klein, M. Lipinski, D. Meuser, A. Pauls, 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, M.M. Defranchis, L. Didukh,D. Dom´ınguez Damiani, G. Eckerlin, D. Eckstein, 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, 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, M. Eich, F. Feindt, A. Fr ¨ohlich,C. Garbers, E. Garutti, P. Gunnellini, J. Haller, A. Hinzmann, A. Karavdina, G. Kasieczka,R. Klanner, R. Kogler, V. Kutzner, J. Lange, T. Lange, A. Malara, C.E.N. Niemeyer, A. Nigamova,K.J. Pena Rodriguez, O. Rieger, P. Schleper, M. Schr ¨oder, J. Schwandt, D. Schwarz, J. Sonneveld,H. Stadie, G. Steinbr ¨uck, A. Tews, 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, J.o. Gosewisch, 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, M. Neukum, G. Quast, K. Rabbertz, J. Rauser, D. Savoiu,D. Sch¨afer, M. Schnepf, D. Seith, I. Shvetsov, H.J. Simonis, R. Ulrich, J. Van Der Linden,R.F. Von Cube, M. Wassmer, M. Weber, S. Wieland, R. Wolf, S. Wozniewski, S. Wunsch 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, 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. 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
M. Bart ´ok , 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 , 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, 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. Jha, V. Kumar, D.K. Mishra, K. Naskar , P.K. Netrakanti, L.M. Pant, P. Shukla Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, S. Dugad, 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 ,44 , 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 ,45 , 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 ,46 , S. Costa a , b , A. Di Mattia a , R. Potenza a , b , A. Tricomi a , b ,46 , 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 ,22 , 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 ,22 , 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 ,22 , P. Paolucci a ,22 , B. Rossi a , C. Sciacca 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 ,47 ,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 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,Universit`a di Siena d , Siena, Italy K. Androsov a , P. Azzurri a , G. Bagliesi a , V. Bertacchi a , c , L. Bianchini a , T. Boccali a , E. Bossini,R. Castaldi a , M.A. Ciocci a , b , R. Dell’Orso a , M.R. Di Domenico a , b , S. Donato a , 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, 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, S. Lee, K. Nam,B.H. Oh, M. Oh, S.B. Oh, H. Seo, U.K. Yang, I. Yoon2
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, S. Ko, H. Kwon, H. Lee, S. Lee, K. Nam,B.H. Oh, M. Oh, S.B. Oh, H. Seo, U.K. Yang, I. Yoon2 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
S. Ha, 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
M. Ambrozas, 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
I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada
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
S. Afanasiev, D. Budkouski, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov, A. Kamenev,V. Karjavine, A. Lanev, A. Malakhov, V. Matveev , V. Palichik, V. Perelygin, M. Savina,D. Seitova, V. Shalaev, S. Shmatov, S. Shulha, V. Smirnov, O. Teryaev, N. Voytishin, 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. Gribushin, V. Klyukhin,O. Kodolova, I. Lokhtin, S. Obraztsov, M. Perfilov, S. Petrushanko, V. Savrin Novosibirsk State University (NSU), Novosibirsk, Russia
V. Blinov , T. Dimova , L. Kardapoltsev , I. Ovtin , Y. Skovpen , S. Zakharov 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 , 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,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. Gonzalez Ca-ballero, E. Palencia Cortezon, C. Ram ´on ´Alvarez, J. Ripoll Sau, V. Rodr´ıguez Bouza, 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, C. Fernandez Madrazo, 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, N. Trevisani, 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. Brondolin, T. Camporesi, M. Capeans Garrido,G. Cerminara, S.S. Chhibra, L. Cristella, D. d’Enterria, A. Dabrowski, N. Daci, 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. 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, M. Pitt, 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, M. Verzetti, K.A. Wozniak, W.D. Zeuner Paul Scherrer Institut, Villigen, Switzerland
L. Caminada , A. Ebrahimi, W. Erdmann, R. Horisberger, Q. Ingram, H.C. Kaestli, D. Kotlinski,U. Langenegger, M. Missiroli, 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 , R. Wallny,D.H. Zhu Universit¨at Z ¨urich, Zurich, Switzerland
C. Amsler , C. Botta, D. Brzhechko, M.F. Canelli, A. De Wit, 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, S. Sanchez Cruz, 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, P.r. Yu
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, A. Bundock, 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, V. Cepaitis, G.S. Chahal , D. Colling, P. Dauncey, G. Davies, M. Della Negra, G. Fedi, G. Hall,M.H. Hassanshahi, 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, A. Tapper, K. Uchida, T. Virdee , N. Wardle, S.N. Webb,D. Winterbottom, A.G. Zecchinelli Brunel University, Uxbridge, United Kingdom
J.E. Cole, A. Khan, P. Kyberd, C.K. Mackay, I.D. Reid, L. Teodorescu, S. Zahid
Baylor University, Waco, USA
S. Abdullin, A. Brinkerhoff, B. Caraway, J. Dittmann, K. Hatakeyama, A.R. Kanuganti,B. McMaster, N. Pastika, S. Sawant, C. Smith, C. Sutantawibul, 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, D. Di Croce, 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, J. Luo, M. Narain, S. Sagir , E. Usai,W.Y. Wong, X. Yan, 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, F. Jensen, O. Kukral, R. Lander, M. Mulhearn,D. Pellett, M. Shi, D. Taylor, M. Tripathi, Y. Yao, F. Zhang
University of California, Los Angeles, USA
M. Bachtis, R. Cousins, A. Dasgupta, A. Datta, 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, 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, L. Giannini,D. Gilbert, V. Krutelyov, J. Letts, M. Masciovecchio, S. May, S. Padhi, M. Pieri, V. Sharma,M. Tadel, A. Vartak, 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, M. Kilpatrick, 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, 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, I. Vorobiev
University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, E. MacDonald, 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, 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, K.F. Di Petrillo, V.D. Elvira, J. Freeman,Z. Gecse, L. Gray, D. Green, S. Gr ¨unendahl, O. Gutsche, R.M. Harris, 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, T. Liu, J. Lykken, C. Madrid,K. Maeshima, C. Mantilla, 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, L. Spiegel, S. Stoynev, J. Strait, L. Taylor,S. Tkaczyk, N.V. Tran, L. Uplegger, E.W. Vaandering, H.A. Weber 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, 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, R. Kumar Verma, 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, 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
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, 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, R. Bi, S. Brandt, W. Busza, I.A. Cali, Y. Chen, M. D’Alfonso, G. Gomez Cebal-los, M. Goncharov, P. Harris, M. Hu, M. Klute, D. Kovalskyi, J. Krupa, Y.-J. Lee, P.D. Luckey,B. Maier, A.C. Marini, C. Mironov, X. Niu, C. Paus, D. Rankin, C. Roland, G. Roland, Z. Shi,G.S.F. Stephans, 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, M. Bryson, 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, L. Hay, I. Iashvili, A. Kharchilava, C. McLean, D. Nguyen,J. Pekkanen, S. Rappoccio
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, K. Lannon,N. Loukas, N. Marinelli, I. Mcalister, F. Meng, K. Mohrman, Y. Musienko , R. Ruchti,P. Siddireddy, 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
F.M. Addesa, B. Bonham, P. Das, G. Dezoort, P. Elmer, A. Frankenthal, B. Greenberg,N. Haubrich, S. Higginbotham, A. Kalogeropoulos, G. Kopp, S. Kwan, D. Lange, M.T. Lucchini,D. Marlow, K. Mei, I. Ojalvo, J. Olsen, C. Palmer, D. Stickland, C. Tully
University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A.S. Bakshi, V.E. Barnes, R. Chawla, S. Das, L. Gutay, M. Jones, A.W. Jung, S. Karmarkar,M. Liu, G. Negro, N. Neumeister, C.C. Peng, S. Piperov, A. Purohit, J.F. Schulte, M. Stojanovic ,J. Thieman, F. Wang, 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, A. Kumar, W. Li, B.P. Padley,R. Redjimi, J. Roberts † , 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, 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, 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, E. Wolfe
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, A. Mohammadi, 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, Egypt, Alexandria, Egypt3: Also at Universit´e Libre de Bruxelles, Bruxelles, Belgium4: Also at IRFU, CEA, Universit´e Paris-Saclay, Gif-sur-Yvette, France
5: 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 Nanjing Normal University Department of Physics, Nanjing, China9: Now at The University of Iowa, Iowa City, USA10: Also at University of Chinese Academy of Sciences, Beijing, China11: Also at Institute for Theoretical and Experimental Physics named by A.I. Alikhanov ofNRC ‘Kurchatov Institute’, Moscow, Russia12: Also at Joint Institute for Nuclear Research, Dubna, Russia13: Also at Cairo University, Cairo, Egypt14: Also at Helwan University, Cairo, Egypt15: Now at Zewail City of Science and Technology, Zewail, Egypt16: Also at Suez University, Suez, Egypt17: Now at British University in Egypt, Cairo, Egypt18: Also at Purdue University, West Lafayette, USA19: Also at Universit´e de Haute Alsace, Mulhouse, France20: Also at Ilia State University, Tbilisi, Georgia21: Also at Erzincan Binali Yildirim University, Erzincan, Turkey22: Also at CERN, European Organization for Nuclear Research, Geneva, Switzerland23: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany24: Also at University of Hamburg, Hamburg, Germany25: Also at Department of Physics, Isfahan University of Technology, Isfahan, Iran, Isfahan,Iran26: Also at Brandenburg University of Technology, Cottbus, Germany27: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University,Moscow, Russia28: 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 Physics, University of Debrecen, Debrecen, Hungary, Debrecen,Hungary31: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary32: Also at MTA-ELTE Lend ¨ulet CMS Particle and Nuclear Physics Group, E ¨otv ¨os Lor´andUniversity, Budapest, Hungary, Budapest, Hungary33: Also at Wigner Research Centre for Physics, Budapest, Hungary34: Also at IIT Bhubaneswar, Bhubaneswar, India, Bhubaneswar, India35: Also at Institute of Physics, Bhubaneswar, India36: Also at G.H.G. Khalsa College, Punjab, India37: Also at Shoolini University, Solan, India38: Also at University of Hyderabad, Hyderabad, India39: Also at University of Visva-Bharati, Santiniketan, India40: Also at Indian Institute of Technology (IIT), Mumbai, India41: Also at Deutsches Elektronen-Synchrotron, Hamburg, Germany42: Also at Sharif University of Technology, Tehran, Iran43: Also at Department of Physics, University of Science and Technology of Mazandaran,Behshahr, Iran44: Now at INFN Sezione di Bari a , Universit`a di Bari b , Politecnico di Bari c , Bari, Italy45: Also at Italian National Agency for New Technologies, Energy and Sustainable EconomicDevelopment, Bologna, Italy46: Also at Centro Siciliano di Fisica Nucleare e di Struttura Della Materia, Catania, Italy
47: Also at Universit`a di Napoli ’Federico II’, NAPOLI, Italy48: Also at Riga Technical University, Riga, Latvia, Riga, Latvia49: Also at Consejo Nacional de Ciencia y Tecnolog´ıa, Mexico City, Mexico50: Also at Institute for Nuclear Research, Moscow, Russia51: Now at National Research Nuclear University ’Moscow Engineering Physics Institute’(MEPhI), Moscow, Russia52: 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 P.N. Lebedev Physical Institute, Moscow, Russia56: Also at California Institute of Technology, Pasadena, USA57: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia58: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia59: Also at Trincomalee Campus, Eastern University, Sri Lanka, Nilaveli, Sri Lanka60: Also at INFN Sezione di Pavia a , Universit`a di Pavia b , Pavia, Italy, Pavia, Italy61: Also at National and Kapodistrian University of Athens, Athens, Greece62: Also at Universit¨at Z ¨urich, Zurich, Switzerland63: Also at Ecole Polytechnique F´ed´erale Lausanne, Lausanne, Switzerland64: Also at Stefan Meyer Institute for Subatomic Physics, Vienna, Austria, Vienna, Austria65: Also at Laboratoire d’Annecy-le-Vieux de Physique des Particules, IN2P3-CNRS, Annecy-le-Vieux, France66: Also at S¸ ırnak University, Sirnak, Turkey67: Also at Department of Physics, Tsinghua University, Beijing, China, Beijing, China68: Also at Near East University, Research Center of Experimental Health Science, Nicosia,Turkey69: Also at Beykent University, Istanbul, Turkey, Istanbul, Turkey70: Also at Istanbul Aydin University, Application and Research Center for Advanced Studies(App. & Res. Cent. for Advanced Studies), Istanbul, Turkey71: Also at Mersin University, Mersin, Turkey72: Also at Piri Reis University, Istanbul, Turkey73: Also at Adiyaman University, Adiyaman, Turkey74: Also at Ozyegin University, Istanbul, Turkey75: Also at Izmir Institute of Technology, Izmir, Turkey76: Also at Necmettin Erbakan University, Konya, Turkey77: Also at Bozok Universitetesi Rekt ¨orl ¨ug ¨u, Yozgat, Turkey, Yozgat, Turkey78: Also at Marmara University, Istanbul, Turkey79: Also at Milli Savunma University, Istanbul, Turkey80: Also at Kafkas University, Kars, Turkey81: Also at Istanbul Bilgi University, Istanbul, Turkey82: Also at Hacettepe University, Ankara, Turkey83: Also at Vrije Universiteit Brussel, Brussel, Belgium84: Also at School of Physics and Astronomy, University of Southampton, Southampton,United Kingdom85: Also at IPPP Durham University, Durham, United Kingdom86: Also at Monash University, Faculty of Science, Clayton, Australia87: Also at Bethel University, St. Paul, Minneapolis, USA, St. Paul, USA88: Also at Karamano ˘glu Mehmetbey University, Karaman, Turkey89: Also at Ain Shams University, Cairo, Egypt90: Also at Bingol University, Bingol, Turkey2