Search for top squarks in final states with two top quarks and several light-flavor jets in proton-proton collisions at \sqrt{s}= 13 TeV
EEUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH (CERN)
CERN-EP-2021-0152021/02/16
CMS-SUS-19-004
Search for top squarks in final states with two top quarksand several light-flavor jets in proton-proton collisions at √ s =
13 TeV
The CMS Collaboration * Abstract
Many new physics models, including versions of supersymmetry characterized by R -parity violation (RPV), compressed mass spectra, long decay chains, or additionalhidden sectors, predict the production of events with top quarks, low missing trans-verse momentum, and many additional quarks or gluons. The results of a searchfor new physics in events with two top quarks and additional jets are reported. Thesearch is performed using events with at least seven jets and exactly one electron ormuon. No requirement on missing transverse momentum is imposed. The studyis based on a sample of proton-proton collisions at √ s =
13 TeV corresponding to137 fb − of integrated luminosity collected with the CMS detector at the LHC in 2016–2018. The data are used to determine best fit values and upper limits on the crosssection for pair production of top squarks in scenarios of RPV and stealth supersym-metry. Top squark masses up to 670 (870) GeV are excluded at 95% confidence levelfor the RPV (stealth) scenario, and the maximum observed local significance is 2.8standard deviations for the RPV scenario with top squark mass of 400 GeV. Submitted to Physical Review D © 2021 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 ] F e b Supersymmetry [1, 2] (SUSY) is an extension of the standard model (SM) that may provide asolution to the gauge hierarchy problem [3]. In the SUSY framework, quadratically divergentradiative corrections to the Higgs boson mass parameter, dominated by loops involving the topquark, are canceled by loops with bosonic top quark superpartners (top squark, (cid:101) t). To avoidfine tuning, the lightest (cid:101) t and the superpartners of the Higgs bosons (higgsinos) must havemasses near the weak scale [3–8], and should therefore have nonnegligible production crosssections at the CERN Large Hadron Collider (LHC).Most searches for the (cid:101) t look for an excess of events with large missing transverse momentum p missT originating from the undetected lightest SUSY particle (LSP) produced in (cid:101) t decays. It istypical in these searches to assume that the LSP is the lightest neutralino (cid:101) χ , which is stable if R -parity [9] is conserved. However, it has been shown [10–12] that this search strategy is notsensitive to well-motivated SUSY models that predict signatures with low p missT via, for exam-ple, gauge mediated SUSY breaking [13], compressed mass spectra [14, 15], hidden valleys [16],or other mechanisms. As searches performed at the LHC using events with high p missT set evermore stringent lower bounds on the (cid:101) t mass [17–22], searches for low- p missT alternatives becomeincreasingly important.Models of R -parity violating (RPV) SUSY produce low- p missT signatures by providing a mech-anism for the LSP, in this case (cid:101) χ , to decay. Among other couplings, RPV SUSY includes atrilinear Yukawa coupling between quarks and squarks that allows the (cid:101) χ to decay into threequarks via an off-shell squark [9]. These couplings are typically referred to as λ (cid:48)(cid:48) ijk where i , j ,and k specify the generations of the participating (s)quarks. The benchmark RPV model usedin this analysis is illustrated in Fig. 1. The (cid:101) t decays in the typical way into a top quark and a (cid:101) χ ,and the (cid:101) χ undergoes an RPV decay via nonzero λ (cid:48)(cid:48) into three light-flavor quarks, (cid:101) χ → uds.However, since this analysis does not distinguish between jets originating from quarks of thefirst and second generation, our results are more broadly applicable to any RPV model withcoupling λ (cid:48)(cid:48) abc with a , b , c ∈ {
1, 2 } .Stealth SUSY models [12, 23, 24] introduce a new hidden “stealth” sector of light particles withsmall or absent couplings to the SUSY breaking sector and finite couplings to the visible sector.Because of the weak connection to the SUSY breaking sector, SUSY is approximately conservedin the stealth sector, resulting in stealth particles that are nearly mass-degenerate with theirsuperpartners. Production and decay of stealth particles via interactions with visible particlescan be achieved through a variety of “portals” including mediation by the Higgs boson or newparticles at a higher mass scale. The benchmark stealth SUSY model used in the interpretationof the results of this search (stealth SYY) [24] assumes a minimal stealth sector containing onlyone scalar particle S with even R -parity and its superpartner (cid:101) S, both of which are singletsunder all SM interactions, and a portal mediated by loop interactions involving a new vector-like messenger field (Y), the gluon (g), (cid:101) χ , S, and (cid:101) S. Decays of the (cid:101) t in the stealth SYY modelare illustrated in Fig. 1. Each (cid:101) t decays to a gluon, top quark, and (cid:101)
S, with subsequent decaysof (cid:101)
S to S and a gravitino (cid:101)
G and S to jets via S → gg. Because of the small mass splittingbetween the S and (cid:101) S, as well as the small (cid:101)
G mass, the undetected (cid:101)
G carries away very littlemomentum. Thus, the stealth SYY model shares the general feature of all stealth SUSY modelsthat it naturally produces a low- p missT signature without R -parity violation or a special tuningof sparticle masses.The RPV and stealth SYY models are characterized by the masses of the particles and branchingfractions in the decay chain. In the benchmark RPV model, we take the (cid:101) χ mass to be 100 GeV. P P ˜t˜t ˜ χ ˜ χ q qqttqqq P P ˜t˜t ˜S S˜S S˜G g g gtgtgg˜G Figure 1: Diagrams of top squark pair production with decays to top quarks and additionallight-flavor quarks for the RPV SUSY model (left) and with decays to top quarks and gluonsfor the stealth SYY model (right).For the benchmark stealth SYY model, the critical small (cid:101)
S-S mass splitting is held constant at10 GeV, and we assume a (cid:101)
S mass of 100 GeV and a (cid:101)
G mass of 1 GeV. For both models, a range of (cid:101) t masses ( m (cid:101) t ) are considered from 300 to 1400 GeV, and all decays described above are assumedto be prompt with unity branching fractions.In this paper, we describe a search for (cid:101) t pair production followed by the decay of each (cid:101) t into atop quark and three light-flavor jets via the benchmark RPV and stealth SYY models describedabove. This is the first search of its kind at the LHC. Previous searches for RPV (cid:101) t decays focusedon final states with dijet resonances [25, 26], lepton-jet resonances [27, 28], intermediate leptonicchargino decays [29], or final states with many b quarks [30]. Previous searches for stealth SUSYtargeted superpartners of light-flavor quarks with decays into gauge bosons and jets [31, 32].Before describing each step in more detail in subsequent sections, we provide an overviewof the analysis strategy here. The main distinguishing feature of the signals in this analysis,in addition to the presence of two top quarks, is high jet multiplicity ( N jets ). The SM back-grounds arise through processes including top quark pair production (tt), multijet productionfrom quantum chromodynamics (QCD), production of tt in association with SM weak gaugebosons or additional top quarks (tt+X), production of weak gauge bosons, and single top quarkproduction (other). These SM processes all include additional jets from initial- and final-stateradiation (ISR and FSR). The QCD background is primarily suppressed by requiring the pres-ence of exactly one charged lepton (e or µ ) arising from the leptonic decay of a top quark.Backgrounds that do not produce any top quarks are suppressed by requiring the presence ofat least one jet identified as arising from the fragmentation of a bottom quark (b-tagged jet),and additionally that the invariant mass of the lepton and a b-tagged jet be consistent with thepresence of a top quark.The signal is distinguished from the dominant and irreducible tt background by means of aneural network (NN) trained to recognize differences in the spatial distribution of jets anddecay kinematic distributions between signal and tt background events. Events are dividedinto 24 categories based on their NN score ( S NN ) and N jets ; categories with higher (lower) S NN and N jets tend to be signal enriched (depleted). We perform a simultaneous fit to the number ofevents in data in S NN and N jets categories to estimate the total numbers of tt and potential signalevents present in the data, as well as the distribution of tt events in S NN and N jets categories.The NN output is designed to have no dependence on N jets , so that the N jets distribution of ttevents can be constrained in the fit to be the same for all S NN categories. This requirement for tt N jets shape invariance is important for the analysis and will be discussed throughout the paper. This paper is organized as follows. We introduce the CMS detector and methods for event re-construction and selection in Section 2. Samples of simulated events are described in Section 3.The estimation and modeling of SM backgrounds are explained in Section 4, and the descrip-tion of the treatment of systematic uncertainties is in Section 5. Finally, the results and theirinterpretation are in Section 6, followed by the summary in Section 7.
The search is performed using a data sample of proton-proton (pp) collisions at √ s =
13 TeV,corresponding to an integrated luminosity of 137 fb − , collected in 2016–2018 with the CMSdetector at the LHC. Data and simulation samples from four periods (2016, 2017, 2018A, 2018B)are treated separately in order to address variations in detector and LHC conditions. Data from2018 are divided into two samples (2018A and 2018B), with 2018B corresponding to the periodwhen a detector malfunction prevented readout from 3% of the hadron calorimeter. In thissection, we define reconstructed physics objects and describe the selection criteria for events inthe signal region (SR) and the control region (CR) of the analysis.The central feature of the CMS apparatus is a superconducting solenoid of 6 m internal diam-eter, providing a magnetic field of 3.8 T. Within the solenoid volume are a silicon pixel andstrip tracker, a lead tungstate crystal electromagnetic calorimeter, and a brass and scintillatorhadron calorimeter, each composed of a barrel and two endcap sections. Forward calorime-ters extend the pseudorapidity coverage provided by the barrel and endcap detectors. Muonsare detected in gas-ionization chambers embedded in the steel flux-return yoke outside thesolenoid. A more detailed description of the CMS detector, together with a definition of thecoordinate system used and the relevant kinematic variables, can be found in Ref. [33].The CMS trigger system is described in Ref. [34]. Events are selected using triggers that re-quire the presence of at least one electron or one muon. The minimum transverse momentum p T threshold is 27 (35) GeV for electrons and 24 (24) GeV for muons in 2016 (2017–2018). Thetriggers at these thresholds require the lepton to be isolated from tracks and calorimeter de-posits in the detector. Events may also be selected from single-lepton triggers with higher p T thresholds, 115 GeV for electrons and 50 GeV for muons, with no isolation requirements. Thecombined trigger efficiency varies from 80% for leptons with p T close to the lower thresholdsto greater than 95% for leptons with p T >
120 GeV.Events are reconstructed using the particle-flow (PF) algorithm [35], which reconstructs parti-cles in an event using an optimized combination of information from the various elements ofthe CMS detector and identifies each as a photon, electron, muon, charged hadron, or neutralhadron. These particles are further clustered into jets as described below.The reconstructed vertex with the largest value of summed physics-object p is taken to be theprimary pp interaction vertex, where the physics objects are the jets, clustered using the anti- k T algorithm [36, 37] with the charged-particle tracks assigned to the vertex as inputs, and theassociated missing transverse momentum, taken as the negative vector sum of the p T of thosejets. Charged-particle tracks associated with vertices from other pp interactions (pileup) areremoved from further consideration. The primary vertex is required to lie within 24 cm of theinteraction point along the beam axis, and within 2 cm in the plane transverse to the beam axis.Electrons and muons must satisfy p T >
30 GeV and | η | < p T threshold is increased to 37 GeV to account for the higher triggerthreshold. The lepton identification requirements are the “tight” criteria for electrons [38] and the “medium” criteria for muons [39]. Leptons must be isolated within a cone of radius R = √ ( ∆ φ ) + ( ∆ η ) that scales as 1/ p T between a maximum of 0.2 for leptons with p T <
50 GeVand a minimum of 0.05 for lepton p T >
200 GeV [40].Jets are clustered from the reconstructed PF particles using the anti- k T algorithm with a dis-tance parameter of 0.4. Criteria are applied to remove events with jets arising from instru-mental effects or reconstruction failures [41, 42]. The reconstructed jet energies are correctedfor the nonlinear response of the detector [43, 44] and for contributions from neutral hadronsfrom pileup [45]. Jets are required to have p T >
30 GeV and | η | < R = p T around 30 GeV, the algorithm has anefficiency of 65% and a misidentification rate for light-flavor jets (including gluon jets) of 1%.In addition to the trigger and vertex criteria above, events in the SR must contain exactly oneisolated electron or muon and at least seven jets, at least one of which should be b tagged.Samples with seven and eight jets include a small number of expected signal events, but areincluded in the SR to constrain the background. To further reject the QCD background, werequire the scalar sum of jet p T ( H T ) to exceed 300 GeV. To suppress non-tt backgrounds, werequire the invariant mass of the system formed by the b-tagged jet and the lepton to be be-tween 50 and 250 GeV. If there is more than one b-tagged jet in the event, the invariant mass ofeach b-tagged jet and the lepton is considered, and at least one combination is required to meetthe above criterion. No requirement is made on the event p missT .In addition to the SR, a signal-depleted control region (CR) dominated by QCD background isdefined with the dual purpose of determining the QCD contribution to the SR and verifyingthe important assumption of tt N jets shape invariance with S NN . Despite being dominated byQCD background, the CR is useful for confirming tt N jets shape invariance because many of thejets used as inputs to the NN arise from QCD radiation, which is common to the tt and QCDbackgrounds; this claim is verified in Section 5. The CR is defined similarly to the SR with thedifferences being that the lepton is required to be a muon; the muon is required to fail the SRisolation requirement; there is no requirement for a b-tagged jet, nor on the invariant mass ofthe lepton and b-tagged jet; the only trigger used is the high-threshold muon trigger withoutan isolation requirement; and the muon p T threshold is 55 GeV. Simulated event samples are used in the estimation of the expected number of SM backgroundand signal events passing the SR selection. Top quark pair and single top quark events pro-duced in the t channel are generated with the next-to-leading-order (NLO) POWHEG v2.0 [47–51] generator, while single top quark events in the tW channel are generated with
POWHEG v1.0 [50]. Single top quark production in the s channel, as well as rare SM processes such asttZ and ttW are generated at NLO accuracy with the M AD G RAPH MC @ NLO v2.2.2 program.The M AD G RAPH MC @ NLO v2.2.2 generator [52, 53] is used in the leading-order (LO) modeto simulate QCD and W + jets events.For the signal, top squark pair production events are generated using M AD G RAPH MC @ NLO in LO mode, including up to two additional partons in the matrix element calculation. The topsquarks are decayed using
PYTHIA v8.212 (2016) or 8.226 (2017–2018) [54] according to thesignal models described in Section 1. The signal production cross section ( σ (cid:101) t (cid:101) t ) is calculated as afunction of m (cid:101) t using approximate next-to-NLO (NNLO) plus next-to-next-to-leading-logarithm (NNLL) calculations [55, 56].The generation of these processes is based on either LO or NLO parton distribution functions(PDFs) using NNPDF3.0 [57] for the simulated samples corresponding to 2016 detector condi-tions, and using the NNLO PDF sets from NNPDF3.1 [58] for the 2017 and 2018 simulatedsamples. Parton showering and hadronization are simulated with PYTHIA using underly-ing event tune CUETP8M1 [59] for 2016 samples, except for tt production which used tuneCUETP8M2T4 [60], or
PYTHIA with tune CP5 (CP2) [61] for all 2017 and 2018 background (sig-nal) samples. To model the effects of pileup, simulated events are generated with a nominaldistribution of pp interactions per bunch crossing and then reweighted to match the corre-sponding distribution in data. The CMS detector response is simulated using a G
EANT H T distribution in tt simulation, parameterized as functions of N jets and H T , are obtained in a signal-depleted sample identical to the SR, except for the requirement5 ≤ N jets ≤
7. Events with N jets = H T with parameters depending linearly on N jets in order to extend the correctioninto the N jets > H T correction is small at low H T and 20–40% at H T = N jets . Simulated background events passing the SR selection requirements predominantly originatefrom tt production, with contributions of less than 10% from QCD, and a few percent fromthe remaining minor backgrounds including tt production in association with a vector boson,single top quark production, and W + jets.As introduced in Section 1, the crux of the analysis is to estimate the dominant tt backgroundin four bins of S NN and six N jets bins using a simultaneous binned maximum-likelihood fitconstraining the tt N jets shape to be the same in all S NN categories. Event yields, as well as the N jets and S NN distributions, are fixed at values determined from a signal-depleted data controlsample for the QCD background and from simulation for the minor backgrounds, as describedlater in this section. The yield and N jets shape of the tt background, along with the signalstrength, are determined in the fit; signal strength is defined as the ratio of the fit signal eventyield to the one predicted by SUSY.The NN is trained to discriminate between signal and tt background by exploiting differencesin the event shape and distributions of the kinematic variables. The gradient reversal tech-nique [70] is used to minimize dependence of the NN output on N jets , as required by theprimary assumption that the tt N jets shape is the same in all S NN categories. All NN inputvariables are computed in an approximate center-of-mass frame defined by all jets in the eventwith p T >
30 GeV and | η | <
5. The NN input variables include the four-vector componentsfor the seven jets in the event with the highest momentum in the center-of-mass frame, thefour-vector components of the lepton in the event, the second through fifth Fox–Wolfram mo-ments [71] normalized by the first moment, and the three eigenvalues of the sphericity ten-sor [72] normalized by the sum of the eigenvalues. The Fox–Wolfram moments and sphericity tensor eigenvalues, which are computed from the same seven highest momentum jets, quan-tify the distribution of jet energy in the event, which tends to be more spherical for signal (cid:101) t pairproduction than for the tt background.For the NN training, simulated tt events are used for the background sample, and a mixture ofRPV and stealth SYY simulated events with m (cid:101) t from 350–850 GeV is used as the signal sample.In this way, the NN can identify common features among all signal samples ensuring a searchwith broad sensitivity. Reflecting differences in simulation between the data taking periods,as described in Section 3, a single training is used for 2017, 2018A, and 2018B, with a separatetraining used for 2016. The S NN distributions for the simulated background, several signalmodels, and the 2016 and 2017+2018 data are shown in Fig. 2. E v en t s + Xtt QCD multijet Other tt Data 4) · t~ t~ s = 450 GeV ( t~ RPV m 16) · t~ t~ s = 850 GeV ( t~ mYStealth SY CMS (13 TeV) -1 NN S D a t a / M C E v en t s + Xtt QCD multijet Other tt Data 4) · t~ t~ s = 450 GeV ( t~ RPV m 16) · t~ t~ s = 850 GeV ( t~ mYStealth SY CMS (13 TeV) -1
101 fb NN S D a t a / M C Figure 2: The S NN distributions for 2016 (left) and 2017+2018 (right) show the data in the SR(black points); simulated background normalized to the number of data events (filled his-tograms); RPV signal model with m (cid:101) t of 450 GeV (red short dashed); and stealth SYY signalmodel with m (cid:101) t of 850 GeV (cyan long dashed). All events shown pass the SR event selection.The band on the total background histogram denotes the dominant systematic uncertaintiesrelated to the modeling of H T , jet mass, and jet p T in the tt simulation, as well as the statisticaluncertainty for the non-tt components. The lower panel shows the ratio of the number of dataevents to the number of normalized simulated events with the band representing the differ-ence between the nominal ratio and the ratio obtained when varying the total background byits uncertainty.For each of the six N jets bins, events are divided into four S NN bins: S NN,1 (lowest S NN ), . . . , S NN,4 (highest S NN ). The S NN bin boundaries are chosen separately for each N jets bin such thatthe expected significance for the 550 GeV RPV signal model, which has expected significanceclose to 5 standard deviations ( σ ), is maximized, under the constraint that the fraction of sim-ulated tt events in each S NN bin is the same for all N jets bins. For example, the fraction of allevents in each N jets bin falling into the S NN,1 bin is constrained to be approximately 56%, whilethe fraction of events falling into the S NN,4 bin is constrained to be approximately 2.4%. Thisconstraint removes the small dependence of N jets on S NN that remains after NN training withgradient reversal.In the maximum-likelihood fit, the tt N jets distribution is parameterized with a function in-spired by QCD jet scaling patterns [73] in which the ratio R ( i ) = M i + / M i , where M i is the number of events with N jets = i , can be described by a falling “Poisson” component at low N jets and a constant “staircase” component at high N jets . This ratio is well modeled by the function f ( i ) = a + (cid:20) ( a − a ) i − ( a − a ) i − (cid:21) .Notice that a = f ( ) , a = f ( ) , and a is the asymptotic value for large i . This particularparameterization was chosen to avoid large correlations between the fit parameters. The N jets distribution for each S NN bin j (see Fig. 4) is modeled using a recursive expression given by M ji = Y j Π i − k = f ( k ) where Y j are normalization parameters that are floating in the fit. The last N jets bin considered is an inclusive N jets ≥
12 bin, such that i ∈ [
7, 12 ] . In the maximum-likelihood fit, the free parameters consist of the three shape parameters a , a , and a ; the fournormalization parameters Y j ; the signal strength; and all nuisance parameters related to sys-tematic uncertainties described in Section 5.The QCD background yield parameters are fixed in the fit at the values determined from theCR. More specifically, the QCD background prediction for each N jets - S NN bin in the SR is givenby the yield for the same bin in the CR in data, after subtraction of the non-QCD backgrounds aspredicted from simulation, multiplied by the ratio of SR to CR yields in simulation ( R QCD ). Thisprocedure is verified with a closure test in the simulation. The yield parameters from the minorbackgrounds are also kept fixed in the fit at the values predicted by simulation. While the yieldparameters are fixed in the fit, the ultimate contributions from QCD and minor backgroundsvary according to the constrained nuisance parameters related to systematic uncertainties inthose fit components.
As described in Section 4, an unbiased estimate of the dominant tt background is obtained fromthe fit to data as long as the tt N jets shape is the same for all four S NN bins. By construction, N jets shape invariance is achieved in the simulation with an N jets -specific S NN binning as describedin the previous section. Thus, systematic uncertainties on the tt background are importantto the degree that they violate the assumption that the S NN binning determined in simulationalso applies to the data. We quantify how each source of uncertainty causes deviations from theassumed N jets shape invariance by comparing the nominal N jets shape to the N jets shapes in all S NN bins after performing the relevant systematic variation to the tt simulation. For each S NN bin, the change in the fraction of events in each N jets bin is taken as the systematic uncertaintyfor that N jets bin, and the change is included as a constrained nuisance parameter in the fit. The24 N jets - S NN bin variations are taken to be completely correlated.Sources of tt shape uncertainty include uncertainty in aspects of event generation includingPDFs, choice of renormalization and factorization scales ( µ R , µ F scales), and parton showermodeling, which is itself composed of aspects related to modeling of ISR, FSR, color reconnec-tion in the parton shower, matrix element-parton shower matching scale (ME-PS), underlyingevent (UE tune), and pileup modeling. The uncertainty due to the choice in ( µ R , µ F ) scales is de-termined by independently varying both by factors of 2.0 and 0.5 excluding the variations (2.0,0.5) and (0.5, 2.0) [53, 74, 75]. The ME-PS uncertainty is obtained by varying the POWHEG pa-rameter that governs ME-PS matching about its nominal value according to h damp = + − times the top quark mass [61]. The UE tune uncertainty comes from variation of the PYTHIA pa-rameters that control the modeling of the underlying event as described in Ref. [61]. The totalinelastic pp cross section is changed by 5% to estimate the uncertainty related to pileup [76].
Sources of tt shape uncertainty related mostly to aspects of detector simulation include deter-mination of jet energy scale (JES) and resolution (JER), modeling of the b tagging efficiency,modeling of the efficiency for lepton triggers, identification, and isolation (lepton efficiencies);residual mismodeling of H T , jet p T , and jet mass; and use of the CR for measuring deviationsfrom the assumption of N jets shape invariance.The uncertainty in the modeling of H T in the tt simulation is composed of four separate com-ponents. The first H T uncertainty (primary) is taken as the full difference in the tt backgroundshape with and without the H T correction. The second H T uncertainty (validation) is taken asthe difference between the simulation with nominal H T correction (described in Section 3) andthe observed H T distribution in the signal-depleted SR sample with N jets =
8. The third andfourth H T uncertainties address the choices of parameterization of the H T correction as func-tions of H T and N jets . For these, we take the uncertainty as the difference between the nominalcorrection and two alternate corrections that use the H T = H T > H T -parameterization) and the N jets = N jets ( N jets -parameterization).Comparisons of data and simulation in the CR show that the simulation predicts distributionswith higher values of jet p T and mass than observed. The observed discrepancy at jet p T (mass)of 400 (50) GeV depends on jet p T rank and is small for the highest p T jet in each event growingto approximately 50% for the jet with sixth-highest p T in each event. This dependence on jet p T rank indicates that the discrepancy arises in event generation rather than simulation of detectorresponse. Similar trends are observed in the signal-depleted, tt-dominated SR with N jets = p T (mass) of each jet is scaled by the value 0.95, 0.95, 0.95, 0.95 (0.95, 1.01, 0.98, 0.98) for 2016, 2017,2018A, and 2018B, respectively. The related tt shape uncertainty is taken to be the resultingdifference between scaled and nominal simulated tt distributions. The H T correction is omittedfrom the determination of these jet p T and mass uncertainties to avoid double counting of H T mismodeling effects. In addition, because the estimation of jet p T and mass uncertainties relieson variable scaling (rather than event reweighting), they include effects of changes in the S NN for each event, which is not included in the H T uncertainty.As mentioned above, the use of N jets -dependent S NN binning ensures that the N jets shape isthe same in all four S NN bins in simulation, and the use of the same binning in the data as-sumes that the N jets - S NN dependence is well modeled in the simulation. This assumption isconfirmed and a related systematic uncertainty is determined by comparing the N jets shapes(in five uniform S NN bins) for data and simulation in the CR. For each of the six N jets bins, wecompute the ratio R M = ( µ i ) ( M all / M i ) as a function of S NN , where M all is the total numberof events in all N jets bins, M i is the total number of events in the N jets = i bin, and µ i is theuncertainty-weighted average of M all / M i in the N jets = i bin used to facilitate comparison ofthe R M shapes between samples and N jets bins with different normalizations. Figure 3 showsa comparison of R M (from N jets = MCCR ), simulation of tt in the SR (tt
MCSR ), and the data in the QCDbackground-dominated CR (Data CR ). Agreement between QCD MCCR and tt
MCSR demonstrates thatQCD background-dominated data in the CR are a good proxy for tt-dominated data in the SR,and agreement between QCD
MCCR and Data CR verifies that the dependence of the N jets shape on S NN is well modeled in the simulation. Similar agreement is found for the R M distributions forthe other N jets bins and data periods. The uncertainty related to the combination of both effectsis taken as the difference between tt MCSR and Data CR . NN S M R MCCR
QCD
MC SR tt CR Data
CMS (13 TeV) -1 = 7 jets N NN S M R MCCR
QCD
MC SR tt CR Data
CMS (13 TeV) -1 = 11 jets N Figure 3: Distribution in S NN of the ratio R M , as defined in the text, for N jets = R M .For the QCD background, the shape is obtained from data in the CR, and the normalizationis set with R QCD . Because the systematic uncertainties in the simulation largely cancel in the R QCD ratio, the uncertainty in R QCD is dominated by the statistical uncertainty of simulatedsamples and ranges from 15–25% depending on data collection period.Sources of systematic uncertainty in the predictions for signals and the minor backgrounds in-clude PDFs, JES, JER, b tagging efficiency, lepton efficiency, trigger efficiency, ( µ R , µ F ) scales,cross sections for the minor backgrounds, and a 2.3–2.5% uncertainty in the integrated lumi-nosity [77–79]. Since the signal and minor backgrounds are estimated directly from simulation,related uncertainties are included as the full effect of the systematic variation on the yields ineach N jets and S NN bin, rather than as a shape uncertainty.Uncertainties derived from comparisons of data and simulation separately in each data takingperiod (related to pileup, JES, JER, b tagging efficiency, lepton efficiencies, H T corrections, N jets shape invariance, and integrated luminosity) are treated as uncorrelated among all data sam-ples. Uncertainties related to parton shower modeling are treated as fully correlated for 2017,2018A, and 2018B, while the corresponding uncertainties for 2016 are uncorrelated with thosefrom the other data taking periods; uncertainties related to ( µ R , µ F ) scales and cross sections forthe minor backgrounds are treated as correlated between all four periods.Table 1 shows the impact of the systematic uncertainties on the expected event yields for the ttbackground, minor backgrounds, and the RPV signal model with m (cid:101) t =
550 GeV. For sources ofuncertainty for which the size of the impact depends on N jets and S NN , a representative rangeof values is listed along with the maximum value from all bins. The results of the fit to 2016, 2017, 2018A, and 2018B data sets with the signal strength fixed tozero (background-only fit) are shown along with the observed number of events in Fig. 4; eachcolumn (row) in the plot array corresponds to a specific S NN bin (data set). The expected dis-0
550 GeV. For sources ofuncertainty for which the size of the impact depends on N jets and S NN , a representative rangeof values is listed along with the maximum value from all bins. The results of the fit to 2016, 2017, 2018A, and 2018B data sets with the signal strength fixed tozero (background-only fit) are shown along with the observed number of events in Fig. 4; eachcolumn (row) in the plot array corresponds to a specific S NN bin (data set). The expected dis-0 Table 1: Summary of the impact of systematic uncertainties in the expected event yields forthe tt background, minor backgrounds (both tt+X and other), and the RPV signal model with m (cid:101) t =
550 GeV. Abbreviated names for each source of uncertainty are explained in the text. Forsources of uncertainty for which the size of the impact depends on N jets and S NN , a represen-tative range of values showing the 16th and 84th percentile of all the corrections is listed withthe maximum value from all bins shown in parentheses. All values are in units of percent.tt Minor RPVSource of uncertainty background background signalPDFs 0–1 (2) 0–1 (8) 0–2 (7)( µ R , µ F ) scales 0–2 (5) 1–8 (18) 0–3 (4)ISR 0–4 (15) — —FSR 0–8 (27) — —Color reconnection 0–10 (44) — —ME-PS 0–14 (82) — —UE tune 0–7 (100) — —Pileup 0–2 (7) 0–7 (28) 0–2 (4)JES 0–4 (18) 5–21 (100) 1–11 (31)JER 0–2 (10) 1–15 (100) 0–6 (14)b tagging 0–1 (3) 0–2 (12) 0–2 (2)Lepton efficiencies 0–1 (1) 3–5 (5) 3–4 (4) H T primary 0–5 (17) — — H T validation 0–1 (4) 0–6 (10) — H T H T -parameterization 0–2 (9) — — H T N jets -parameterization 0–7 (27) — —Jet p T N jets shape invariance 0–12 (37) — —Integrated luminosity — 2.3–2.5 2.3–2.5Theoretical cross section — 30 — -
10 110 E v en t s NN,1 S CMS ‡ - d ( da t a - f i t ) / - NN,2 S ‡ - d ( da t a - f i t ) / - NN,3 S ‡ - d ( da t a - f i t ) / - NN,4 S (13 TeV) -1 Bkg FitN observed = 450 GeV t~ RPV m = 850 GeV t~ SYY m _ ‡ jets N - -
10 110 E v en t s NN,1 S CMS ‡ - d ( da t a - f i t ) / - NN,2 S ‡ - d ( da t a - f i t ) / - NN,3 S ‡ - d ( da t a - f i t ) / - NN,4 S (13 TeV) -1 Bkg FitN observed = 450 GeV t~ RPV m = 850 GeV t~ SYY m _ ‡ jets N - -
10 110 E v en t s NN,1 S CMS ‡ - d ( da t a - f i t ) / - NN,2 S ‡ - d ( da t a - f i t ) / - NN,3 S ‡ - d ( da t a - f i t ) / - NN,4 S (13 TeV) -1 Bkg FitN observed = 450 GeV t~ RPV m = 850 GeV t~ SYY m _ ‡ jets N - -
10 110 E v en t s NN,1 S CMS ‡ - d ( da t a - f i t ) / - NN,2 S ‡ - d ( da t a - f i t ) / - NN,3 S ‡ - d ( da t a - f i t ) / - NN,4 S (13 TeV) -1 Bkg FitN observed = 450 GeV t~ RPV m = 850 GeV t~ SYY m _ ‡ jets N - Figure 4: Fitted background prediction and observed data counts for 2016, 2017, 2018A, and2018B (from upper to lower rows) as functions of N jets in each of the four bins in S NN . The signaldistributions normalized to the predicted cross section for the RPV model with m (cid:101) t =
450 GeVand the stealth SYY model with m (cid:101) t =
850 GeV are shown for comparison. The lower panel ofeach plot displays the difference between the number of observed events and the number ofevents determined by the fit divided by the statistical uncertainty associated with the observednumber of events ( δ ) as black points with error bars denoting δ . The blue band shows the totalsystematic uncertainty in the fit from all nuisance parameters. E v en t s / b i n + X tt QCD multijet Other tt Data = 450 GeV t~ RPV m = 850 GeV t~ mYStealth SY CMS (13 TeV) -1
137 fb ‡ jets N D a t a / P r ed . Figure 5: Background prediction from the background-only fit and observed data counts as afunction of N jets summed over data periods and S NN bins. Overlaid are expected distributionsfor the RPV and stealth SYY models with m (cid:101) t =
450 and 850 GeV, respectively, normalized ac-cording to the top squark pair production cross section. For visualization purposes, the hatchedband in the lower panel shows the quadrature sum of all of the uncertainties on the backgroundprediction.tributions for top squark pair production in the specific RPV ( m (cid:101) t =
450 GeV) and stealth SYYmodels ( m (cid:101) t =
850 GeV) described in Section 1 are overlaid for illustration purposes. The lowerpanel of each plot displays the difference between the observed number of events and the totalnumber of expected events determined by the fit divided by the statistical uncertainty associ-ated with the observed number of events ( δ ) as black points with error bars denoting δ . Theblue band shows the total uncertainty in the fit determined from the full fit covariance matrixin order to account for the correlations among fit parameters. Figure 5 shows the results of thesame background-only fit summed over S NN bins and data periods with separate contributionsfrom each background, as obtained from or fixed in the fit.The data are also used to determine the 95% confidence level (CL) upper limits on σ (cid:101) t (cid:101) t andthe signal strength p -values [80] for both the RPV and stealth SYY models obtained using theCL s approach [81–83] with asymptotic formulae [84] and the profile likelihood ratio as the teststatistic. Figure 6 shows the expected and observed cross section limits as a function of m (cid:101) t forthe benchmark RPV and stealth SYY signal models. Comparing to the predicted cross section,these limits correspond to the exclusion of top squark masses in the range 300–670 and 300–870 GeV for the benchmark RPV and stealth SYY models, respectively. Figure 7 shows thelocal p -value [80] of the signal strength, as a function of m (cid:101) t , obtained from fits to the data witheach signal strength as a free parameter for both the RPV and stealth SYY models. The p -valuequantifies the probability for the background to produce an upward fluctuation at least as largeas that observed. Fits are performed and p -values obtained separately for each data set, as wellas in a simultaneous fit to all data sets. We observe the most extreme p -value to be 0.003, whichcorresponds to a local significance of 2.8 σ and a best fit signal strength of 0.21 ± m (cid:101) t =
400 GeV assuming unity branching fractions for the decays described inSection 1.
300 400 500 600 700 800 900 1000 1100 1200 [GeV] t~ m - - -
10 110 [ pb ] t ~ t ~ s % C L uppe r li m i t on ) = 1.0 c~ t fi t~( B jjj) = 1.0 fi c~ ( B = 100 GeV c~ m jjj fi c~ , c~ t fi t~, t~ t~ fi pp 68% expected95% expectedObserved limit (NNLO+NNLL) t~ t~ s (13 TeV) -1
137 fb
CMS
300 400 500 600 700 800 900 1000 1100 1200 [GeV] t~ m - - -
10 110 [ pb ] t ~ t ~ s % C L uppe r li m i t on g) = 1.0S~ t fi t~( B gg) = 1.0 fi (S B ) = 1.0, G~ S fi S~( B = 90 GeV S = 1 GeV, m G~ = 100 GeV, m S~ m gg fi , S G~ S fi S~g, S~ t fi t~, t~ t~ fi pp 68% expected95% expectedObserved limit (NNLO+NNLL) t~ t~ s (13 TeV) -1
137 fb
CMS
Figure 6: Expected and observed 95% CL upper limit on the top squark pair production crosssection as a function of the top squark mass for the RPV (left) and stealth SYY (right) SUSYmodels. Particle masses and branching fractions assumed for each model are included on eachplot. The expected cross section computed at NNLO+NNLL accuracy is shown in the redcurve.
300 400 500 600 700 800 900 1000 1100 1200 [GeV] t~ m - - - -
10 1 Lo c a l p - v a l ue s s s ) -1 All Years (137 fb ) -1 -1 -1 -1 fi c~ , c~ t fi t~, t~ t~ fi pp CMS (13 TeV) -1
137 fb
300 400 500 600 700 800 900 1000 1100 1200 [GeV] t~ m p r ed . s / m ea s . s
300 400 500 600 700 800 900 1000 1100 1200 [GeV] t~ m - - - -
10 1 Lo c a l p - v a l ue s s s ) -1 All Years (137 fb ) -1 -1 -1 -1 fi , S G~ S fi S~g, S~ t fi t~, t~ t~ fi pp CMS (13 TeV) -1
137 fb
300 400 500 600 700 800 900 1000 1100 1200 [GeV] t~ m p r ed . s / m ea s . s Figure 7: Local p -value as a function of top squark mass for the RPV (left) and stealth SYYmodels (right). The colored lines show the p -values for separate fits of the 2016 (red dashdotted), 2017 (blue dotted), 2018A (green short dashed), and 2018B (orange long dashed) datasets; the black line shows the p -value for the simultaneous fit of data sets. The lower panelsshow the best fit signal strength ( σ meas. / σ pred. ) as a function of top squark mass with uncertaintydenoted by the green band.The 2.8 σ local significance for the RPV model with m (cid:101) t =
400 GeV is understood to arise froma combination of two effects. First, although the level of agreement between the observed dataand the background expectation shown in Fig. 4 is reasonable, the agreement improves whenthe signal is included in the fit, contributing approximately 1.1 σ to the significance. Second,the constrained nuisance parameters (NP) are pulled less from their initial values when thesignal is included in the fit, contributing approximately 1.7 σ to the significance. This secondeffect is illustrated in Fig. 8 which shows for each of a selection of NP : the fit value ( θ ) anduncertainty ( δ θ ) from both the background-only fit (b) and the signal+background fit (s+b), as U E t une ( + ) ( - ) p r i m a r y ( ) T H U E t une ( + ) ( ) U E t une (-) ( - ) M E - PS ( + ) ( - ) M E - PS (-) ( - ) J E R (-) ( B ) - pa r a m e t e r i z a t i on ( A ) j e t s N T H p r i m a r y ( A ) T H M E - PS ( + ) ( ) s hape i n v a r i an c e ( ) j e t s N I S R ( ) / m a ss ( A ) T J e t p p r i m a r y ( ) T H J ES (-) ( ) - - - q (13 TeV) -1
137 fb
CMS b-only fits+b fit U E t une ( + ) ( - ) p r i m a r y ( ) T H U E t une ( + ) ( ) U E t une (-) ( - ) M E - PS ( + ) ( - ) M E - PS (-) ( - ) J E R (-) ( B ) - pa r a m e t e r i z a t i on ( A ) j e t s N T H p r i m a r y ( A ) T H M E - PS ( + ) ( ) s hape i n v a r i an c e ( ) j e t s N I S R ( ) / m a ss ( A ) T J e t p p r i m a r y ( ) T H R e m a i nde r - ( b ) c ( s + b ) - c (b) c (s+b) - c cD Cumulative
Figure 8: The upper panel shows the fit values ( θ ) and uncertainties ( δ θ ) for a selection of nui-sance parameters (NP) from both the background-only fit (purple) and signal+background fit(blue) for the RPV model with m (cid:101) t =
400 GeV. The x-axis labels refer to the NP sources describedin Section 5, the data period (16, 17, etc.), and the direction of variation ( + , − ). The lower panelshows the ∆ χ ≡ χ ( s + b ) − χ ( b ) difference of χ ≡ ( θ / δ θ ) from the signal+background(s+b) and background-only (b) fits as a red point for each NP and the cumulative sum of ∆ χ from left to right as a blue shaded histogram. The fourteen selected NP are those with | ∆ χ | > | ∆ χ | . The rightmost bin,separated by a vertical solid line, shows the sum of ∆ χ for all NP not displayed in the figure(red point) and the sum of ∆ χ for all NP (blue shaded histogram).well as the ∆ χ ≡ χ ( s + b ) − χ ( b ) difference of χ ≡ ( θ / δ θ ) from the two fits. A θ valueof one indicates that the fit value of the nuisance parameter is one standard deviation from itsnominal value. In addition, the cumulative and total sums of ∆ χ are shown for the NPs, withthe sum for all NP of ∑ ∆ χ = − (cid:112) | ∑ ∆ χ | = σ . A first of its kind search for top squark pair production with subsequent decay characterizedby two top quarks, additional gluons or light-flavor quarks, and low missing transverse mo-mentum ( p missT ) is described. Events containing exactly one electron or muon and at least sevenjets, of which at least one should be b tagged, are selected from a sample of proton-proton col-lisions at √ s =
13 TeV corresponding to an integrated luminosity of 137 fb − collected with theCMS detector in 2016–2018. No requirement is made on p missT . The dominant tt background ispredicted from data using a simultaneous fit of the jet multiplicity distribution across four binsof a neural network score.The results are interpreted in terms of top squark pair production in the context of R -parityviolating (RPV) and stealth supersymmetry models. Top squark masses ( m (cid:101) t ) up to 670 GeV areexcluded at 95% confidence level for the RPV model in which the top squark decays to a topquark and the lightest neutralino, which subsequently decays to three light-flavor quarks via anoff-shell squark through a trilinear coupling λ (cid:48)(cid:48) . Top squark masses up to 870 GeV are excludedfor the stealth supersymmetry model in which the top squark decays to a top quark, threegluons, and a gravitino via intermediate hidden sector particles. The maximum observed local significance is 2.8 standard deviations corresponding to a best fit signal strength of 0.21 ± m (cid:101) t =
400 GeV.
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 Gridand other centers for delivering so effectively the computing infrastructure essential to ouranalyses. Finally, we acknowledge the enduring support for the construction and operationof the LHC, the CMS detector, and the supporting computing infrastructure provided by thefollowing funding agencies: BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq,CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, andNSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT(Ecuador); MoER, ERC PUT and ERDF (Estonia); Academy of Finland, MEC, and HIP (Fin-land); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece); NK-FIA (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, CIN-VESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (NewZealand); 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); ThEPCen-ter, 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 (EuropeanUnion); the Leventis Foundation; the Alfred 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, and under project number400140256 - GRK2497; the Lend ¨ulet (“Momentum”) Program and the J´anos Bolyai ResearchScholarship of the Hungarian Academy of Sciences, 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 Industrial Research, India; the HOMINGPLUS program of the Foundation for Polish Science, cofinanced from European Union, Re-gional Development Fund, the Mobility Plus program of the Ministry of Science and HigherEducation, the National Science Center (Poland), contracts Harmonia 2014/14/M/ST2/00428,Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis2012/07/E/ST2/01406; the National Priorities Research Program by Qatar National ResearchFund; the Ministry of Science and Higher Education, project no. 0723-2020-0041 (Russia); thePrograma Estatal de Fomento de la Investigaci ´on Cient´ıfica y T´ecnica de Excelencia Mar´ıa deMaeztu, grant MDM-2015-0509 and the Programa Severo Ochoa del Principado de Asturias;the Thalis and Aristeia programs cofinanced by EU-ESF and the Greek NSRF; the RachadapisekSompot Fund for Postdoctoral Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); the Kavli Foundation;the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C-1845;and the Weston Havens Foundation (USA).
References [1] P. Fayet and S. Ferrara, “Supersymmetry”,
Phys. Rept. (1977) 249, doi:10.1016/0370-1573(77)90066-7 .[2] S. P. Martin, “A supersymmetry primer”, Adv. Ser. Direct. High Energy Phys. (1997) 1, doi:10.1142/9789812839657_0001 , arXiv:hep-ph/9709356 .[3] S. Dimopoulos and G. F. Giudice, “Naturalness constraints in supersymmetric theorieswith nonuniversal soft terms”, Phys. Lett. B (1995) 573, doi:10.1016/0370-2693(95)00961-J , arXiv:hep-ph/9507282 .[4] R. Barbieri and G. F. Giudice, “Upper bounds on supersymmetric particle masses”, Nucl.Phys. B (1988) 63, doi:10.1016/0550-3213(88)90171-X .[5] A. Pomarol and D. Tommasini, “Horizontal symmetries for the supersymmetric flavorproblem”,
Nucl. Phys. B (1996) 3, doi:10.1016/0550-3213(96)00074-0 , arXiv:hep-ph/9507462 .[6] A. G. Cohen, D. B. Kaplan, and A. E. Nelson, “The more minimal supersymmetricstandard model”, Phys. Lett. B (1996) 588, doi:10.1016/S0370-2693(96)01183-5 , arXiv:hep-ph/9607394 .[7] M. Papucci, J. T. Ruderman, and A. Weiler, “Natural SUSY endures”, JHEP (2012) 035, doi:10.1007/JHEP09(2012)035 , arXiv:1110.6926 .[8] C. Brust, A. Katz, S. Lawrence, and R. Sundrum, “SUSY, the third generation and theLHC”, JHEP (2012) 103, doi:10.1007/JHEP03(2012)103 , arXiv:1110.6670 .[9] R. Barbier et al., “ R -parity violating supersymmetry”, Phys. Rept. (2005) 1, doi:10.1016/j.physrep.2005.08.006 , arXiv:hep-ph/0406039 .[10] D. S. M. Alves, E. Izaguirre, and J. G. Wacker, “Where the sidewalk ends: Jets andmissing energy search strategies for the 7 TeV LHC”, JHEP (2011) 012, doi:10.1007/JHEP10(2011)012 , arXiv:1102.5338 .[11] M. Lisanti, P. Schuster, M. Strassler, and N. Toro, “Study of LHC searches for a lepton andmany jets”, JHEP (2012) 081, doi:10.1007/JHEP11(2012)081 , arXiv:1107.5055 .[12] J. Fan, M. Reece, and J. T. Ruderman, “Stealth supersymmetry”, JHEP (2011) 012, doi:10.1007/JHEP11(2011)012 , arXiv:1105.5135 .[13] G. F. Giudice and R. Rattazzi, “Theories with gauge mediated supersymmetry breaking”, Phys. Rept. (1999) 419, doi:10.1016/S0370-1573(99)00042-3 , arXiv:hep-ph/9801271 .[14] S. P. Martin, “Compressed supersymmetry and natural neutralino dark matter from topsquark-mediated annihilation to top quarks”, Phys. Rev. D (2007) 115005, doi:10.1103/PhysRevD.75.115005 , arXiv:hep-ph/0703097 . eferences [15] T. J. LeCompte and S. P. Martin, “Large Hadron Collider reach for supersymmetricmodels with compressed mass spectra”, Phys. Rev. D (2011) 015004, doi:10.1103/PhysRevD.84.015004 , arXiv:1105.4304 .[16] M. J. Strassler, “Why unparticle models with mass gaps are examples of hidden valleys”,(2008). arXiv:0801.0629 .[17] CMS Collaboration, “Search for supersymmetry in proton-proton collisions at 13 TeVusing identified top quarks”, Phys. Rev. D (2018) 012007, doi:10.1103/PhysRevD.97.012007 , arXiv:1710.11188 .[18] CMS Collaboration, “Search for direct production of supersymmetric partners of the topquark in the all-jets final state in proton-proton collisions at √ s =
13 TeV”,
JHEP (2017) 005, doi:10.1007/JHEP10(2017)005 , arXiv:1707.03316 .[19] CMS Collaboration, “Search for top squark pair production in pp collisions at √ s = JHEP (2017) 019, doi:10.1007/JHEP10(2017)019 , arXiv:1706.04402 .[20] CMS Collaboration, “Search for top squarks and dark matter particles, in opposite-chargedilepton final states at √ s =
13 TeV”,
Phys. Rev. D (2018) 032009, doi:10.1103/PhysRevD.97.032009 , arXiv:1711.00752 .[21] ATLAS Collaboration, “Search for a scalar partner of the top quark in the jets plusmissing transverse momentum final state at √ s =
13 TeV with the ATLAS detector”,
JHEP (2017) 085, doi:10.1007/JHEP12(2017)085 , arXiv:1709.04183 .[22] ATLAS Collaboration, “Search for top-squark pair production in final states with onelepton, jets, and missing transverse momentum using 36 fb − of √ s =
13 TeV pp collisiondata with the ATLAS detector”,
JHEP (2018) 108, doi:10.1007/JHEP06(2018)108 , arXiv:1711.11520 .[23] J. Fan, M. Reece, and J. T. Ruderman, “A stealth supersymmetry sampler”, JHEP (2012) 196, doi:10.1007/JHEP07(2012)196 , arXiv:1201.4875 .[24] J. Fan et al., “Stealth supersymmetry simplified”, JHEP (2016) 016, doi:10.1007/JHEP07(2016)016 , arXiv:1512.05781 .[25] ATLAS Collaboration, “A search for pair-produced resonances in four-jet final states at √ s =
13 TeV with the ATLAS detector”,
Eur. Phys. J. C (2018) 250, doi:10.1140/epjc/s10052-018-5693-4 , arXiv:1710.07171 .[26] CMS Collaboration, “Search for pair-produced resonances decaying to quark pairs inproton-proton collisions at √ s =
13 TeV”,
Phys. Rev. D (2018) 112014, doi:10.1103/PhysRevD.98.112014 , arXiv:1808.03124 .[27] ATLAS Collaboration, “Search for B-L R -parity-violating top squarks in √ s =
13 TeV ppcollisions with the ATLAS experiment”,
Phys. Rev. D (2018) 032003, doi:10.1103/PhysRevD.97.032003 , arXiv:1710.05544 .[28] CMS Collaboration, “Search for pair production of third-generation scalar leptoquarksand top squarks in proton–proton collisions at √ s = Phys. Lett. B (2014) 229, doi:10.1016/j.physletb.2014.10.063 , arXiv:1408.0806 . [29] CMS Collaboration, “Search for R -parity violating decays of a top squark inproton-proton collisions at √ s = Phys. Lett. B (2016) 178, doi:10.1016/j.physletb.2016.06.039 , arXiv:1602.04334 .[30] ATLAS Collaboration, “Search for new phenomena in a lepton plus high jet multiplicityfinal state with the ATLAS experiment using √ s =
13 TeV proton-proton collision data”,
JHEP (2017) 088, doi:10.1007/JHEP09(2017)088 , arXiv:1704.08493 .[31] CMS Collaboration, “Search for stealth supersymmetry in events with jets, either photonsor leptons, and low missing transverse momentum in pp collisions at 8 TeV”, Phys. Lett.B (2015) 503, doi:10.1016/j.physletb.2015.03.017 , arXiv:1411.7255 .[32] CMS Collaboration, “Search for supersymmetry in events with photons and low missingtransverse energy in pp collisions at √ s = Phys. Lett. B (2013) 42, doi:10.1016/j.physletb.2012.12.055 , arXiv:1210.2052 .[33] CMS Collaboration, “The CMS experiment at the CERN LHC”, JINST (2008) S08004, doi:10.1088/1748-0221/3/08/S08004 .[34] CMS Collaboration, “The CMS trigger system”, JINST (2017) P01020, doi:10.1088/1748-0221/12/01/P01020 , arXiv:1609.02366 .[35] CMS Collaboration, “Particle-flow reconstruction and global event description with theCMS detector”, JINST (2017) P10003, doi:10.1088/1748-0221/12/10/P10003 , arXiv:1706.04965 .[36] M. Cacciari, G. P. Salam, and G. Soyez, “The anti- k T jet clustering algorithm”, JHEP (2008) 063, doi:10.1088/1126-6708/2008/04/063 , arXiv:0802.1189 .[37] M. Cacciari, G. P. Salam, and G. Soyez, “FastJet user manual”, Eur. Phys. J. C (2012)1896, doi:10.1140/epjc/s10052-012-1896-2 , arXiv:1111.6097 .[38] CMS Collaboration, “Performance of electron reconstruction and selection with the CMSdetector in proton-proton collisions at √ s = 8 TeV”, JINST (2015) P06005, doi:10.1088/1748-0221/10/06/P06005 , arXiv:1502.02701 .[39] CMS Collaboration, “Performance of the CMS muon detector and muon reconstructionwith proton-proton collisions at √ s =
13 TeV”,
JINST (2018) P06015, doi:10.1088/1748-0221/13/06/P06015 , arXiv:1804.04528 .[40] K. Rehermann and B. Tweedie, “Efficient identification of boosted semileptonic topquarks at the LHC”, JHEP (2011) 059, doi:10.1007/JHEP03(2011)059 , arXiv:1007.2221 .[41] CMS Collaboration, “Jet performance in pp collisions at 7 TeV”, CMS Physics AnalysisSummary CMS-PAS-JME-10-003, 2010.[42] CMS Collaboration, “Jet algorithms performance in 13 TeV data”, CMS Physics AnalysisSummary CMS-PAS-JME-16-003, 2017.[43] CMS Collaboration, “Jet energy scale and resolution in the CMS experiment in ppcollisions at 8 TeV”, JINST (2017) P02014, doi:10.1088/1748-0221/12/02/P02014 , arXiv:1607.03663 . eferences [44] CMS Collaboration, “Jet energy scale and resolution performance with 13 TeV datacollected by CMS in 2016–2018”, CMS Detector Performance Note CMS-DP-2020-019,2020.[45] M. Cacciari and G. P. Salam, “Pileup subtraction using jet areas”, Phys. Lett. B (2008)119, doi:10.1016/j.physletb.2007.09.077 , arXiv:0707.1378 .[46] CMS Collaboration, “Identification of heavy-flavour jets with the CMS detector in ppcollisions at 13 TeV”, JINST (2018) P05011, doi:10.1088/1748-0221/13/05/P05011 , arXiv:1712.07158 .[47] P. Nason, “A new method for combining NLO QCD with shower Monte Carloalgorithms”, JHEP (2004) 040, doi:10.1088/1126-6708/2004/11/040 , arXiv:hep-ph/0409146 .[48] S. Frixione, P. Nason, and C. Oleari, “Matching NLO QCD computations with partonshower simulations: the POWHEG method”,
JHEP (2007) 070, doi:10.1088/1126-6708/2007/11/070 , arXiv:0709.2092 .[49] S. Alioli, P. Nason, C. Oleari, and E. Re, “A general framework for implementing NLOcalculations in shower Monte Carlo programs: the POWHEG box”,
JHEP (2010) 043, doi:10.1007/JHEP06(2010)043 , arXiv:1002.2581 .[50] S. Frixione, P. Nason, and G. Ridolfi, “A positive-weight next-to-leading-order MonteCarlo for heavy flavour hadroproduction”, JHEP (2007) 126, doi:10.1088/1126-6708/2007/09/126 , arXiv:0707.3088 .[51] R. Frederix, E. Re, and P. Torrielli, “Single-top t -channel hadroproduction in thefour-flavour scheme with POWHEG and amc@nlo”,
JHEP (2012) 130, doi:10.1007/JHEP09(2012)130 , arXiv:1207.5391 .[52] J. Alwall et al., “The automated computation of tree-level and next-to-leading orderdifferential cross sections, and their matching to parton shower simulations”, JHEP (2014) 079, doi:10.1007/JHEP07(2014)079 , arXiv:1405.0301 .[53] A. Kalogeropoulos and J. Alwall, “The SysCalc code: A tool to derive theoreticalsystematic uncertainties”, (2018). arXiv:1801.08401 .[54] T. Sj ¨ostrand et al., “An introduction to PYTHIA
Comput. Phys. Commun. (2015)159, doi:10.1016/j.cpc.2015.01.024 , arXiv:1410.3012 .[55] C. Borschensky et al., “Squark and gluino production cross sections in pp collisions at √ s =
13, 14, 33 and 100 TeV”,
Eur. Phys. J. C (2014) 3174, doi:10.1140/epjc/s10052-014-3174-y , arXiv:1407.5066 .[56] W. Beenakker et al., “NNLL-fast: predictions for coloured supersymmetric particleproduction at the LHC with threshold and Coulomb resummation”, JHEP (2016) 133, doi:10.1007/JHEP12(2016)133 , arXiv:1607.07741 .[57] NNPDF Collaboration, “Parton distributions for the LHC Run II”, JHEP (2015) 040, doi:10.1007/JHEP04(2015)040 , arXiv:1410.8849 .[58] NNPDF Collaboration, “Parton distributions from high-precision collider data”, Eur.Phys. J. C (2017) 663, doi:10.1140/epjc/s10052-017-5199-5 , arXiv:1706.00428 .0
Eur. Phys. J. C (2014) 3174, doi:10.1140/epjc/s10052-014-3174-y , arXiv:1407.5066 .[56] W. Beenakker et al., “NNLL-fast: predictions for coloured supersymmetric particleproduction at the LHC with threshold and Coulomb resummation”, JHEP (2016) 133, doi:10.1007/JHEP12(2016)133 , arXiv:1607.07741 .[57] NNPDF Collaboration, “Parton distributions for the LHC Run II”, JHEP (2015) 040, doi:10.1007/JHEP04(2015)040 , arXiv:1410.8849 .[58] NNPDF Collaboration, “Parton distributions from high-precision collider data”, Eur.Phys. J. C (2017) 663, doi:10.1140/epjc/s10052-017-5199-5 , arXiv:1706.00428 .0 [59] CMS Collaboration, “Event generator tunes obtained from underlying event andmultiparton scattering measurements”, Eur. Phys. J. C (2016) 155, doi:10.1140/epjc/s10052-016-3988-x , arXiv:1512.00815 .[60] CMS Collaboration, “Investigations of the impact of the parton shower tuning in PYTHIA √ s = PYTHIA
Eur. Phys. J. C (2020) 4, doi:10.1140/epjc/s10052-019-7499-4 , arXiv:1903.12179 .[62] GEANT4 Collaboration, “G EANT
Nucl. Instrum. Meth. A (2003) 250, doi:10.1016/S0168-9002(03)01368-8 .[63] M. Czakon and A. Mitov, “Top++: A program for the calculation of the top-paircross-section at hadron colliders”,
Comput. Phys. Commun. (2014) 2930, doi:10.1016/j.cpc.2014.06.021 , arXiv:1112.5675 .[64] P. Kant et al., “HATHOR for single top-quark production: Updated predictions anduncertainty estimates for single top-quark production in hadronic collisions”, Comput.Phys. Commun. (2015) 74, doi:10.1016/j.cpc.2015.02.001 , arXiv:1406.4403 .[65] M. Aliev et al., “HATHOR: HAdronic Top and Heavy quarks crOss section calculatoR”, Comput. Phys. Commun. (2011) 1034, doi:10.1016/j.cpc.2010.12.040 , arXiv:1007.1327 .[66] T. Gehrmann et al., “W + W − production at hadron colliders in next to next to leadingorder QCD”, Phys. Rev. Lett. (2014) 212001, doi:10.1103/PhysRevLett.113.212001 , arXiv:1408.5243 .[67] J. M. Campbell and R. K. Ellis, “An update on vector boson pair production at hadroncolliders”, Phys. Rev. D (1999) 113006, doi:10.1103/PhysRevD.60.113006 , arXiv:hep-ph/9905386 .[68] J. M. Campbell, R. K. Ellis, and C. Williams, “Vector boson pair production at the LHC”, JHEP (2011) 018, doi:10.1007/JHEP07(2011)018 , arXiv:1105.0020 .[69] Y. Li and F. Petriello, “Combining QCD and electroweak corrections to dileptonproduction in FEWZ ”, Phys. Rev. D (2012) 094034, doi:10.1103/PhysRevD.86.094034 , arXiv:1208.5967 .[70] Y. Ganin and V. Lempitsky, “Unsupervised domain adaptation by backpropagation”,(2014). arXiv:1409.7495 .[71] G. C. Fox and S. Wolfram, “Observables for the analysis of event shapes in e + e − annihilation and other processes”, Phys. Rev. Lett. (1978) 1581, doi:10.1103/PhysRevLett.41.1581 .[72] J. D. Bjorken and S. J. Brodsky, “Statistical model for electron-positron annihilation intohadrons”, Phys. Rev. D (1970) 1416, doi:10.1103/PhysRevD.1.1416 .[73] E. Gerwick, T. Plehn, S. Schumann, and P. Schichtel, “Scaling patterns for QCD jets”, JHEP (2012) 162, doi:10.1007/JHEP10(2012)162 , arXiv:1208.3676 . eferences [74] M. Cacciari et al., “The tt cross-section at 1.8 TeV and 1.96 TeV: A study of the systematicsdue to parton densities and scale dependence”, JHEP (2004) 068, doi:10.1088/1126-6708/2004/04/068 , arXiv:hep-ph/0303085 .[75] S. Catani, D. de Florian, M. Grazzini, and P. Nason, “Soft gluon resummation for Higgsboson production at hadron colliders”, JHEP (2003) 028, doi:10.1088/1126-6708/2003/07/028 , arXiv:hep-ph/0306211 .[76] CMS Collaboration, “Measurement of the inelastic proton-proton cross section at √ s = JHEP (2018) 161, doi:10.1007/JHEP07(2018)161 , arXiv:1802.02613 .[77] CMS Collaboration, “CMS luminosity measurements for the 2016 data taking period”,CMS Physics Analysis Summary CMS-PAS-LUM-17-001, 2017.[78] CMS Collaboration, “CMS luminosity measurements for the 2017 data taking period at √ s =
13 TeV”, CMS Physics Analysis Summary CMS-PAS-LUM-17-004, 2017.[79] CMS Collaboration, “CMS luminosity measurements for the 2018 data taking period at √ s =
13 TeV”, CMS Physics Analysis Summary CMS-PAS-LUM-18-002, 2018.[80] L. Demortier, “ p -values and nuisance parameters”, in Statistical issues for LHC physics.Proceedings, Workshop, PHYSTAT-LHC, Geneva, Switzerland, June 27-29, 2007 , p. 23. 2008. doi:10.5170/CERN-2008-001 .[81] ATLAS and CMS Collaborations, The LHC Higgs Combination Group, “Procedure forthe LHC Higgs boson search combination in Summer 2011”, Technical ReportCMS-NOTE-2011-005, ATL-PHYS-PUB-2011-11, 2011.[82] T. Junk, “Confidence level computation for combining searches with small statistics”,
Nucl. Instrum. Meth. A (1999) 435, doi:10.1016/S0168-9002(99)00498-2 , arXiv:hep-ex/9902006 .[83] A. L. Read, “Presentation of search results: The CL s technique”, J. Phys. G (2002) 2693, doi:10.1088/0954-3899/28/10/313 .[84] G. Cowan, K. Cranmer, E. Gross, and O. Vitells, “Asymptotic formulae forlikelihood-based tests of new physics”, Eur. Phys. J. C (2011) 1554, doi:10.1140/epjc/s10052-011-1554-0 , arXiv:1007.1727 . [Erratum: doi:10.1140/epjc/s10052-013-2501-z ]. A The CMS Collaboration
Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan † , A. Tumasyan Institut f ¨ur Hochenergiephysik, Wien, Austria
W. Adam, J.W. Andrejkovic, T. Bergauer, S. Chatterjee, 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 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, M. Delcourt, 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, L. Favart, A. Grebenyuk, A.K. Kalsi, K. Lee,M. Mahdavikhorrami, I. Makarenko, L. Moureaux, L. P´etr´e, A. Popov, N. Postiau, E. Starling,L. Thomas, M. Vanden Bemden, C. Vander Velde, P. Vanlaer, D. Vannerom, L. Wezenbeek
Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, M. Gruchala, 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, 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, M. Barroso Ferreira Filho, H. BRANDAO MALBOUISSON, W. Carvalho,J. Chinellato , E.M. Da Costa, G.G. Da Silveira , D. De Jesus Damiao, S. Fonseca De Souza,D. Matos Figueiredo, C. Mora Herrera, K. Mota Amarilo, 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, T. Javaid , M. Mittal, H. Wang, L. Yuan Department of Physics, Tsinghua University, Beijing, China
M. Ahmad, G. Bauer, C. Dozen , Z. Hu, J. Martins , Y. Wang, K. Yi Institute of High Energy Physics, Beijing, China
E. Chapon, G.M. Chen , H.S. Chen , M. Chen, 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
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
Charles University, Prague, Czech Republic
M. Finger , M. Finger Jr. , A. Kveton Escuela Politecnica Nacional, Quito, Ecuador
E. Ayala
Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin
Academy of Scientific Research and Technology of the Arab Republic of Egypt, EgyptianNetwork of High Energy Physics, Cairo, Egypt
S. Abu Zeid , S. Khalil , E. Salama Center for High Energy Physics (CHEP-FU), Fayoum University, El-Fayoum, Egypt
A. Lotfy, Y. Mohammed
National Institute of Chemical Physics and Biophysics, Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M. Kadastik, 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, H. Petrow, 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, S. Ghosh, R. Granier de Cassagnac, A. Hakimi, I. Kucher, A. Lobanov,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
G. Adamov, 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, 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, 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, A.J. R´adl,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 , P. Saha, 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, 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, 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 ,41 , 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 ,L. Brigliadori a , 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 ,42 ,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 ,43 , S. Costa a , b , A. Di Mattia a , R. Potenza a , b , A. Tricomi a , b ,43 , 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 , b , 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 , F. Brivio a , b , F. Cetorelli a , b , V. Ciriolo a , b ,19 , 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 ,19 , 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 , F. Carnevali a , b , N. Cavallo a , c , A. De Iorio a , b , F. Fabozzi a , c , A.O.M. Iorio a , b ,L. Lista a , b , S. Meola a , d ,19 , P. Paolucci a ,19 , 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 ,44 ,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 , Universit`adi Siena d , Pisa, Italy 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 , d , 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 , d , 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 ,K. Shchelina 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
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, B.H. Oh,M. Oh, S.B. Oh, H. Seo, U.K. Yang, I. Yoon
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. Watson0
D. Jeon, J.H. Kim, B. Ko, J.S.H. Lee, I.C. Park, Y. Roh, D. Song, I.J. Watson0 Yonsei University, Department of Physics, Seoul, Korea
S. Ha, H.D. Yoo
Sungkyunkwan University, Suwon, Korea
Y. Choi, Y. Jeong, H. Lee, Y. Lee, I. Yu
College of Engineering and Technology, American University of the Middle East (AUM),Kuwait
T. Beyrouthy, 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, 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 , A. Oskin, P. Parygin, S. Polikarpov , E. Popova, E. Tarkovskii 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, M. Dubinin , L. Dudko, A. Ershov, A. Gribushin, V. Klyukhin,O. Kodolova, I. Lokhtin, S. Obraztsov, S. Petrushanko, V. Savrin, A. Snigirev Novosibirsk State University (NSU), Novosibirsk, Russia
V. Blinov , T. Dimova , L. Kardapoltsev , I. Ovtin , Y. Skovpen Institute for High Energy Physics of National Research Centre ‘Kurchatov Institute’,Protvino, Russia
I. Azhgirey, I. Bayshev, V. Kachanov, A. Kalinin, D. Konstantinov, V. Petrov, R. Ryutin, A. Sobol,S. Troshin, N. Tyurin, A. Uzunian, A. Volkov
National Research Tomsk Polytechnic University, Tomsk, Russia
A. Babaev, 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, V. 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
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, J. Alimena, 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, H. Qu, 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
K. Androsov , 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, C. Martin Perez, 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, 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, S. Fayer, G. Fedi, G. Hall,M.H. Hassanshahi, G. Iles, J. Langford, L. Lyons, A.-M. Magnan, S. Malik, A. Martelli, 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
C. Brainerd, R. Breedon, M. Calderon De La Barca Sanchez, M. Chertok, J. Conway, P.T. Cox,R. Erbacher, F. Jensen, O. Kukral, R. Lander, M. Mulhearn, D. Pellett, 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, J. Guiang, R. Kansal, V. Krutelyov, R. Lee, J. Letts, M. Masciovecchio, S. May,S. Padhi, M. Pieri, B.V. Sathia Narayanan, V. Sharma, M. Tadel, A. Vartak, F. W ¨urthwein,Y. Xiang, 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, 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, A. Woodard University of Florida, Gainesville, USA
D. Acosta, P. Avery, D. Bourilkov, L. Cadamuro, V. Cherepanov, F. Errico, R.D. Field,D. Guerrero, B.M. Joshi, M. Kim, J. Konigsberg, A. Korytov, K.H. Lo, K. Matchev, N. Menendez,G. Mitselmakher, D. Rosenzweig, K. Shi, J. Sturdy, J. Wang, 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, K. Nam
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, G. Andreassi, R. Bi, S. Brandt, W. Busza, I.A. Cali, Y. Chen, M. D’Alfonso,G. Gomez Ceballos, M. Goncharov, P. Harris, M. Hu, M. Klute, D. Kovalskyi, J. Krupa, Y.-J. Lee, B. Maier, A.C. Marini, C. Mironov, C. Paus, D. Rankin, C. Roland, G. Roland, Z. Shi,G.S.F. Stephans, K. Tatar, J. 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, A. Williams
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. Band, 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, M. Zarucki, L. Zygala The Ohio State University, Columbus, USA
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, G. Paspalaki, 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, F. Fienga,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, Egypt
3: Also at Universit´e Libre de Bruxelles, Bruxelles, Belgium4: Also at Universidade Estadual de Campinas, Campinas, Brazil5: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil6: Also at University of Chinese Academy of Sciences, Beijing, China7: Also at Department of Physics, Tsinghua University, Beijing, China, Beijing, China8: Also at UFMS, Nova Andradina, Brazil9: Also at Nanjing Normal University Department of Physics, Nanjing, China10: Now at The University of Iowa, Iowa City, USA11: 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 Ain Shams University, Cairo, Egypt14: Also at Zewail City of Science and Technology, Zewail, Egypt15: Also at British University in Egypt, Cairo, Egypt16: Also at Purdue University, West Lafayette, USA17: Also at Universit´e de Haute Alsace, Mulhouse, France18: Also at Erzincan Binali Yildirim University, Erzincan, Turkey19: Also at CERN, European Organization for Nuclear Research, Geneva, Switzerland20: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany21: Also at University of Hamburg, Hamburg, Germany22: Also at Department of Physics, Isfahan University of Technology, Isfahan, Iran, Isfahan,Iran23: Also at Brandenburg University of Technology, Cottbus, Germany24: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University,Moscow, Russia25: Also at Physics Department, Faculty of Science, Assiut University, Assiut, Egypt26: Also at Eszterhazy Karoly University, Karoly Robert Campus, Gyongyos, Hungary27: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary, Debrecen,Hungary28: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary29: Also at MTA-ELTE Lend ¨ulet CMS Particle and Nuclear Physics Group, E ¨otv ¨os Lor´andUniversity, Budapest, Hungary, Budapest, Hungary30: Also at Wigner Research Centre for Physics, Budapest, Hungary31: Also at IIT Bhubaneswar, Bhubaneswar, India, Bhubaneswar, India32: Also at Institute of Physics, Bhubaneswar, India33: Also at G.H.G. Khalsa College, Punjab, India34: Also at Shoolini University, Solan, India35: Also at University of Hyderabad, Hyderabad, India36: Also at University of Visva-Bharati, Santiniketan, India37: Also at Indian Institute of Technology (IIT), Mumbai, India38: Also at Deutsches Elektronen-Synchrotron, Hamburg, Germany39: Also at Sharif University of Technology, Tehran, Iran40: Also at Department of Physics, University of Science and Technology of Mazandaran,Behshahr, Iran41: Now at INFN Sezione di Bari a , Universit`a di Bari b , Politecnico di Bari c , Bari, Italy42: Also at Italian National Agency for New Technologies, Energy and Sustainable EconomicDevelopment, Bologna, Italy43: Also at Centro Siciliano di Fisica Nucleare e di Struttura Della Materia, Catania, Italy44: Also at Universit`a di Napoli ’Federico II’, NAPOLI, Italy
45: Also at Riga Technical University, Riga, Latvia, Riga, Latvia46: Also at Consejo Nacional de Ciencia y Tecnolog´ıa, Mexico City, Mexico47: Also at IRFU, CEA, Universit´e Paris-Saclay, Gif-sur-Yvette, France48: Also at Institute for Nuclear Research, Moscow, Russia49: Now at National Research Nuclear University ’Moscow Engineering Physics Institute’(MEPhI), Moscow, Russia50: Also at St. Petersburg State Polytechnical University, St. Petersburg, Russia51: Also at University of Florida, Gainesville, USA52: Also at Imperial College, London, United Kingdom53: Also at P.N. Lebedev Physical Institute, Moscow, Russia54: Also at California Institute of Technology, Pasadena, USA55: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia56: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia57: Also at Trincomalee Campus, Eastern University, Sri Lanka, Nilaveli, Sri Lanka58: Also at INFN Sezione di Pavia a , Universit`a di Pavia b , Pavia, Italy, Pavia, Italy59: Also at National and Kapodistrian University of Athens, Athens, Greece60: Also at Universit¨at Z ¨urich, Zurich, Switzerland61: Also at Ecole Polytechnique F´ed´erale Lausanne, Lausanne, Switzerland62: Also at Stefan Meyer Institute for Subatomic Physics, Vienna, Austria, Vienna, Austria63: Also at Laboratoire d’Annecy-le-Vieux de Physique des Particules, IN2P3-CNRS, Annecy-le-Vieux, France64: Also at S¸ ırnak University, Sirnak, Turkey65: Also at Near East University, Research Center of Experimental Health Science, Nicosia,Turkey66: Also at Konya Technical University, Konya, Turkey67: Also at Istanbul University - Cerraphasa, Faculty of Engineering, Istanbul, Turkey68: Also at Mersin University, Mersin, Turkey69: Also at Piri Reis University, Istanbul, Turkey70: Also at Adiyaman University, Adiyaman, Turkey71: Also at Ozyegin University, Istanbul, Turkey72: Also at Izmir Institute of Technology, Izmir, Turkey73: Also at Necmettin Erbakan University, Konya, Turkey74: Also at Bozok Universitetesi Rekt ¨orl ¨ug ¨u, Yozgat, Turkey, Yozgat, Turkey75: Also at Marmara University, Istanbul, Turkey76: Also at Milli Savunma University, Istanbul, Turkey77: Also at Kafkas University, Kars, Turkey78: Also at Istanbul Bilgi University, Istanbul, Turkey79: Also at Hacettepe University, Ankara, Turkey80: Also at Vrije Universiteit Brussel, Brussel, Belgium81: Also at School of Physics and Astronomy, University of Southampton, Southampton,United Kingdom82: Also at IPPP Durham University, Durham, United Kingdom83: Also at Monash University, Faculty of Science, Clayton, Australia84: Also at Bethel University, St. Paul, Minneapolis, USA, St. Paul, USA85: Also at Karamano ˘glu Mehmetbey University, Karaman, Turkey86: Also at Bingol University, Bingol, Turkey87: Also at Georgian Technical University, Tbilisi, Georgia88: Also at Sinop University, Sinop, Turkey89: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey0