Search for R-parity violating supersymmetry in pp collisions at \sqrt{s}=13\,\text{TeV} using b jets in a final state with a single lepton, many jets, and high sum of large-radius jet masses
Abstract

Results are reported from a search for physics beyond the standard model in proton-proton collisions at a center-of-mass energy of . The search uses a signature of a single lepton, large jet and bottom quark jet multiplicities, and high sum of large-radius jet masses, without any requirement on the missing transverse momentum in an event. The data sample corresponds to an integrated luminosity of 35.9 recorded by the CMS experiment at the LHC. No significant excess beyond the prediction from standard model processes is observed. The results are interpreted in terms of upper limits on the production cross section for -parity violating supersymmetric extensions of the standard model using a benchmark model of gluino pair production, in which each gluino decays promptly via . Gluinos with a mass below 1610 are excluded at 95% confidence level.

EUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH (CERN)


CERN-EP-2017-312 2019/\two@digits7/\two@digits15

CMS-SUS-16-040                                         


Search for -parity violating supersymmetry in pp collisions at using b jets in a final state with a single lepton, many jets, and high sum of large-radius jet masses


The CMS Collaboration111See Appendix A for the list of collaboration members



Abstract

Please replace the default abstract using the abstract command.


Submitted to Physics Letters B

© 2019 CERN for the benefit of the CMS Collaboration. CC-BY-4.0 license

1 Introduction

Searches for physics beyond the standard model (SM) are motivated by several considerations, including theoretical problems associated with explaining the observed mass of the Higgs boson in the presence of quantum corrections (the hierarchy problem) [1], and astrophysical evidence for dark matter [2]. While the SM has been successful in describing a vast range of phenomena, its inability to address these theoretical and experimental issues makes it an incomplete description of fundamental particles and their interactions.

Supersymmetry (SUSY), a proposed extension of the SM, provides possible solutions to these problems [3, 4, 5, 6, 7, 8, 9, 10, 11, 12]. The hierarchy problem can be addressed by SUSY models with a sufficiently low-mass top squark and gluino, and the lightest supersymmetric particle (LSP), if stable, is a potential dark matter candidate. That stability is assured in -parity conserving (RPC) SUSY models, where the -parity of a particle is defined as with , , and denoting the spin, baryon number, and lepton number of the particle, respectively [13].

Recent searches at the CERN LHC have set stringent limits on RPC SUSY production, as mass limits for the models studied are reaching for the top squark [14, 15] and  [16, 17, 18, 19, 20, 21] for the gluino. Due to these limits, there is mounting tension in the ability of RPC SUSY models to explain the hierarchy problem with little fine tuning. These RPC SUSY searches, however, typically require signatures with significant missing transverse momentum () resulting from the undetected LSPs, while in -parity violating (RPV) SUSY, the LSP is not stable and decays to SM particles, which removes the large signature. Though this disfavors the LSP as a dark matter candidate, it allows RPV SUSY models to evade constraints from typical RPC SUSY searches.

Given that there is no fundamental theoretical reason for -parity conservation, RPV SUSY yields an important class of models that can ease the tension between natural solutions to the hierarchy problem and current experimental limits. In addition, the absence of a requirement can allow RPV SUSY searches to be sensitive to a parameter space of RPC SUSY where only a small amount of is expected, such as in models where the mass splitting between the supersymmetric particles is small. Therefore, RPV SUSY searches help to complete the coverage of SUSY parameter space.

This search is motivated by a particular model of -parity violation, minimal flavor violating (MFV) SUSY [22], in which the -parity violating couplings arise from the SM Yukawa couplings. This makes the third generation couplings large and those of the first two generations small, which is consistent with the strong experimental constraints on baryon and lepton number violation involving the lightest two generations [23]. With these couplings, the additional -parity violating terms in the superpotential are

(1)

Here , , and are SU(2) doublets corresponding to leptons, quarks, and the Higgs boson, respectively. The fields , , and are the charged lepton, up-type quark, and down-type quark SU(2) singlets, while the various and factors denote the coupling strengths for their corresponding interaction. Color indices are suppressed and letters , , denote generation indices. More details on RPV SUSY can be found in Ref. [23].

The coupling must be antisymmetric in the last two indices because of color conservation, which excludes gluinos decaying to . Therefore, the largest allowed MFV coupling is to a top, bottom, and strange quark, and the gluino decays primarily via . Pair production of gluinos that decay in this way is used as the benchmark signal for this analysis.

The simplified model [24, 25] that is used in the interpretation makes several assumptions about the SUSY mass spectrum. It is assumed that squarks other than the top squark are much heavier than the gluino, so they do not affect the gluino decay, and the branching ratio of is . The top squark is assumed to be off-shell in its decay. This results in a three-body decay, so searches for dijet resonances, i.e., , are not applicable in this scenario. It is further assumed that the gluinos decay promptly. An example diagram for this simplified model is shown in Fig. 1. Although this benchmark is used for interpreting results, the search is structured to be generically sensitive to high-mass signatures with large jet and bottom quark jet multiplicities and either little or no , which are potential features of other models of physics beyond the SM. Previous limits on such MFV models were obtained by the ATLAS and CMS Collaborations at  [26, 27, 28] and by the ATLAS Collaboration at  [29], excluding gluino masses below and , respectively.

This analysis searches in a single-lepton (electron or muon) final state for an excess of events with a large number of identified bottom quark (b-tagged) jets in regions determined as a function of the jet multiplicity and the sum of masses of large-radius jets, . Signal events are expected to contribute to this final state through the leptonic decay of one of the top quarks while populating the high jet multiplicity and high kinematic regions due to the hadronic decay of the second top quark and the additional bottom and strange quark jets. The four b quarks, two from the top quark decays and two from the top squark decays, provide a high b-tagged jet multiplicity signature. The quantity was proposed in phenomenological studies [30, 31, 32] and was used for RPC SUSY searches by the ATLAS Collaboration in all-hadronic final states [33, 34] and by the CMS Collaboration in single-lepton events [35, 21].

Figure 1: Example diagram for the simplified model used as the benchmark signal in this analysis.

2 The CMS detector, samples, and event selection

This search uses a sample of proton-proton collision data at a center-of-mass energy of corresponding to an integrated luminosity of 35.9, which was collected by the CMS experiment during 2016. The central feature of the CMS detector is a superconducting solenoid of 6 m internal diameter, providing a magnetic field of 3.8 T. Within the solenoid volume are the charged particle tracking systems, composed of silicon-pixel and silicon-strip detectors, and the calorimeter systems, consisting of a lead tungstate crystal electromagnetic calorimeter (ECAL), and a brass and scintillator hadron calorimeter. Muons are identified and measured by gas-ionization detectors embedded in the magnetic flux-return yoke outside the solenoid. A more detailed description of the CMS detector, together with a definition of the coordinate system used and the relevant kinematic variables, is given in Ref. [36].

The background predictions use Monte Carlo (MC) simulation samples with corrections to the normalization and shape of distributions measured in data control samples. MadGraph5_amc@nlo 2.2.2 is used in leading-order mode [37, 38] to generate the , +jets, quantum chromodynamics multijet (QCD), and Drell–Yan background processes with extra partons. Comparison to a powheg 2.0 [39, 40, 41] sample generated at next-to-leading order (NLO) shows that the NLO effects do not have a significant impact. The W, Z, , and -channel single top quark production backgrounds are generated with MadGraph5_amc@nlo 2.2.2 in NLO mode [42], while the , , and -channel single top quark processes are generated with powheg 2.0. The generated samples are interfaced with PYTHIA 8.205 [43] for fragmentation and parton showering and use the CUETP8M1 tune to describe the underlying event [44]. The detector response is simulated with Geant4 [45]. Simulated samples are processed through the same reconstruction algorithms as the data.

The signal samples are generated by MadGraph5_amc@nlo 2.2.2 in leading-order mode and follow the same procedure for fragmentation, parton showering, simulation, and event reconstruction as the background samples.

The reconstruction of objects in an event proceeds from the candidate particles identified by the particle-flow (PF) algorithm [46], which uses information from the tracker, calorimeters, and muon systems to identify the candidates as charged or neutral hadrons, photons, electrons, or muons. Charged-particle tracks are required to originate from the event primary vertex (PV), which is the reconstructed vertex with the largest value of summed physics-object squared transverse momentum (). The physics objects used for the PV reconstruction are those returned by a jet finding algorithm [47, 48] with the tracks assigned to the vertex as inputs, and the associated missing transverse momentum, taken as the negative vector sum of the of those objects.

Electrons are reconstructed by pairing a charged-particle track with an ECAL supercluster [49]. The resulting electron candidates are required to have and , and to satisfy identification criteria designed to remove hadrons misidentified as electrons, photon conversions, and electrons from heavy-flavor hadron decays. Muons are reconstructed by associating tracks in the muon system with those found in the silicon tracker [50]. Muon candidates are required to satisfy , , and identification criteria designed to select a high-purity muon sample.

To preferentially select leptons that originate in the decay of and bosons, leptons are required to be isolated from other PF candidates. The relative isolation of a particle is quantified using an optimized version of the mini-isolation variable . Mini-isolation is computed as the scalar sum of the of charged hadrons from the PV, neutral hadrons, and photons that are within a cone of radius surrounding the lepton momentum vector in - space [51]. The cone radius varies with according to

(2)

The -dependent cone size reduces the rate of accidental overlaps between the lepton and jets in high-multiplicity or highly Lorentz-boosted events, particularly overlaps between bottom quark jets and leptons originating from a boosted top quark. Relative isolation is computed as after subtraction of the average contribution from additional proton-proton collisions in the same bunch-crossing (pileup). To be considered isolated, electrons (muons) must satisfy ().

The combined efficiency for the electron reconstruction, identification, and isolation requirements is about 50% at of , increasing to 65% at , and reaching a plateau of 80% above . The corresponding efficiency for muons is about 70% at of , increasing to 80% at , and reaching a plateau of 95% for . Data-to-simulation corrections (scale factors) are applied for both electrons and muons to correct the simulated lepton selection efficiency to match that observed in data.

The charged PF candidates associated with the PV and the neutral PF candidates are clustered into jets using the anti- algorithm [47] with distance parameter , as implemented in the fastjet package [48]. The estimated contribution to the jet from neutral PF candidates produced by pileup is removed with a correction based on the area of the jet and the average energy density of the event [52]. The jet energy is calibrated using - and -dependent corrections; the resulting calibrated jets are selected if they satisfy and . Each jet must also meet loose identification requirements [53] to suppress, for example, calorimeter noise. Finally, jets that have PF constituents matched to the selected lepton are removed from the jet collection. These resulting jets are considered to be “small-” jets.

The combined secondary vertex algorithm [54, 55] is applied to each jet to create a subset of b-tagged jets. The tagging efficiency for jets in the range to is 60–67% (51–57%) in the barrel (endcap) and increases with . Above the efficiency decreases to %. The probability to misidentify jets arising from quarks is 13–15% (11–13%) in the barrel (endcap), while the misidentification probability for light-flavor quarks or gluons is 1–2%. Data-derived scale factors for the  tag efficiency and mistag rate are applied to simulation such that the simulated  tagging performance matches that observed in data.

“Large-” () jets are created by clustering small- jets and the selected lepton using the anti- algorithm. Leptons are included to encompass the full kinematics of the event. Clustering small- jets instead of PF candidates incorporates the jet pileup corrections, thereby reducing the dependence of the large- jet mass on pileup. This technique of clustering small- jets into large- jets has been used previously in Refs. [56, 35]. The variable is defined as the sum of all large- jet masses, where is the mass of a single large-R jet:

(3)

The quantity is used as a measure of the mass-scale of an event. Signal events tend to have large as the large-R jets capture the kinematic information of the high-mass gluinos. Comparatively, SM background processes tend to have smaller values of due to their lower mass-scales. SM events, however, can have large values of in the presence of significant initial-state-radiation (ISR). For example, in events, ISR jets can either overlap with daughter jets or boost the system such that the system is collimated, both of which result in high-mass large-R jets and, correspondingly, high . The distributions for and signal are shown in Fig. 2, which uses events with to ensure similar distributions for both and signal.

Events are selected with triggers [57] that require either at least one jet with or the scalar sum of the of all small- jets () above . Trigger efficiencies are over for signal events passing the analysis selection defined below.

These events are further selected with a baseline requirement of exactly one electron or muon, , , that the number of small- jets () be at least 4, and that the number of those jets that are tagged as bottom quark jets () be at least 1.

Figure 2: Distributions of , normalized to the same area, for events and signal events with two different gluino masses in a selection of , , , , and .

3 Background prediction

After the baseline selection, the dominant background contribution is from the process, with small contributions from +jets and QCD events with a misidentified lepton. Rare background contributions, classified below as “Other”, come from single top quark, , , , , and Drell–Yan production.

To search for signal events arising from new high-mass particles decaying with large jet and b-jet multiplicities, the distribution is examined in different kinematic regions based on and . The bins are defined to be 4–5, 6–7, and . The bins are , , and , with the two highest  bins merged for the case due to the limited data sample size in the region. The low-, low- bins are expected to be background-dominated and are used as control regions to constrain systematic uncertainties, while the high-, high- bins are used as signal regions. The distribution is separated into , 2, 3, and bins for each region. The two highest  bins are the most sensitive to signal due to larger signal-to-background ratios, while the lower  bins provide constraints on the background normalizations and systematic uncertainties. The signal efficiency for the bin requiring and is and for and 1600, respectively.

A global maximum-likelihood fit is performed to obtain predictions for the SM background processes. This fit is carried out both for a background-only hypothesis and for signal-plus-background hypotheses, in which an additional signal contribution is extracted. The model is constructed using the poisson probabilities of the bin contents of the distribution for all , regions, while systematic uncertainties are applied as nuisance parameters. The shape for each process is taken from simulation, but varied to assess the impact of mismodeling of relevant parameters, including the rate of gluon splitting to and tagging efficiencies for heavy- and light-flavor jets [54, 55]. The appropriate ranges for these parameters are determined based on measurements in dedicated control samples and then constrained by a simultaneous fit across all bins of and in a correlated manner. Various studies with simulated pseudo-experiments were conducted to validate the likelihood model.

Because the kinematic tails of the and variables are difficult to model reliably, the and QCD normalizations are individually allowed to freely vary in each (, ) bin. The normalizations are constrained in each bin by the background-dominated bins, while the QCD normalizations are constrained by control regions with no identified leptons (). These control regions follow the same kinematic binning as the bins, but are integrated in for and use offset  bins of 6–7, 8–9, and to account for differences in the distributions between the and samples. The QCD contribution in a particular bin is then constrained by the corresponding bin. To avoid biasing the normalization measurement, the small contribution of background to the control regions is included using the normalization from the corresponding bins, while contributions from other processes are taken from simulation.

The shape of the +jets background is taken from simulation and allowed to vary based on the data-to-simulation agreement in a kinematically similar +jets sample selected with ( or ), , , , and , where is the invariant mass of the two leptons. The distribution and data/simulation yields ratio for this sample are shown in Fig. 3. The +jets background is then determined in the fit with one global normalization parameter and two parameters to adjust the bin-to-bin normalization based on the difference between the ratios in adjacent bins – 17% between and and 62% between and . After correcting the spectrum, the residual mismodeling is expected to be small, so no further correction is applied.

The “Other” component is estimated from simulation. Its contribution is less than 20% of the total backgrounds in all kinematic regions considered.

Figure 3: Jet multiplicity distribution for data and MC simulation in a +jets control sample selected by requiring , , , , and . The total yield from simulation is normalized to the number of events in data. The uncertainty in the ratio of data to simulation yields (lower panel) is statistical only.

4 Systematic uncertainties

4.1 Background systematic uncertainties

The nominal simulated shape of the distribution is allowed to vary by the inclusion of systematic uncertainties. Each uncertainty is incorporated in the fit with template histograms to account for the effects of the systematic variation and a nuisance parameter to control the variation amplitude. The nuisance parameters are subject to Gaussian constraints, normalized so that corresponds to the nominal shape and corresponds to standard deviation (s.d.) variation of the systematic uncertainty. These uncertainties affect only the shape for , QCD, and W+jets backgrounds, because their normalizations are determined from data, while for the other (subleading) backgrounds the uncertainties affect both the shape and normalization.

The primary source of systematic uncertainty is from the modeling of gluon splitting, which can produce additional  quarks in events and may not be properly simulated. An uncertainty in the gluon splitting rate is determined using a fit to the distribution, defined as the between two b-tagged jets in the event, in a control sample selected with , , , , and . Since the measurement is not limited by the data sample size, this control sample is formed from a subset of the data that is selected to be most stable in the  tagging algorithm performance. This choice isolates the physical effects of gluon splitting from the potential time dependence of the  tagging performance due to variations in experimental conditions, which are separately incorporated by the b-tag scale factor uncertainties.

Events where both of the b-tagged jets originate from one gluon splitting populate the low- region, while events without a gluon splitting or where the splitting yields one or no b-tagged jets populate both the low- and high- regions roughly equally. Gluon splittings can sometimes be reconstructed with fewer than two b-tagged jets either because the quarks are collimated into a single jet, one of the b jets is not tagged, or because one of the quarks is not within the kinematic acceptance.

A fit to the distribution is used to extract the relative contributions of events with and without gluon splitting and is performed in four equal bins in the range . This binning is chosen to avoid relying on the fine details of the simulated shape. The instances of gluon splitting in simulation are identified by requiring a gluon with that decays to  quarks. Three categories are then defined: events with gluon splitting resulting in two b-tagged jets (denoted GSbb), with gluon splitting resulting in one or fewer b-tagged jets (GSb), and without any gluon splitting (no GS). In the fit, the GSbb and GSb contributions are varied together with a single normalization parameter.

The fit extracts a weight of for gluon splitting events and a weight of for non-gluon splitting events. The post-fit distributions are shown in Figure 4. The GSbb and GSb categories are plotted separately to demonstrate the difference in shapes. The discrepancy in the last bin does not significantly impact the fit because the higher yield bins at lower values of constrain the fit. These weights, summed in quadrature with their post-fit uncertainty, are propagated to the final likelihood fit as a systematic uncertainty with  s.d. variations formed by applying weights of to gluon splitting events and to non-gluon splitting events in an anti-correlated manner. The fit results are used as a measure of the uncertainty on modelling of the GS rate as opposed to a correction to the central value, since the variable may not be a perfect proxy for the GS rate.

Various tests are conducted to assess the stability of the fit results. To test the dependence of the gluon splitting weights across kinematic regions, the fit is repeated both with a higher threshold and with different bins. Additionally the fit is conducted with finer binning to test the dependence of the results on the binning of the distribution. The resulting weights are all consistent with those of the nominal fit.

Figure 4: Post-fit distributions in a selection with , , , , and with the post-fit uncertainty represented by a hatched band. The ratio of data to simulation yields is shown in the lower panel.

Another significant systematic uncertainty is the uncertainty in the data-to-simulation scale factors (SF) for  tagging efficiency and mistag rates. These scale factors are derived from data in various QCD and control samples and are binned in jet and jet flavor (light + , , and [58]. The  s.d. templates for these scale factors are assessed by varying them according to the uncertainties in their measurements.

Other experimental uncertainties are small and include lepton selection efficiency, lepton misidentification rate, jet energy scale, jet energy resolution, and integrated luminosity. The uncertainty associated with lepton selection efficiency is determined by varying the efficiency to select a lepton within its uncertainty determined from data. The distribution for QCD events may not be simulated well because it relies on modeling the tail of the fragmentation function and various detector effects. To account for this, an uncertainty of 20% is assigned to the relative normalization of QCD events in the 0- and 1-lepton bins, which is motivated by data-to-simulation studies of lepton isolation distributions. Jet energy scale uncertainties [53, 59] are assessed by varying the of small-R jets as a function of and . The uncertainty arising from jet energy resolution  [53, 59] is determined by applying an -dependent factor to the jet to match the jet energy resolution observed in data. The integrated luminosity is varied according to its uncertainty of 2.5% [60], affecting only the backgrounds estimated from simulation. No uncertainty is applied for the amount of pileup as its effect is negligible in this high- selection. The uncertainties due to the limited size of simulation samples are incorporated as uncorrelated nuisance parameters in the fit.

Theoretical systematic uncertainties are applied and include independent and correlated variations of the renormalization and factorization scales. Additionally, uncertainties on the parton distribution function (PDF) are incorporated by considering variations in the NNPDF 3.0 scheme [61]. The size of these uncertainties is typically small as the effect of these variations is largely to modify the cross section of processes, which for the main backgrounds are constrained by data.

The background systematic uncertainties that affect the shape are shown in Fig. 5 (left) for the most sensitive search bin.

Figure 5: Background (left) and signal (right) systematic uncertainties affecting the shape (in percent) in the and bin. The uncertainties for other bins are similar.

4.2 Signal systematic uncertainties

Several of the systematic uncertainties affecting the signal yield are evaluated in the same way as the background yield. These are the uncertainties due to gluon splitting, lepton selection efficiency, jet energy scale, jet energy resolution,  tag scale factors, simulation sample size, integrated luminosity, and theoretical uncertainties. All systematic variations affect both the shape and normalization, except for the gluon splitting uncertainty, which is taken to affect only the shape.

The number of jets from ISR produced in the signal simulation is reweighted based on comparisons between data and simulated samples. The reweighting factors vary between 0.92 and 0.51 for the number of ISR jets between and . One half of the deviation from unity is taken as the systematic uncertainty in these reweighting factors.

The systematic uncertainties affecting the signal shape are shown in Fig. 5 (right) for the most sensitive bin in a model with . The dominant signal systematic uncertainties arise from the limited simulation sample size, the  tagging efficiency scale factors, and the ISR modeling. There is no systematic uncertainty taken for pileup reweighting, as the signal efficiency is found to be insensitive to the number of pileup interactions.

5 Results

The results of a background-only fit of the observed distributions are shown in Figs. 6 and 7. These figures separately show the control and signal regions, although the fit includes all bins simultaneously. The distributions in data are well described by the fit, and examination of the nuisance parameters shows that none of them are significantly changed by the fit. The post-fit yields are presented in Table 2.

Figure 6: Data and the background-only post-fit distribution for bins with low expected signal contribution: , (upper-left), , (upper-right), , (lower-left), and , (lower-right). The expected signal distribution is also shown for a gluino mass of 1600. The ratio of data to post-fit yields is shown in the lower panel. The post-fit uncertainty is depicted as a hatched band.
Figure 7: Data and the background-only post-fit distribution for bins with large expected signal contribution: , (upper-left), , (upper-right), , (lower-left), and , (lower-right). The expected signal distribution is also shown for a gluino mass of 1600. The ratio of data to post-fit yields is shown in the lower panel. The post-fit uncertainty is depicted as a hatched band.
QCD +jets Other All bkg. Data Expected
Table 2: Post-fit yields for the background-only fit, observed data, and expected yields for in each search bin.

A signal-plus-background fit is performed for gluino masses ranging from 1000 to 2000. For all masses, the post-fit distribution describes the data well, and the fit extracts at most a small and insignificant signal contribution. For example, with a 1600 gluino, the extracted signal yield relative to the model prediction is . The change of nuisance parameters by the fit is small and consistent with those of the background-only fit. Limits on the signal production cross section are calculated at 95% confidence level (CL) using the asymptotic approximation of the criterion [62, 63, 64, 65] and shown in Fig. 8. Comparing the observed limit to the gluino pair production cross section [66], gluino masses below 1610 are excluded in the benchmark model.

Figure 8: Cross section upper limits at 95% CL for a model of gluino pair production with compared to the gluino pair production cross section. The theoretical uncertainties in the cross section are shown as a band around the red line [66]. The expected limits (dashed line) and their  s.d. and  s.d. variations are shown as green and yellow bands, respectively. The observed limit is shown by the solid line with dots.

6 Summary

Results are presented from a search for new phenomena in events with a single lepton, large jet and bottom quark jet multiplicities, and high sum of large-radius jet masses, without a missing transverse momentum requirement. The background is predicted using a simultaneous fit in bins of the number of jets, number of b-tagged jets, and the sum of masses of large radius jets, using Monte Carlo simulated predictions with corrections measured in data control samples for the normalizations of the dominant backgrounds and nuisance parameters for theoretical and experimental uncertainties. Statistical uncertainties dominate in the signal regions, while the most important systematic uncertainties arise from the modeling of gluon splitting and the b quark tagging efficiency and mistag rate. The observed data are consistent with the background-only hypothesis. An upper limit of approximately 10 fb is determined for the gluino-gluino production cross section using a benchmark -parity violating supersymmetry model of gluino pair production with a prompt three-body decay to quarks, as predicted in minimal flavor violating models. For this model, gluinos are observed (expected) to be excluded up to at a 95% confidence level, which improves upon previous searches at  [26, 27, 28] and is comparable to recent results at  [29].

Acknowledgments

We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies: BMWFW and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF (Cyprus); SENESCYT (Ecuador); MoER, ERC IUT, and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece); OTKA and NIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS, RFBR and RAEP (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI and FEDER (Spain); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU and SFFR (Ukraine); STFC (United Kingdom); DOE and NSF (USA).

Individuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, contract No. 675440 (European Union); the Leventis Foundation; the A. P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Council of Science and Industrial Research, India; the HOMING PLUS program of the Foundation for Polish Science, cofinanced from European Union, Regional Development Fund, the Mobility Plus program of the Ministry of Science and Higher Education, 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-bis 2012/07/E/ST2/01406; the National Priorities Research Program by Qatar National Research Fund; the Programa Clarín-COFUND del Principado de Asturias; the Thalis and Aristeia programs cofinanced by EU-ESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA).

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E.A. De Wolf, D. Di Croce, X. Janssen, J. Lauwers, M. Van De Klundert, H. Van Haevermaet, P. Van Mechelen, N. Van Remortel Vrije Universiteit Brussel, Brussel, Belgium
S. Abu Zeid, F. Blekman, J. D’Hondt, I. De Bruyn, J. De Clercq, K. Deroover, G. Flouris, D. Lontkovskyi, S. Lowette, I. Marchesini, S. Moortgat, L. Moreels, Q. Python, K. Skovpen, S. Tavernier, W. Van Doninck, P. Van Mulders, I. Van Parijs Université Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, B. Dorney, G. Fasanella, L. Favart, R. Goldouzian, A. Grebenyuk, A.K. Kalsi, T. Lenzi, J. Luetic, T. Maerschalk, A. Marinov, T. Seva, E. Starling, C. Vander Velde, P. Vanlaer, D. Vannerom, R. Yonamine, F. Zenoni Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, A. Fagot, M. Gul, I. Khvastunov\@textsuperscript2, D. Poyraz, C. Roskas, S. Salva, D. Trocino, M. Tytgat, W. Verbeke, M. Vit, N. Zaganidis Université Catholique de Louvain, Louvain-la-Neuve, Belgium
H. Bakhshiansohi, O. Bondu, S. Brochet, G. Bruno, C. Caputo, A. Caudron, P. David, S. De Visscher, C. Delaere, M. Delcourt, B. Francois, A. Giammanco, M. Komm, G. Krintiras, V. Lemaitre, A. Magitteri, A. Mertens, M. Musich, K. Piotrzkowski, L. Quertenmont, A. Saggio, M. Vidal Marono, S. Wertz, J. Zobec Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
W.L. Aldá Júnior, F.L. Alves, G.A. Alves, L. Brito, G. Correia Silva, C. Hensel, A. Moraes, M.E. Pol, P. Rebello Teles Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato\@textsuperscript3, E. Coelho, E.M. Da Costa, G.G. Da Silveira\@textsuperscript4, D. De Jesus Damiao, S. Fonseca De Souza, L.M. Huertas Guativa, H. Malbouisson, M. Melo De Almeida, C. Mora Herrera, L. Mundim, H. Nogima, L.J. Sanchez Rosas, A. Santoro, A. Sznajder, M. Thiel, E.J. Tonelli Manganote\@textsuperscript3, F. Torres Da Silva De Araujo, A. Vilela Pereira Universidade Estadual Paulista ,  Universidade Federal do ABC ,  São Paulo, Brazil
S. Ahuja, C.A. Bernardes, T.R. Fernandez Perez Tomei, E.M. Gregores, P.G. Mercadante, S.F. Novaes, Sandra S. Padula, D. Romero Abad, J.C. Ruiz Vargas Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia, Bulgaria
A. Aleksandrov, R. Hadjiiska, P. Iaydjiev, M. Misheva, M. Rodozov, M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
A. Dimitrov, L. Litov, B. Pavlov, P. Petkov Beihang University, Beijing, China
W. Fang\@textsuperscript5, X. Gao\@textsuperscript5, L. Yuan Institute of High Energy Physics, Beijing, China
M. Ahmad, J.G. Bian, G.M. Chen, H.S. Chen, M. Chen, Y. Chen, C.H. Jiang, D. Leggat, H. Liao, Z. Liu, F. Romeo, S.M. Shaheen, A. Spiezia, J. Tao, C. Wang, Z. Wang, E. Yazgan, H. Zhang, J. Zhao State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing, China
Y. Ban, G. Chen, J. Li, Q. Li, S. Liu, Y. Mao, S.J. Qian, D. Wang, Z. Xu, F. Zhang\@textsuperscript5 Tsinghua University, Beijing, China
Y. Wang Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C.A. Carrillo Montoya, L.F. Chaparro Sierra, C. Florez, C.F. González Hernández, J.D. Ruiz Alvarez, M.A. Segura Delgado University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, Split, Croatia
B. Courbon, N. Godinovic, D. Lelas, I. Puljak, P.M. Ribeiro Cipriano, T. Sculac University of Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, K. Kadija, B. Mesic, A. Starodumov\@textsuperscript6, T. Susa University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H. Rykaczewski Charles University, Prague, Czech Republic
M. Finger\@textsuperscript7, M. Finger Jr.\@textsuperscript7 Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
A.A. Abdelalim\@textsuperscript8\@textsuperscript9, S. Elgammal\@textsuperscript10, S. Khalil\@textsuperscript9 National Institute of Chemical Physics and Biophysics, Tallinn, Estonia
S. Bhowmik, R.K. Dewanjee, M. Kadastik, L. Perrini, M. Raidal, C. Veelken Department of Physics, University of Helsinki, Helsinki, Finland
P. Eerola, H. Kirschenmann, J. Pekkanen, M. Voutilainen Helsinki Institute of Physics, Helsinki, Finland
J. Havukainen, J.K. Heikkilä, T. Järvinen, V. Karimäki, R. Kinnunen, T. Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, P. Luukka, T. Mäenpää, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta University of Technology, Lappeenranta, Finland
T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F. Ferri, S. Ganjour, S. Ghosh, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry, C. Leloup, E. Locci, M. Machet, J. Malcles, G. Negro, J. Rander, A. Rosowsky, M.Ö. Sahin, M. Titov Laboratoire Leprince-Ringuet, Ecole polytechnique, CNRS/IN2P3, Université Paris-Saclay, Palaiseau, France
A. Abdulsalam\@textsuperscript11, C. Amendola, I. Antropov, S. Baffioni, F. Beaudette, P. Busson, L. Cadamuro, C. Charlot, R. Granier de Cassagnac, M. Jo, I. Kucher, S. Lisniak, A. Lobanov, J. Martin Blanco, M. Nguyen, C. Ochando, G. Ortona, P. Paganini, P. Pigard, R. Salerno, J.B. Sauvan, Y. Sirois, A.G. Stahl Leiton, T. Strebler, Y. Yilmaz, A. Zabi, A. Zghiche Université de Strasbourg, CNRS, IPHC UMR 7178, F-67000 Strasbourg, France
J.-L. Agram\@textsuperscript12, J. Andrea, D. Bloch, J.-M. Brom, M. Buttignol, E.C. Chabert, N. Chanon, C. Collard, E. Conte\@textsuperscript12, X. Coubez, F. Drouhin\@textsuperscript12, J.-C. Fontaine\@textsuperscript12, D. Gelé, U. Goerlach, M. Jansová, P. Juillot, A.-C. Le Bihan, N. Tonon, P. Van Hove Centre de Calcul de l’Institut National de Physique Nucleaire et de Physique des Particules, CNRS/IN2P3, Villeurbanne, France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1,  CNRS-IN2P3, Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, R. Chierici, D. Contardo, P. Depasse, H. El Mamouni, J. Fay, L. Finco, S. Gascon, M. Gouzevitch, G. Grenier, B. Ille, F. Lagarde, I.B. Laktineh, M. Lethuillier, L. Mirabito, A.L. Pequegnot, S. Perries, A. Popov\@textsuperscript13, V. Sordini, M. Vander Donckt, S. Viret, S. Zhang Georgian Technical University, Tbilisi, Georgia
A. Khvedelidze\@textsuperscript7 Tbilisi State University, Tbilisi, Georgia
L. Rurua RWTH Aachen University, I. Physikalisches Institut, Aachen, Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, M. Preuten, C. Schomakers, J. Schulz, M. Teroerde, B. Wittmer, V. Zhukov\@textsuperscript13 RWTH Aachen University, III. Physikalisches Institut A,  Aachen, Germany
A. Albert, D. Duchardt, M. Endres, M. Erdmann, S. Erdweg, T. Esch, R. Fischer, A. Güth, T. Hebbeker, C. Heidemann, K. Hoepfner, S. Knutzen, M. Merschmeyer, A. Meyer, P. Millet, S. Mukherjee, T. Pook, M. Radziej, H. Reithler, M. Rieger, F. Scheuch, D. Teyssier, S. Thüer RWTH Aachen University, III. Physikalisches Institut B,  Aachen, Germany
G. Flügge, B. Kargoll, T. Kress, A. Künsken, T. Müller, A. Nehrkorn, A. Nowack, C. Pistone, O. Pooth, A. Stahl\@textsuperscript14 Deutsches Elektronen-Synchrotron, Hamburg, Germany
M. Aldaya Martin, T. Arndt, C. Asawatangtrakuldee, K. Beernaert, O. Behnke, U. Behrens, A. Bermúdez Martínez, A.A. Bin Anuar, K. Borras\@textsuperscript15, V. Botta, A. Campbell, P. Connor, C. Contreras-Campana, F. Costanza, C. Diez Pardos, G. Eckerlin, D. Eckstein, T. Eichhorn, E. Eren, E. Gallo\@textsuperscript16, J. Garay Garcia, A. Geiser, J.M. Grados Luyando, A. Grohsjean, P. Gunnellini, M. Guthoff, A. Harb, J. Hauk, M. Hempel\@textsuperscript17, H. Jung, M. Kasemann, J. Keaveney, C. Kleinwort, I. Korol, D. Krücker, W. Lange, A. Lelek, T. Lenz, K. Lipka, W. Lohmann\@textsuperscript17, R. Mankel, I.-A. Melzer-Pellmann, A.B. Meyer, M. Missiroli, G. Mittag, J. Mnich, A. Mussgiller, E. Ntomari, D. Pitzl, A. Raspereza, M. Savitskyi, P. Saxena, R. Shevchenko, N. Stefaniuk, G.P. Van Onsem, R. Walsh, Y. Wen, K. Wichmann, C. Wissing, O. Zenaiev University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, V. Blobel, M. Centis Vignali, T. Dreyer, E. Garutti, D. Gonzalez, J. Haller, A. Hinzmann, M. Hoffmann, A. Karavdina, R. Klanner, R. Kogler, N. Kovalchuk, S. Kurz, D. Marconi, M. Meyer, M. Niedziela, D. Nowatschin, F. Pantaleo\@textsuperscript14, T. Peiffer, A. Perieanu, C. Scharf, P. Schleper, A. Schmidt, S. Schumann, J. Schwandt, J. Sonneveld, H. Stadie, G. Steinbrück, F.M. Stober, M. Stöver, H. Tholen, D. Troendle, E. Usai, A. Vanhoefer, B. Vormwald Institut für Experimentelle Kernphysik, Karlsruhe, Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, E. Butz, R. Caspart, T. Chwalek, F. Colombo, W. De Boer, A. Dierlamm, N. Faltermann, B. Freund, R. Friese, M. Giffels, M.A. Harrendorf, F. Hartmann\@textsuperscript14, S.M. Heindl, U. Husemann, F. Kassel\@textsuperscript14, S. Kudella, H. Mildner, M.U. Mozer, Th. Müller, M. Plagge, G. Quast, K. Rabbertz, M. Schröder, I. Shvetsov, G. Sieber, H.J. Simonis, R. Ulrich, S. Wayand, M. Weber, T. Weiler, S. Williamson, C. Wöhrmann, R. Wolf Institute of Nuclear and Particle Physics (INPP),  NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas, I. Topsis-Giotis National and Kapodistrian University of Athens, Athens, Greece
G. Karathanasis, S. Kesisoglou, A. Panagiotou, N. Saoulidou, E. Tziaferi National Technical University of Athens, Athens, Greece
K. Kousouris University of Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, N. Manthos, I. Papadopoulos, E. Paradas, J. Strologas, F.A. Triantis, D. Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös Loránd University, Budapest, Hungary
M. Csanad, N. Filipovic, G. Pasztor, O. Surányi, G.I. Veres\@textsuperscript18 Wigner Research Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath\@textsuperscript19, Á. Hunyadi, F. Sikler, V. Veszpremi, G. Vesztergombi\@textsuperscript18 Institute of Nuclear Research ATOMKI, Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi\@textsuperscript20, A. Makovec, J. Molnar, Z. Szillasi Institute of Physics, University of Debrecen, Debrecen, Hungary
M. Bartók\@textsuperscript18, P. Raics, Z.L. Trocsanyi, B. Ujvari Indian Institute of Science (IISc),  Bangalore, India
S. Choudhury, J.R. Komaragiri National Institute of Science Education and Research, Bhubaneswar, India
S. Bahinipati\@textsuperscript21, P. Mal, K. Mandal, A. Nayak\@textsuperscript22, D.K. Sahoo\@textsuperscript21, N. Sahoo, S.K. Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, R. Chawla, N. Dhingra, A. Kaur, M. Kaur, S. Kaur, R. Kumar, P. Kumari, A. Mehta, J.B. Singh, G. Walia University of Delhi, Delhi, India
Ashok Kumar, Aashaq Shah, A. Bhardwaj, S. Chauhan, B.C. Choudhary, R.B. Garg, S. Keshri, A. Kumar, S. Malhotra, M. Naimuddin, K. Ranjan, R. Sharma Saha Institute of Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj\@textsuperscript23, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep\@textsuperscript23, D. Bhowmik, S. Dey, S. Dutt\@textsuperscript23, S. Dutta, S. Ghosh, N. Majumdar, A. Modak, K. Mondal, S. Mukhopadhyay, S. Nandan, A. Purohit, P.K. Rout, A. Roy, S. Roy Chowdhury, S. Sarkar, M. Sharan, B. Singh, S. Thakur\@textsuperscript23 Indian Institute of Technology Madras, Madras, India
P.K. Behera Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, A.K. Mohanty\@textsuperscript14, P.K. Netrakanti, L.M. Pant, P. Shukla, A. Topkar Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, S. Dugad, B. Mahakud, S. Mitra, G.B. Mohanty, N. Sur, B. Sutar Tata Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, Sa. Jain, S. Kumar, M. Maity\@textsuperscript24, G. Majumder, K. Mazumdar, T. Sarkar\@textsuperscript24, N. Wickramage\@textsuperscript25 Indian Institute of Science Education and Research (IISER),  Pune, India
S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, S. Sharma Institute for Research in Fundamental Sciences (IPM),  Tehran, Iran
S. Chenarani\@textsuperscript26, E. Eskandari Tadavani, S.M. Etesami\@textsuperscript26, M. Khakzad, M. Mohammadi Najafabadi, M. Naseri, S. Paktinat Mehdiabadi\@textsuperscript27, F. Rezaei Hosseinabadi, B. Safarzadeh\@textsuperscript28, M. Zeinali University College Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari , Università di Bari , Politecnico di Bari ,  Bari, Italy
M. Abbrescia, C. Calabria, A. Colaleo, D. Creanza, L. Cristella, N. De Filippis, M. De Palma, F. Errico, L. Fiore, G. Iaselli, S. Lezki, G. Maggi, M. Maggi, G. Miniello, S. My, S. Nuzzo, A. Pompili, G. Pugliese, R. Radogna, A. Ranieri, G. Selvaggi, A. Sharma, L. Silvestris\@textsuperscript14, R. Venditti, P. Verwilligen INFN Sezione di Bologna , Università di Bologna ,  Bologna, Italy
G. Abbiendi, C. Battilana, D. Bonacorsi, L. Borgonovi, S. Braibant-Giacomelli, R. Campanini, P. Capiluppi, A. Castro, F.R. Cavallo, S.S. Chhibra, G. Codispoti, M. Cuffiani, G.M. Dallavalle, F. Fabbri, A. Fanfani, D. Fasanella, P. Giacomelli, C. Grandi, L. Guiducci, F. Iemmi, S. Marcellini, G. Masetti, A. Montanari, F.L. Navarria, A. Perrotta, A.M. Rossi, T. Rovelli, G.P. Siroli, N. Tosi INFN Sezione di Catania , Università di Catania ,  Catania, Italy
S. Albergo, S. Costa, A. Di Mattia, F. Giordano, R. Potenza, A. Tricomi, C. Tuve INFN Sezione di Firenze , Università di Firenze ,  Firenze, Italy
G. Barbagli, K. Chatterjee, V. Ciulli, C. Civinini, R. D’Alessandro, E. Focardi, P. Lenzi, M. Meschini, S. Paoletti, L. Russo\@textsuperscript29, G. Sguazzoni, D. Strom, L. Viliani INFN Laboratori Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, F. Fabbri, D. Piccolo, F. Primavera\@textsuperscript14 INFN Sezione di Genova , Università di Genova ,  Genova, Italy
V. Calvelli, F. Ferro, F. Ravera, E. Robutti, S. Tosi INFN Sezione di Milano-Bicocca , Università di Milano-Bicocca ,  Milano, Italy
A. Benaglia, A. Beschi, L. Brianza, F. Brivio, V. Ciriolo\@textsuperscript14, M.E. Dinardo, S. Fiorendi, S. Gennai, A. Ghezzi, P. Govoni, M. Malberti, S. Malvezzi, R.A. Manzoni, D. Menasce, L. Moroni, M. Paganoni, K. Pauwels, D. Pedrini, S. Pigazzini\@textsuperscript30, S. Ragazzi, T. Tabarelli de Fatis INFN Sezione di Napoli , Università di Napoli ’Federico II’ , Napoli, Italy, Università della Basilicata , Potenza, Italy, Università G. Marconi , Roma, Italy
S. Buontempo, N. Cavallo, S. Di Guida\@textsuperscript14, F. Fabozzi, F. Fienga, A.O.M. Iorio, W.A. Khan, L. Lista, S. Meola\@textsuperscript14, P. Paolucci\@textsuperscript14, C. Sciacca, F. Thyssen INFN Sezione di Padova , Università di Padova , Padova, Italy, Università di Trento , Trento, Italy
P. Azzi, N. Bacchetta, S. Badoer, L. Benato, D. Bisello, A. Boletti, R. Carlin, A. Carvalho Antunes De Oliveira, P. Checchia, M. Dall’Osso, P. De Castro Manzano, T. Dorigo, U. Dosselli, U. Gasparini, S. Lacaprara, P. Lujan, M. Margoni, A.T. Meneguzzo, N. Pozzobon, P. Ronchese, R. Rossin, F. Simonetto, A. Tiko, E. Torassa, M. Zanetti, P. Zotto, G. Zumerle INFN Sezione di Pavia , Università di Pavia ,  Pavia, Italy
A. Braghieri, A. Magnani, P. Montagna, S.P. Ratti, V. Re, M. Ressegotti, C. Riccardi, P. Salvini, I. Vai, P. Vitulo INFN Sezione di Perugia , Università di Perugia ,  Perugia, Italy
L. Alunni Solestizi, M. Biasini, G.M. Bilei, C. Cecchi, D. Ciangottini, L. Fanò, P. Lariccia, R. Leonardi, E. Manoni, G. Mantovani, V. Mariani, M. Menichelli, A. Rossi, A. Santocchia, D. Spiga INFN Sezione di Pisa , Università di Pisa , Scuola Normale Superiore di Pisa ,  Pisa, Italy
K. Androsov, P. Azzurri\@textsuperscript14, G. Bagliesi, L. Bianchini, T. Boccali, L. Borrello, R. Castaldi, M.A. Ciocci, R. Dell’Orso, G. Fedi, L. Giannini, A. Giassi, M.T. Grippo\@textsuperscript29, F. Ligabue, T. Lomtadze, E. Manca, G. Mandorli, A. Messineo, F. Palla, A. Rizzi, A. Savoy-Navarro\@textsuperscript31, P. Spagnolo, R. Tenchini, G. Tonelli, A. Venturi, P.G. Verdini INFN Sezione di Roma , Sapienza Università di Roma ,  Rome, Italy
L. Barone, F. Cavallari, M. Cipriani, N. Daci, D. Del Re, E. Di Marco, M. Diemoz, S. Gelli, E. Longo, F. Margaroli, B. Marzocchi, P. Meridiani, G. Organtini, R. Paramatti, F. Preiato, S. Rahatlou, C. Rovelli, F. Santanastasio INFN Sezione di Torino , Università di Torino , Torino, Italy, Università del Piemonte Orienta