Measurement of differential t t ¯ production cross sections using top quarks at large transverse momenta in pp collisions at s √ = 13 TeV
EEUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH (CERN)
CERN-EP-2020-1212020/08/19
CMS-TOP-18-013
Measurement of differential tt production cross sectionsusing top quarks at large transverse momenta in ppcollisions at √ s =
13 TeV
The CMS Collaboration ∗ Abstract
A measurement is reported of differential top quark pair (tt) production cross sec-tions, where top quarks are produced at large transverse momenta. The data col-lected with the CMS detector at the LHC are from pp collisions at a center-of-massenergy of 13 TeV corresponding to an integrated luminosity of 35.9 fb − . The mea-surement uses events where at least one top quark decays as t → Wb → qq (cid:48) b and isreconstructed as a large-radius jet with transverse momentum in excess of 400 GeV.The second top quark is required to decay either in a similar way, or leptonically, asinferred from a reconstructed electron or muon, a bottom quark jet, and a missingtransverse momentum due to the undetected neutrino. The cross section is extractedas a function of kinematic variables of individual top quarks or of the tt system. Theresults are presented at the particle level, within a region of phase space close to thatof the experimental acceptance, and at the parton level, and are compared to varioustheoretical models. In both decay channels the observed absolute cross sections aresignificantly lower than the predictions from theory, while the normalized differentialmeasurements are well described. Submitted to Physical Review D c (cid:13) ∗ See Appendix A for the list of collaboration members a r X i v : . [ h e p - e x ] A ug The top quark completes the third generation of quarks in the standard model (SM), and aprecise understanding of its properties is critical for the overall consistency of the theory. Mea-surements of the top quark-antiquark pair (tt) production cross section confront the expecta-tions from quantum chromodynamics (QCD), but could also be sensitive to effects of physicsbeyond the SM. In particular, tt production constitutes a dominant SM background to manydirect searches for beyond-the-SM phenomena, and its detailed characterization is thereforeimportant for confirming possible discoveries.The large tt yield expected in pp collisions at the CERN LHC enables measurements of the ttproduction rate as functions of kinematic variables of individual top quarks and the tt system.Such measurements have been performed at the ATLAS [1–9] and CMS [10–19] experimentsat 7, 8, and 13 TeV center-of-mass energies, assuming a resolved final state where the decayproducts of the tt system can be reconstructed individually. Resolved top quark reconstruc-tion is possible for top quark transverse momenta ( p T ) up to about 500 GeV. At higher p T , thetop quark decay products are highly collimated (“Lorentz boosted”) and they can no longerbe reconstructed separately. To explore the highly boosted phase space, top quark decays arereconstructed as large-radius ( R ) jets in this analysis. Previous efforts in this domain by AT-LAS [20, 21] and CMS [22] confirm that it is feasible to perform precise differential measure-ments of high- p T tt production and have also indicated possibly interesting deviations fromtheory.This paper reports a measurement of the differential tt production cross section in the boostedregime in the all-jet and lepton+jets final states. The results are based on pp collisions at √ s =
13 TeV in the CMS detector, corresponding to a total integrated luminosity of 35.9 fb − .In the all-jet decay channel, each W boson arising from the t → Wb transition decays into aquark (q) and antiquark (q (cid:48) ). As a result, the final state consists of at least six quarks, two ofwhich are bottom quarks. Additional partons, gluons or quarks, can arise from initial-stateradiation (ISR) and final-state radiation (FSR). The sizable boost of the top quarks in this mea-surement ( p T >
400 GeV) provides two top quarks reconstructed as large- R jets and the finalstate therefore consists of at least two such jets. In the lepton+jets channel, one top quark de-cays according to t → Wb → qq (cid:48) b and is reconstructed as a single large- R jet, while the secondtop quark decays to a bottom quark and a W boson that in turn decays to a charged lepton ( (cid:96) ),either an electron (e) or a muon ( µ ), and a neutrino (t → Wb → (cid:96) ν b). Decays of W bosons via τ leptons to electrons or muons are treated as signal. The measurements were performed us-ing larger integrated luminosity and higher center-of-mass energy compared to previous CMSresults [22]. This provides a sharper confrontation with theory over data in a wider region ofphase space.The paper is organized as follows: Section 2 describes the main features of the CMS detectorand the triggering system. Section 3 gives the details of the Monte Carlo (MC) simulations.Event reconstruction and selection are outlined in Sections 4 and 5, respectively. In Section 6,we discuss the estimation of the background contributions, followed by a description of sig-nal extraction in Section 7. Systematic uncertainties are discussed in Section 8. The unfoldingprocedure used to obtain the particle- and parton-level cross sections and the resulting mea-surements are presented in Section 9. Finally, Section 10 provides a brief summary of the paper. The central feature of the CMS apparatus is a superconducting solenoid of 6 m internal diame-ter, providing a magnetic field of 3.8 T. A silicon pixel and strip tracker, a lead tungstate crystalelectromagnetic calorimeter (ECAL), and a brass and scintillator hadron calorimeter (HCAL),each composed of a barrel and two endcap sections, reside within the magnetic volume. For-ward calorimeters extend the pseudorapidity ( η ) coverage provided by the barrel and endcapdetectors. Muons are detected in gas-ionization chambers embedded in the steel flux-returnyoke outside the solenoid. A more detailed description of the CMS detector, together with adefinition of the coordinate system and kinematic variables can be found in Ref. [23].Events of interest are selected using a two-tiered trigger system [24]. The first level (L1), com-posed of specialized hardware processors, uses information from the calorimeters and muondetectors to select events at a rate of about 100 kHz within a fixed time interval of 4 µ s. Thesecond level, known as the high-level trigger (HLT), consists of a farm of processors that runthe full event reconstruction software in a configuration for fast processing, and reduces theevent rate to about 1 kHz before data storage. We use MC simulation to generate event samples for the tt signal and also to model the contri-butions from some of the background processes. The tt events are generated at next-to-leadingorder (NLO) in QCD using
POWHEG (v2) [25–29], assuming a top quark mass m t = t channel and in association with a W boson is simulated atNLO with POWHEG [30]. The production of W and Z bosons in association with jets (V+jets), aswell as multijet events, are simulated using the M AD G RAPH MC @ NLO [31] (v2.2.2) genera-tor at leading order (LO), with the MLM matching algorithm [32] to match the jets after partonshowering to the original partons. Samples of diboson (WW, WZ, or ZZ) events are simulatedat LO using
PYTHIA (v8.212) [33, 34].All simulated events are processed using
PYTHIA to model parton showering, hadronization,and the underlying event (UE). The NNPDF 3.0 [35] parton distribution functions (PDFs) areused to generate the events, and the CUETP8M1 UE tune [36] is used for all but the tt and singletop quark processes. For these, the CUETP8M2T4 tune [37] with an adjusted value of the strongcoupling α S is used, yielding an improved modeling of tt event properties. The simulation ofthe response of the CMS detector is based on G EANT
PYTHIA and overlaidwith events generated according to the pileup distribution measured in data. An average of 27pileup interactions was observed for the collected data.The simulated processes are normalized to their best known theoretical cross sections. Specifi-cally, the tt, V+jets, and single top quark event samples are normalized to next-to-NLO (NNLO)precision in QCD [39–41].The measured differential cross sections for tt production are compared with state-of-the-arttheoretical expectations provided by the NLO
POWHEG generator, combined with
PYTHIA for parton showering, as described above, or combined with NLO
HERWIG ++ [42] and thecorresponding EE5C UE tune [43]. In addition, a comparison is performed with M AD -G RAPH MC @ NLO [31] using
PYTHIA for the parton showering.
Global event reconstruction, also called particle-flow (PF) event reconstruction [44], aims to re-construct and identify each individual particle in an event through an optimized combinationof information from all subdetectors. In this process, the particle type (photon, electron, muon,and charged or neutral hadron) plays an important role in the determination of particle direc-tion and energy. Photons are identified as ECAL energy clusters not linked to the extrapolationof any charged-particle trajectory to the ECAL. Electrons are identified as primary charged par-ticle tracks and potentially many ECAL energy clusters corresponding to extrapolation of thesetracks to the ECAL and to possible bremsstrahlung photons emitted along the way through thetracker material. Muons are identified as tracks in the central tracker consistent with eithera track or several hits in the muon system associated with calorimeter deposition compatiblewith the muon hypothesis. Charged hadrons are identified as charged-particle tracks that areneither identified as electrons nor as muons. Finally, neutral hadrons are identified as HCALenergy clusters not linked to any charged-hadron trajectory, or as a combined ECAL and HCALenergy excess relative to the expected deposit of the charged-hadron energy.The energy of photons is obtained from the ECAL measurement. The energy of electrons isdetermined from a combination of the track momentum at the main interaction vertex, theenergy of the corresponding ECAL cluster, and the energy sum of all bremsstrahlung photonsspatially compatible with originating from the electron track. The energy of muons is obtainedfrom the curvature of the corresponding track. The energy of charged hadrons is determinedfrom a combination of their momentum measured in the tracker and the matching ECAL andHCAL energy deposits, corrected for the response function of the calorimeters to hadronicshowers. Finally, the energy of neutral hadrons is obtained from the corresponding correctedECAL and HCAL energies.Leptons and charged hadrons are required to be compatible with originating from the primaryinteraction vertex. The candidate vertex with the largest value of summed physics-object p is taken to be the primary pp interaction vertex. For this purpose the physics objects are thejets, clustered using the jet finding algorithm [45, 46] with the tracks assigned to candidatevertices as inputs, and the negative vector p T sum of those jets. Charged hadrons that areassociated with a pileup vertex are classified as pileup candidates and are ignored in the subse-quent event reconstruction. Electron and muon objects are first identified from correspondingelectron or muon PF candidates. Next, jet clustering is performed on all PF candidates that arenot classified as pileup candidates. The jet clustering does not exclude the electron and muonPF candidates, even if these have already been assigned to electron/muon objects. A dedicatedremoval of overlapping physics objects is therefore used at the analysis level to avoid doublecounting.Electrons and muons selected in the (cid:96) +jets channel must have p T >
50 GeV and | η | < p T >
20 GeV and | η | < I mini )algorithm, which requires the scalar p T sum of tracks in a cone around the electron or muonto be less than a given fraction of the lepton p T ( p (cid:96) T ) [47]. The width of the cone ( ∆ R ) dependson the lepton p T , being defined as ∆ R = (
10 GeV ) / p (cid:96) T for p (cid:96) T <
200 GeV and ∆ R = p (cid:96) T >
200 GeV. This algorithm retains high isolation efficiency for leptons originating fromdecays of highly-boosted top quarks. A value of I mini < ≈
95% efficiency. For vetoing additional leptons in the (cid:96) +jets channel, the same lepton selectionis used with the isolation requirement removed. Correction factors are applied to account fordifferences between data and simulation in the modeling of lepton identification, isolation, and trigger efficiencies, determined as functions of | η | and p T of the electron or muon using a“tag-and-probe” method [48].In each event, jets are clustered using the reconstructed PF candidates through the infrared-and collinear-safe anti- k T algorithm [45, 46]. Two jet collections are considered to identify band t jet candidates. Small- R jets are clustered using a distance parameter of 0.4 in the (cid:96) +jetschannel and large- R jets using a distance parameter of 0.8 in the all-jet and (cid:96) +jets channels.The jet momenta are determined through the vector sum of all particle momenta in the jet,and found from simulation to be typically within 5–10% of the true momentum over the entirespectrum and detector acceptance. Additional pp interactions can contribute more tracks andcalorimetric energy depositions to the jet momentum. To mitigate this effect, the pileup candi-dates are discarded before the clustering and an offset correction is applied to correct for theremaining contributions from neutral particles [49].Jet energy corrections are obtained from simulation to bring the average measured responseof jets to that of particle-level jets. In situ measurements of the momentum balance in dijet,photon+jet, Z+jet, and multijet events are used to account for any residual differences in the jetenergy scale (JES) between data and simulation [50]. The jet energy resolution (JER) amountstypically to 15–20% at 30 GeV, 10% at 100 GeV, and 5% at 1 TeV. Additional criteria are appliedto remove jets that are due to anomalous signals in the subdetectors or due to reconstructionfailures [51].A grooming technique is used to remove soft, wide-angle radiation from the large- R jets and tothereby improve the mass resolution. The algorithm employed is the “modified mass drop tag-ger” [52, 53], also known as the “soft-drop” (SD) algorithm [54], with angular exponent β = z cut < R = m SD . The subjets within large- R jets are identified through a reclus-tering of their constituents using the Cambridge–Aachen algorithm [55, 56] and then reversingthe last step of the clustering history.To identify jets originating from top quarks that decay according to t → Wb → qq (cid:48) b (t tag-ging), we use the N -subjettiness variables [57] τ , τ , and τ computed using the jet constituentsaccording to τ N = ∑ j p T, j R ∑ k p T, k min { ∆ R k , ∆ R k , . . . ∆ R N , k } , (1)where N denotes the number of reconstructed candidate subjets and k runs over the constituentparticles in the jet [58]. The term min refers to the minimum value of the items within the curlyparentheses, and the variable ∆ R i , k = √ ( ∆ η i , k ) + ( ∆ φ i , k ) , where φ is the azimuthal angle, isthe angular distance between the candidate subjet i axis and the jet constituent k . The variable R corresponds to the characteristic jet distance parameter ( R = k T algorithm [59, 60] to the jet constituentsbefore proceeding with jet grooming techniques.Small- R jets and subjets of large- R jets are identified as bottom quark candidates (b-tagged)using the combined secondary vertex (CSV) algorithm [61]. Data-to-simulation correction fac-tors are used to match the b tagging efficiency observed in simulation to that measured in data.The typical efficiencies of the b tagging algorithm for small- R jets and subjets of large- R jetsare, respectively, 63 and 58% for genuine b (sub)jets, while the misidentification probability forlight-flavor (sub)jets is 1%. For the subjets of large- R jets, the efficiency for tagging genuineb subjets drops from 65 to 40% as the p T increases from 20 GeV to 1 TeV.The missing transverse momentum vector (cid:126) p missT is defined as the projection onto the plane per- pendicular to the beam axis of the negative momentum vector sum of all PF candidates in anevent. Its magnitude is referred to as p missT , which is calculated after applying the aforemen-tioned jet energy corrections. Different triggers were employed to collect signal events in the all-jet and (cid:96) +jets channels, ac-cording to each event topology. The trigger used in the all-jet channel required the presence of ajet with p T >
180 GeV at L1. At the HLT, large- R jets were reconstructed from PF candidates us-ing the anti- k T algorithm with a distance parameter of 0.8. The mass of the jets at the HLT, afterremoval of soft particles, was required to be greater than 30 GeV. Selected events had to containat least two such jets with p T >
280 and 200 GeV for the leading and trailing jets, respectively.Finally, at least one of these jets had to be b-tagged using the CSV algorithm suitably adjustedfor the HLT at an average identification efficiency of 90% for b jets. The aforementioned triggerran for the entire 2016 data run, collecting an integrated luminosity of 35.9 fb − . A second trig-ger with identical kinematic criteria but without any b tagging requirement was employed andran on average every 21 bunch crossings, collecting an integrated luminosity of 1.67 fb − . Theevents collected with the latter trigger were intended for use as a control data sample to esti-mate the multijet background in the all-jet channel, as described below. For the (cid:96) +jets channel,the data were selected using triggers requiring a single lepton without imposing any isolationcriteria, either an electron with p T >
45 GeV and | η | < p T >
40 GeV and | η | < R jets with p T >
200 and 50 GeV.
The events considered in the all-jet final state are required to fulfill a common baseline selection.This requires the presence of at least two large- R jets in the event with p T >
400 GeV, | η | < < m SD <
300 GeV. In addition, events with at least one lepton are vetoed to suppressleptonic final states originating from top quarks.Jet substructure variables are used to discriminate between events that originate from tt decaysand multijet production. These are sensitive to the type of jet, and in particular to whether thejet arises from a single parton, such as those in the case of ordinary quark or gluon evolutionsinto jets, or from three partons, such as in the t → Wb → qq (cid:48) b decay considered here. The τ variables of the two large- R jets with highest p T are combined through a neural network (NN)to form a multivariate discriminant that characterizes each event, with values close to zero in-dicating dijet production, and values close to one favoring tt production. These variables arechosen such that the correlation with the number of b-tagged subjets, which is used to definecontrol regions for the multijet background, is minimal. The NN consists of two hidden layerswith 16 and 4 nodes, implemented in the TMVA toolkit [62]. More complex architectures donot improve the discriminating capabilities of the NN. The training of the NN is performedwith simulated multijet (background) and tt (signal) events that satisfy the baseline selection,through the back-propagation method and a sigmoid activation function for the nodes. Excel-lent agreement between data and simulation is observed for the input variables in the phasespace of the training.Besides the baseline selection, sub-regions are defined based on the NN output, the m SD of thejets, and the number of b-tagged subjets in each large- R jet. The signal region (SR) used toextract the differential measurements contains events collected with the signal trigger where both large- R jets contain a b-tagged subjet, have masses in the range of 120–220 GeV, and NNoutput values greater than 0.8. This value is chosen to ensure that the ratio of tt signal tobackground is large, while keeping a sufficient number of signal events with a top quark p T > A ) and control region A(CR A ) are the same as the SR and CR, but have an extended requirement on the m SD of large- R jets of 50–300 GeV. It should be noted that the events selected in SR A and CR A were collectedwith the signal and control triggers, respectively. Finally, signal region B (SR B ) has the sameselection criteria as the SR, except without an NN requirement, and is used to constrain someof the signal modeling uncertainties. (cid:96) +jets channel The (cid:96) +jets final state is identified through the presence of an electron or a muon, a small- R jet that reflects the bottom quark emitted in the t → Wb → (cid:96) ν b decay, and a large- R jetcorresponding to the top quark decaying according to t → Wb → qq (cid:48) b. Small- R (large- R ) jetsare required to have p T > ( ) GeV and | η | < R jet near the lepton, with 0.3 < ∆ R ( (cid:96) , jet ) < π /2;iv. At least one large- R jet away from the lepton, with ∆ R ( (cid:96) , jet ) > π /2;v. p missT >
50 or 35 GeV for the electron or muon channel, and;vi. For events in the electron channel, a cutoff to ensure that (cid:126) p missT does not point along thetransverse direction of the electron or the leading jet: | ∆ φ ( (cid:126) p X T , (cid:126) p missT ) | < p missT /110 GeV,where X stands for the electron or the leading small- R jet.The more stringent p missT selection and criterion (vi) in the electron channel are applied to fur-ther reduce background from multijet production.Events that fulfill the preselection criteria are categorized according to whether the jet can-didates pass or fail the relevant b or t tagging criteria. The b jet candidate is the highest- p T leptonic-side jet in the event while the t jet candidate is the highest- p T jet on the non-leptonicside. The N -subjettiness ratio τ / τ (abbreviated as τ ) is used to distinguish a three-prongedtop quark decay from background processes by requiring τ < < m SD <
220 GeV. A data-to-simulation efficiency correction factor isextracted simultaneously with the integrated signal yield, as described in Section 7, to correctthe t tagging efficiency in simulation to match that in data.Events are divided into the following categories:i. No t tags (0t): the t jet candidate fails the t tagging requirement; ii. 1 t tag, no b tags (1t0b): the t jet candidate passes the t tagging requirement, but the b jetcandidate fails the b tagging requirement, and;iii. 1 t tag, 1 b tag (1t1b): both the t jet candidate and the b jet candidate pass their respectivetagging requirement.These event categories are designed to produce different admixtures of signal and background,with the 0t region having most background and the 1t1b region most signal.
The dominant background in the all-jet channel is multijet production, while in the (cid:96) +jets chan-nel the dominant sources of background include nonsignal tt, single top quark, W+jets, andmultijet production events. Nonsignal tt events, referred to as ”tt other”, comprise dilepton(where one lepton is not identified) and all-jet final states (where a lepton arises from one ofthe jets), in addition to τ +jets events where the τ lepton decays hadronically.In the all-jet channel, the background from multijet production is significantly suppressedthrough a combination of b tagging requirements for the subjets within the large- R jets andthe event NN output. The remaining contribution is estimated from a control data sample. Thetwo items determined from data are the shape of the multijet background as a function of anobservable of interest x , and the absolute normalization N multijet . The shape is taken from CR A ,where the tt signal contamination, based on simulation, is about 1%. The value of N multijet isextracted through a binned maximum likelihood fit of the data in SR A of the m SD of the t jetcandidate, m t , where the t jet candidate is taken as the large- R jet with highest p T . The expectednumber of events is modeled according to D ( m t ) = N tt T ( m t ; k scale , k res ) + N multijet ( + k slope m t ) Q ( m t ) + N bkg B ( m t ) , (2)which contains the Poisson intensity function in data D ( m t ) , the distributions T ( m t ) and B ( m t ) of the signal and the subdominant backgrounds, respectively, taken from MC simulation, andthe distribution Q ( m t ) of the multijet background. To account for a possible discrepancy in themultijet m t dependence in the CR A and SR A , a multiplicative factor ( + k slope m t ) is introduced,inspired by the simulation, but with the slope parameter k slope left free in the fit. Also free in thefit are the normalization factors N tt , N multijet , and N bkg . Two additional nuisance parametersare introduced in the analytic parametrization of the m t distribution for simulated tt events, k scale and k res , which account for possible differences between data and simulation in the scaleand resolution in the m t parameter. The fit is performed using the R OO F IT toolkit [63] and theresults are shown in Fig. 1 and Table 1. The fitted tt yield of 6238 ±
181 is significantly lowerthan the 9885 events expected in the SR A according to tt simulation and the theoretical crosssection discussed in Section 3, which implies that the fiducial cross section is smaller than the POWHEG + PYTHIA r = ± p T spectrum compared to NLO predictions thathas been reported in previous measurements [10, 13]. The fitted signal strength is used to scaledown the expected tt signal yields from the POWHEG + PYTHIA E v en t s / G e V All-jet channelDataFit modelttMultijetOther backgrounds (GeV) t m
50 100 150 200 250 300 ( D a t a - F i t ) / U n c . - - (13 TeV) -1 CMS
Figure 1: Result of the fit of m SD of the t jet candidate, m t , in the signal region SR A to datain the all-jet events. The shaded area shows the tt contribution, the dashed line the multijetbackground, and the dash-dotted line the other subdominant backgrounds. The solid line isthe fit to the combined signal+background model, and the data points are represented by thefilled circles. The lower panel shows the difference between the data and the fit model, dividedby the uncertainty in the fit.Table 1: Fitted values of the nuisance parameters for the fit to data in the SR A in the all-jetchannel. Parameter Value k res ± k scale ± k slope ( ± ) × − N bkg ± N multijet ± N tt ± <
1% in the entirephase space) and are fixed to the predictions from simulation.Figure 2 shows the distribution in the NN output in the SR B , and Figs. 3 and 4 show the p T and absolute rapidity | y | of the two top quark candidates and the mass, p T , and rapidity y ofthe tt system, respectively. Also, the m SD values of the two jets are shown in Fig. 5. The tt andmultijet processes are normalized according to the results of the fit in SR A described above,while the yields in subdominant backgrounds are taken from simulation. Table 2 summarizesthe event yields in the SR.In the (cid:96) +jets channel, background events from tt other, single top quark, V+jets, and dibosonproduction are estimated from simulation. The multijet background is modeled using a datasideband region defined by inverting the isolation requirement on the lepton and relaxing thelepton identification criteria. The predicted contributions from signal and other backgroundevents are subtracted from the data distribution in the sideband region to obtain the kinematicdistributions for multijet events. The normalization of the multijet background is extractedfrom a maximum likelihood fit, discussed in Section 7.2; an initial estimate of its normalizationis taken as the simulated prediction. The normalizations of the other background processes arealso constrained via the fit. E v en t s / . All-jet channelDatattMultijetSingle tW + jetsZ + jetsMC stat. unc. (13 TeV) -1 CMS
NN output D a t a / p r ed . Figure 2: Comparison between data and prediction in the signal region SR B (same as the SR,but without an NN requirement) of the NN output distribution for the all-jet channel. Thecontributions from tt and multijet production are normalized according to the fitted values oftheir respective yields and shown as stacked histograms. The data points are represented byfilled circles, while the shaded band represents the statistical uncertainty in simulation. Thelower panel shows the data divided by the sum of the predictions.Table 2: Observed and predicted event yields with their respective statistical uncertainties inthe signal region SR for the all-jet channel. The tt and multijet yields are obtained from the fitin SR A . Process Number of eventstt 4244 ± ± ± ± ± ± In the all-jet channel, the tt signal is extracted from data by subtracting the contribution fromthe background. The signal is extracted as a function of seven separate variables: p T and | y | ofthe leading and subleading t jet, as well as the mass, p T , and y of the tt system, according to: S ( x ) = D ( x ) − R yield N multijet Q ( x ) − B ( x ) , (3)where x corresponds to one of the variables p t i T , | y t i | , m tt , p ttT , or y tt , S ( x ) is the tt signal distri-bution, D ( x ) is the measured distribution in data, Q ( x ) is the multijet distribution, and B ( x ) is the contribution from the subdominant backgrounds (for which both the distribution andthe normalization are taken from simulation). These distributions refer to the SR. The variable N multijet is the fitted number of multijet events in the SR A . The factor R yield is used to extractthe number of multijet events in the SR from N multijet and it is found (in simulation) to be inde-pendent of the b tagging requirement. This allows its estimate from the multijet control data as0
NN output D a t a / p r ed . Figure 2: Comparison between data and prediction in the signal region SR B (same as the SR,but without an NN requirement) of the NN output distribution for the all-jet channel. Thecontributions from tt and multijet production are normalized according to the fitted values oftheir respective yields and shown as stacked histograms. The data points are represented byfilled circles, while the shaded band represents the statistical uncertainty in simulation. Thelower panel shows the data divided by the sum of the predictions.Table 2: Observed and predicted event yields with their respective statistical uncertainties inthe signal region SR for the all-jet channel. The tt and multijet yields are obtained from the fitin SR A . Process Number of eventstt 4244 ± ± ± ± ± ± In the all-jet channel, the tt signal is extracted from data by subtracting the contribution fromthe background. The signal is extracted as a function of seven separate variables: p T and | y | ofthe leading and subleading t jet, as well as the mass, p T , and y of the tt system, according to: S ( x ) = D ( x ) − R yield N multijet Q ( x ) − B ( x ) , (3)where x corresponds to one of the variables p t i T , | y t i | , m tt , p ttT , or y tt , S ( x ) is the tt signal distri-bution, D ( x ) is the measured distribution in data, Q ( x ) is the multijet distribution, and B ( x ) is the contribution from the subdominant backgrounds (for which both the distribution andthe normalization are taken from simulation). These distributions refer to the SR. The variable N multijet is the fitted number of multijet events in the SR A . The factor R yield is used to extractthe number of multijet events in the SR from N multijet and it is found (in simulation) to be inde-pendent of the b tagging requirement. This allows its estimate from the multijet control data as0 E v en t s / G e V All-jet channelDatattMultijetSingle tW + jetsZ + jetsMC stat. unc. (13 TeV) -1 CMS (GeV) T Leading jet p
400 600 800 1000 1200 1400 D a t a / p r ed . E v en t s / G e V All-jet channelDatattMultijetSingle tW + jetsZ + jetsMC stat. unc. (13 TeV) -1 CMS (GeV) T Subleading jet p
400 600 800 1000 1200 1400 D a t a / p r ed . E v en t s / . All-jet channelDatattMultijetSingle tW + jetsZ + jetsMC stat. unc. (13 TeV) -1 CMS
Leading jet |y| D a t a / p r ed . E v en t s / . All-jet channelDatattMultijetSingle tW + jetsZ + jetsMC stat. unc. (13 TeV) -1 CMS
Subleading jet |y| D a t a / p r ed . Figure 3: Comparison between data and prediction in the signal region SR for the p T (upperrow) and absolute rapidity (lower row) of the leading (left column) and subleading (right col-umn) large- R jets in the all-jet channel. The contributions from tt and multijet production arenormalized according to the fitted values of the respective yields and are shown as stacked his-tograms. The data points are shown with filled circles, while the shaded band represents thestatistical uncertainty in the simulation. The lower panel shows the data divided by the sum ofthe predictions. .1 All-jet channel E v en t s / G e V All-jet channelDatattMultijetSingle tW + jetsZ + jetsMC stat. unc. (13 TeV) -1 CMS
Dijet m ass (GeV) D a t a / p r ed . E v en t s / G e V All-jet channelDatattMultijetSingle tW + jetsZ + jetsMC stat. unc. (13 TeV) -1 CMS (GeV) T Dijet p D a t a / p r ed . E v en t s / . All-jet channelDatattMultijetSingle tW + jetsZ + jetsMC stat. unc. (13 TeV) -1 CMS
Dijet y - - D a t a / p r ed . Figure 4: Comparison between data and prediction in the signal region SR of the all-jet channelfor the kinematic properties of the system of the two leading large- R jets (tt candidates). Specif-ically, the invariant mass (upper left), p T (upper right), and rapidity (lower). The contributionsfrom tt and multijet production are normalized according to the fitted values of the respectiveyields and are shown as stacked histograms. The data points are shown with filled circles,while the shaded band represents the statistical uncertainty in the simulation. The lower panelshows the data divided by the sum of the predictions. E v en t s / G e V All-jet channelDatattMultijetSingle tW + jetsZ + jetsMC stat. unc. (13 TeV) -1 CMS
Leading jet mass (GeV)
100 120 140 160 180 200 220 240 D a t a / p r ed . E v en t s / G e V All-jet channelDatattMultijetSingle tW + jetsZ + jetsMC stat. unc. (13 TeV) -1 CMS
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100 120 140 160 180 200 220 240 D a t a / p r ed . Figure 5: Comparison between data and prediction in the signal region SR for the mass ofthe leading (left) and subleading (right) large- R jets in the all-jet channel. The tt and multijetproduction are normalized according to the fitted values of the respective yields and are dis-played as stacked histograms. The data points are shown with filled circles, while the shadedband represents the statistical uncertainty in the simulation. The lower panel shows the datadivided by the sum of the predictions. R yield ≡ N SRmultijet / N SR A multijet = N CRmultijet / N CR A multijet = ± R yield includesthe statistical uncertainty of the data and the systematic uncertainty of the method as obtainedwith simulated events. (cid:96) +jets channel In the (cid:96) +jets channel, the tt signal strength, the scale factor for the t tagging efficiency, and thebackground normalizations are extracted through a simultaneous binned maximum-likelihoodfit to the data across the different analysis categories. The 0t, 1t0b, and 1t1b categories are fittedsimultaneously, normalizing each background component to the same cross section in all cat-egories. The resulting fit is expressed in terms of a multiplicative factor, the signal strength r ,applied to the input tt cross section. Different variables are used to discriminate the tt sig-nal from the background processes. The small- R jet η distribution is used in the 0t and 1t0bcategories, while the large- R jet m SD distribution is used in the 1t1b region. These distribu-tions were chosen to have good discrimination between tt, W+jets, and multijet production, astt events tend to be produced more centrally than the background, and the m SD distributionpeaks near the top quark mass. The tt signal and tt background contributions merge into asingle distribution in the fit, essentially constraining the leptonic branching fraction to equalthat provided in the simulation.Background normalizations and experimental sources of systematic uncertainty are treated asnuisance parameters in the fit. The uncertainties from reweighting pileup, lepton scale factors,JES, JER, and b and t tagging efficiencies are treated as uncertainties in the input distributions.Two separate nuisance parameters are used to describe the t tagging uncertainty: one for thet tagging scale factor applied to the tt and single top quark (tW) events, where we expect thet-tagged jet to correspond to a genuine top quark, while the t misidentification scale factoris applied to the remaining background. The uncertainties in the integrated luminosity andbackground normalizations are treated as uncertainties in the production cross sections of thebackgrounds. The event categories in the fit are designed such that the t tagging efficiencyis constrained by the relative population of events in the three categories. The different ad- .2 (cid:96) +jets channel mixtures of signal and background between the event categories provide constraints on thebackground normalizations. The measurement of the signal strength is correlated with vari-ous nuisance parameters, with the strongest correlation being with the t tagging efficiency, asexpected. To determine the uncertainties in distributions, the nuisance parameter is used tointerpolate between the nominal distribution and distributions corresponding to ± µ +jets channels, with most nuisance parameters constrainedto be identical in both channels. The nuisance parameters associated with the electron andmuon scale factors are treated separately, as are the electron and muon multijet normalizations.The event yields that account for all posterior parameters are given in Table 3. The posteriorkinematic distributions for the three event categories are shown in Fig. 6.Table 3: Posterior signal and background event yields in the 0t, 1t0b, and 1t1b categories,together with the observed yields in data. The uncertainties include all posterior experimentalcontributions. Process Number of events (e+jets channel)0t 1t0b 1t1btt 10710 ±
940 2840 ±
120 2670 ± ±
400 191 ±
47 107 ± ± ±
190 62 ± ±
300 118 ±
37 17 ± ±
110 22 ± ± ±
740 242 ±
80 31 ± ± ±
250 2889 ± µ +jets channel)0t 1t0b 1t1btt 16800 ± ±
170 3905 ± ±
590 282 ±
68 153 ± ± ±
320 105 ± ±
680 234 ±
69 19 ± ±
160 31 ±
10 2 ± ± ±
76 43 ± ± ±
380 4228 ± p T and y distributions for the t jet candidate in each of the three event cate-gories for the combined (cid:96) +jets channel. All distributions use the posterior t tagging scale factorsand background normalizations, but not the posterior values of other nuisance parameters. Theposterior t tagging efficiency and misidentification scale factors are 1.04 ± ± p T - and η -dependent uncertainty in the ranges of 1–8 and 1–13%. The fittedbackground normalizations are generally in good agreement with their corresponding pre-fitvalues.The posterior signal strength determined in the fit is 0.81 ± E v en t s / . DatattSingle tW+jets Z+jetsDibosonMultijetMC stat. unc.
CMS (13 TeV) -1 e+jets0t - - - - - h Small-R jet D a t a / p r ed . E v en t s / . DatattSingle tW+jets Z+jetsDibosonMultijetMC stat. unc.
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100 120 140 160 180 200 220 (GeV) SD t jet m D a t a / p r ed . Figure 6: Posterior kinematic distributions in the maximum-likelihood fit. Different eventcategories and variables are fitted: η distribution for small- R jets in 0t events (upper row), η distribution of the b jet candidate in 1t0b events (middle row), and m SD of the t jet candidate in1t1b events (lower row), in the e+jets (left column) and µ +jets (right column) channels. The datapoints are indicated by filled circles, while the signal and background predictions are shownas stacked histograms. The lower panels show data divided by the sum of the predictions andtheir systematic uncertainties as obtained from the fit (shaded band). .2 (cid:96) +jets channel E v en t s / G e V Data signaltt otherttSingle tW+jetsZ+jetsDibosonMultijetMC stat. unc. l+jets0t
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CMS (13 TeV) -1 - - - - - Large-R jet y D a t a / p r ed . E v en t s / G e V Data signaltt otherttSingle tW+jetsZ+jetsDibosonMultijetMC stat. unc. l+jets1t0b
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CMS (13 TeV) -1 - - - - - t jet y D a t a / p r ed . Figure 7: Distributions of the p T (left column) and y (right column) of the t jet candidate forthe 0t (upper row), 1t0b (middle row), and 1t1b (lower row) events in the combined (cid:96) +jetschannel that use the posterior t tag scale factors and background normalizations. The datapoints are given by the filled circles, while the signal and background predictions are shownas stacked histograms. The lower panels show data divided by the sum of the predictions andtheir systematic uncertainties as obtained from the fit (shaded band). served to overestimate the data by roughly 25% in the region of the fiducial phase space. Themeasured signal strength extrapolated from the fit serves as an indicator of the level of agree-ment between the measured integrated tt cross section and the prediction from simulation. The systematic uncertainties originate from both experimental and theoretical sources. Theformer include all those related to differences in performance in particle reconstruction andidentification between data and simulation, as well as in the modeling of background. Thelatter are related to the MC simulation of the tt signal process and affect primarily the unfoldedresults through the acceptance, efficiency, and migration matrices. Each systematic variationproduces a change in the measured differential cross section and that difference relative to thenominal result defines the effect of this variation on the measurement.The dominant experimental sources of the systematic uncertainty in the all-jet channel are theJES and the subjet b tagging efficiency. In the (cid:96) +jets channel, the efficiencies in t and b tag-ging provide the largest contributions to the uncertainties. The different sources are describedbelow:i.
Multijet background (all-jet):
The fitted multijet yield as well as the uncertainty in R yield in Eq. (3) impact the distributed uncertainties. These are estimated to be about 1% froma comparison of the distribution in each variable of the SR with its CR (as described inSection 5) in simulated events, as well as for different pileup profiles in data collected withthe control trigger relative to the signal trigger. The uncertainty in R yield is dominated bythe assumption of the extraction method (estimated through simulated events), while thestatistical contribution is smaller.ii. Subdominant backgrounds (all-jet):
The expected yield from the subdominant backgroundsestimated from simulation (single top quark production and vector bosons produced inassociation with jets) is changed by ± < Background estimate ( (cid:96) +jets):
An a priori uncertainty of 30% is applied to the single topquark and W+jets background normalizations. An additional uncertainty in flavor com-position of the W+jets process is estimated by changing the light- and heavy-flavor com-ponents independently by their 30% normalization uncertainties. For the multijet nor-malization, an a priori uncertainty of 50% is used to reflect the combined uncertainty inthe normalization and the extraction of the kinematic contributions from the sideband re-gion in data. These background sources and the corresponding systematic uncertaintiesare all constrained in the maximum likelihood fit.iv.
JES:
The uncertainty in the energy scale of each reconstructed large- R jet is a leading ex-perimental contribution in the all-jet channel. It is divided into 24 independent sources [50]and each change is used to provide a new jet collection that affects the repeated event in-terpretation. This results not only in changes in the p T scale, but can also lead to differentt jet candidates. The p T - and η -dependent JES uncertainty is about 1–2% per jet. The re-sulting uncertainty in the measured cross section is typically about 10% but can be muchlarger at high top quark p T . For the (cid:96) +jets channel, the uncertainty in JES is estimatedfor both small- R and large- R jets by shifting the jet energy in simulation up or down bytheir p T - and η -dependent uncertainties, with a resulting impact on the differential crosssection of 1–10%. v. JER:
The impact on the JER is determined by smearing the jets according to the JER un-certainty [50]. The effect on the cross section is relatively small, at the level of 2%.vi. t tagging efficiency ( (cid:96) +jets):
The t tagging efficiency and its associated uncertainty are ex-tracted simultaneously with the signal strength and background normalizations in thelikelihood fit of the (cid:96) +jets analysis, discussed in Section 7. The uncertainty in the t tag-ging efficiency is in the range 6–10%, while for the misidentification rate it is 8–15%,depending on the p T and η of the t jet.vii. Subjet b tagging efficiency (all-jet):
The uncertainty in the identification of b subjets withinthe large- R jets (estimated in Ref. [61]) is the leading experimental uncertainty in the all-jet channel. The effect on the cross sections is about 10%, relatively independent of theobservables. Unlike the uncertainty associated with JES, the b-subjet tagging uncertaintylargely cancels in the normalized cross sections.viii. b tagging efficiency ( (cid:96) +jets): For the (cid:96) +jets channel, the small- R jet b tagging efficiency inthe simulation is corrected to match that measured in data using p T - and η -dependentscale factors [61]. The resulting uncertainty in the differential cross sections is about 1–2%. The b tagging efficiency and non-b jet misidentification uncertainties are treated asfully correlated.ix. Pileup:
The uncertainty related to the modeling of additional pileup interactions is sub-dominant. The impact on the measurement is estimated by changing the total inelasticcross section used to reweight the simulated events by ± < Trigger (all-jet):
The uncertainty associated with the trigger, accounting for the differencebetween the simulated and observed trigger efficiency, is well below 1% in the phasespace of the all-jet channel. The measurement of the trigger efficiency is performed inevents collected with an orthogonal trigger that requires the presence of an isolated muonwith p T greater than 27 GeV.xi. Lepton identification and trigger ( (cid:96) +jets):
The performance of the lepton identification, re-construction, trigger, and isolation constitutes a small source of systematic uncertainty.Correction factors used to modify the simulation to match the efficiencies observed indata are estimated through a tag-and-probe method using Z → (cid:96)(cid:96) decays. The corre-sponding uncertainty is determined by changing the correction factors up or down bytheir uncertainties. The resulting systematic uncertainties depend on lepton p T and η ,and are in the range 1–7 (1–5)% for electrons (muons).xii. Integrated luminosity:
The uncertainty in the measurement of the integrated luminosity is2.5% [65].The theoretical uncertainties are divided into two sub-categories: sources of systematic un-certainty related to the matrix element calculations of the hard scattering process and sourcesrelated to the modeling of the parton shower and the underlying event. The first category(consisting of the first three sources below) is evaluated using variations of the simulated eventweights, while the second category is evaluated with dedicated, alternative MC samples withmodified parameters. These sources are:i.
Parton distribution functions:
The uncertainty from PDFs is estimated by applying eventweights corresponding to the 100 replicas of the NNPDF PDFs [35]. For each observablewe compute its standard deviation from the 100 variants. ii. QCD renormalization and factorization scales:
This source of systematic uncertainty is es-timated by applying event weights corresponding to different renormalization and fac-torization scale options. Both scales are changed independently by a factor of two up ordown in the event generation, omitting the two cases where the scales are changed inopposite directions, and taking the envelope of the six results.iii.
Strong coupling ( α S ): The uncertainty associated with α S is estimated by applying eventweights corresponding to higher or lower values of α S for the matrix element using thechanged NNPDF PDFs [35] values of α S = ISR and FSR:
The uncertainty in the ISR and FSR is estimated from alternative MC sam-ples with reduced or increased values of α S used in PYTHIA to generate that radiation.The scale in the ISR is changed by factors of 2 and 0.5, and the scale in the FSR by factorsof √ √ B , using the NN output that is sensitive to the modeling of FSR. This leadsto a reduced uncertainty that is 0.3 times the variations from the alternative MC samples.v. Matching of the matrix element to the parton shower : In the
POWHEG matching of the matrixelement to the parton shower (ME-PS), the resummed gluon damping factor h damp is usedto regulate high- p T radiation. The nominal value is h damp = m t . Uncertainties in h damp are parameterized by considering alternative simulated samples with h damp = m t and h damp = m t [37].vi. Underlying event tune:
This uncertainty is estimated from alternative MC samples usingthe CUETP8M2T4 parameters varied by ± Here, we discuss the differential tt production cross sections measured in the all-jet and (cid:96) +jetschannels as a function of different kinematic variables of the top quark or tt system, corrected tothe particle and parton levels using an unfolding procedure. The measurements are comparedto predictions from different MC event generators.
The parton-level phase space to which the measurement is unfolded is constrained by the kine-matic requirements of the detector-level fiducial region. Namely, in the all-jet decay channel,the t and t must have p T >
400 GeV and | η | < m tt >
800 GeV is required toavoid extreme events with large top quark p T and small m tt .The parton-level definition for the (cid:96) +jets channel differs in that it is defined for (cid:96) +jets events,where one top quark decays according to t → Wb → qq (cid:48) b and has p T >
400 GeV to match thefiducial requirement at the detector level, and the other top quark decays as t → Wb → (cid:96) ν bwithout any p T requirement.The so-called particle level represents the state of quasi-stable particles with a mean lifetimegreater than 30 ps originating from the pp collision after hadronization but before the interac-tion of these particles in the detector. The observables computed from the momenta of particlesare typically better defined than those computed from parton-level information. Also, the asso-ciated phase space is closer to the fiducial phase space of the measurement at the detector level,which provides smaller theoretical uncertainties. In the context of this analysis, particle jets are .2 Unfolding reconstructed from quasi-stable particles, excluding neutrinos, using the anti- k T algorithm at adistance parameter of 0.8—identical to reconstruction at detector level—and just the particlesoriginating from the primary interaction. Subsequently, jets that are geometrically matchedto generated leptons within ∆ R < η - φ (i.e., from the leptonic decays of W bosons) areremoved from the particle jet collection.For the all-jet channel, the two particle jets with highest p T are considered the particle-level tjet candidates. To match the fiducial phase space as closely as possible, the same kinematicselection criteria are applied as for the detector-level events. In particular, the particle-level jetsmust have p T >
400 GeV and | η | < (cid:96) +jets channel is set up to mimic the kinematic selectionsat the detector level. Particle-level large- R jets are selected if they fulfill p T >
400 GeV, | η | < R jets are selected if they have p T >
50 GeV, | η | < p T >
50 GeV and | η | < f , where f is the fraction of reconstructed events that pass the selec-tion at the unfolded level (parton or particle) in the same observable range, and f is the fractionof generated events at the unfolded level that are selected at the reconstruction level. Figure 8presents these fractions at the parton and particle levels for the all-jet channel, as a function ofthe leading top quark p T and | y | . The fraction f is a function of the leading reconstructed topquark and the f is a function of the leading top quark at parton or particle level. The distri-bution of f vs. p T shows a characteristic threshold behavior due to the resolution in p T , while f is independent of | y | . The f value decreases with p T , primarily due to the inefficiency ofsubjet b tagging and the NN output dependence on the p T (at high jet p T it is more difficult todifferentiate between ordinary jets and highly boosted top quarks). Also, f decreases at high | y | values due to the increased inefficiency in b tagging at the edges of the CMS tracker. We extract the differential cross sections by applying an unfolding procedure, which is neces-sary due to the finite resolution of the detector. The unfolded cross sections are evaluated asfollows d σ unf i d x = L ∆ x i f i ∑ j (cid:16) R − ij f j S j (cid:17) , (4)where L is the total integrated luminosity and ∆ x i is the width of the i -th bin of the observable x . The quantity R − ij is the inverse of the migration matrix between the i - and j -th bins, and S j is the signal yield in the j -th bin computed from Eq. (3). The binning of the various observablesis chosen such that the purity (fraction of reconstructed events for which the true value of theobservable lies in the same bin) and the stability (fraction of true events where the reconstructedobservable lies in the same bin) are well above 50% for most of the bins. This choice results inmigration matrices with suppressed nondiagonal elements, shown for the all-jet channel inFig. 9 and for the (cid:96) +jets channel in Fig. 10. To minimize biases introduced by the various0
50 GeV and | η | < f , where f is the fraction of reconstructed events that pass the selec-tion at the unfolded level (parton or particle) in the same observable range, and f is the fractionof generated events at the unfolded level that are selected at the reconstruction level. Figure 8presents these fractions at the parton and particle levels for the all-jet channel, as a function ofthe leading top quark p T and | y | . The fraction f is a function of the leading reconstructed topquark and the f is a function of the leading top quark at parton or particle level. The distri-bution of f vs. p T shows a characteristic threshold behavior due to the resolution in p T , while f is independent of | y | . The f value decreases with p T , primarily due to the inefficiency ofsubjet b tagging and the NN output dependence on the p T (at high jet p T it is more difficult todifferentiate between ordinary jets and highly boosted top quarks). Also, f decreases at high | y | values due to the increased inefficiency in b tagging at the edges of the CMS tracker. We extract the differential cross sections by applying an unfolding procedure, which is neces-sary due to the finite resolution of the detector. The unfolded cross sections are evaluated asfollows d σ unf i d x = L ∆ x i f i ∑ j (cid:16) R − ij f j S j (cid:17) , (4)where L is the total integrated luminosity and ∆ x i is the width of the i -th bin of the observable x . The quantity R − ij is the inverse of the migration matrix between the i - and j -th bins, and S j is the signal yield in the j -th bin computed from Eq. (3). The binning of the various observablesis chosen such that the purity (fraction of reconstructed events for which the true value of theobservable lies in the same bin) and the stability (fraction of true events where the reconstructedobservable lies in the same bin) are well above 50% for most of the bins. This choice results inmigration matrices with suppressed nondiagonal elements, shown for the all-jet channel inFig. 9 and for the (cid:96) +jets channel in Fig. 10. To minimize biases introduced by the various0 (GeV) t,1T p
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Figure 8: Simulated fractions f and f for the parton-level (upper row) and particle-level(lower row) selection in the all-jet channel as a function of the leading top quark p T (left col-umn) and | y | (right column). The fraction f is a function of the leading reconstructed top quarkand the f is a function of the leading top quark at parton or particle level. .3 All-jet channel unfolding methods utilizing regularization, we use migration-matrix inversion, as written inEq. (4) and implemented in the TU NFOLD framework [67], for the price of a moderate increasein statistical uncertainty. (GeV) t,1T
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Figure 9: Migration matrices determined from simulation for the leading top quark p T (upperrow) and m tt (lower row) at the parton level (left) and particle level (right) in the all-jet channel.Each column is normalized to unity. For the all-jet channel, the measurement of the unfolded differential cross section in bin j ofthe variable x is performed using Eq. (4). To estimate the uncertainty in the measurement,the entire procedure of the signal extraction, unfolding with different response matrices, andextrapolation to the particle- or parton-level phase space is repeated for every source of un-certainty discussed in Section 8. The unfolded cross sections at the particle (parton) level areshown in Figs. 11–13 (14–16). Figures 17 and 18 show a summary of the statistical and thedominant systematic uncertainties in the differential cross section, as a function of the leadingtop quark p T and | y | at the particle and parton levels, respectively. (cid:96) +jets channel In the (cid:96) +jets channel, the differential tt cross section is measured as a function of the p T and | y | of the top quark that decays according to t → Wb → qq (cid:48) b. The measurement at the particlelevel defines a region of phase space that mimics the event selection criteria as detailed inSection 9.1, but at the parton level corresponds to the phase space where the non-leptonicallydecaying top quark has p T >
400 GeV. The (cid:96) +jets tt events are selected at the parton level, andthe properties of the non-leptonically decaying top quarks are defined to represent the true topquark p T values.
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Figure 10: Migration matrices determined from simulation for top quark p T (upper row) andrapidity (lower row) at the parton level (left) and particle level (right) in the (cid:96) +jets channel.Each column is normalized to unity. .4 (cid:96) +jets channel ( pb / G e V ) t, T / dp s d - - - - - Particle levelDataTotal unc.Powheg+Pythia8aMC@NLO+Pythia8Powheg+Herwig++
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Figure 11: Differential cross section unfolded to the particle level, absolute (left) and normal-ized (right), as a function of the leading (upper row) and subleading (lower row) top quark p T in the all-jet channel. The lower panel shows the ratio (MC/data) −
1. The vertical bars on thedata and in the ratio represent the statistical uncertainty in data, while the shaded band showsthe total statistical and systematic uncertainty added in quadrature. | ( pb ) t, / d | y s d Particle levelDataTotal unc.Powheg+Pythia8aMC@NLO+Pythia8Powheg+Herwig++
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Figure 12: Differential cross section unfolded to the particle level, absolute (left) and normal-ized (right), as a function of the leading (upper row) and subleading (lower row) top quark | y | in the all-jet channel. The lower panel shows the ratio (MC/data) −
1. The vertical bars on thedata and in the ratio represent the statistical uncertainty in data, while the shaded band showsthe total statistical and systematic uncertainty added in quadrature. .4 (cid:96) +jets channel ( pb / G e V ) tt / d m s d - - - - - Particle levelDataTotal unc.Powheg+Pythia8aMC@NLO+Pythia8Powheg+Herwig++
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All-jet channel (GeV) tt m ( M C / da t a )- - (13 TeV) -1 CMS ( pb / G e V ) tt T / dp s d - - - - - -
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All-jet channel (GeV) ttT p ( M C / da t a )- - - (13 TeV) -1 CMS ( pb ) tt / d y s d - -
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All-jet channel tt y - - - - ( M C / da t a )- - (13 TeV) -1 CMS
Figure 13: Differential cross section unfolded to the particle level, absolute (left) and normal-ized (right), as a function of m tt (upper row), p ttT (middle row), and y tt (lower row) in the all-jetchannel. The lower panel shows the ratio (MC/data) −
1. The vertical bars on the data and inthe ratio represent the statistical uncertainty in data, while the shaded band shows the totalstatistical and systematic uncertainty added in quadrature. ( pb / G e V ) t, T / dp s d - - - - - Parton levelDataTotal unc.Powheg+Pythia8aMC@NLO+Pythia8Powheg+Herwig++
All-jet channel (GeV) t,1T p
400 600 800 1000 1200 1400 ( M C / da t a )- - - (13 TeV) -1 CMS ) - ( G e V t, T / dp s ) d s ( / - - - - - - Parton levelDataTotal unc.Powheg+Pythia8aMC@NLO+Pythia8Powheg+Herwig++
All-jet channel (GeV) t,1T p
400 600 800 1000 1200 1400 ( M C / da t a )- - (13 TeV) -1 CMS ( pb / G e V ) t, T / dp s d - - - - - Parton levelDataTotal unc.Powheg+Pythia8aMC@NLO+Pythia8Powheg+Herwig++
All-jet channel (GeV) t,2T p
400 600 800 1000 1200 1400 ( M C / da t a )- - (13 TeV) -1 CMS ) - ( G e V t, T / dp s ) d s ( / - - - - - Parton levelDataTotal unc.Powheg+Pythia8aMC@NLO+Pythia8Powheg+Herwig++
All-jet channel (GeV) t,2T p
400 600 800 1000 1200 1400 ( M C / da t a )- - - (13 TeV) -1 CMS
Figure 14: Differential cross section unfolded to the parton level, absolute (left) and normalized(right), as a function of the leading (upper row) and subleading (lower row) top quark p T in theall-jet channel. The lower panel shows the ratio (MC/data) −
1. The vertical bars on the dataand in the ratio represent the statistical uncertainty in data, while the shaded band shows thetotal statistical and systematic uncertainty added in quadrature. .4 (cid:96) +jets channel | ( pb ) t, / d | y s d Parton levelDataTotal unc.Powheg+Pythia8aMC@NLO+Pythia8Powheg+Herwig++
All-jet channel | t,1 |y ( M C / da t a )- - - (13 TeV) -1 CMS | t, / d | y s ) d s ( / Parton levelDataTotal unc.Powheg+Pythia8aMC@NLO+Pythia8Powheg+Herwig++
All-jet channel | t,1 |y ( M C / da t a )- - - (13 TeV) -1 CMS | ( pb ) t, / d | y s d Parton levelDataTotal unc.Powheg+Pythia8aMC@NLO+Pythia8Powheg+Herwig++
All-jet channel | t,2 |y ( M C / da t a )- - - (13 TeV) -1 CMS | t, / d | y s ) d s ( / Parton levelDataTotal unc.Powheg+Pythia8aMC@NLO+Pythia8Powheg+Herwig++
All-jet channel | t,2 |y ( M C / da t a )- - - (13 TeV) -1 CMS
Figure 15: Differential cross section unfolded to the parton level, absolute (left) and normalized(right), as a function of the leading (upper row) and subleading (lower row) top quark | y | in theall-jet channel. The lower panel shows the ratio (MC/data) −
1. The vertical bars on the dataand in the ratio represent the statistical uncertainty in data, while the shaded band shows thetotal statistical and systematic uncertainty added in quadrature. ( pb / G e V ) tt / d m s d - - - - - Parton levelDataTotal unc.Powheg+Pythia8aMC@NLO+Pythia8Powheg+Herwig++
All-jet channel (GeV) tt m ( M C / da t a )- - - (13 TeV) -1 CMS ) - ( G e V tt / d m s ) d s ( / - - - - - Parton levelDataTotal unc.Powheg+Pythia8aMC@NLO+Pythia8Powheg+Herwig++
All-jet channel (GeV) tt m ( M C / da t a )- - - (13 TeV) -1 CMS ( pb / G e V ) tt T / dp s d - - - - -
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All-jet channel (GeV) ttT p ( M C / da t a )- - (13 TeV) -1 CMS ) - ( G e V tt T / dp s ) d s ( / - - - - - Parton levelDataTotal unc.Powheg+Pythia8aMC@NLO+Pythia8Powheg+Herwig++
All-jet channel (GeV) ttT p ( M C / da t a )- - - (13 TeV) -1 CMS ( pb ) tt / d y s d -
10 110 Parton levelDataTotal unc.Powheg+Pythia8aMC@NLO+Pythia8Powheg+Herwig++
All-jet channel tt y - - - - ( M C / da t a )- - (13 TeV) -1 CMS tt / d y s ) d s ( / - -
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All-jet channel tt y - - - - ( M C / da t a )- - (13 TeV) -1 CMS
Figure 16: Differential cross section unfolded to the parton level, absolute (left) and normalized(right), as a function of m tt (upper row), p ttT (middle row), and y tt (lower row) in the all-jetchannel. The lower panel shows the ratio (MC/data) −
1. The vertical bars on the data and inthe ratio represent the statistical uncertainty in data, while the shaded band shows the totalstatistical and systematic uncertainty added in quadrature. .4 (cid:96) +jets channel (GeV) t,1T p
400 600 800 1000 1200 1400 R e l a t i v e un c e r t a i n t y ( % ) Particle level (all-jet channel)Stat. uncertaintyJES+JER+pileupFlavor taggingParton showerHard scattering Absolute cross section (13 TeV) -1 CMS (GeV) t,1T p
400 600 800 1000 1200 1400 R e l a t i v e un c e r t a i n t y ( % ) Particle level (all-jet channel)Stat. uncertaintyJES+JER+pileupFlavor taggingParton showerHard scattering Normalized cross section (13 TeV) -1 CMS | t,1 |y R e l a t i v e un c e r t a i n t y ( % ) Particle level (all-jet channel)Stat. uncertaintyJES+JER+pileupFlavor taggingParton showerHard scattering Absolute cross section (13 TeV) -1 CMS | t,1 |y R e l a t i v e un c e r t a i n t y ( % ) Particle level (all-jet channel)Stat. uncertaintyJES+JER+pileupFlavor taggingParton showerHard scattering Normalized cross section (13 TeV) -1 CMS
Figure 17: Breakdown of the uncertainties in the absolute (left column) and normalized (rightcolumn) measurement at the particle level, as a function of the leading top quark p T (upperrow) and | y | (lower row) in the all-jet channel. The shaded band shows the statistical uncer-tainty, while the solid lines show the systematic uncertainties grouped in four categories: a)uncertainty due to pileup and the JES and JER of the large- R jets, b) uncertainty due to fla-vor tagging of the subjets, c) uncertainty due to the modeling of the parton shower, and d)uncertainty due to the modeling of the hard scattering.0
Figure 17: Breakdown of the uncertainties in the absolute (left column) and normalized (rightcolumn) measurement at the particle level, as a function of the leading top quark p T (upperrow) and | y | (lower row) in the all-jet channel. The shaded band shows the statistical uncer-tainty, while the solid lines show the systematic uncertainties grouped in four categories: a)uncertainty due to pileup and the JES and JER of the large- R jets, b) uncertainty due to fla-vor tagging of the subjets, c) uncertainty due to the modeling of the parton shower, and d)uncertainty due to the modeling of the hard scattering.0 (GeV) t,1T p
400 600 800 1000 1200 1400 R e l a t i v e un c e r t a i n t y ( % ) Parton level (all-jet channel)Stat. uncertaintyJES+JER+pileupFlavor taggingParton showerHard scattering Absolute cross section (13 TeV) -1 CMS (GeV) t,1T p
400 600 800 1000 1200 1400 R e l a t i v e un c e r t a i n t y ( % ) Parton level (all-jet channel)Stat. uncertaintyJES+JER+pileupFlavor taggingParton showerHard scattering Normalized cross section (13 TeV) -1 CMS | t,1 |y R e l a t i v e un c e r t a i n t y ( % ) Parton level (all-jet channel)Stat. uncertaintyJES+JER+pileupFlavor taggingParton showerHard scattering Absolute cross section (13 TeV) -1 CMS | t,1 |y R e l a t i v e un c e r t a i n t y ( % ) Parton level (all-jet channel)Stat. uncertaintyJES+JER+pileupFlavor taggingParton showerHard scattering Normalized cross section (13 TeV) -1 CMS
Figure 18: Breakdown of the uncertainties in the absolute (left column) and normalized (rightcolumn) measurement at the parton level, as a function of the leading top quark p T (upper row)and | y | (lower row) in the all-jet channel. The shaded band shows the statistical uncertainty,while the solid lines show the systematic uncertainties grouped in four categories: a) uncer-tainty due to pileup and the JES and JER of the large- R jets, b) uncertainty due to flavor taggingof the subjets, c) uncertainty due to the modeling of the parton shower, and d) uncertainty dueto the modeling of the hard scattering. .5 Discussion The differential cross section is extracted from the signal-dominated 1t1b category. The distri-bution in the measured signal is determined by subtracting the estimated background contri-butions from the distribution in data, using the posterior normalizations from the fit given inTable 3. To account for reconstruction efficiencies and bin migrations in signal, we use unreg-ularized unfolding, as implemented in the TU
NFOLD framework [67], which was found to beoptimal for the measurement. The unfolding relies on response matrices that map the p T and | y | distributions for the t-tagged jet to corresponding properties for either the particle-level t jetcandidate or the parton-level top quark.Systematic uncertainties in the unfolded measurement receive contributions from the experi-mental and theoretical sources discussed in Section 8. The posterior values from the likelihoodfit are used for the t tagging efficiency, background normalizations, and lepton efficiencies,while the a priori values are used for the remaining uncertainties. For each systematic changethat affects the distribution in p T or | y | , we define a separate response matrix that is used tounfold the data. The resulting uncertainties are added in quadrature to obtain the total uncer-tainty in the unfolded distribution.The data in the electron and muon channels are combined before the unfolding by adding themeasured distributions and their response matrices into a single channel. The background con-tributions are also merged before subtracting these from the measured distributions, with theexception of the electron and muon multijet backgrounds that are treated as separate sources.The unfolded cross sections for top quarks are shown in Figs. 19–20 as a function of p T and | y | for the particle and parton levels, respectively, and compared to results from POWHEG inter-faced with
PYTHIA or HERWIG ++ and from M AD G RAPH MC @ NLO interfaced with
PYTHIA .The breakdown of sources of systematic uncertainty are given in Figs. 21 and 22. The cross sec-tion at the parton level as a function of the p T of the top quark that decays as t → Wb → qq (cid:48) bpresented in this paper can also be compared to the corresponding measurement from CMSin the resolved final state [19]. The two measurements are observed to be in agreement in theregion of phase space where they overlap. The unfolded cross sections at the particle and parton levels reveal some important features.Theory predictions of the integrated cross sections are 56 and 25% higher than our measure-ment for the all-jet and (cid:96) +jets channels, respectively, which agrees with previous results [20]. Itshould be noted that the two channels probe different phase spaces of the tt production, dueto the kinematic requirement on the subleading top quark in the all-jet channel, and thereforethe integrated cross sections are not expected to be the same. That is, the phase space probedin the all-jet channel requires two top quarks with p T above 400 GeV, while the (cid:96) +jets channelphase space only requires one such high- p T top quark. In terms of the normalized differentialdistributions, there is agreement between the data and theory within the uncertainties of themeasurement and some qualitative observations can be made by comparing the central valuesof the data and theory. There is good agreement for the leading top quark (all-jet channel)and the p T of the top quark that decays as t → Wb → qq (cid:48) b ( (cid:96) +jets channel), while the crosssection as a function of the p T of the subleading top quark in the all-jet channel appears to besofter in data than for the POWHEG predictions, with M AD G RAPH MC @ NLO providing thebest description. The distributions in y are well described by theory in both channels, with asmall deviation for the subleading top quark that is related to the difference in the p T spectrum.Finally, the measured distributions for the tt system are mostly in agreement with theory, witha possible deviation in the m tt variable, where POWHEG tends to produce a harder spectrum, - - - ( pb / G e V ) t T / dp s d Particle levelDataTotal unc.Powheg+Pythia8aMC@NLO+Pythia8Powheg+Herwig++ l+jets channel
400 500 600 700 800 900 1000 1100 1200 (GeV) tT p - - ( M C / da t a )- (13 TeV) -1 CMS - - - - ) - ( G e V t T / dp s d s / Particle levelDataTotal unc.Powheg+Pythia8aMC@NLO+Pythia8Powheg+Herwig++ l+jets channel
400 500 600 700 800 900 1000 1100 1200 (GeV) tT p - - ( M C / da t a )- (13 TeV) -1 CMS ( pb ) t / d y s d Particle levelDataTotal unc.Powheg+Pythia8aMC@NLO+Pythia8Powheg+Herwig++ l+jets channel | t |y - ( M C / da t a )- (13 TeV) -1 CMS t / d y s d s / Particle levelDataTotal unc.Powheg+Pythia8aMC@NLO+Pythia8Powheg+Herwig++ l+jets channel | t |y - ( M C / da t a )- (13 TeV) -1 CMS
Figure 19: Differential cross section measurements at the particle level, as a function of theparticle-level t jet p T (upper row) and | y | (lower row) for the (cid:96) +jets channel. Both absolute (leftcolumn) and normalized (right column) cross sections are shown. The lower panel shows theratio (MC/data) −
1. The vertical bars on the data and in the ratio represent the statistical un-certainty in data, while the shaded band shows the total statistical and systematic uncertaintyadded in quadrature. - - - - - ( pb / G e V ) t T / dp s d Parton levelDataTotal unc.Powheg+Pythia8aMC@NLO+Pythia8Powheg+Herwig++ l+jets channel
400 500 600 700 800 900 1000 1100 1200 (GeV) tT p - - ( M C / da t a )- (13 TeV) -1 CMS - - - - ) - ( G e V t T / dp s d s / Parton levelDataTotal unc.Powheg+Pythia8aMC@NLO+Pythia8Powheg+Herwig++ l+jets channel
400 500 600 700 800 900 1000 1100 1200 (GeV) tT p - - ( M C / da t a )- (13 TeV) -1 CMS ( pb ) t / d y s d Parton levelDataTotal unc.Powheg+Pythia8aMC@NLO+Pythia8Powheg+Herwig++ l+jets channel | t |y - ( M C / da t a )- (13 TeV) -1 CMS t / d y s d s / Parton levelDataTotal unc.Powheg+Pythia8aMC@NLO+Pythia8Powheg+Herwig++ l+jets channel | t |y - ( M C / da t a )- (13 TeV) -1 CMS
Figure 20: Differential cross section measurements at the parton level, as a function of theparton-level top quark p T (upper row) and | y | (lower row) for the (cid:96) +jets channel. Both abso-lute (left column) and normalized (right column) cross sections are shown. The lower panelshows the ratio (MC/data) −
1. The vertical bars on the data and in the ratio represent the sta-tistical uncertainty in data, while the shaded band shows the total statistical and systematicuncertainty added in quadrature.while M AD G RAPH MC @ NLO is fully consistent with the data. Regarding systematic uncer-tainties, it should be noted that they are in general larger for the all-jet channel because the twoleading experimental sources in JES and b tagging enter twice (two large- R jets). In contrast, theuncertainty in parton showering is smaller for the all-jet channel because its main contribution(FSR) is constrained through a dedicated analysis, as discussed in Section 8.
10 Summary
A measurement was presented of the top quark pair (tt) cross section for top quarks with hightransverse momentum ( p T ) produced in pp collisions at 13 TeV. The measurement uses eventsin which either one or both top quarks decay to jets, and where the decay products cannot beresolved but are instead clustered in a single large-radius ( R ) jet with p T >
400 GeV. The all-jetfinal state contains two such large- R jets, while the lepton+jets final state is identified throughthe presence of an electron or muon, a b-tagged jet, missing transverse momentum from theescaping neutrino, and a single t-tagged, large- R jet. The measurement utilizes a larger data set
400 500 600 700 800 900 1000 1100 1200 (GeV) tT p0102030405060708090100 R e l a t i v e un c e r t a i n t y ( % ) Stat. uncertaintyJES+JER+b taggingt taggingOther experimentalParton showerHard scattering
CMS (13 TeV) -1 Particle level (l+jets channel)Absolute cross section
400 500 600 700 800 900 1000 1100 1200 (GeV) tT p0102030405060708090100 R e l a t i v e un c e r t a i n t y ( % ) Stat. uncertaintyJES+JER+b taggingt taggingOther experimentalParton showerHard scattering
CMS (13 TeV) -1 Particle level (l+jets channel)Normalized cross section t |y0102030405060 R e l a t i v e un c e r t a i n t y ( % ) Stat. uncertaintyJES+JER+b taggingt taggingOther experimentalParton showerHard scattering
CMS (13 TeV) -1 Particle level (l+jets channel)Absolute cross section t |y0102030405060 R e l a t i v e un c e r t a i n t y ( % ) Stat. uncertaintyJES+JER+b taggingt taggingOther experimentalParton showerHard scattering
CMS (13 TeV) -1 Particle level (l+jets channel)Normalized cross section
Figure 21: Breakdown of the sources of systematic uncertainty affecting the differential crosssection measurements in the (cid:96) +jets channel at the particle level as a function of the particle-levelt jet p T (upper row) or | y | (lower row). Both the systematic uncertainties in the absolute (leftcolumn) and the normalized (right column) cross sections are shown. ”JES+JER+b tagging”includes uncertainties due to the JES, JER, and small- R jet b tagging efficiency; ”t tagging”is the uncertainty associated with the large- R jet t tagging efficiency; ”Other experimental”includes the uncertainties originating from the background estimate, pileup modeling, leptonidentification and trigger efficiency, and measurement of the integrated luminosity; ”Partonshower” includes contributions from ISR and FSR, underlying event tune, ME-PS matching,and color reconnection; ”Hard scattering” includes the uncertainty due to PDFs, as well asrenormalization and factorization scales. The grey bands shows the statistical uncertainty.relative to previous results to explore a wider phase space of tt production and to elucidate anydiscrepancies with theory that were reported in previous publications. For the all-jet channel,absolute and normalized differential cross sections are measured as functions of the leadingand subleading top quark p T and absolute rapidity | y | , and as a function of the invariant mass, p T , and y of the tt system, unfolded to the particle level within a fiducial phase space and tothe parton level. For the lepton+jets channel, the differential cross sections are measured asfunctions of the p T and | y | of the top quark that decays according to t → Wb → qq (cid:48) b, both atthe particle and parton levels. The results are compared with theory using the POWHEG matrixelement generator, interfaced to either
PYTHIA or HERWIG ++ for the underlying event andparton showering, and with the M AD G RAPH MC @ NLO matrix element generator, interfaced
400 500 600 700 800 900 1000 1100 1200 (GeV) tT p0102030405060708090100 R e l a t i v e un c e r t a i n t y ( % ) Stat. uncertaintyJES+JER+b taggingt taggingOther experimentalParton showerHard scattering
CMS (13 TeV) -1 Parton level (l+jets channel)Absolute cross section
400 500 600 700 800 900 1000 1100 1200 (GeV) tT p0102030405060708090100 R e l a t i v e un c e r t a i n t y ( % ) Stat. uncertaintyJES+JER+b taggingt taggingOther experimentalParton showerHard scattering
CMS (13 TeV) -1 Parton level (l+jets channel)Normalized cross section t |y0102030405060 R e l a t i v e un c e r t a i n t y ( % ) Stat. uncertaintyJES+JER+b taggingt taggingOther experimentalParton showerHard scattering
CMS (13 TeV) -1 Parton level (l+jets channel)Absolute cross section t |y0102030405060 R e l a t i v e un c e r t a i n t y ( % ) Stat. uncertaintyJES+JER+b taggingt taggingOther experimentalParton showerHard scattering
CMS (13 TeV) -1 Parton level (l+jets channel)Normalized cross section
Figure 22: Breakdown of the sources of systematic uncertainty affecting the differential crosssection measurements in the (cid:96) +jets channel at the parton level as a function of the top quark p T (upper row) or | y | (lower row). Both the systematic uncertainties in the absolute (left column)and the normalized (right column) cross sections are shown. ”JES+JER+b tagging” includesuncertainties due to the JES, JER, and small- R jet b tagging efficiency; ”t tagging” is the un-certainty associated with the large- R jet t tagging efficiency; ”Other experimental” includesthe uncertainties originating from the background estimate, pileup modeling, lepton identifi-cation and trigger efficiency, and measurement of the integrated luminosity; ”Parton shower”includes contributions from ISR and FSR, underlying event tune, ME-PS matching, and colorreconnection; ”Hard scattering” includes the uncertainty due to PDFs, as well as renormaliza-tion and factorization scales.to PYTHIA . All the models significantly exceed the absolute cross section in the phase spacesof the measurements. However, the normalized differential cross sections are consistently welldescribed. The most notable discrepancies are observed in the invariant mass of the tt systemand the subleading top quark p T in the all-jet channel, where theory predicts a higher crosssection at high mass and at high p T , respectively. To further investigate the severity of thisdiscrepancy, more data are needed to enhance the statistical significance of the measurementin this region of phase space. Acknowledgments
We congratulate our colleagues in the CERN accelerator departments for the excellent perfor-mance of the LHC and thank the technical and administrative staffs at CERN and at other CMSinstitutes for their contributions to the success of the CMS effort. In addition, we gratefullyacknowledge the computing centers and personnel of the Worldwide LHC Computing Gridfor delivering so effectively the computing infrastructure essential to our analyses. Finally,we acknowledge the enduring support for the construction and operation of the LHC and theCMS detector provided by the following funding agencies: BMBWF and FWF (Austria); FNRSand FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria);CERN; CAS, MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia);RIF (Cyprus); SENESCYT (Ecuador); MoER, ERC IUT, PUT and ERDF (Estonia); Academy ofFinland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF(Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland);INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LAS (Lithuania); MOE and UM(Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Mon-tenegro); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal);JINR (Dubna); MON, RosAtom, RAS, RFBR, and NRC KI (Russia); MESTD (Serbia); SEIDI,CPAN, PCTI, and FEDER (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland);MST (Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK (Turkey);NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA).Individuals have received support from the Marie-Curie program and the European ResearchCouncil and Horizon 2020 Grant, contract Nos. 675440, 752730, and 765710 (European Union);the Leventis Foundation; the A.P. Sloan Foundation; the Alexander von Humboldt Founda-tion; the Belgian Federal Science Policy Office; the Fonds pour la Formation `a la Recherchedans l’Industrie et dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie doorWetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the“Excellence of Science – EOS” – be.h project n. 30820817; the Beijing Municipal Science &Technology Commission, No. Z191100007219010; the Ministry of Education, Youth and Sports(MEYS) of the Czech Republic; the Deutsche Forschungsgemeinschaft (DFG) under Germany’sExcellence Strategy – EXC 2121 “Quantum Universe” – 390833306; the Lend ¨ulet (“Momen-tum”) Program and the J´anos Bolyai Research Scholarship of the Hungarian Academy of Sci-ences, the New National Excellence Program ´UNKP, the NKFIA research grants 123842, 123959,124845, 124850, 125105, 128713, 128786, and 129058 (Hungary); the Council of Science and In-dustrial Research, India; the HOMING PLUS program of the Foundation for Polish Science,cofinanced from European Union, Regional Development Fund, the Mobility Plus program ofthe Ministry of Science and Higher Education, the National Science Center (Poland), contractsHarmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406; the National Priorities ResearchProgram by Qatar National Research Fund; the Ministry of Science and Higher Education,project no. 02.a03.21.0005 (Russia); the Programa Estatal de Fomento de la Investigaci ´onCient´ıfica y T´ecnica de Excelencia Mar´ıa de Maeztu, grant MDM-2015-0509 and the ProgramaSevero Ochoa del Principado de Asturias; the Thalis and Aristeia programs cofinanced by EU-ESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chula-longkorn University and the Chulalongkorn Academic into Its 2nd Century Project Advance-ment Project (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Cor-poration; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA). eferences References [1] ATLAS Collaboration, “Measurements of top-quark pair single- and double-differentialcross-sections in the all-hadronic channel in pp collisions at √ s =
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Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan † , A. Tumasyan Institut f ¨ur Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, T. Bergauer, M. Dragicevic, J. Er ¨o, A. Escalante Del Valle, R. Fr ¨uhwirth ,M. Jeitler , N. Krammer, L. Lechner, D. Liko, T. Madlener, I. Mikulec, N. Rad, J. Schieck ,R. Sch ¨ofbeck, M. Spanring, S. Templ, W. Waltenberger, C.-E. Wulz , M. Zarucki Institute for Nuclear Problems, Minsk, Belarus
V. Chekhovsky, A. Litomin, V. Makarenko, J. Suarez Gonzalez
Universiteit Antwerpen, Antwerpen, Belgium
M.R. Darwish , E.A. De Wolf, D. Di Croce, X. Janssen, T. Kello , A. Lelek, M. Pieters,H. Rejeb Sfar, H. Van Haevermaet, P. Van Mechelen, S. Van Putte, N. Van Remortel Vrije Universiteit Brussel, Brussel, Belgium
F. Blekman, E.S. Bols, S.S. Chhibra, J. D’Hondt, J. De Clercq, D. Lontkovskyi, S. Lowette,I. Marchesini, S. Moortgat, Q. Python, S. Tavernier, W. Van Doninck, P. Van Mulders
Universit´e Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, B. Clerbaux, G. De Lentdecker, H. Delannoy, B. Dorney, L. Favart,A. Grebenyuk, A.K. Kalsi, I. Makarenko, L. Moureaux, L. P´etr´e, A. Popov, N. Postiau,E. Starling, L. Thomas, C. Vander Velde, P. Vanlaer, D. Vannerom, L. Wezenbeek
Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, I. Khvastunov , M. Niedziela, C. Roskas, K. Skovpen, M. Tytgat,W. Verbeke, B. Vermassen, M. Vit Universit´e Catholique de Louvain, Louvain-la-Neuve, Belgium
G. Bruno, F. Bury, C. Caputo, P. David, C. Delaere, M. Delcourt, I.S. Donertas, A. Giammanco,V. Lemaitre, J. Prisciandaro, A. Saggio, A. Taliercio, M. Teklishyn, P. Vischia, S. Wuyckens,J. Zobec
Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
G.A. Alves, G. Correia Silva, C. Hensel, A. Moraes
Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
W.L. Ald´a J ´unior, E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato , E. Coelho,E.M. Da Costa, G.G. Da Silveira , D. De Jesus Damiao, S. Fonseca De Souza, H. Malbouisson,J. Martins , D. Matos Figueiredo, M. Medina Jaime , M. Melo De Almeida, C. Mora Herrera,L. Mundim, H. Nogima, P. Rebello Teles, L.J. Sanchez Rosas, A. Santoro, S.M. Silva Do Amaral,A. Sznajder, M. Thiel, E.J. Tonelli Manganote , F. Torres Da Silva De Araujo, A. Vilela Pereira Universidade Estadual Paulista a , Universidade Federal do ABC b , S˜ao Paulo, Brazil C.A. Bernardes a , L. Calligaris a , T.R. Fernandez Perez Tomei a , E.M. Gregores b , D.S. Lemos a ,P.G. Mercadante 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
M. Bonchev, A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov, A. Petrov Beihang University, Beijing, China
W. Fang , Q. Guo, H. Wang, L. Yuan Department of Physics, Tsinghua University, Beijing, China
M. Ahmad, Z. Hu, Y. Wang
Institute of High Energy Physics, Beijing, China
E. Chapon, G.M. Chen , H.S. Chen , M. Chen, C.H. Jiang, D. Leggat, H. Liao, Z. Liu, R. Sharma,A. Spiezia, J. Tao, 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, G. Chen, A. Levin, J. Li, L. Li, Q. Li, X. Lyu, Y. Mao, S.J. Qian,D. Wang, Q. Wang, J. Xiao
Sun Yat-Sen University, Guangzhou, China
Z. You
Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beamApplication (MOE) - Fudan University, Shanghai, China
X. Gao Zhejiang University, Hangzhou, China
M. Xiao
Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C. Florez, J. Fraga, A. Sarkar, M.A. Segura Delgado
Universidad de Antioquia, Medellin, Colombia
J. 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, T. Sculac
University of Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac
Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, D. Majumder, B. Mesic, M. Roguljic, A. Starodumov , T. Susa University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, E. Erodotou, A. Ioannou, G. Kole, M. Kolosova, S. Konstantinou,G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H. Rykaczewski, H. Saka,D. Tsiakkouri
Charles University, Prague, Czech Republic
M. Finger , M. Finger Jr. , A. Kveton, J. Tomsa Escuela Politecnica Nacional, Quito, Ecuador
E. Ayala
Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin
Academy of Scientific Research and Technology of the Arab Republic of Egypt, EgyptianNetwork of High Energy Physics, Cairo, Egypt
A.A. Abdelalim , S. Abu Zeid , S. Khalil Center for High Energy Physics (CHEP-FU), Fayoum University, El-Fayoum, Egypt
M.A. Mahmoud, Y. Mohammed National Institute of Chemical Physics and Biophysics, Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M. Kadastik,M. Raidal, C. Veelken
Department of Physics, University of Helsinki, Helsinki, Finland
P. Eerola, L. Forthomme, H. Kirschenmann, K. Osterberg, M. Voutilainen
Helsinki Institute of Physics, Helsinki, Finland
E. Br ¨ucken, F. Garcia, J. Havukainen, V. Karim¨aki, M.S. Kim, R. Kinnunen, T. Lamp´en,K. Lassila-Perini, S. Laurila, S. Lehti, T. Lind´en, H. Siikonen, E. Tuominen, J. Tuominiemi
Lappeenranta University of Technology, Lappeenranta, Finland
P. Luukka, T. Tuuva
IRFU, CEA, Universit´e Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F. Ferri, S. Ganjour, A. Givernaud,P. Gras, G. Hamel de Monchenault, P. Jarry, C. Leloup, 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, Paris, France
S. Ahuja, C. Amendola, F. Beaudette, M. Bonanomi, P. Busson, C. Charlot, O. Davignon, B. Diab,G. Falmagne, R. Granier de Cassagnac, I. Kucher, A. Lobanov, C. Martin Perez, M. Nguyen,C. Ochando, P. Paganini, J. Rembser, R. Salerno, J.B. Sauvan, Y. Sirois, A. Zabi, A. Zghiche
Universit´e de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France
J.-L. Agram , J. Andrea, D. Bloch, G. Bourgatte, J.-M. Brom, E.C. Chabert, C. Collard, J.-C. Fontaine , D. Gel´e, U. Goerlach, C. Grimault, A.-C. Le Bihan, P. Van Hove Universit´e de Lyon, Universit´e Claude Bernard Lyon 1, CNRS-IN2P3, Institut de PhysiqueNucl´eaire de Lyon, Villeurbanne, France
E. Asilar, S. Beauceron, C. Bernet, G. Boudoul, C. Camen, A. Carle, N. Chanon, R. Chierici,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, L. Torterotot, G. Touquet,M. Vander Donckt, S. Viret
Georgian Technical University, Tbilisi, Georgia
T. Toriashvili Tbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze RWTH Aachen University, I. Physikalisches Institut, Aachen, Germany
L. Feld, K. Klein, M. Lipinski, D. Meuser, A. Pauls, M. Preuten, M.P. Rauch, J. Schulz,M. Teroerde
RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany
D. Eliseev, M. Erdmann, P. Fackeldey, B. Fischer, S. Ghosh, T. Hebbeker, K. Hoepfner, H. Keller,L. Mastrolorenzo, M. Merschmeyer, A. Meyer, P. Millet, G. Mocellin, S. Mondal, S. Mukherjee,D. Noll, A. Novak, T. Pook, A. Pozdnyakov, T. Quast, M. Radziej, Y. Rath, H. Reithler, J. Roemer,A. Schmidt, S.C. Schuler, A. Sharma, S. Wiedenbeck, S. Zaleski RWTH Aachen University, III. Physikalisches Institut B, Aachen, Germany
C. Dziwok, G. Fl ¨ugge, W. Haj Ahmad , O. Hlushchenko, T. Kress, A. Nowack, C. Pistone,O. Pooth, D. Roy, H. Sert, A. Stahl , T. Ziemons Deutsches Elektronen-Synchrotron, Hamburg, Germany
H. Aarup Petersen, M. Aldaya Martin, P. Asmuss, I. Babounikau, S. Baxter, O. Behnke,A. Berm ´udez Mart´ınez, A.A. Bin Anuar, K. Borras , V. Botta, D. Brunner, A. Campbell,A. Cardini, P. Connor, S. Consuegra Rodr´ıguez, V. Danilov, A. De Wit, M.M. Defranchis,L. Didukh, D. Dom´ınguez Damiani, G. Eckerlin, D. Eckstein, T. Eichhorn, A. Elwood,L.I. Estevez Banos, E. Gallo , A. Geiser, A. Giraldi, A. Grohsjean, M. Guthoff, M. Haranko,A. Harb, A. Jafari , N.Z. Jomhari, H. Jung, A. Kasem , M. Kasemann, H. Kaveh, J. Keaveney,C. Kleinwort, J. Knolle, D. Kr ¨ucker, W. Lange, T. Lenz, J. Lidrych, K. Lipka, W. Lohmann ,R. Mankel, I.-A. Melzer-Pellmann, J. Metwally, A.B. Meyer, M. Meyer, M. Missiroli, J. Mnich,A. Mussgiller, V. Myronenko, Y. Otarid, D. P´erez Ad´an, S.K. Pflitsch, D. Pitzl, A. Raspereza,A. Saibel, M. Savitskyi, V. Scheurer, P. Sch ¨utze, C. Schwanenberger, R. Shevchenko, A. Singh,R.E. Sosa Ricardo, H. Tholen, N. Tonon, O. Turkot, A. Vagnerini, M. Van De Klundert, R. Walsh,D. Walter, Y. Wen, K. Wichmann, C. Wissing, S. Wuchterl, O. Zenaiev, R. Zlebcik University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, K. De Leo, T. Dreyer, A. Ebrahimi, F. Feindt,A. Fr ¨ohlich, C. Garbers, E. Garutti, D. Gonzalez, P. Gunnellini, J. Haller, A. Hinzmann,A. Karavdina, G. Kasieczka, R. Klanner, R. Kogler, S. Kurz, V. Kutzner, J. Lange, T. Lange,A. Malara, J. Multhaup, C.E.N. Niemeyer, A. Nigamova, K.J. Pena Rodriguez, A. Reimers,O. Rieger, P. Schleper, S. Schumann, J. Schwandt, D. Schwarz, J. Sonneveld, H. Stadie,G. Steinbr ¨uck, B. Vormwald, I. Zoi
Karlsruher Institut fuer Technologie, Karlsruhe, Germany
M. Akbiyik, M. Baselga, S. Baur, J. Bechtel, T. Berger, E. Butz, R. Caspart, T. Chwalek,W. De Boer, A. Dierlamm, A. Droll, K. El Morabit, N. Faltermann, K. Fl ¨oh, M. Giffels,A. Gottmann, F. Hartmann , C. Heidecker, U. Husemann, M.A. Iqbal, I. Katkov , P. Keicher,R. Koppenh ¨ofer, S. Kudella, S. Maier, M. Metzler, S. Mitra, M.U. Mozer, D. M ¨uller, Th. M ¨uller,M. Musich, G. Quast, K. Rabbertz, J. Rauser, D. Savoiu, D. Sch¨afer, M. Schnepf, M. Schr ¨oder,D. Seith, I. Shvetsov, H.J. Simonis, R. Ulrich, M. Wassmer, M. Weber, C. W ¨ohrmann, R. Wolf,S. Wozniewski Institute of Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia Paraskevi,Greece
G. Anagnostou, P. Asenov, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas, G. Paspalaki,A. Stakia
National and Kapodistrian University of Athens, Athens, Greece
M. Diamantopoulou, D. Karasavvas, G. Karathanasis, P. Kontaxakis, C.K. Koraka,A. Manousakis-katsikakis, A. Panagiotou, I. Papavergou, N. Saoulidou, K. Theofilatos,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, S. Mallios, K. Manitara,N. Manthos, I. Papadopoulos, J. Strologas, D. Tsitsonis MTA-ELTE Lend ¨ulet CMS Particle and Nuclear Physics Group, E ¨otv ¨os Lor´and University,Budapest, Hungary
M. Bart ´ok , R. Chudasama, M. Csanad, M.M.A. Gadallah , P. Major, K. Mandal, A. Mehta,G. Pasztor, O. Sur´anyi, G.I. Veres Wigner Research Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath , F. Sikler, V. Veszpremi, G. Vesztergombi † Institute of Nuclear Research ATOMKI, Debrecen, Hungary
N. Beni, 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, S. L ¨ok ¨os , F. Nemes, T. Novak Indian Institute of Science (IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri, D. Kumar, L. Panwar, P.C. Tiwari
National Institute of Science Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati , D. Dash, C. Kar, P. Mal, T. Mishra, V.K. Muraleedharan Nair Bindhu,A. Nayak , D.K. Sahoo , N. Sur, S.K. Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, N. Dhingra , R. Gupta, A. Kaur, A. Kaur, S. Kaur,P. Kumari, M. Lohan, M. Meena, K. Sandeep, S. Sharma, J.B. Singh, A.K. Virdi University of Delhi, Delhi, India
A. Ahmed, A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, A. Kumar,M. Naimuddin, P. Priyanka, K. Ranjan, A. Shah
Saha Institute of Nuclear Physics, HBNI, Kolkata, India
M. Bharti , R. Bhattacharya, S. Bhattacharya, D. Bhowmik, S. Dutta, S. Ghosh, B. Gomber ,M. Maity , K. Mondal, S. Nandan, P. Palit, A. Purohit, P.K. Rout, G. Saha, 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, M.A. Bhat, S. Dugad, R. Kumar Verma, U. Sarkar
Tata Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, S. Karmakar, S. Kumar,G. Majumder, K. Mazumdar, S. Mukherjee, D. Roy, N. Sahoo
Indian Institute of Science Education and Research (IISER), Pune, India
S. Dube, B. Kansal, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, A. Rastogi, S. Sharma
Department of Physics, Isfahan University of Technology, Isfahan, Iran
H. Bakhshiansohi Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
S. Chenarani , S.M. Etesami, M. Khakzad, M. Mohammadi Najafabadi, M. Naseri 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 ,39 , C. Aruta a , b , C. Calabria 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 , G. Iaselli a , c , M. Ince a , b , S. Lezki a , b , G. Maggi a , c , M. Maggi a , I. Margjeka 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 , b , S. Braibant-Giacomelli a , b ,L. Brigliadori a , b , R. Campanini a , b , P. Capiluppi a , b , A. Castro a , b , F.R. Cavallo 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 ,40 , S. Marcellini a , G. Masetti a ,F.L. Navarria a , b , A. Perrotta a , F. Primavera 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 ,41 , S. Costa a , b , A. Di Mattia a , R. Potenza a , b , A. Tricomi a , b ,41 , C. Tuve a , b INFN Sezione di Firenze a , Universit`a di Firenze b , Firenze, Italy G. Barbagli a , A. Cassese a , R. Ceccarelli a , b , V. Ciulli a , b , C. Civinini a , R. D’Alessandro a , b , F. Fiori a ,E. Focardi a , b , G. Latino a , b , P. Lenzi a , b , M. Lizzo a , b , M. Meschini a , S. Paoletti a , R. Seidita a , b ,G. Sguazzoni a , L. Viliani a INFN Laboratori Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, D. Piccolo
INFN Sezione di Genova a , Universit`a di Genova b , Genova, Italy M. Bozzo a , b , F. Ferro a , R. Mulargia a , b , E. Robutti a , S. Tosi a , b INFN Sezione di Milano-Bicocca a , Universit`a di Milano-Bicocca b , Milano, Italy A. Benaglia a , A. Beschi a , b , F. Brivio a , b , F. Cetorelli a , b , V. Ciriolo a , b ,20 , 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 , 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 ,20 , D. Zuolo a , b INFN Sezione di Napoli a , Universit`a di Napoli ’Federico II’ b , Napoli, Italy, Universit`a dellaBasilicata c , Potenza, Italy, Universit`a G. Marconi d , Roma, Italy S. Buontempo a , N. Cavallo a , c , A. De Iorio a , b , F. Fabozzi a , c , F. Fienga a , A.O.M. Iorio a , b ,L. Layer a , b , L. Lista a , b , S. Meola a , d ,20 , P. Paolucci a ,20 , B. Rossi a , C. Sciacca a , b , E. Voevodina a , b INFN Sezione di Padova a , Universit`a di Padova b , Padova, Italy, Universit`a di Trento c ,Trento, Italy P. Azzi a , N. Bacchetta a , A. Boletti a , b , A. Bragagnolo a , b , R. Carlin a , b , P. Checchia a ,P. De Castro Manzano a , T. Dorigo a , U. Dosselli a , F. Gasparini a , b , U. Gasparini a , b , S.Y. Hoh a , b ,M. Margoni a , b , A.T. Meneguzzo a , b , M. Presilla b , P. Ronchese a , b , R. Rossin a , b , F. Simonetto a , b ,G. Strong, A. Tiko 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 A. Braghieri a , S. Calzaferri a , b , D. Fiorina a , b , P. Montagna a , b , S.P. Ratti a , b , V. Re a , M. Ressegotti a , b ,C. Riccardi a , b , P. Salvini a , I. Vai a , P. Vitulo a , b INFN Sezione di Perugia a , Universit`a di Perugia b , Perugia, Italy M. Biasini a , b , G.M. Bilei a , D. Ciangottini a , b , L. Fan `o a , b , P. Lariccia a , b , G. Mantovani a , b ,V. Mariani a , b , M. Menichelli a , F. Moscatelli a , A. Rossi a , b , A. Santocchia a , b , D. Spiga a ,T. Tedeschi a , b INFN Sezione di Pisa a , Universit`a di Pisa b , Scuola Normale Superiore di Pisa c , Pisa, Italy K. Androsov a , P. Azzurri a , G. Bagliesi a , V. Bertacchi a , c , L. Bianchini a , T. Boccali a , R. Castaldi a ,M.A. Ciocci a , b , R. Dell’Orso a , M.R. Di Domenico a , b , S. Donato a , L. Giannini a , c , A. Giassi a ,M.T. Grippo a , F. Ligabue a , c , E. Manca a , c , G. Mandorli a , c , A. Messineo a , b , F. Palla a , A. Rizzi a , b ,G. Rolandi a , c , S. Roy Chowdhury a , c , A. Scribano a , N. Shafiei a , b , P. Spagnolo a , R. Tenchini a ,G. Tonelli a , b , N. Turini a , A. Venturi a , P.G. Verdini a INFN Sezione di Roma a , Sapienza Universit`a di Roma b , Rome, Italy F. Cavallari a , M. Cipriani a , b , D. Del Re a , b , E. Di Marco a , M. Diemoz a , E. Longo a , b , P. Meridiani a ,G. Organtini a , b , F. Pandolfi a , R. Paramatti a , b , C. Quaranta a , b , S. Rahatlou a , b , C. Rovelli a ,F. Santanastasio a , b , L. Soffi a , b , R. Tramontano a , b INFN Sezione di Torino a , Universit`a di Torino b , Torino, Italy, Universit`a del PiemonteOrientale c , Novara, Italy N. Amapane a , b , R. Arcidiacono a , c , S. Argiro a , b , M. Arneodo a , c , N. Bartosik a , R. Bellan a , b ,A. Bellora a , b , C. Biino a , A. Cappati a , b , N. Cartiglia a , S. Cometti a , M. Costa a , b , R. Covarelli a , b ,N. Demaria a , B. Kiani a , b , F. Legger a , C. Mariotti a , S. Maselli a , E. Migliore a , b , V. Monaco a , b ,E. Monteil a , b , M. Monteno a , M.M. Obertino a , b , G. Ortona a , L. Pacher a , b , N. Pastrone a ,M. Pelliccioni a , G.L. Pinna Angioni a , b , M. Ruspa a , c , R. Salvatico a , b , F. Siviero a , b , V. Sola a ,A. Solano a , b , D. Soldi a , b , A. Staiano a , 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,S. Sekmen, Y.C. Yang
Chonnam National University, Institute for Universe and Elementary Particles, Kwangju,Korea
H. Kim, D.H. Moon
Hanyang University, Seoul, Korea
B. Francois, T.J. Kim, J. Park
Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, S. Ha, B. Hong, K. Lee, K.S. Lee, J. Lim, J. Park, S.K. Park, Y. Roh, J. Yoo
Kyung Hee University, Department of Physics, Seoul, Republic of Korea
J. Goh, A. Gurtu
Sejong University, Seoul, Korea
H.S. Kim, Y. Kim0
H.S. Kim, Y. Kim0 Seoul National University, Seoul, Korea
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, S. Ko, H. Kwon, H. Lee, K. Lee, S. Lee,K. Nam, B.H. Oh, M. Oh, S.B. Oh, B.C. Radburn-Smith, 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, I.J. Watson
Sungkyunkwan University, Suwon, Korea
Y. Choi, C. Hwang, Y. Jeong, H. Lee, J. Lee, Y. Lee, I. Yu
Riga Technical University, Riga, Latvia
V. Veckalns Vilnius University, Vilnius, Lithuania
A. Juodagalvis, A. Rinkevicius, G. Tamulaitis
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
H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz , R. Lopez-Fernandez,A. Sanchez-Hernandez Universidad Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, M. Ramirez-Garcia, F. Vazquez Valencia
Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada
Universidad Aut ´onoma de San Luis Potos´ı, San Luis Potos´ı, Mexico
A. Morelos Pineda
University of Montenegro, Podgorica, Montenegro
J. Mijuskovic , N. Raicevic University of Auckland, Auckland, New Zealand
D. Krofcheck
University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler
National Centre for Physics, Quaid-I-Azam University, Islamabad, Pakistan
A. Ahmad, M.I. Asghar, M.I.M. Awan, Q. Hassan, 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, A. Byszuk , K. Doroba, A. Kalinowski, M. Konecki, J. Krolikowski,M. Olszewski, M. Walczak Laborat ´orio de Instrumenta¸c˜ao e F´ısica Experimental de Part´ıculas, Lisboa, Portugal
M. Araujo, P. Bargassa, D. Bastos, A. Di Francesco, P. Faccioli, B. Galinhas, M. Gallinaro,J. Hollar, N. Leonardo, T. Niknejad, J. Seixas, K. Shchelina, O. Toldaiev, J. Varela
Joint Institute for Nuclear Research, Dubna, Russia
V. Alexakhin, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov, V. Karjavine, A. Lanev,A. Malakhov, V. Matveev , P. Moisenz, V. Palichik, V. Perelygin, M. Savina, D. Seitova,S. Shmatov, S. Shulha, V. Smirnov, O. Teryaev, N. Voytishin, B.S. Yuldashev , A. Zarubin 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, I. Pozdnyakov,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
O. Bychkova, M. Chadeeva , D. Philippov, E. Popova, V. Rusinov P.N. Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin, M. Kirakosyan, A. Terkulov
Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,Russia
A. Baskakov, A. Belyaev, E. Boos, V. Bunichev, M. Dubinin , L. Dudko, A. Gribushin,V. Klyukhin, I. Lokhtin, S. Obraztsov, M. Perfilov, V. Savrin, P. Volkov Novosibirsk State University (NSU), Novosibirsk, Russia
V. Blinov , T. Dimova , L. Kardapoltsev , I. Ovtin , Y. Skovpen Institute for High Energy Physics of National Research Centre ‘Kurchatov Institute’,Protvino, Russia
I. Azhgirey, I. Bayshev, V. Kachanov, A. Kalinin, D. Konstantinov, V. Petrov, R. Ryutin, A. Sobol,S. Troshin, N. Tyurin, A. Uzunian, A. Volkov
National Research Tomsk Polytechnic University, Tomsk, Russia
A. Babaev, A. Iuzhakov, V. Okhotnikov
Tomsk State University, Tomsk, Russia
V. Borchsh, V. Ivanchenko, E. Tcherniaev University of Belgrade: Faculty of Physics and VINCA Institute of Nuclear Sciences,Belgrade, Serbia
P. Adzic , P. Cirkovic, M. Dordevic, P. Milenovic, J. Milosevic, M. Stojanovic 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, J.A. Brochero Cifuentes, 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, D. Moran, ´A. Navarro Tobar,A. P´erez-Calero Yzquierdo, J. Puerta Pelayo, I. Redondo, L. Romero, S. S´anchez Navas,M.S. Soares, A. Triossi, C. Willmott
Universidad Aut ´onoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Troc ´oniz, R. Reyes-Almanza
Universidad de Oviedo, Instituto Universitario de Ciencias y Tecnolog´ıas Espaciales deAsturias (ICTEA), Oviedo, Spain
B. Alvarez Gonzalez, J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras, I. Gonzalez Ca-ballero, E. Palencia Cortezon, C. Ram ´on ´Alvarez, V. Rodr´ıguez Bouza, S. Sanchez Cruz
Instituto de F´ısica de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, J. Duarte Campderros, M. Fernandez,P.J. Fern´andez Manteca, A. Garc´ıa Alonso, G. Gomez, C. Martinez Rivero, P. Mar-tinez Ruiz del Arbol, F. Matorras, J. Piedra Gomez, C. Prieels, F. Ricci-Tam, T. Rodrigo, A. Ruiz-Jimeno, L. Russo , L. Scodellaro, I. Vila, J.M. Vizan Garcia University of Colombo, Colombo, Sri Lanka
MK Jayananda, B. Kailasapathy , D.U.J. Sonnadara, DDC Wickramarathna University of Ruhuna, Department of Physics, Matara, Sri Lanka
W.G.D. Dharmaratna, K. Liyanage, N. Perera, N. Wickramage
CERN, European Organization for Nuclear Research, Geneva, Switzerland
T.K. Aarrestad, D. Abbaneo, B. Akgun, E. Auffray, G. Auzinger, J. Baechler, P. Baillon, A.H. Ball,D. Barney, J. Bendavid, M. Bianco, A. Bocci, P. Bortignon, E. Bossini, E. Brondolin, T. Camporesi,G. Cerminara, L. Cristella, D. d’Enterria, A. Dabrowski, N. Daci, V. Daponte, A. David,A. De Roeck, M. Deile, R. Di Maria, M. Dobson, M. D ¨unser, N. Dupont, A. Elliott-Peisert,N. Emriskova, F. Fallavollita , D. Fasanella, S. Fiorendi, G. Franzoni, J. Fulcher, W. Funk,S. Giani, D. Gigi, K. Gill, F. Glege, L. Gouskos, M. Gruchala, M. Guilbaud, D. Gulhan,J. Hegeman, Y. Iiyama, V. Innocente, T. James, P. Janot, J. Kaspar, J. Kieseler, M. Komm,N. Kratochwil, C. Lange, P. Lecoq, K. Long, C. Lourenc¸o, L. Malgeri, M. Mannelli, A. Massironi,F. Meijers, S. Mersi, E. Meschi, F. Moortgat, M. Mulders, J. Ngadiuba, J. Niedziela, S. Orfanelli,L. Orsini, F. Pantaleo , L. Pape, E. Perez, M. Peruzzi, A. Petrilli, G. Petrucciani, A. Pfeiffer,M. Pierini, F.M. Pitters, D. Rabady, A. Racz, M. 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 ,J. Steggemann, S. Summers, V.R. Tavolaro, D. Treille, A. Tsirou, G.P. Van Onsem, A. Vartak,M. Verzetti, K.A. Wozniak, W.D. Zeuner Paul Scherrer Institut, Villigen, Switzerland
L. Caminada , W. Erdmann, R. Horisberger, Q. Ingram, H.C. Kaestli, D. Kotlinski,U. Langenegger, T. Rohe ETH Zurich - Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland
M. Backhaus, P. Berger, A. Calandri, N. Chernyavskaya, G. Dissertori, M. Dittmar, M. Doneg`a,C. Dorfer, T. Gadek, T.A. G ´omez Espinosa, C. Grab, D. Hits, W. Lustermann, A.-M. Lyon,R.A. Manzoni, M.T. Meinhard, F. Micheli, P. Musella, F. Nessi-Tedaldi, F. Pauss, V. Perovic,G. Perrin, L. Perrozzi, S. Pigazzini, M.G. Ratti, M. Reichmann, C. Reissel, T. Reitenspiess,B. Ristic, D. Ruini, D.A. Sanz Becerra, M. Sch ¨onenberger, L. Shchutska, V. Stampf,M.L. Vesterbacka Olsson, R. Wallny, D.H. Zhu
Universit¨at Z ¨urich, Zurich, Switzerland
C. Amsler , C. Botta, D. Brzhechko, M.F. Canelli, A. De Cosa, R. Del Burgo, J.K. Heikkil¨a,M. Huwiler, A. Jofrehei, B. Kilminster, S. Leontsinis, A. Macchiolo, V.M. Mikuni, U. Molinatti,I. Neutelings, G. Rauco, P. Robmann, K. Schweiger, Y. Takahashi, S. Wertz National Central University, Chung-Li, Taiwan
C. Adloff , C.M. Kuo, W. Lin, A. Roy, T. Sarkar , S.S. Yu National Taiwan University (NTU), Taipei, Taiwan
L. Ceard, P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.y. Li, R.-S. Lu, E. Paganis,A. Psallidas, A. Steen, E. Yazgan
Chulalongkorn University, Faculty of Science, Department of Physics, Bangkok, Thailand
B. Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas
C¸ ukurova University, Physics Department, Science and Art Faculty, Adana, Turkey
F. Boran, S. Damarseckin , Z.S. Demiroglu, F. Dolek, C. Dozen , I. Dumanoglu , E. Eskut,G. Gokbulut, Y. Guler, E. Gurpinar Guler , I. Hos , C. Isik, E.E. Kangal , O. Kara,A. Kayis Topaksu, U. Kiminsu, G. Onengut, K. Ozdemir , A. Polatoz, A.E. Simsek, B. Tali ,U.G. Tok, S. Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle East Technical University, Physics Department, Ankara, Turkey
B. Isildak , G. Karapinar , K. Ocalan , M. Yalvac Bogazici University, Istanbul, Turkey
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, D. Burns , 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, J. Taylor, A. Titterton Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev , C. Brew, R.M. Brown, D.J.A. Cockerill, K.V. Ellis, K. Harder, S. Harper, J. Linacre, K. Manolopoulos, D.M. Newbold, E. Olaiya, D. Petyt, T. Reis, T. Schuh,C.H. Shepherd-Themistocleous, A. Thea, I.R. Tomalin, T. Williams
Imperial College, London, United Kingdom
R. Bainbridge, P. Bloch, S. Bonomally, J. Borg, S. Breeze, O. Buchmuller, A. Bundock, V. Cepaitis,G.S. Chahal , D. Colling, P. Dauncey, G. Davies, M. Della Negra, P. Everaerts, G. Fedi,G. Hall, G. Iles, J. Langford, L. Lyons, A.-M. Magnan, S. Malik, A. Martelli, V. Milosevic,A. Morton, J. Nash , V. Palladino, M. Pesaresi, D.M. Raymond, A. Richards, A. Rose, E. Scott,C. Seez, A. Shtipliyski, M. Stoye, A. Tapper, K. Uchida, T. Virdee , N. Wardle, S.N. Webb,D. Winterbottom, A.G. Zecchinelli, S.C. Zenz Brunel University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, C.K. Mackay, I.D. Reid, L. Teodorescu, S. Zahid
Baylor University, Waco, USA
A. Brinkerhoff, K. Call, B. Caraway, J. Dittmann, K. Hatakeyama, C. Madrid, B. McMaster,N. Pastika, C. Smith
Catholic University of America, Washington, DC, USA
R. Bartek, A. Dominguez, R. Uniyal, A.M. Vargas Hernandez
The University of Alabama, Tuscaloosa, USA
A. Buccilli, O. Charaf, S.I. Cooper, S.V. Gleyzer, C. Henderson, P. Rumerio, C. West
Boston University, Boston, USA
A. Akpinar, A. Albert, D. Arcaro, C. Cosby, Z. Demiragli, D. Gastler, C. Richardson, J. Rohlf,K. Salyer, D. Sperka, D. Spitzbart, I. Suarez, S. Yuan, D. Zou
Brown University, Providence, USA
G. Benelli, B. Burkle, X. Coubez , D. Cutts, Y.t. Duh, M. Hadley, U. Heintz, J.M. Hogan ,K.H.M. Kwok, E. Laird, G. Landsberg, K.T. Lau, J. Lee, M. Narain, S. Sagir , R. Syarif, E. Usai,W.Y. Wong, D. Yu, W. Zhang University of California, Davis, Davis, USA
R. Band, C. Brainerd, R. Breedon, M. Calderon De La Barca Sanchez, M. Chertok, J. Conway,R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk, F. Jensen, W. Ko † , O. Kukral, R. Lander,M. Mulhearn, D. Pellett, J. Pilot, M. Shi, D. Taylor, K. Tos, M. Tripathi, Y. Yao, F. Zhang University of California, Los Angeles, USA
M. Bachtis, C. Bravo, R. Cousins, A. Dasgupta, A. Florent, D. Hamilton, J. Hauser, M. Ignatenko,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, S.M.A. Ghiasi Shirazi, G. Hanson, G. Karapostoli,O.R. Long, N. Manganelli, M. Olmedo Negrete, M.I. Paneva, W. Si, S. Wimpenny, Y. Zhang
University of California, San Diego, La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, S. Cooperstein, N. Deelen, M. Derdzinski, J. Duarte,R. Gerosa, D. Gilbert, B. Hashemi, D. Klein, V. Krutelyov, J. Letts, M. Masciovecchio, S. May,S. Padhi, M. Pieri, V. Sharma, M. Tadel, F. W ¨urthwein, A. Yagil
University of California, Santa Barbara - Department of Physics, Santa Barbara, USA
N. Amin, R. Bhandari, C. Campagnari, M. Citron, A. Dorsett, V. Dutta, J. Incandela, B. Marsh,H. Mei, A. Ovcharova, H. Qu, M. Quinnan, J. Richman, U. Sarica, D. Stuart, S. Wang California Institute of Technology, Pasadena, USA
D. Anderson, A. Bornheim, O. Cerri, I. Dutta, J.M. Lawhorn, N. Lu, J. Mao, H.B. Newman,T.Q. Nguyen, J. Pata, M. Spiropulu, J.R. Vlimant, S. Xie, Z. Zhang, R.Y. Zhu
Carnegie Mellon University, Pittsburgh, USA
J. Alison, M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, M. Sun, I. Vorobiev,M. Weinberg
University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, E. MacDonald, T. Mulholland, R. Patel, A. Perloff, K. Stenson,K.A. Ulmer, S.R. Wagner
Cornell University, Ithaca, USA
J. Alexander, Y. Cheng, J. Chu, D.J. Cranshaw, A. Datta, A. Frankenthal, K. Mcdermott,J. Monroy, J.R. Patterson, D. Quach, A. Ryd, W. Sun, S.M. Tan, Z. Tao, J. Thom, P. Wittich,M. Zientek
Fermi National Accelerator Laboratory, Batavia, USA
S. Abdullin, M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S. Banerjee,L.A.T. Bauerdick, A. Beretvas, D. Berry, J. Berryhill, P.C. Bhat, K. Burkett, J.N. Butler, A. Canepa,G.B. Cerati, H.W.K. Cheung, F. Chlebana, M. Cremonesi, V.D. Elvira, J. Freeman, Z. Gecse,E. Gottschalk, L. Gray, D. Green, S. Gr ¨unendahl, O. Gutsche, R.M. Harris, S. Hasegawa,R. Heller, T.C. Herwig, J. Hirschauer, B. Jayatilaka, S. Jindariani, M. Johnson, U. Joshi,T. Klijnsma, B. Klima, M.J. Kortelainen, S. Lammel, J. Lewis, D. Lincoln, R. Lipton, M. Liu,T. Liu, J. Lykken, K. Maeshima, D. Mason, P. McBride, P. Merkel, S. Mrenna, S. Nahn, V. O’Dell,V. Papadimitriou, K. Pedro, C. Pena , O. Prokofyev, F. Ravera, A. Reinsvold Hall, L. Ristori,B. Schneider, E. Sexton-Kennedy, N. Smith, A. Soha, W.J. Spalding, L. Spiegel, S. Stoynev,J. Strait, L. Taylor, S. Tkaczyk, N.V. Tran, L. Uplegger, E.W. Vaandering, M. Wang, 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. Wang, S. Wang, X. Zuo
Florida International University, Miami, USA
Y.R. Joshi
Florida State University, Tallahassee, USA
T. Adams, A. Askew, D. Diaz, R. Habibullah, S. Hagopian, V. Hagopian, K.F. Johnson,R. Khurana, T. Kolberg, G. Martinez, H. Prosper, C. Schiber, R. Yohay, J. Zhang
Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, S. Butalla, T. Elkafrawy , M. Hohlmann, D. Noonan, M. Rahmani,M. Saunders, F. Yumiceva University of Illinois at Chicago (UIC), Chicago, USA
M.R. Adams, L. Apanasevich, H. Becerril Gonzalez, R. Cavanaugh, X. Chen, S. Dittmer,O. Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, C. Mills, G. Oh, T. Roy, M.B. Tonjes,N. Varelas, J. Viinikainen, H. Wang, X. Wang, Z. Wu
The University of Iowa, Iowa City, USA
M. Alhusseini, B. Bilki , 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, K. Yi Johns Hopkins University, Baltimore, USA
O. Amram, B. Blumenfeld, L. Corcodilos, M. Eminizer, A.V. Gritsan, S. Kyriacou,P. Maksimovic, C. Mantilla, J. Roskes, M. Swartz, T. ´A. V´ami
The University of Kansas, Lawrence, USA
C. Baldenegro Barrera, P. Baringer, A. Bean, A. Bylinkin, T. Isidori, S. Khalil, J. King,G. Krintiras, A. Kropivnitskaya, C. Lindsey, W. Mcbrayer, 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, D.R. Mendis, T. Mitchell, A. Modak,A. Mohammadi
Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright
University of Maryland, College Park, USA
E. Adams, A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, N.J. Hadley, S. Jabeen, G.Y. Jeng,R.G. Kellogg, T. Koeth, A.C. Mignerey, S. Nabili, M. Seidel, A. Skuja, S.C. Tonwar, L. Wang,K. Wong
Massachusetts Institute of Technology, Cambridge, USA
D. Abercrombie, B. Allen, R. Bi, S. Brandt, W. Busza, I.A. Cali, Y. Chen, M. D’Alfonso,G. Gomez Ceballos, M. Goncharov, P. Harris, D. Hsu, M. Hu, M. Klute, D. Kovalskyi, J. Krupa,Y.-J. Lee, P.D. Luckey, B. Maier, A.C. Marini, C. Mcginn, C. Mironov, S. Narayanan, X. Niu,C. Paus, D. Rankin, C. Roland, G. Roland, Z. Shi, G.S.F. Stephans, K. Sumorok, K. Tatar,D. Velicanu, J. Wang, T.W. Wang, Z. Wang, B. Wyslouch
University of Minnesota, Minneapolis, USA
R.M. Chatterjee, A. Evans, S. Guts † , P. Hansen, J. Hiltbrand, Sh. Jain, M. Krohn, Y. Kubota,Z. Lesko, J. Mans, M. Revering, R. Rusack, R. Saradhy, N. Schroeder, N. Strobbe, M.A. Wadud University of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros
University of Nebraska-Lincoln, Lincoln, USA
K. Bloom, S. Chauhan, D.R. Claes, C. Fangmeier, L. Finco, F. Golf, J.R. Gonz´alez Fern´andez,I. Kravchenko, J.E. Siado, G.R. Snow † , B. Stieger, W. Tabb State University of New York at Buffalo, Buffalo, USA
G. Agarwal, C. Harrington, I. Iashvili, A. Kharchilava, C. McLean, D. Nguyen, A. Parker,J. Pekkanen, S. Rappoccio, B. Roozbahani
Northeastern University, Boston, USA
G. Alverson, E. Barberis, C. Freer, Y. Haddad, A. Hortiangtham, G. Madigan, B. Marzocchi,D.M. Morse, V. Nguyen, T. Orimoto, L. Skinnari, A. Tishelman-Charny, T. Wamorkar, B. Wang,A. Wisecarver, D. Wood
Northwestern University, Evanston, USA
S. Bhattacharya, J. Bueghly, Z. Chen, A. Gilbert, T. Gunter, K.A. Hahn, N. Odell, M.H. Schmitt,K. Sung, M. Velasco University of Notre Dame, Notre Dame, USA
R. Bucci, N. Dev, R. Goldouzian, M. Hildreth, K. Hurtado Anampa, C. Jessop, D.J. Karmgard,K. Lannon, W. Li, N. Loukas, N. Marinelli, I. Mcalister, F. Meng, K. Mohrman, Y. Musienko ,R. Ruchti, P. Siddireddy, S. Taroni, M. Wayne, A. Wightman, M. Wolf, L. Zygala The Ohio State University, Columbus, USA
J. Alimena, B. Bylsma, B. Cardwell, L.S. Durkin, B. Francis, C. Hill, W. Ji, A. Lefeld, B.L. Winer,B.R. Yates
Princeton University, Princeton, USA
G. Dezoort, P. Elmer, B. Greenberg, N. Haubrich, S. Higginbotham, A. Kalogeropoulos,G. Kopp, S. Kwan, D. Lange, M.T. Lucchini, J. Luo, D. Marlow, K. Mei, I. Ojalvo, J. Olsen,C. Palmer, P. Pirou´e, D. Stickland, C. Tully
University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg
Purdue University, West Lafayette, USA
V.E. Barnes, R. Chawla, S. Das, L. Gutay, M. Jones, A.W. Jung, B. Mahakud, G. Negro,N. Neumeister, C.C. Peng, S. Piperov, H. Qiu, J.F. Schulte, N. Trevisani, F. Wang, R. Xiao, W. Xie
Purdue University Northwest, Hammond, USA
T. Cheng, J. Dolen, N. Parashar
Rice University, Houston, USA
A. Baty, S. Dildick, K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Kilpatrick, A. Kumar, W. Li,B.P. Padley, R. Redjimi, J. Roberts † , J. Rorie, W. Shi, A.G. Stahl Leiton, Z. Tu, A. Zhang 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 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, D. Marley, R. Mueller, D. Overton, L. Perni`e, D. Rathjens, A. Safonov Texas Tech University, Lubbock, USA
N. Akchurin, J. Damgov, V. Hegde, S. Kunori, K. Lamichhane, S.W. Lee, T. Mengke,S. Muthumuni, T. Peltola, S. Undleeb, I. Volobouev, Z. Wang, A. Whitbeck
Vanderbilt University, Nashville, USA
E. Appelt, S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H. Ni, K. Padeken,F. Romeo, P. Sheldon, S. Tuo, J. Velkovska, M. Verweij
University of Virginia, Charlottesville, USA
L. Ang, M.W. Arenton, B. Cox, G. Cummings, J. Hakala, R. Hirosky, M. Joyce, A. Ledovskoy,C. Neu, B. Tannenwald, Y. Wang, E. Wolfe, F. Xia Wayne State University, Detroit, USA
P.E. Karchin, N. Poudyal, J. Sturdy, P. Thapa
University of Wisconsin - Madison, Madison, WI, USA
K. Black, T. Bose, J. Buchanan, C. Caillol, S. Dasu, I. De Bruyn, L. Dodd, C. Galloni,H. He, M. Herndon, A. Herv´e, U. Hussain, A. Lanaro, A. Loeliger, R. Loveless,J. Madhusudanan Sreekala, A. Mallampalli, D. Pinna, T. Ruggles, 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 Department of Basic and Applied Sciences, Faculty of Engineering, Arab Academyfor Science, Technology and Maritime Transport, Alexandria, Egypt3: Also at Universit´e Libre de Bruxelles, Bruxelles, Belgium4: Also at IRFU, CEA, Universit´e Paris-Saclay, Gif-sur-Yvette, France5: Also at Universidade Estadual de Campinas, Campinas, Brazil6: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil7: Also at UFMS, Nova Andradina, Brazil8: Also at Universidade Federal de Pelotas, Pelotas, Brazil9: Also at University of Chinese Academy of Sciences, Beijing, China10: Also at Institute for Theoretical and Experimental Physics named by A.I. Alikhanov ofNRC ‘Kurchatov Institute’, Moscow, Russia11: Also at Joint Institute for Nuclear Research, Dubna, Russia12: Also at Helwan University, Cairo, Egypt13: Now at Zewail City of Science and Technology, Zewail, Egypt14: Also at Ain Shams University, Cairo, Egypt15: Now at Fayoum University, El-Fayoum, Egypt16: Also at Purdue University, West Lafayette, USA17: Also at Universit´e de Haute Alsace, Mulhouse, France18: Also at Tbilisi State University, Tbilisi, Georgia19: Also at Erzincan Binali Yildirim University, Erzincan, Turkey20: Also at CERN, European Organization for Nuclear Research, Geneva, Switzerland21: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany22: Also at University of Hamburg, Hamburg, Germany23: Also at Department of Physics, Isfahan University of Technology, Isfahan, Iran, Isfahan,Iran24: Also at Brandenburg University of Technology, Cottbus, Germany25: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University,Moscow, Russia26: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary, Debrecen,Hungary27: Also at Physics Department, Faculty of Science, Assiut University, Assiut, Egypt28: 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 IIT Bhubaneswar, Bhubaneswar, India, Bhubaneswar, India31: Also at Institute of Physics, Bhubaneswar, India32: Also at G.H.G. Khalsa College, Punjab, India33: Also at Shoolini University, Solan, India34: Also at University of Hyderabad, Hyderabad, India
35: Also at University of Visva-Bharati, Santiniketan, India36: Also at Indian Institute of Technology (IIT), Mumbai, India37: Also at Deutsches Elektronen-Synchrotron, Hamburg, Germany38: Also at Department of Physics, University of Science and Technology of Mazandaran,Behshahr, Iran39: Now at INFN Sezione di Bari a , Universit`a di Bari b , Politecnico di Bari c , Bari, Italy40: Also at Italian National Agency for New Technologies, Energy and Sustainable EconomicDevelopment, Bologna, Italy41: Also at Centro Siciliano di Fisica Nucleare e di Struttura Della Materia, Catania, Italy42: Also at Riga Technical University, Riga, Latvia, Riga, Latvia43: Also at Consejo Nacional de Ciencia y Tecnolog´ıa, Mexico City, Mexico44: Also at Warsaw University of Technology, Institute of Electronic Systems, Warsaw, Poland45: Also at Institute for Nuclear Research, Moscow, Russia46: Now at National Research Nuclear University ’Moscow Engineering Physics Institute’(MEPhI), Moscow, Russia47: Also at Institute of Nuclear Physics of the Uzbekistan Academy of Sciences, Tashkent,Uzbekistan48: Also at St. Petersburg State Polytechnical University, St. Petersburg, Russia49: Also at University of Florida, Gainesville, USA50: Also at Imperial College, London, United Kingdom51: Also at P.N. Lebedev Physical Institute, Moscow, Russia52: Also at California Institute of Technology, Pasadena, USA53: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia54: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia55: Also at Universit`a degli Studi di Siena, Siena, Italy56: Also at Trincomalee Campus, Eastern University, Sri Lanka, Nilaveli, Sri Lanka57: Also at INFN Sezione di Pavia a , Universit`a di Pavia b , Pavia, Italy, Pavia, Italy58: Also at National and Kapodistrian University of Athens, Athens, Greece59: Also at Universit¨at Z ¨urich, Zurich, Switzerland60: Also at Stefan Meyer Institute for Subatomic Physics, Vienna, Austria, Vienna, Austria61: Also at Laboratoire d’Annecy-le-Vieux de Physique des Particules, IN2P3-CNRS, Annecy-le-Vieux, France62: Also at S¸ ırnak University, Sirnak, Turkey63: Also at Department of Physics, Tsinghua University, Beijing, China, Beijing, China64: Also at Near East University, Research Center of Experimental Health Science, Nicosia,Turkey65: Also at Beykent University, Istanbul, Turkey, Istanbul, Turkey66: Also at Istanbul Aydin University, Application and Research Center for Advanced Studies(App. & Res. Cent. for Advanced Studies), Istanbul, Turkey67: Also at Mersin University, Mersin, Turkey68: Also at Piri Reis University, Istanbul, Turkey69: Also at Adiyaman University, Adiyaman, Turkey70: Also at Ozyegin University, Istanbul, Turkey71: Also at Izmir Institute of Technology, Izmir, Turkey72: Also at Necmettin Erbakan University, Konya, Turkey73: Also at Bozok Universitetesi Rekt ¨orl ¨ug ¨u, Yozgat, Turkey74: Also at Marmara University, Istanbul, Turkey75: Also at Milli Savunma University, Istanbul, Turkey76: Also at Kafkas University, Kars, Turkey0