TOP-17-019

$Revision$ $HeadURL$ $Id$

TOP-17-019

# Search for the production of four top quarks in the single-lepton and
opposite-sign dilepton final states in proton-proton collisions at
$\sqrt{s}=13$

###### Abstract

A search for the standard model production of four top quarks
($\Pp\Pp\to\cPqt\cPaqt\cPqt\cPaqt$) is reported using single-lepton plus jets
and opposite-sign dilepton plus jets signatures. Proton-proton collisions are
recorded with the CMS detector at the LHC at a center-of-mass energy of 13in a
sample corresponding to an integrated luminosity of 35.8. A multivariate
analysis exploiting global event and jet properties is used to discriminate
$\cPqt\cPaqt\cPqt\cPaqt$ from $\ttbar$ production. No significant deviation is
observed from the predicted background. An upper limit is set on the cross
section for $\cPqt\cPaqt\cPqt\cPaqt$ production in the standard model of 48fb
at 95% confidence level. When combined with a previous measurement by the CMS
experiment from an analysis of other final states, the observed signal
significance is 1.4 standard deviations, and the combined cross section
measurement is $13^{+11}_{-9}$fb. The result is also interpreted in the
framework of effective field theory.

##  0.1 Introduction

Many models of physics beyond the standard model (BSM) predict enhanced or
modified couplings of top quarks to other particles. This is particularly
relevant for processes that have small production cross sections and,
therefore, are yet to be observed, such as the production of four top quarks,
$\cPqt\cPaqt\cPqt\cPaqt$. There is considerable interest in the measurement of
the $\cPqt\cPaqt\cPqt\cPaqt$ cross section because of its sensitivity to BSM
physics, including supersymmetry [1, 2], composite models [3], top quark
compositeness [4], two-Higgs-doublet models [5, 6, 7], and models with extra
spatial dimensions [8, 9]. Within the effective field theory (EFT) framework,
the contribution of any BSM process to $\cPqt\cPaqt\cPqt\cPaqt$ production can
be parameterized in terms of nonrenormalizable effective couplings of the
standard model (SM) fields, if the characteristic energy scale, $\Lambda$, of
the BSM physics is much larger than the typical energy scale of
$\cPqt\cPaqt\cPqt\cPaqt$ production at the LHC. A generic interpretation of
the $\cPqt\cPaqt\cPqt\cPaqt$ production can be done using the EFT predictions
[10].

The production of four top quarks from proton-proton ($\Pp\Pp$) interactions
$\Pp\Pp\to\cPqt\cPaqt\cPqt\cPaqt$ has not yet been observed. The SM predicts a
cross section, at next-to-leading order (NLO), with electroweak corrections
(EWK), of $\sigma_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{SM}}$ of $12.0$fb at the
center-of-mass energy of 13 [11]. To facilitate comparison with published
ATLAS and CMS analyses using comparable data sets, the NLO quantum
chromodynamics (QCD) calculation with a value of
$\sigma_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{SM}}=9.2$fb is used [12, 13].
Consequently, the experiments at the CERN LHC may be just approaching
sensitivity to the process, provided that it can be separated from the
overwhelming background from SM $\ttbar$ events. The lowest-order Feynman
diagrams illustrating typical contributions to SM four top quark production in
$\Pp\Pp$ collisions are shown in Fig. 1.

Figure 1: Representative Feynman diagrams for
$\Pp\Pp\to\cPqt\cPaqt\cPqt\cPaqt$ production at lowest order in the SM.

Searches for $\cPqt\cPaqt\cPqt\cPaqt$ production have been performed at 8by
ATLAS [14, 15, 16] and CMS[17], and also at 13(ATLAS (36.1[18, 19]) and CMS
(2.6[20])). The CMS Collaboration measured the $\cPqt\cPaqt\cPqt\cPaqt$
production cross section in a search exploiting same-sign dilepton and
multilepton signatures [21, 22] using 13data (35.9) collected in 2016. The
ATLAS Collaboration investigated anomalous $\cPqt\cPaqt\cPqt\cPaqt$ production
in events with Lorentz-boosted top quarks identified with top quark tagging
techniques [23] using 13data (36.1) collected in 2015–2016.

This paper presents a new search in the single-lepton (SL) (, )+jets and
opposite-sign dilepton (DL) ($\MM$, $\PGmpm\Pemp$, or $\EE$)+jets
$\cPqt\cPaqt\cPqt\cPaqt$ decay channels using $\Pp\Pp$ collisions at
13collected by the CMS experiment in 2016 and corresponding to an integrated
luminosity of $35.8$ . For this analysis, only final states containing one or
two leptons are considered, which constitute about 40% of all
$\cPqt\cPaqt\cPqt\cPaqt$ decays. Compared to the previous analysis [20], we
have implemented a number of important changes which combine to give a much
improved analysis sensitivity. The training process and selection of the input
variables for the event-discriminating MVA’s (Section 0.4.2) in both the SL
and OS dilepton channels has been re-optimized. A new categorization of the
signal sensitive regions at large jet and b-tag multiplicities has been
introduced, and a revised binning scheme is used to decrease the statistical
uncertainties, and improve the signal sensitivity. The categorisation provides
additional discrimination against the rare +boson (H, Z, W, WW/WZ/ZZ)
backgrounds. Lastly, a much larger simulated data set is used to populate the
discriminant bins with high jet multiplicity and high b-tag multiplicity.

##  0.2 The CMS detector

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. Within the solenoid
volume are a silicon pixel and strip tracker, a lead tungstate crystal
electromagnetic calorimeter, and a brass and scintillator hadron calorimeter,
each composed of a barrel and two endcap sections. Forward calorimeters extend
the pseudorapidity coverage ($\eta$) provided by the barrel and endcap
detectors. Muons are measured in gas-ionization detectors embedded in the
steel flux-return yoke outside the solenoid, in the range $\abs{\eta}<2.4$. A
more detailed description of the CMS detector, together with a definition of
the coordinate system used and the relevant kinematic variables, can be found
in Ref. [24].

##  0.3 Simulated samples

The acceptance for the SM $\Pp\Pp\to\cPqt\cPaqt\cPqt\cPaqt$ process is
estimated using samples simulated at NLO precision by the 2.2.2 generator [13,
25]. Only diagrams arising from quantum chromodynamics interactions were taken
into account in the simulation. The cross section used to normalize the
simulation is the NLO calculation of $9.2^{+2.9}_{-2.4}$ fb [13], where the
quoted uncertainty incorporates the variation of factorization and
renormalization scales used in the calculation of the matrix elements (ME),
and the dependence on the choice of parton distribution functions (PDFs). The
signal model includes MadSpin [26] and uses the default dynamic scale choice
in , defined as $\mu_{\mathrm{R,F}}=\frac{1}{2}\Sigma_{\PQt}m_{\mathrm{T}}$.
This is the sum of $m_{\mathrm{T}}$ over each outgoing parton (the four top
quarks), divided by two, where $m_{\mathrm{T}}=\sqrt{m^{2}+\pt^{2}}$, in which
$m$ is the mass of the parton, and is the transverse momentum.

The most important background process is top quark pair production with
additional jets ($\ttbar{+}\text{jets}$), that comprises over 90-95% of the
background. Next in importance are single top (ST) quark processes including
$t$-channel and $\PQt\PW$ production. These are followed by
$\PZ{+}\text{jets}$ and $\PW{+}\text{jets}$ electroweak processes (EW), where
only the leptonic decay modes of the bosons are considered. Next are rare
processes involving the production of a top quark-antiquark pair and a , , or
Higgs bosons, namely, +,,. Finally, production in association with dibosons,
$\ttbar\PW\PW$, $\ttbar\PW\PZ$, $\ttbar\PZ\PZ$, $\ttbar\PW\PH$,
$\ttbar\PZ\PW$, $\ttbar\PH\PH$, and triple top quark production
($\ttbar\PQt{+}\text{jets}$ and $\ttbar\PQt\PW$) are considered, processes we
collectively denote as $\cPqt\cPaqt\text{XY}$. Based on their signature
resemblance and comparability of production rates to the
$\cPqt\cPaqt\cPqt\cPaqt$ signal, +and +are grouped together while + and
$\cPqt\cPaqt\text{XY}$ are grouped together in the simulation.

Several Monte Carlo (MC) event generators are used to simulate these
processes. The $\ttbar{+}\text{jets}$ process is simulated using the -box v2
generator [27, 28, 29, 30, 31] at NLO accuracy for the ME, but the cross
section is normalized to its predicted value at next-to-next-to-leading order
(NNLO), which includes soft-gluon corrections, at next-to-next-to-leading-
logarithm accuracy, obtained with Top++ 2.0 [32, 33, 34, 35, 36, 37, 38]. The
-box simulations are interfaced with 8.212 using the CUETP8M2T4 tune [39, 40,
41]. Recent calculations [38] suggest that next-to-next-to-leading-order
effects have an important consequence on the shape of the top quark spectrum
that NLO ME generators are unable to reproduce. To allow for this, a parton-
level reweighting of the simulation has been applied to match the predictions
to the data [42, 43]. The correction is applied as a function of the
transverse momenta of the parton-level top quark and antiquark after initial-
and final-state radiation. Specifically for this result, additional dedicated
samples were created that populate the tails in high multiplicity with a
factor of 10 more events.

Single top quark $\PQt\PW$ processes are simulated with the -box v1 generator
[44], while $t$-channel processes are simulated with -box v2. Both are
interfaced with 8.212 using the CUETP8M2T4 tune, with the cross sections
normalized to the NLO calculations [45, 46]. The analysis has been shown [20]
to be insensitive to other ST quark production processes, such as $s$-channel
production.

Events with massive gauge bosons and no top quarks ($\PZ{+}\text{jets}$,
$\PW{+}\text{jets}$) are simulated using [13] at leading-order (LO) accuracy,
with up to four additional partons in the ME calculations, and using the MLM
matching scheme [47]. The tune CUETP8M1 is used for the parton shower (PS) and
underlying event (UE) modeling. These samples are normalized to their NNLO
cross sections [48].

The production of a pair in association with a , and up to one extra parton is
simulated using the generator at LO accuracy and matched with the PS
predictions using the MLM matching scheme. Top quark pair production in
association with a Higgs boson, $\ttbar\PH$, is modeled using -box v2,
interfaced with 8.212 with the CUETP8M2T4 tune. In this sample, only the
dominant $\PH\to\bbbar$ decays are taken into account. These three samples are
normalized to the NLO cross sections [49]. Top quark pair production in
association with one or two massive bosons is simulated using the LO ME in the
generator, and the CUETP8M2T4 tune of 8.212 to provide the PS. The cross
sections are scaled to their LO values [49].

For the samples with NLO MEs, the NNPDF3.0NLO [50] PDFs are used, while for LO
MEs, the corresponding NNPDF3.0LO PDFs are used. The parton shower,
hadronization, and underlying event models implemented in 8.212 [51] are used
to simulate higher-order processes and nonperturbative aspects of $\Pp\Pp$
collisions. The NLO simulations use strong coupling constant values of
$\alpS(M_{\PZ})=0.137$ and $\alpS(M_{\PZ})=0.113$ for the ME and PS modeling,
and the LO simulations use $\alpS(M_{\PZ})=0.130$ for the ME. In all
simulations involving the top quark, a mass $m_{\PQt}$ of 172.5is used.

The CUETP8M2T4 tune [39, 40, 41] currently provides the best description of
the data [52, 53]. The -box calculation describes the high-multiplicity tail
when this tune is used. The uncertainties cover the differences due to
alternative choices of the PS and hadronization models [54].

All of the simulated samples include an estimate of the additional $\Pp\Pp$
interactions per bunch crossing (pileup), modeled with the 8.212 program.
Corrections are applied to make the simulation of the number of additional
interactions representative of that observed in the data. The simulated events
are propagated through a simulation of the CMS detector based on (v.9.4) [55]
and reconstructed using the same algorithms as for the collider data.

##  0.4 Data analysis

###  0.4.1 Event selection

The final states considered in this analysis are the single-lepton channel
with exactly one muon or electron, (, )+jets, and the opposite-sign dilepton
channel, ($\MM$, $\PGmpm\Pemp$, $\EE$)+jets. In all cases, the leptons are
expected to originate from the bosons arising from top quark decays and thus
tend to be isolated, unlike the leptons produced in the decay of unstable
hadrons within jets.

Single-lepton events were recorded using a trigger [56] that required at least
one isolated muon with $\PT>24\GeV$ and $\abs{\eta}<2.4$, or one isolated
electron with $\PT>32\GeV$ and $\abs{\eta}<2.1$. Dilepton events were recorded
using either single-lepton or dilepton triggers. In the case of dilepton
triggers, the thresholds for the leading and subleading leptons for the dimuon
triggers are 17 and 8, respectively, 23 and 12for dielectron triggers, and 23
and 8for muon-electron triggers, regardless of lepton flavor. Dilepton
triggers require $\abs{\eta}<2.4$ for muons and $\abs{\eta}<2.5$ for
electrons. The single-lepton triggers were also used in the dilepton channel
to increase the efficiency, while retaining the orthogonality of the
selections addressing the two final states.

Offline event reconstruction uses the CMS particle-flow (PF) algorithm [57]
for particle reconstruction and identification. Single-lepton events are
required to have exactly one isolated muon with $\PT>26\GeV$ or one isolated
electron with $\PT>35\GeV$, either within $\abs{\eta}<2.1$. In the dilepton
channel, events are required to contain exactly two isolated leptons of
opposite sign with $\PT>25\GeV$ for the leading and $\PT>20\GeV$ for the
subleading lepton, within $\abs{\eta}<2.4$. Muons must satisfy the criteria
described in Ref. [58] and have a relative isolation, $I_{\text{rel}}<0.15$.
Electron candidates must satisfy stringent identification criteria, including
$I_{\text{rel}}$, which are described in Ref. [59]. The $I_{\text{rel}}$ is
defined as the scalar sum of the additional particles consistent with the same
vertex as the lepton, within a cone of angular radius $\Delta
R=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}$ = 0.4 around the
lepton, divided by the of the lepton, where $\Delta\eta$ and $\Delta\phi$ (in
radians) are the differences in pseudorapidity and azimuthal angle,
respectively, between the directions of the lepton and the additional
particle. The sum is corrected for the neutral particle contribution from
pileup on an event-by-event basis [59, 58]. To suppress background events from
decays of low-mass resonances and bosons, the lepton pairs are required to
have an invariant mass greater than 20and be outside of a 30window centered on
the boson mass in both the $\MM$ and $\EE$ channels. Events containing
additional muons with looser relative isolation, $I_{\text{rel}}<0.25$, or
isolated electrons are vetoed.

Each event is required to contain at least one reconstructed vertex. The
reconstructed vertex with the largest value of the quadratic sum of the of its
associated tracks is considered the primary $\Pp\Pp$ interaction vertex. Jets
are reconstructed from the PF candidates using the infrared- and collinear-
safe anti-algorithm [60, 61] with a distance parameter of 0.4. Pileup
interactions can contribute tracks and calorimetric energy depositions to the
jet momentum. To mitigate this effect, charged particles identified as
originating from pileup vertices are discarded and the jet is corrected for
the remaining contributions [62, 63]. Jet energy corrections are derived from
simulations to bring the measured response of jets to that of particle level
jets on average. In situ measurements of the momentum balance in dijet,
$\text{photon}{+}\text{jet}$, $\PZ{+}\text{jet}$, and multijet events are used
to account for any residual differences in jet energy scale between real and
simulated data [64]. The jet energy resolution is typically 15% at 10, 8% at
100, and 4% at 1. The missing transverse momentum vector is computed as the
negative vector sum of of all the PF candidates in an event, and its magnitude
is denoted as [65]. The quantity is modified to account for corrections to the
energy of the reconstructed jets in the event.

A minimum of seven jets for the single-muon and eight jets for the single-
electron channel are required, each of which must have $\PT>30\GeV$ and
$\abs{\eta}<2.5$. The difference in the jet multiplicity is motivated by the
need to reduce the residual contamination from multijet QCD background in the
electron channel due to a higher lepton misidentification rate. In the
selected events, at least two jets must be tagged as originating from the
hadronization of bottom quarks ( jets) using the combined secondary vertex
(CSVv2) algorithm at its medium working point [66]. Additional jet candidates
are identified using the CSVv2 algorithm at its loose working point. The two
working points, loose and medium, provide different levels of purity and
efficiency. The loose working point gives a misidentification rate of
approximately 10% for light-quark and gluon jets, with a tagging efficiency of
about 80%. The medium working point has a misidentification rate of about 1%
with a tagging efficiency of about 68%. The efficiency to tag quarks is 12%.
To suppress the small residual QCD background, is required to be larger than
$50$ . Studies on the estimation of non-prompt leptons from QCD multijet
background by inverting lepton isolation selection criteria have verified that
this background is negligible after applying the selection requirements. In
addition, a requirement on the scalar sum of the of all jets, $\HT>500\GeV$,
is applied. The requirement is used to suppress the background, while having
little effect on the signal acceptance [20].

In the dilepton channels, a minimum of four jets is required, each with
$\abs{\eta}<2.4$. Of these, at least two must be -tagged using the same CSVv2
algorithm with medium working point as was used in the single-lepton channel.
While the threshold for non-tagged jets is $30\GeV$ (as for the single-lepton
channel), the threshold for -tagged jets is lowered to $25\GeV$ to increase
the acceptance for events with multiple jets. The $\HT>500\GeV$ requirement is
also applied to the dilepton channels.

Figures 2–5 show the comparison of the data and simulations after these
selections have been applied for both the single-lepton and dilepton analyses.
The simulation of $\ttbar{+}\text{jets}$ process is split into three
categories: top quark pair associated with two additional light flavor or
gluon jets (+jj), top quark pair associated with a charm quark pair
(+$\ccbar$), and top quark pair associated with a bottom quark pair
(+$\bbbar$) [67]. The definitions of the variables in the figures are given in
the next section.

###  0.4.2 Multivariate discriminants

Boosted decision trees (BDTs) [68, 69] are used in two roles in this analysis:
to identify the top quarks and to improve the discrimination between signal
and background. The jet multiplicity, jet properties and the number of the
jets, as well as associated kinematic variables, feature strongly in the
choice of BDT input variables. The method is based on the strategies developed
for the previous 13CMS analyses in the single-lepton and opposite-sign
dilepton final states [20]. All BDTs are trained using the AdaBoost algorithm
[70], as implemented in the tmva package [71], and return a discriminant as
output.

The BDT for identifying hadronically decaying top quarks classifies
combinations of three jets (trijet) on how consistent they are with the trijet
originating from the all-hadronic decay of a top quark, rather than from other
sources such as initial-state radiation (ISR) or final-state radiation (FSR).
Its input variables consist of the invariant dijet and trijet masses, the
tagging information for the jet not associated to the dijet, and the angles
between the three jets. This BDT is trained to distinguish between the three
jets from a hadronically decaying top quark and any other permutation of 3-jet
combinations using the ME information in tt+jets simulations.

Because of the high jet multiplicity in both signal and background events,
many three-jet combinations are possible. The trijet permutations for each
event are ranked according to their discriminant value, from highest to
lowest. In the single-lepton channel, each background event contains a genuine
hadronic top quark decay, so the jets included in the first-ranked trijet
($T_{\text{trijet1}}$) are removed and the highest-ranked discriminant using
the remaining jets ($T_{\text{trijet2}}$) is used. In the dilepton channels,
the background contains no hadronic top quark decays, so only the output for
$T_{\text{trijet1}}$ is used as the discriminant.

The BDTs, yielding the discriminants for the single-lepton channel
($D_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{SL}}$) and for the dilepton channel
($D_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{DL}}$), use the discriminant from the
trijet associations, described above, as one of its input variables. In the
single-lepton channel, $D_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{SL}}$ is trained
separately for each jet multiplicity, and inclusively over the number of
-tagged jets. In the dilepton channel, the training is done unitarily for all
jet multiplicities while separately in $\MM,\PGmpm\Pemp$, and $\EE$ states.
The choice of input variables is optimized separately for the two channels and
is based on the characteristics of the lepton and jet activity in the events.
The resulting variable lists are different for the two channels. The variables
can be grouped into three categories: event activity, event topology, and
quark multiplicity. Although many of the input variables are correlated, each
one contributes some additional discrimination between the background and the
$\cPqt\cPaqt\cPqt\cPaqt$ signal.

Studies of the differences between the simulated and $\cPqt\cPaqt\cPqt\cPaqt$
events have led to the selection of the following variables describing the
hadronic activity in the event:

  1. 1.

The number of jets present in the event, $N_{\mathrm{j}}$.

  2. 2.

The scalar sum of the of all medium working point jets in the event,
$\HT^{\text{\PQb}}$.

  3. 3.

The ratio of the sum of the four highest jets in the event in the single-
lepton channel, or the two jets with the highest tagging discriminant in the
dilepton channel, to the of the other jets in the event, $\HT^{\text{ratio}}$.

  4. 4.

The sum in the event, subtracting the scalar sum of the two highest jets,
$\HT^{\text{2m}}$.

  5. 5.

The transverse momenta of the jets with the third- and fourth-largest in the
event, $\pt^{\text{j3}}$ and $\pt^{\text{j4}}$.

  6. 6.

The reduced event mass, $M_{\text{red}}^{\text{h}}$, defined as the invariant
mass of the system comprising all the jets in the reduced event, where the
reduced event is constructed by removing the jets contained in
$T_{\text{trijet1}}$ in single-lepton events. In events, the reduced event
will typically only contain the jet from the semileptonic top quark decay and
jets arising from ISR and FSR. Conversely, a reduced $\cPqt\cPaqt\cPqt\cPaqt$
event can contain up to two hadronically decaying top quarks and, as a result,
a relatively high reduced event mass.

  7. 7.

The reduced event , $H_{\mathrm{T}}^{\mathrm{x}}$, is defined as the of all
jets in the single-lepton event selection excluding those contained in
$T_{\text{trijet1}}$.

The event topology is characterized by the two variables:

  1. 1.

Event sphericity, $S$, [72], calculated from all of the jets in the event in
terms of the normalized tensor
$M^{\alpha\beta}={\sum_{i}p_{i}^{\alpha}p_{i}^{\beta}}/{\sum_{i}\abs{\vec{p_{i}}}^{2}},$
where $\alpha$ and $\beta$ refer to the three-components of the momentum of
the $i$th jet. The sphericity is defined as
$S=(3/2)(\lambda_{2}+\lambda_{3})$, where $\lambda_{2}$ and $\lambda_{3}$ are
the two smallest eigenvalues of $M^{\alpha\beta}$. The sphericity in
$\cPqt\cPaqt\cPqt\cPaqt$ events should differ from that in background events
of the same energy, since the jets in events will be less isotropically
distributed because of their recoil from sources such as ISR.

  2. 2.

Hadronic centrality, $C$, defined as the value of divided by the sum of the
energies of all jets in the event.

Since all these variables rely only on the hadronic information in the event,
sensitivity to the lepton information is provided through the and $\eta$ of
the highest lepton (or the only lepton for the single-lepton channel)
$\left(\pt^{\ell 1},\eta^{\ell 1}\right)$ and the angular difference ($\Delta
R_{\ell\ell}$) between the leptons in dilepton events. The jet multiplicity is
characterized in terms of the number of jets tagged by the CSVv2 algorithm
operating at its loose ($N_{\text{tags}}^{\text{l}}$) and medium
($N_{\text{tags}}^{\text{m}}$) operating points, and the angular separation
$\Delta R_{\PQb\PQb}$ between the -tagged jets with the highest CSVv2
discriminants. Finally, the third- and fourth-highest tagging discriminant
values are used as they allow separation between +light jets, and genuine
additional heavy-flavor jets, as present in $\cPqt\cPaqt\cPqt\cPaqt$ events.

The training variables were not changed as a function of final state or jet
multiplicity. In the single-lepton channel, the optimal variable set, listed
in the order of their discriminating power, is $T_{\text{trijet2}}$;
$\HT^{\text{\PQb}}$; $C$; $\pt^{\ell 1}$; $M_{\text{red}}^{\text{h}}$;
$H_{\mathrm{T}}^{\mathrm{x}}$; the third- and fourth-highest CSVv2
discriminants, and the of those tagged jets; the $\PT$ for the first, second,
fifth, and sixth jet. In the dilepton channel, the optimal variable set,
listed in the order of their discriminating power, is $N_{\mathrm{j}}$,
$T_{\text{trijet1}}$, $\HT^{\text{2m}}$, $\pt^{\text{j4}}$,
$N_{\text{tags}}^{\text{l}}$, $\HT^{\text{ratio}}$, $\HT^{\text{\PQb}}$, $S$,
$\Delta R_{\PQb\PQb}$, $N_{\text{tags}}^{\text{m}}$, $\Delta R_{\ell\ell}$,
$C$, $\pt^{\text{j3}}$, $\pt^{\ell 1}$ and $\eta^{\ell 1}$. The MC modeling of
the individual observables utilized in the discriminants
$D_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{SL}}$ and
$D_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{DL}}$ was verified using samples of events
and found to be in agreement with the data for all the jet and jet
multiplicities.

Figure 2: Distributions of $N_{\mathrm{j}}$, $N_{\text{tags}}^{\text{m}}$,
$\pt^{\ell 1}$ and $T_{\text{trijet2}}$ in the combined single-lepton
channels. In the upper panels, the data are shown as dots with error bars
representing statistical uncertainties, MC simulations are shown as a
histogram. The lower panels show the relative difference between the data and
the sum of all of the standard model backgrounds. In each panel, the shaded
band represents the total uncertainty in the dominant background estimate. See
Section 0.4.2 for the definitions of the variables.

Figure 3: Distributions of , $\HT^{\text{\PQb}}$,
$H_{\mathrm{T}}^{\mathrm{x}}$ and $M_{\text{red}}^{\text{h}}$ in the combined
single-lepton channel. In the upper panels, the data are shown as dots with
error bars representing statistical uncertainties, MC simulations are shown as
a histogram. The lower panels show the relative difference between the data
and the sum of all of the standard model backgrounds. In each panel, the
shaded band represents the total uncertainty in the dominant background
estimate. See Section 0.4.2 for the definitions of the variables.

Figure 4: Distributions of $N_{\mathrm{j}}$ and $T_{\text{trijet1}}$ in the
$\MM$ (upper row) and $\PGmpm\Pemp$ (lower row) channels. In the upper panels
of each figure, the data are shown as dots with error bars representing
statistical uncertainties, MC simulations are shown as a histogram. The lower
panels show the relative difference between the data and the sum of all of the
standard model backgrounds. In each panel, the shaded band represents the
total uncertainty in the dominant background estimate. See Section 0.4.2 for
the definitions of the variables.

Figure 5: Distributions of $N_{\mathrm{j}}$ and $T_{\text{trijet1}}$ in the
$\EE$ channel. In the upper panels of each figure, the data are shown as dots
with error bars representing statistical uncertainties, MC simulations are
shown as a histogram. The lower panels show the relative difference between
the data and the sum of all of the standard model backgrounds. In each panel,
the shaded band represents the total uncertainty in the dominant background
estimate. See Section 0.4.2 for the definitions of the variables.

##  0.5 Systematic uncertainties

The systematic uncertainties that affect this analysis can change the shape,
or the normalization, or both, of the $D_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{SL}}$
and $D_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{DL}}$ discriminants. The uncertainties
are characterized in Table 0.5. Each of the systematic uncertainty sources is
modeled by one nuisance parameter. The normalization-dependent terms account
for the uncertainties in the background yields, while the effect of the shape-
dependent terms is evaluated using discriminant distributions whose shape has
been modified by each of the uncertainties.

Uncertainties that affect the normalization of the data sets and shapes of the
$D_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{SL}}$ and
$D_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{DL}}$ discriminants. Their contribution to
different effects are marked by X.  Systematic uncertainty Normalization Shape
Integrated luminosity X Pileup modeling X X Lepton reconstruction and
identification X Jet energy corrections X X tagging X X Ren. and fact. scales
X X PS scales X ME-PS matching X UE X Jet multiplicity correction X Parton
distribution functions X X Top quark reweighting X Heavy-flavor reweighting X
X Rare process X

The experimental uncertainties considered are:

  * •

Integrated luminosity: A 2.5% normalization uncertainty on the integrated
luminosity [73].

  * •

Pileup modeling: The number of pileup events in the simulation is matched to
that of the data. The uncertainty due to this correction is estimated by using
two sets of alternative weights derived with a variation of $\pm 4.6\%$ on the
total inelastic $\Pp\Pp$ cross section [74].

  * •

Lepton reconstruction and identification: The uncertainties in lepton
identification, isolation, trigger efficiencies, and tracking efficiencies
were examined. After a comparison between data and simulations, we assign a
normalization uncertainty of 3% to take into account these effects.

  * •

Jet energy corrections and resolutions: The uncertainties due to limited
knowledge of the jet energy scale (JES) and the jet energy resolution (JER)
are estimated by varying the $\eta$\- and $\pt$-dependent JES and JER
corrections of all jets by $\pm$1 standard deviation [64]. In the case of JES
uncertainty, it was split into 6 components, which include uncertainties owing
to the absolute jet-energy scale, the pileup offset, the extrapolation between
samples of different jet-flavor composition, the parton fragmentation and
underlying event modeling and residual time and $\eta$-dependent variations.
Each component represents a quadratic sum of subsets of jet energy correction
uncertainties from different sources. The effect of each component is
evaluated separately.

  * •

tagging: The uncertainty in the tagging discriminant shape is estimated by
varying the shape of the discriminant distribution according to its one
standard deviation uncertainties in terms of the , $\eta$, and flavor of the
jets [67]. The variations correspond to uncertainties in the jet energy scale,
background contamination of the samples used to derive them, and statistical
uncertainties of these data samples.

Sources of systematic uncertainties originating from theory are listed below.

  * •

Renormalization and factorization scales: In order to estimate the uncertainty
arising from missing higher-order terms in the calculation of the signal and
background cross sections, renormalization and factorization scales are each
modified, independently, up and down by a factor of two relative to their
nominal values. The cases in which the two scales are varied in opposite
directions are excluded. This is estimated for both the and
$\cPqt\cPaqt\cPqt\cPaqt$ processes.

  * •

Parton shower scales: The evolution scales in the initial- and final-state PSs
are separately varied by a factor of 2 and $\sqrt{2}$, respectively, up and
down relative to their nominal values, in order to estimate the uncertainty
attributed to the shower model. This is estimated for both the and
$\cPqt\cPaqt\cPqt\cPaqt$ processes.

  * •

ME-PS matching: The uncertainty resulting from this source is estimated by
varying the -box PS scale parameter, $h_{\text{damp}}$, that controls the ME
and PS matching and regulates the high-$\pt$ radiation, within its uncertainty
by $\pm$1 standard deviation of the measured value
$h_{\text{damp}}=1.581^{+0.658}_{-0.585}m_{\PQt}$ [54]. This is estimated for
the process.

  * •

Underlying event: The uncertainty from the UE tune of event generator is
evaluated by using simulations with varied parameters that are related to the
CUETP8M2T4 tune [39]. This is estimated for the process.

  * •

Jet multiplicity correction: The modeling of $\ttbar{+}\text{jets}$ production
in -box is insufficient to describe the data in the regions of large jet
multiplicity. To allow for this, scale factors are determined from fits to the
single-lepton data in the signal depleted regions ($N_{\mathrm{j}}=8$,9, and
$N_{\text{tags}}^{\text{m}}=2$,3), and propagated to the signal sensitive
regions. The scale factors determined in the single-lepton channel are also
used in the dilepton channel taking into account the difference in the jet
multiplicity between the two channels. The uncertainty resulting from this
correction is 10% for the process [53].

  * •

Parton distribution functions: The PDF uncertainty [75] in production is
estimated by evaluating the shape difference between the nominal simulation
and simulations based on the NNPDF [50], MMHT14 [76], and CT10 [77] PDF sets.
This is estimated for the process.

  * •

Top quark reweighting: The simulation is corrected to match the observed
spectra [78, 79]. The uncertainty from the corrections made to the shape of
the top quark distribution is estimated by allowing the correction function to
vary within a $\pm$1 standard deviation uncertainty. This is estimated for the
process.

  * •

Heavy-flavor reweighting: To correctly model the rate of additional heavy-
flavor jets in production, the uncertainty in the rate of +is taken from the
$\pm$1 standard deviation uncertainty in the measured value [80]. This is
estimated for the process. As a cross-check, an independent uncertainty on
+$\ccbar$ production was added. The resulting effect on the expected
sensitivity of the search was found to be negligible.

  * •

Rare processes: Uncertainties from the cross sections of rare processes of
pair production in association with one or two massive gauge bosons and triple
top quark production are taken into account by allowing them to vary within
50% of their SM value [21].

The simulated samples used to evaluate the PS, ME-PS and UE uncertainties are
statistically limited, so these uncertainties are estimated conservatively by
assigning the larger value between the statistical uncertainty of these
simulated samples and the rate change of these simulated sample from the
nominal simulation as uncertainty, independently for different jet
multiplicities.

##  0.6 Results

A simultaneous binned maximum-likelihood template fit to the single-lepton,
dilepton, and combined experimental results was used to determine the signal
strength parameter, which is defined as the ratio of the observed and
predicted SM $\cPqt\cPaqt\cPqt\cPaqt$ cross sections,
$\mu=\sigma_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{obs}}/\sigma_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{SM}}$.
To increase the sensitivity of the analysis, events are categorized depending
on their jet and -tagged jet multiplicities. In the single-lepton channel
these categories are: $N_{\mathrm{j}}=7$, 8, 9, and $\geq$10 and
$N_{\text{tags}}^{\text{m}}=2$, 3, and $\geq$4 in each jet multiplicity
region. In the dilepton channel these are $N_{\mathrm{j}}=4$–5, 6–7, and
$\geq$8 and $N_{\text{tags}}^{\text{m}}=2$, and $\geq$3 in each jet
multiplicity region. In each category the binning was chosen to ensure at
least 4 predicted background events per bin.

The likelihood function incorporates each of the systematic uncertainties in
the signal and background $D_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{DL}}$ and
$D_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{SL}}$ templates as nuisance parameters in
the fit. The systematic uncertainties attributed to the trigger or specific to
the jet or lepton reconstruction were treated as fully correlated among the
different final states. The normalization uncertainties are included assuming
a log-normal distribution for the nuisance parameters, while the shape
uncertainties are included as Gaussian-distributed parameters.

All of the post-fit nuisance parameter values were found to be consistent with
their initial values to well within their quoted uncertainties, indicating the
consistency of the fit model with the observed data. Two of the post-fit
nuisance parameters are significantly constrained by the fit. These correspond
to the heavy-flavor reweighting and initial-state parton-shower radiation
scale, which are reduced by 65% and 30%, respectively. The sensitivity of the
analysis is affected almost equally by the statistical uncertainty and the
combined systematic uncertainties. The leading sources of systematic
uncertainty are the +heavy-flavor production reweighting, the jet multiplicity
correction, and the PS and UE modeling in simulation. The post-fit
distributions in signal-enriched $N_{\mathrm{j}}$ and
$N_{\text{tags}}^{\text{m}}$ categories are shown in Figs. 6–9 for the single-
lepton channel and Figs. 11–12 for the dilepton channel.

Figure 6:  Post-fit $D_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{SL}}$ distribution in
the single-muon channel for events satisfying baseline single-lepton selection
and $N_{\mathrm{j}}=7$, $N_{\text{tags}}^{\text{m}}=2$, 3, $\geq$4\. Non-
uniform binning of the BDT discriminant was chosen to achieve approximately
uniform distribution of the background. Dots represent data. Vertical error
bars show the statistical uncertainties in data. The post-fit background
predictions are shown as shaded histograms. Open boxes demonstrate the size of
the pre-fit uncertainty in the total background and are centered around the
pre-fit expectation value of the prediction. The hatched area shows the size
of the post-fit uncertainty in the background prediction. The signal histogram
template is shown as a solid line. The lower panel shows the relative
difference of the observed number of events over the post-fit background
prediction.

Figure 7:  Post-fit $D_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{SL}}$ distribution in
the (upper row) single-muon and (lower row) single-electron channels for
events satisfying baseline single-lepton selection and $N_{\mathrm{j}}=8$,
$N_{\text{tags}}^{\text{m}}=2$, 3, $\geq$4\. Non-uniform binning of the BDT
discriminant was chosen to achieve approximately uniform distribution of the
background. Dots represent data. Vertical error bars show the statistical
uncertainties in data. The post-fit background predictions are shown as shaded
histograms. Open boxes demonstrate the size of the pre-fit uncertainty in the
total background and are centered around the pre-fit expectation value of the
prediction. The hatched area shows the size of the post-fit uncertainty in the
background prediction. The signal histogram template is shown as a solid line.
The lower panel shows the relative difference of the observed number of events
over the post-fit background prediction.

Figure 8:  Post-fit $D_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{SL}}$ distribution in
the (upper row) single-muon and (lower row) single-electron channels for
events satisfying baseline single-lepton selection and $N_{\mathrm{j}}=9$,
$N_{\text{tags}}^{\text{m}}=2$, 3, $\geq$4\. Non-uniform binning of the BDT
discriminant was chosen to achieve approximately uniform distribution of the
background. Dots represent data. Vertical error bars show the statistical
uncertainties in data. The post-fit background predictions are shown as shaded
histograms. Open boxes demonstrate the size of the pre-fit uncertainty in the
total background and are centered around the pre-fit expectation value of the
prediction. The hatched area shows the size of the post-fit uncertainty in the
background prediction. The signal histogram template is shown as a solid line.
The lower panel shows the relative difference of the observed number of events
over the post-fit background prediction.

Figure 9:  Post-fit $D_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{SL}}$ distribution in
the (upper row) single-muon and (lower row) single-electron channels for
events satisfying baseline single-lepton selection and $N_{\mathrm{j}}\geq
10$, $N_{\text{tags}}^{\text{m}}=2$, 3, $\geq$4\. Non-uniform binning of the
BDT discriminant was chosen to achieve approximately uniform distribution of
the background. Dots represent data. Vertical error bars show the statistical
uncertainties in data. The post-fit background predictions are shown as shaded
histograms. Open boxes demonstrate the size of the pre-fit uncertainty in the
total background and are centered around the pre-fit expectation value of the
prediction. The hatched area shows the size of the post-fit uncertainty in the
background prediction. The signal histogram template is shown as a solid line.
The lower panel shows the relative difference of the observed number of events
over the post-fit background prediction.

Figure 10:  Post-fit $D_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{DL}}$ distributions in
the $\MM$ channel for events satisfying baseline opposite-sign dilepton
selection and (upper row) $N_{\mathrm{j}}=4$–5,
$N_{\text{tags}}^{\text{m}}=2$, $\geq$3, $N_{\mathrm{j}}=6$–7,
$N_{\text{tags}}^{\text{m}}=2$ and (lower row) $N_{\mathrm{j}}=6$–7,
$N_{\text{tags}}^{\text{m}}\geq 3$, $N_{\mathrm{j}}\geq 8$,
$N_{\text{tags}}^{\text{m}}=2$, $\geq$3\. Dots represent data. Vertical error
bars show the statistical uncertainties in data. The post-fit background
predictions are shown as shaded histograms. Open boxes demonstrate the size of
the pre-fit uncertainty in the total background and are centered around the
pre-fit expectation value of the prediction. The hatched area shows the size
of the post-fit uncertainty in the background prediction. The signal histogram
template is shown as a solid line. The lower panel shows the relative
difference of the observed number of events over the post-fit background
prediction.

Figure 11:  Post-fit $D_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{DL}}$ distributions in
the $\PGmpm\Pemp$ channel for events satisfying baseline opposite-sign
dilepton selection and (upper row) $N_{\mathrm{j}}=4$–5,
$N_{\text{tags}}^{\text{m}}=2$, $\geq$3, $N_{\mathrm{j}}=6$–7,
$N_{\text{tags}}^{\text{m}}=2$ and (lower row) $N_{\mathrm{j}}=6$–7,
$N_{\text{tags}}^{\text{m}}\geq 3$, $N_{\mathrm{j}}\geq 8$,
$N_{\text{tags}}^{\text{m}}=2$, $\geq$3\. Dots represent data. Vertical error
bars show the statistical uncertainties in data. The post-fit background
predictions are shown as shaded histograms. Open boxes demonstrate the size of
the pre-fit uncertainty in the total background and are centered around the
pre-fit expectation value of the prediction. The hatched area shows the size
of the post-fit uncertainty in the background prediction. The signal histogram
template is shown as a solid line. The lower panel shows the relative
difference of the observed number of events over the post-fit background
prediction.

Figure 12:  Post-fit $D_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{DL}}$ distributions in
the $\EE$ channel for events satisfying baseline opposite-sign dilepton
selection and (upper row) $N_{\mathrm{j}}=4$–5,
$N_{\text{tags}}^{\text{m}}=2$, $\geq$3, $N_{\mathrm{j}}=6$–7,
$N_{\text{tags}}^{\text{m}}=2$ and (lower row) $N_{\mathrm{j}}=6$–7,
$N_{\text{tags}}^{\text{m}}\geq 3$, $N_{\mathrm{j}}\geq 8$,
$N_{\text{tags}}^{\text{m}}=2$, $\geq$3\. Dots represent data. Vertical error
bars show the statistical uncertainties in data. The post-fit background
predictions are shown as shaded histograms. Open boxes demonstrate the size of
the pre-fit uncertainty in the total background and are centered around the
pre-fit expectation value of the prediction. The hatched area shows the size
of the post-fit uncertainty in the background prediction. The signal histogram
template is shown as a solid line. The lower panel shows the relative
difference of the observed number of events over the post-fit background
prediction.

Maximum-likelihood signal strength, $\mu$, and cross section estimates, as
well as the expected and observed significance of SM $\cPqt\cPaqt\cPqt\cPaqt$
production. Both $\mu$ and $\sigma_{\cPqt\cPaqt\cPqt\cPaqt}$ are constrained
to be positive. The results for the two analyses from this paper are shown
separately and combined. The results from a previous CMS multilepton
measurement are also given [21]. The values quoted for the uncertainties on
the signal strengths and cross sections are the one standard deviation (s.d.)
values and include all statistical and systematic uncertainties. The expected
significance is calculated assuming that the data are distributed according to
the prediction of the SM with nominal $\cPqt\cPaqt\cPqt\cPaqt$ production
cross section value $\sigma_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{SM}}$, which
corresponds to the assumed signal strength modifier value $\mu=1$.  Channel
Best fit $\mu$ Best fit $\sigma_{\cPqt\cPaqt\cPqt\cPaqt}$ Exp. significance
Obs. significance  (fb) s.d. s.d.  Single-lepton $1.6^{\,+\,4.6}_{\,-\,1.6}$
$15^{\,+\,42}_{\,-\,15}$ $0.21$ $0.36$ OS dilepton $0.0^{\,+\,2.7}$
$0^{\,+\,25}$ $0.36$ $0.0$ Combined $0.0^{\,+\,2.2}$ $0^{\,+\,20}$ $0.40$
$0.0$ (this analysis) SS dilepton + multilepton $1.8^{\,+\,1.5}_{\,-\,1.2}$
$17^{\,+\,14}_{\,-\,11}$ $1.0$ $1.6$ Combined $1.4^{\,+\,1.2}_{\,-\,1.0}$
$13^{\,+\,11}_{\,-\,9}$ $1.1$ $1.4$ (this analysis + [21])

No statistically significant deviation from the SM background prediction is
observed in the $D_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{DL}}$ or
$D_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{SL}}$ distributions. The corresponding
observed and expected significance of the signal and the best fit value of the
signal strength parameter are given together with the $\cPqt\cPaqt\cPqt\cPaqt$
cross section in Table 0.6. In order to quantify the experimental sensitivity
of the search, the median expected significance is calculated assuming that
the data are distributed according to the SM prediction with a nominal
$\cPqt\cPaqt\cPqt\cPaqt$ production cross section value
$\sigma_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{SM}}$, corresponding to the signal
strength modifier value $\mu=1$. An upper limit on the
$\cPqt\cPaqt\cPqt\cPaqt$ production cross section is derived using the
asymptotic approximation of the $\CLs$ method [81, 82, 83, 84, 85]. The
observed and expected 95% confidence level (CL) upper limits from the two
analyses and their combination are listed in Table 0.6. The expected upper
limit on the $\cPqt\cPaqt\cPqt\cPaqt$ production is calculated under
assumption of a background-only hypothesis, corresponding to the signal
strength modifier $\mu=0$.

Expected and observed 95% CL upper limits on SM $\cPqt\cPaqt\cPqt\cPaqt$
production as a multiple of $\sigma_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{SM}}$ and
in fb. The results for the two analyses from this paper are shown separately
and combined. The results from a previous CMS multilepton search are also
given [21]. The values quoted for the uncertainties in the expected limits
indicate the regions containing 68% of the distribution of limits expected
under the background-only hypothesis. The expected upper limits are calculated
assuming that the data are distributed according to the prediction of the
background-only model corresponding to the scenario with signal strength
modifier value $\mu=0$.  Channel Expected limit, $\mu$ Observed limit, $\mu$
Expected limit Observed limit  (fb) (fb)  Single-lepton
$9.4^{\,+\,4.4}_{\,-\,2.9}$ $10.6$ $86^{\,+\,40}_{\,-\,26}$ $97$ OS dilepton
$7.3^{\,+\,4.5}_{\,-\,2.5}$ $6.9$ $67^{\,+\,41}_{\,-\,23}$ $64$ Combined
$5.7^{\,+\,2.9}_{\,-\,1.8}$ $5.2$ $52^{\,+\,26}_{\,-\,17}$ $48$ (this
analysis) SS dilepton + multilepton $2.5_{\,-\,0.8}^{\,+\,1.4}$ $4.6$
$21_{\,-\,7}^{\,+\,11}$ $42$ Combined $2.2^{\,+\,1.1}_{\,-\,0.7}$ $3.6$
$20^{\,+\,10}_{\,-\,6}$ $33$ (this analysis + [21])

###  0.6.1 Combination with the same-sign dilepton and multileptons channels

An independent search for the SM $\cPqt\cPaqt\cPqt\cPaqt$ production has been
performed previously in same-sign dilepton and multilepton channels [21]. This
search is characterized by a different background composition which, in
contrast to the single-lepton and opposite-sign dilepton searches, is composed
of mainly the $\ttbar\PZ$ and $\ttbar\PW$ processes. In order to exploit the
complementarity of this analysis, a combination of the results from single-
lepton, opposite-sign, same-sign and multilepton channels has been performed.
The combination is based on a binned likelihood function equal to a product of
likelihood terms over all search regions considered in single-lepton,
opposite-sign and same-sign dilepton and multilepton channels.

Because of different origins of the dominant background processes, the main
systematic uncertainties in the three analyses are independent and can be
treated as uncorrelated. Nevertheless, the stability of the combination with
respect to the assumption on the correlations between common sources of
systematic uncertainty was tested by repeating the fit with and without
correlations between the corresponding nuisance parameters. The resulting
changes in the signal strength and expected limit were found to be less than
1% of the corresponding total uncertainties and were therefore not included.

The combined expected and observed 95% CL upper limits on the
$\cPqt\cPaqt\cPqt\cPaqt$ production are $20^{\,+\,10}_{\,-\,6}\unit{fb}$ and
$33\unit{fb}$, respectively, which is about a 10% improvement on the precision
of the measurement with respect to the multilepton analysis alone. A summary
of upper limit determinations from the individual analyses and their
combination is provided in Table 0.6.

###  0.6.2 Effective field theory interpretation

New physics may manifest itself as modified interactions of SM fields, even if
the associated particles are too heavy to be directly probed at the LHC. Such
interactions can be modeled by extending the SM Lagrangian with terms
involving composite operators of SM fields. Assuming that these terms preserve
the gauge symmetries of the SM, possible new interactions can be classified
according to their scaling dimension and the SM fields content [86, 87, 88].
The EFT Lagrangian reads

$\mathcal{L}_{\text{EFT}}=\mathcal{L}_{\text{SM}}^{\left(4\right)}+\frac{1}{\Lambda}\sum\limits_{k}C_{k}^{\left(5\right)}\mathcal{O}_{k}^{\left(5\right)}+\frac{1}{\Lambda^{2}}\sum\limits_{k}C_{k}^{\left(6\right)}\mathcal{O}_{k}^{\left(6\right)}+\ldots,$
(1)

where $\mathcal{L}_{\text{SM}}^{\left(4\right)}$ is the SM Lagrangian, while
$\mathcal{O}_{k}^{\left(n\right)}$ and $C_{k}^{\left(n\right)}$ denote
dimension-$n$ (dim-$n$) composite operators and their coupling parameters,
respectively. Each term in the sum is suppressed by $\Lambda^{n-4}$, where
$\Lambda$ is an energy scale that characterizes the new physics and $n$ is the
scaling dimension of the corresponding operator. The energy scale, $\Lambda$,
is the scale below which on-shell effects of BSM physics can be neglected and
is typically related to the mass scale of the hypothetical BSM states. The EFT
approach is generic and, in principle, experimental constraints obtained
within the EFT framework can be recast into bounds on parameters of any
ultraviolet-complete new physics model.

The production of four top quarks is a unique signature that provides
information about models that predict enhanced interactions of the third
generation quarks, such as four-fermion $\cPqt\cPaqt\cPqt\cPaqt$ coupling. The
dim-5 operators do not contribute to $\cPqt\cPaqt\cPqt\cPaqt$ production
because they do not couple to top quarks [89]. A minimal basis of composite
dim-6 operators contributing in Eq. (1) was derived in Ref. [87]. Only a small
subset of these operators lead to four top quark production at LO in the EFT
perturbation series. In a restricted scenario [90, 10], assuming that new
physics couples predominantly to the left-handed doublet and right-handed up-
type quark singlet of the third generation, only four operators are expected
to contribute significantly to $\cPqt\cPaqt\cPqt\cPaqt$ production, namely,

$\displaystyle\mathcal{O}^{1}_{\cPqt\cPqt}$
$\displaystyle=\\!\left(\cPaqt_{\mathrm{R}}\gamma^{\mu}\cPqt_{\mathrm{R}}\right)\\!\left(\cPaqt_{\mathrm{R}}\gamma_{\mu}\cPqt_{\mathrm{R}}\right),$
(2) $\displaystyle\mathcal{O}_{\cmsSymbolFace{Q}\cmsSymbolFace{Q}}^{1}$
$\displaystyle=\\!\left(\overline{\cmsSymbolFace{Q}}_{\cmsSymbolFace}{L}\gamma^{\mu}\cmsSymbolFace{Q}_{\cmsSymbolFace}{L}\right)\\!\left(\overline{\cmsSymbolFace{Q}}_{\cmsSymbolFace}{L}\gamma_{\mu}\cmsSymbolFace{Q}_{\cmsSymbolFace}{L}\right),$
$\displaystyle\mathcal{O}_{\cmsSymbolFace{Q}\cPqt}^{1}$
$\displaystyle=\\!\left(\overline{\cmsSymbolFace{Q}}_{\cmsSymbolFace}{L}\gamma^{\mu}\cmsSymbolFace{Q}_{\cmsSymbolFace}{L}\right)\\!\left(\cPaqt_{\mathrm{R}}\gamma_{\mu}\cPqt_{\mathrm{R}}\right),$
$\displaystyle\mathcal{O}_{\cmsSymbolFace{Q}\cPqt}^{8}$
$\displaystyle=\\!\left(\overline{\cmsSymbolFace{Q}}_{\cmsSymbolFace}{L}\gamma^{\mu}T^{\mathrm{A}}\cmsSymbolFace{Q}_{\cmsSymbolFace}{L}\right)\\!\left(\cPaqt_{\mathrm{R}}\gamma_{\mu}T^{\mathrm{A}}\cPqt_{\mathrm{R}}\right),$

where $\cmsSymbolFace{Q}_{\cmsSymbolFace}{L}$ and $\cPqt_{\mathrm{R}}$ denote
the left-handed third generation quark doublet and the right-handed top quark
singlet, respectively. The 4-fermion $\ttbar\bbbar$ operators were not
included because of the negligible quark parton density in the proton. Leading
order predictions for the $\Pp\Pp\to\cPqt\cPaqt\cPqt\cPaqt$ cross section can
be parameterized using the equation

$\sigma_{\cPqt\cPaqt\cPqt\cPaqt}=\sigma_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{SM}}+\frac{1}{\Lambda^{2}}\sum_{k}{C_{k}\sigma_{k}^{\left(1\right)}}+\frac{1}{\Lambda^{4}}\sum_{{j}\leq{k}}{{C_{j}C_{k}\sigma_{{j},{k}}^{\left(2\right)}}},$
(3)

where the linear terms, $C_{k}\sigma_{{k}}^{\left(1\right)}$, represent the
interference of the SM production with the dim-6 EFT contribution, while the
quadratic terms include two components: the square of the diagrams containing
one EFT operator, and the interference term for two diagrams, each with one
EFT operator. Representing $C_{k}$ as a column-vector, $\vec{\textbf{C}}$, Eq.
(3) can be expressed in a matrix form as

$\sigma_{\cPqt\cPaqt\cPqt\cPaqt}=\sigma_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{SM}}+\frac{1}{\Lambda^{2}}\vec{\boldsymbol{C}}^{\boldsymbol{T}}\cdot\boldsymbol{\vec{\sigma}}^{\left(1\right)}+\frac{1}{\Lambda^{4}}\vec{\textbf{C}}^{\textbf{T}}{\boldsymbol{\sigma}}^{\left(2\right)}\vec{\boldsymbol{C}}.$
(4)

In order to find $\boldsymbol{\vec{\sigma}}^{\left(1\right)}$ and
${\boldsymbol{\sigma}}^{\left(2\right)}$, a system of linear equations has to
be solved. It is obtained by substituting linearly-independent vectors
$\vec{\boldsymbol{C}}$ into Eq. (4). In the cross section calculation, the EFT
interactions are implemented in the FeynRules [90, 91] package and interfaced
with [13]. The NNPDF3.0LO [50] PDF set and $\alpS(M_{\PZ})=0.138$ were used in
the calculation. In the EFT predictions, the SM contribution,
$\sigma_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{SM}}$ in Eqs. (3) and (4), was
rescaled to the NLO cross section of $9.2\unit{fb}$ for the collision energy
of 13 . The linear and quadratic coefficients, $\sigma_{k}^{\left(1\right)}$
and $\sigma_{{j},{k}}^{\left(2\right)}$, in Eq. (3) can be found in Table
0.6.2.

Linear (left) and quadratic (right) parameterization coefficients,
$\sigma_{k}^{\left(1\right)}$ and $\sigma_{{j},{k}}^{\left(2\right)}$, of Eq.
(3). The coefficients $\sigma_{k}^{\left(1\right)}$ are in units (fb
${\TeVns}^{2}$), while the coefficients $\sigma_{{j},{k}}^{\left(2\right)}$
are in units (fb ${\TeVns}^{4}$).

| $\sigma_{k}^{\left(1\right)}$ |  |  | $\sigma_{{j},{k}}^{\left(2\right)}$ |
---|---|---|---|---|---
Operator |  | $\mathcal{O}^{1}_{\cPqt\cPqt}$ | $\mathcal{O}_{\cmsSymbolFace{Q}\cmsSymbolFace{Q}}^{1}$ | $\mathcal{O}_{\cmsSymbolFace{Q}\cPqt}^{1}$ | $\mathcal{O}_{\cmsSymbolFace{Q}\cPqt}^{8}$
$\mathcal{O}^{1}_{\cPqt\cPqt}$ | 0.39 | 5.59 | 0.36 |  $-$0.39 | 0.3
$\mathcal{O}_{\cmsSymbolFace{Q}\cmsSymbolFace{Q}}^{1}$ | 0.47 |  | 5.49 |  $-$0.45 | 0.13
$\mathcal{O}_{\cmsSymbolFace{Q}\cPqt}^{1}$ | 0.03 |  |  | 1.9 |  $-$0.08
$\mathcal{O}_{\cmsSymbolFace{Q}\cPqt}^{8}$ | 0.28 |  |  |  | 0.45

The observed limit of $3.6\sigma_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{SM}}$, with a
corresponding expected limit of
$3.2\sigma_{\cPqt\cPaqt\cPqt\cPaqt}^{\text{SM}}$ (assuming $\mu=1$), from the
combined experimental results, is used to constrain possible contributions of
EFT operators. Since the data are only sensitive to the ratios,
$C_{k}/\Lambda^{2}$, the constraints are presented only for such ratios. In
the limit setting, SM kinematics of the $\cPqt\cPaqt\cPqt\cPaqt$ final state
were assumed and only rate information was utilized to calculate the
constraints. Besides the NLO scale uncertainty from the SM
$\cPqt\cPaqt\cPqt\cPaqt$ NLO prediction, no further scale uncertainties were
added because other uncertainties on $\cPqt\cPaqt\cPqt\cPaqt$ production are
already included in the experimental limit.

Independent limits were obtained under the assumption that only one operator
contributes to the $\cPqt\cPaqt\cPqt\cPaqt$ cross section with the
coefficients of the other operators set to zero. The intervals obtained are
summarized in Table 0.6.2. More conservative estimates were obtained by
marginalizing the contribution of other operators within the interval
$C_{k}/\Lambda^{2}\in[-4\pi,4\pi]$, defined by the stability of perturbation
series. The corresponding limits are listed in Table 0.6.2. The results
obtained are only slightly weaker than independent constraints because of the
small correlations between the operators.

Expected and observed 95% CL intervals for selected coupling parameters. The
intervals are extracted from upper limit on the $\cPqt\cPaqt\cPqt\cPaqt$
production cross section in the EFT model, where only one selected operator
has a nonvanishing contribution.

Operator | Expected $C_{k}/\Lambda^{2}$ (${\TeVns}^{-2}$) | Observed (${\TeVns}^{-2}$)
---|---|---
$\mathcal{O}^{1}_{\cPqt\cPqt}$ | [$-$2.0, 1.8] | [$-$2.1, 2.0]
$\mathcal{O}_{\cmsSymbolFace{Q}\cmsSymbolFace{Q}}^{1}$ | [$-$2.0, 1.8] | [$-$2.2, 2.0]
$\mathcal{O}_{\cmsSymbolFace{Q}\cPqt}^{1}$ | [$-$3.3, 3.2] | [$-$3.5, 3.5]
$\mathcal{O}_{\cmsSymbolFace{Q}\cPqt}^{8}$ | [$-$7.3, 6.1] | [$-$7.9, 6.6]

Expected and observed 95% CL intervals for selected coupling parameters when
contribution of other operators is marginalized.

Operator | Expected $C_{k}/\Lambda^{2}$ (${\TeVns}^{-2}$) | Observed (${\TeVns}^{-2}$)
---|---|---
$\mathcal{O}^{1}_{\cPqt\cPqt}$ | [$-$2.0, 1.9] | [$-$2.2, 2.1]
$\mathcal{O}_{\cmsSymbolFace{Q}\cmsSymbolFace{Q}}^{1}$ | [$-$2.0, 1.9] | [$-$2.2, 2.0]
$\mathcal{O}_{\cmsSymbolFace{Q}\cPqt}^{1}$ | [$-$3.4, 3.3] | [$-$3.7, 3.5]
$\mathcal{O}_{\cmsSymbolFace{Q}\cPqt}^{8}$ | [$-$7.4, 6.3] | [$-$8.0, 6.8]

As shown in Tables 0.6.2 and 0.6.2, the data have highest sensitivity to the
contribution of $\mathcal{O}^{1}_{\cPqt\cPqt}$ and
$\mathcal{O}_{\cmsSymbolFace{Q}\cmsSymbolFace{Q}}^{1}$. The allowed intervals
for the coupling parameters are almost independent of the other considered
operators and stay stable after marginalization.

##  0.7 Summary

A search for standard model $\cPqt\cPaqt\cPqt\cPaqt$ production has been
performed in final states with one or two oppositely signed muons or electrons
plus jets. The observed yields attributed to $\cPqt\cPaqt\cPqt\cPaqt$
production are consistent with the background predictions. An upper limit at
95% confidence level of $48$fb is set on the cross section for
$\cPqt\cPaqt\cPqt\cPaqt$ production. Combining this result with a previous
same-sign dilepton and multilepton search [21] the resulting cross section is
$13^{\,+\,11}_{\,-\,9}\unit{fb}$ with an observed significance of $1.4$
standard deviations. The combined result constitutes one of the most stringent
constraints from CMS on the production of four top quarks and can be used for
phenomenological reinterpretation of a wide range of new physics models. The
experimental results are interpreted in the effective field theory framework
and yield limits on dimension-6 four-fermion operators coupling to third
generation quarks competitive with the latest ATLAS interpretation [18].

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing centers and personnel of the Worldwide LHC Computing Grid for
delivering so effectively the computing infrastructure essential to our
analyses. Finally, we acknowledge the enduring support for the construction
and operation of the LHC and the CMS detector provided by the following
funding agencies: BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq,
CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST,
and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF
(Cyprus); SENESCYT (Ecuador); MoER, ERC IUT, PUT and ERDF (Estonia); Academy
of Finland, 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 (Montenegro); 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 and SFFR (Ukraine); STFC (United Kingdom); DOE and NSF (USA).
Individuals have received support from the Marie-Curie program and the
European Research Council and Horizon 2020 Grant, contract Nos. 675440,
752730, and 765710 (European Union); the Leventis Foundation; the A.P. Sloan
Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science
Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et
dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door
Wetenschap en Technologie (IWT-Belgium); the 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. Z181100004218003; the
Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the
Lendület (“Momentum”) Program and the János Bolyai Research Scholarship of the
Hungarian Academy of Sciences, the New National Excellence Program ÚNKP, the
NKFIA research grants 123842, 123959, 124845, 124850, 125105, 128713, 128786,
and 129058 (Hungary); the Council of Science and Industrial Research, India;
the HOMING PLUS program of the Foundation for Polish Science, cofinanced from
European Union, Regional Development Fund, the Mobility Plus program of the
Ministry of Science and Higher Education, the National Science Center
(Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543,
2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406;
the National Priorities Research Program by Qatar National Research Fund; the
Ministry of Science and Education, grant no. 3.2989.2017 (Russia); the
Programa Estatal de Fomento de la Investigación Científica y Técnica de
Excelencia María de Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa
del Principado de Asturias; the Thalis and Aristeia programs cofinanced by EU-
ESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral
Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its
2nd Century Project Advancement Project (Thailand); the Welch Foundation,
contract C-1845; and the Weston Havens Foundation (USA).

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##  .8 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan${}^{\textrm{\textdagger}}$, A. Tumasyan Institut für
Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, T. Bergauer, J. Brandstetter, M. Dragicevic, J. Erö, A.
Escalante Del Valle, M. Flechl, R. Frühwirth1, M. Jeitler1, N. Krammer, I.
Krätschmer, D. Liko, T. Madlener, I. Mikulec, N. Rad, J. Schieck1, R.
Schöfbeck, M. Spanring, D. Spitzbart, W. Waltenberger, C.-E. Wulz1, M. Zarucki
Institute for Nuclear Problems, Minsk, Belarus
V. Drugakov, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
M.R. Darwish, E.A. De Wolf, D. Di Croce, X. Janssen, J. Lauwers, 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, L. Moreels, Q. Python, K. Skovpen, S.
Tavernier, W. Van Doninck, P. Van Mulders, I. Van Parijs Université Libre de
Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, B.
Dorney, L. Favart, A. Grebenyuk, A.K. Kalsi, J. Luetic, A. Popov, N. Postiau,
E. Starling, L. Thomas, C. Vander Velde, P. Vanlaer, D. Vannerom, Q. Wang
Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, I. Khvastunov2, C. Roskas, D. Trocino, M. Tytgat, W.
Verbeke, B. Vermassen, M. Vit, N. Zaganidis Université Catholique de Louvain,
Louvain-la-Neuve, Belgium
O. Bondu, G. Bruno, C. Caputo, P. David, C. Delaere, M. Delcourt, A.
Giammanco, V. Lemaitre, A. Magitteri, J. Prisciandaro, A. Saggio, M. Vidal
Marono, P. Vischia, J. Zobec Centro Brasileiro de Pesquisas Fisicas, Rio de
Janeiro, Brazil
F.L. Alves, G.A. Alves, G. Correia Silva, C. Hensel, A. Moraes, P. Rebello
Teles Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, C. De Oliveira Martins, S.
Fonseca De Souza, L.M. Huertas Guativa, H. Malbouisson, J. Martins5, D. Matos
Figueiredo, M. Medina Jaime6, M. Melo De Almeida, C. Mora Herrera, L. Mundim,
H. Nogima, W.L. Prado Da Silva, L.J. Sanchez Rosas, A. Santoro, A. Sznajder,
M. Thiel, E.J. Tonelli Manganote3, F. Torres Da Silva De Araujo, A. Vilela
Pereira Universidade Estadual Paulista a, Universidade Federal do ABC b, São
Paulo, Brazil
S. Ahujaa, C.A. Bernardesa, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M.
Gregoresb, D.S. Lemos, P.G. Mercadanteb, S.F. Novaesa, SandraS. Padulaa
Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of
Sciences, Sofia, Bulgaria
A. Aleksandrov, G. Antchev, R. Hadjiiska, P. Iaydjiev, A. Marinov, 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 Beihang
University, Beijing, China
W. Fang7, X. Gao7, L. Yuan Institute of High Energy Physics, Beijing, China
M. Ahmad, G.M. Chen, H.S. Chen, M. Chen, C.H. Jiang, D. Leggat, H. Liao, Z.
Liu, S.M. Shaheen8, A. Spiezia, J. Tao, E. Yazgan, H. Zhang, S. Zhang8, J.
Zhao State Key Laboratory of Nuclear Physics and Technology, Peking
University, Beijing, China
A. Agapitos, Y. Ban, G. Chen, A. Levin, J. Li, L. Li, Q. Li, Y. Mao, S.J.
Qian, D. Wang Tsinghua University, Beijing, China
Z. Hu, Y. Wang Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, L.F. Chaparro Sierra, C. Florez, C.F. González
Hernández, M.A. Segura Delgado Universidad de Antioquia, Medellin, Colombia
J. Mejia Guisao, J.D. Ruiz Alvarez, C.A. Salazar González, N. Vanegas Arbelaez
University of Split, Faculty of Electrical Engineering, Mechanical Engineering
and Naval Architecture, Split, Croatia
D. Giljanović, 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, S. Ceci, D. Ferencek, K. Kadija, B. Mesic, M. Roguljic, A.
Starodumov9, T. Susa University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, E. Erodotou, A. Ioannou, M. Kolosova, S. Konstantinou,
G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H.
Rykaczewski, D. Tsiakkouri Charles University, Prague, Czech Republic
M. Finger10, M. Finger Jr.10, 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, Egyptian Network of High Energy Physics, Cairo, Egypt
H. Abdalla11, A.A. Abdelalim12,13 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
F. Garcia, J. Havukainen, J.K. Heikkilä, T. Järvinen, V. Karimäki, R.
Kinnunen, T. Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, P.
Luukka, T. Mäenpää, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta
University of Technology, Lappeenranta, Finland
T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, B. Fabbro, J.L. Faure, F.
Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
C. Leloup, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin, A. Savoy-
Navarro14, M. Titov Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole
Polytechnique, Institut Polytechnique de Paris
C. Amendola, F. Beaudette, P. Busson, C. Charlot, 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 Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France
J.-L. Agram15, J. Andrea, D. Bloch, G. Bourgatte, J.-M. Brom, E.C. Chabert, C.
Collard, E. Conte15, J.-C. Fontaine15, D. Gelé, U. Goerlach, M. Jansová, A.-C.
Le Bihan, N. Tonon, P. Van Hove Centre de Calcul de l’Institut National de
Physique Nucleaire et de Physique des Particules, CNRS/IN2P3, Villeurbanne,
France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, C. Camen, N. Chanon, R. Chierici, D.
Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M. Gouzevitch, B.
Ille, Sa. Jain, F. Lagarde, I.B. Laktineh, H. Lattaud, M. Lethuillier, L.
Mirabito, S. Perries, V. Sordini, G. Touquet, M. Vander Donckt, S. Viret
Georgian Technical University, Tbilisi, Georgia
A. Khvedelidze10 Tbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze10 RWTH Aachen University, I. Physikalisches Institut, Aachen,
Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, D. Meuser, A.
Pauls, M. Preuten, M.P. Rauch, C. Schomakers, J. Schulz, M. Teroerde, B.
Wittmer RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
A. Albert, M. Erdmann, S. Erdweg, T. Esch, B. Fischer, R. 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, M. Rieger, J.
Roemer, A. Schmidt, S.C. Schuler, A. Sharma, S. Thüer, S. Wiedenbeck RWTH
Aachen University, III. Physikalisches Institut B, Aachen, Germany
G. Flügge, W. Haj Ahmad16, O. Hlushchenko, T. Kress, T. Müller, A. Nehrkorn,
A. Nowack, C. Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl17 Deutsches
Elektronen-Synchrotron, Hamburg, Germany
M. Aldaya Martin, P. Asmuss, I. Babounikau, H. Bakhshiansohi, K. Beernaert, O.
Behnke, U. Behrens, A. Bermúdez Martínez, D. Bertsche, A.A. Bin Anuar, K.
Borras18, V. Botta, A. Campbell, A. Cardini, P. Connor, S. Consuegra
Rodríguez, C. Contreras-Campana, V. Danilov, A. De Wit, M.M. Defranchis, C.
Diez Pardos, D. Domínguez Damiani, G. Eckerlin, D. Eckstein, T. Eichhorn, A.
Elwood, E. Eren, E. Gallo19, A. Geiser, J.M. Grados Luyando, A. Grohsjean, M.
Guthoff, M. Haranko, A. Harb, A. Jafari, N.Z. Jomhari, H. Jung, A. Kasem18, M.
Kasemann, H. Kaveh, J. Keaveney, C. Kleinwort, J. Knolle, D. Krücker, W.
Lange, T. Lenz, J. Leonard, J. Lidrych, K. Lipka, W. Lohmann20, R. Mankel,
I.-A. Melzer-Pellmann, A.B. Meyer, M. Meyer, M. Missiroli, G. Mittag, J.
Mnich, A. Mussgiller, V. Myronenko, D. Pérez Adán, S.K. Pflitsch, D. Pitzl, A.
Raspereza, A. Saibel, M. Savitskyi, V. Scheurer, P. Schütze, C.
Schwanenberger, R. Shevchenko, A. Singh, H. Tholen, O. Turkot, A. Vagnerini,
M. Van De Klundert, G.P. Van Onsem, R. Walsh, Y. Wen, K. Wichmann, C. Wissing,
O. Zenaiev, R. Zlebcik University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, V. Blobel, T. Dreyer, A.
Ebrahimi, A. Fröhlich, C. Garbers, E. Garutti, D. Gonzalez, P. Gunnellini, J.
Haller, A. Hinzmann, A. Karavdina, G. Kasieczka, R. Klanner, R. Kogler, N.
Kovalchuk, S. Kurz, V. Kutzner, J. Lange, T. Lange, A. Malara, D. Marconi, J.
Multhaup, M. Niedziela, C.E.N. Niemeyer, D. Nowatschin, A. Perieanu, A.
Reimers, O. Rieger, C. Scharf, P. Schleper, S. Schumann, J. Schwandt, J.
Sonneveld, H. Stadie, G. Steinbrück, F.M. Stober, M. Stöver, B. Vormwald, I.
Zoi Karlsruher Institut fuer Technologie, Karlsruhe, Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, T. Berger, E. Butz, R. Caspart, T.
Chwalek, W. De Boer, A. Dierlamm, K. El Morabit, N. Faltermann, M. Giffels, P.
Goldenzweig, A. Gottmann, M.A. Harrendorf, F. Hartmann17, U. Husemann, S.
Kudella, S. Mitra, M.U. Mozer, Th. Müller, M. Musich, A. Nürnberg, G. Quast,
K. Rabbertz, M. Schröder, I. Shvetsov, H.J. Simonis, R. Ulrich, M. Weber, C.
Wöhrmann, R. Wolf 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 National and Kapodistrian University of Athens, Athens, Greece
M. Diamantopoulou, G. Karathanasis, P. Kontaxakis, A. Panagiotou, I.
Papavergou, N. Saoulidou, A. Stakia, K. Theofilatos, K. Vellidis National
Technical University of Athens, Athens, Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis University of
Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, K.
Manitara, N. Manthos, I. Papadopoulos, J. Strologas, F.A. Triantis, D.
Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
M. Bartók21, M. Csanad, P. Major, K. Mandal, A. Mehta, M.I. Nagy, G. Pasztor,
O. Surányi, G.I. Veres Wigner Research Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath22, F. Sikler, T.Á. Vámi, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi21, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
P. Raics, D. Teyssier, Z.L. Trocsanyi, B. Ujvari Eszterhazy Karoly University,
Karoly Robert Campus, Gyongyos, Hungary
T. Csorgo, W.J. Metzger, F. Nemes, T. Novak Indian Institute of Science
(IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri, P.C. Tiwari National Institute of Science
Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati24, C. Kar, G. Kole, P. Mal, V.K. Muraleedharan Nair Bindhu, A.
Nayak25, D.K. Sahoo24, S.K. Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, R. Chawla, N. Dhingra, R.
Gupta, A. Kaur, M. Kaur, S. Kaur, P. Kumari, M. Lohan, M. Meena, K. Sandeep,
S. Sharma, J.B. Singh, A.K. Virdi, G. Walia University of Delhi, Delhi, India
A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, Ashok Kumar, S.
Malhotra, M. Naimuddin, P. Priyanka, K. Ranjan, Aashaq Shah, R. Sharma Saha
Institute of Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj26, M. Bharti26, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep26,
D. Bhowmik, S. Dey, S. Dutta, S. Ghosh, M. Maity27, K. Mondal, S. Nandan, A.
Purohit, P.K. Rout, A. Roy, G. Saha, S. Sarkar, T. Sarkar27, M. Sharan, B.
Singh26, S. Thakur26 Indian Institute of Technology Madras, Madras, India
P.K. Behera, P. Kalbhor, A. Muhammad, P.R. Pujahari, A. Sharma, A.K. Sikdar
Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, D.K. Mishra, P.K. Netrakanti, L.M.
Pant, P. Shukla Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, G.B. Mohanty, N. Sur, RavindraKumar Verma 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, N. Sahoo, S. Sawant Indian Institute of
Science Education and Research (IISER), Pune, India
S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, A.
Rastogi, S. Sharma Institute for Research in Fundamental Sciences (IPM),
Tehran, Iran
S. Chenarani28, E. Eskandari Tadavani, S.M. Etesami28, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, F. Rezaei Hosseinabadi University College
Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L. Cristellaa,b,
N. De Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, L. Fiorea, A. Gelmia,b, G.
Iasellia,c, M. Incea,b, S. Lezkia,b, G. Maggia,c, M. Maggia, G. Minielloa,b,
S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, R. Radognaa, A.
Ranieria, G. Selvaggia,b, L. Silvestrisa, R. Vendittia, P. Verwilligena INFN
Sezione di Bologna a, Università di Bologna b, Bologna, Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia,b, S. Braibant-
Giacomellia,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
C. Cioccaa, G. Codispotia,b, M. Cuffiania,b, G.M. Dallavallea, F. Fabbria, A.
Fanfania,b, E. Fontanesi, P. Giacomellia, C. Grandia, L. Guiduccia,b, F.
Iemmia,b, S. Lo Meoa,29, S. Marcellinia, G. Masettia, F.L. Navarriaa,b, A.
Perrottaa, F. Primaveraa,b, A.M. Rossia,b, T. Rovellia,b, G.P. Sirolia,b, N.
Tosia INFN Sezione di Catania a, Università di Catania b, Catania, Italy
S. Albergoa,b,30, S. Costaa,b, A. Di Mattiaa, R. Potenzaa,b, A. Tricomia,b,30,
C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b, Firenze, Italy
G. Barbaglia, R. Ceccarelli, K. Chatterjeea,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, G. Latino, P. Lenzia,b, M. Meschinia, S.
Paolettia, G. Sguazzonia, D. Stroma, L. Viliania INFN Laboratori Nazionali di
Frascati, Frascati, Italy
L. Benussi, S. Bianco, D. Piccolo INFN Sezione di Genova a, Università di
Genova b, Genova, Italy
M. Bozzoa,b, F. Ferroa, R. Mulargiaa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschia,b, F. Brivioa,b, V. Cirioloa,b,17, S. Di Guidaa,b,17,
M.E. Dinardoa,b, P. Dinia, S. Fiorendia,b, S. Gennaia, A. Ghezzia,b, P.
Govonia,b, L. Guzzia,b, M. Malbertia, S. Malvezzia, D. Menascea, F. Montia,b,
L. Moronia, G. Ortonaa,b, M. Paganonia,b, D. Pedrinia, S. Ragazzia,b, T.
Tabarelli de Fatisa,b, D. Zuoloa,b INFN Sezione di Napoli a, Università di
Napoli ’Federico II’ b, Napoli, Italy, Università della Basilicata c, Potenza,
Italy, Università G. Marconi d, Roma, Italy
S. Buontempoa, N. Cavalloa,c, A. De Iorioa,b, A. Di Crescenzoa,b, F.
Fabozzia,c, F. Fiengaa, G. Galatia, A.O.M. Iorioa,b, L. Listaa,b, S.
Meolaa,d,17, P. Paoluccia,17, B. Rossia, C. Sciaccaa,b, E. Voevodinaa,b INFN
Sezione di Padova a, Università di Padova b, Padova, Italy, Università di
Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, A. Bolettia,b, A. Bragagnolo, R. Carlina,b, P.
Checchiaa, P. De Castro Manzanoa, T. Dorigoa, U. Dossellia, F. Gasparinia,b,
U. Gasparinia,b, A. Gozzelinoa, S.Y. Hoh, P. Lujan, M. Margonia,b, A.T.
Meneguzzoa,b, J. Pazzinia,b, N. Pozzobona,b, M. Presillab, P. Ronchesea,b, R.
Rossina,b, F. Simonettoa,b, A. Tiko, M. Tosia,b, M. Zanettia,b, P. Zottoa,b,
G. Zumerlea,b INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
A. Braghieria, P. Montagnaa,b, S.P. Rattia,b, V. Rea, M. Ressegottia,b, C.
Riccardia,b, P. Salvinia, I. Vaia,b, P. Vituloa,b INFN Sezione di Perugia a,
Università di Perugia b, Perugia, Italy
M. Biasinia,b, G.M. Bileia, C. Cecchia,b, D. Ciangottinia,b, L. Fanòa,b, P.
Laricciaa,b, R. Leonardia,b, E. Manonia, G. Mantovania,b, V. Mariania,b, M.
Menichellia, A. Rossia,b, A. Santocchiaa,b, D. Spigaa INFN Sezione di Pisa a,
Università di Pisa b, Scuola Normale Superiore di Pisa c, Pisa, Italy
K. Androsova, P. Azzurria, G. Bagliesia, V. Bertacchia,c, L. Bianchinia, T.
Boccalia, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, L.
Gianninia,c, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c, G.
Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, G. Rolandi31, S. Roy
Chowdhury, A. Scribanoa, P. Spagnoloa, R. Tenchinia, G. Tonellia,b, N. Turini,
A. Venturia, P.G. Verdinia INFN Sezione di Roma a, Sapienza Università di Roma
b, Rome, Italy
F. Cavallaria, M. Cipriania,b, D. Del Rea,b, E. Di Marcoa,b, M. Diemoza, E.
Longoa,b, B. Marzocchia,b, P. Meridiania, G. Organtinia,b, F. Pandolfia, R.
Paramattia,b, C. Quarantaa,b, S. Rahatloua,b, C. Rovellia, F.
Santanastasioa,b, L. Soffia,b INFN Sezione di Torino a, Università di Torino
b, Torino, Italy, Università del Piemonte Orientale c, Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, C. Biinoa, A. Cappatia,b, N. Cartigliaa, S. Comettia, M.
Costaa,b, R. Covarellia,b, N. Demariaa, B. Kiania,b, C. Mariottia, S.
Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M. Montenoa, M.M.
Obertinoa,b, L. Pachera,b, N. Pastronea, M. Pelliccionia, G.L. Pinna
Angionia,b, A. Romeroa,b, M. Ruspaa,c, R. Sacchia,b, R. Salvaticoa,b, V.
Solaa, A. Solanoa,b, D. Soldia,b, A. Staianoa INFN Sezione di Trieste a,
Università di Trieste b, Trieste, Italy
S. Belfortea, V. Candelisea,b, M. Casarsaa, F. Cossuttia, A. Da Rolda,b, G.
Della Riccaa,b, F. Vazzolera,b, A. Zanettia Kyungpook National University,
Daegu, Korea
B. Kim, D.H. Kim, G.N. Kim, M.S. Kim, J. Lee, S.W. Lee, C.S. Moon, Y.D. Oh,
S.I. Pak, S. Sekmen, D.C. Son, Y.C. Yang Chonnam National University,
Institute for Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon, G. Oh Hanyang University, Seoul, Korea
B. Francois, T.J. Kim, J. Park Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, K. Lee, K.S. Lee, J. Lim, J.
Park, S.K. Park, Y. Roh Kyung Hee University, Department of Physics
J. Goh Sejong University, Seoul, Korea
H.S. Kim Seoul National University, Seoul, Korea
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, H. Lee, K. Lee, S.
Lee, K. Nam, M. Oh, S.B. Oh, B.C. Radburn-Smith, U.K. Yang, H.D. Yoo, I. Yoon,
G.B. Yu University of Seoul, Seoul, Korea
D. Jeon, H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park, I. Watson Sungkyunkwan
University, Suwon, Korea
Y. Choi, C. Hwang, Y. Jeong, J. Lee, Y. Lee, I. Yu Riga Technical University,
Riga, Latvia
V. Veckalns32 Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, J. Vaitkus National Centre for Particle Physics,
Universiti Malaya, Kuala Lumpur, Malaysia
Z.A. Ibrahim, F. Mohamad Idris33, 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 Cruz34, 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
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Montenegro, Podgorica, Montenegro
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. Ahmad, 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 and Telecommunications, Krakow, Poland
V. Avati, L. Grzanka, M. Malawski National Centre for Nuclear Research,
Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, M. Górski, M. Kazana, M. Szleper, P.
Zalewski Institute of Experimental Physics, Faculty of Physics, University of
Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk35, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Misiura, M. Olszewski, A. Pyskir, M. Walczak Laboratório de
Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
M. Araujo, P. Bargassa, D. Bastos, A. Di Francesco, P. Faccioli, B. Galinhas,
M. Gallinaro, J. Hollar, N. Leonardo, J. Seixas, K. Shchelina, G. Strong, O.
Toldaiev, J. Varela Joint Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov, A. Kamenev,
V. Karjavine, A. Lanev, A. Malakhov, V. Matveev36,37, P. Moisenz, V. Palichik,
V. Perelygin, M. Savina, S. Shmatov, S. Shulha, N. Skatchkov, V. Smirnov, N.
Voytishin, A. Zarubin Petersburg Nuclear Physics Institute, Gatchina (St.
Petersburg), Russia
L. Chtchipounov, V. Golovtsov, Y. Ivanov, V. Kim38, E. Kuznetsova39, P.
Levchenko, V. Murzin, V. Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L.
Uvarov, A. Vorobyev Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, 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. Nikitenko40, 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, R. Chistov41, M. Danilov41, S. Polikarpov41, E. Tarkovskii P.N.
Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin, M. Kirakosyan, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia
A. Baskakov, A. Belyaev, E. Boos, V. Bunichev, M. Dubinin42, L. Dudko, V.
Klyukhin, N. Korneeva, I. Lokhtin, S. Obraztsov, M. Perfilov, V. Savrin, P.
Volkov Novosibirsk State University (NSU), Novosibirsk, Russia
A. Barnyakov43, V. Blinov43, T. Dimova43, L. Kardapoltsev43, Y. Skovpen43
Institute for High Energy Physics of National Research Centre ‘Kurchatov
Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, V. Kachanov, D. Konstantinov, P.
Mandrik, V. Petrov, R. Ryutin, S. Slabospitskii, 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
P. Adzic44, P. Cirkovic, D. Devetak, M. Dordevic, P. Milenovic, J. Milosevic,
M. Stojanovic Centro de Investigaciones Energéticas Medioambientales y
Tecnológicas (CIEMAT), Madrid, Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M.
Barrio Luna, J.A. Brochero Cifuentes, C.A. Carrillo Montoya, M. Cepeda, M.
Cerrada, N. Colino, B. De La Cruz, A. Delgado Peris, C. Fernandez Bedoya, J.P.
Fernández Ramos, J. Flix, M.C. Fouz, O. Gonzalez Lopez, S. Goy Lopez, J.M.
Hernandez, M.I. Josa, D. Moran, Á. Navarro Tobar, A. Pérez-Calero Yzquierdo,
J. Puerta Pelayo, I. Redondo, L. Romero, S. Sánchez Navas, M.S. Soares, A.
Triossi, C. Willmott Universidad Autónoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz Universidad de Oviedo, Instituto Universitario de
Ciencias y Tecnologías Espaciales de Asturias (ICTEA), Oviedo, Spain
B. Alvarez Gonzalez, J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras,
I. Gonzalez Caballero, J.R. González Fernández, E. Palencia Cortezon, V.
Rodríguez Bouza, S. Sanchez Cruz Instituto de Física de Cantabria (IFCA),
CSIC-Universidad de Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, J. Duarte Campderros, M.
Fernandez, P.J. Fernández Manteca, A. García Alonso, G. Gomez, C. Martinez
Rivero, P. Martinez Ruiz del Arbol, F. Matorras, J. Piedra Gomez, C. Prieels,
T. Rodrigo, A. Ruiz-Jimeno, L. Russo45, L. Scodellaro, N. Trevisani, I. Vila,
J.M. Vizan Garcia University of Colombo, Colombo, Sri Lanka
K. Malagalage University of Ruhuna, Department of Physics, Matara, Sri Lanka
W.G.D. Dharmaratna, N. Wickramage CERN, European Organization for Nuclear
Research, Geneva, Switzerland
D. Abbaneo, B. Akgun, E. Auffray, G. Auzinger, J. Baechler, P. Baillon, A.H.
Ball, D. Barney, J. Bendavid, M. Bianco, A. Bocci, E. Bossini, C. Botta, E.
Brondolin, T. Camporesi, A. Caratelli, G. Cerminara, E. Chapon, G. Cucciati,
D. d’Enterria, A. Dabrowski, N. Daci, V. Daponte, A. David, O. Davignon, A. De
Roeck, N. Deelen, M. Deile, M. Dobson, M. Dünser, N. Dupont, A. Elliott-
Peisert, F. Fallavollita46, D. Fasanella, G. Franzoni, J. Fulcher, W. Funk, S.
Giani, D. Gigi, A. Gilbert, K. Gill, F. Glege, M. Gruchala, M. Guilbaud, D.
Gulhan, J. Hegeman, C. Heidegger, Y. Iiyama, V. Innocente, P. Janot, O.
Karacheban20, J. Kaspar, J. Kieseler, M. Krammer1, C. Lange, P. Lecoq, C.
Lourenço, L. Malgeri, M. Mannelli, A. Massironi, F. Meijers, J.A. Merlin, S.
Mersi, E. Meschi, F. Moortgat, M. Mulders, J. Ngadiuba, S. Nourbakhsh, S.
Orfanelli, L. Orsini, F. Pantaleo17, L. Pape, E. Perez, M. Peruzzi, A.
Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, F.M. Pitters, D. Rabady, A.
Racz, M. Rovere, H. Sakulin, C. Schäfer, C. Schwick, M. Selvaggi, A. Sharma,
P. Silva, W. Snoeys, P. Sphicas47, J. Steggemann, V.R. Tavolaro, D. Treille,
A. Tsirou, A. Vartak, M. Verzetti, W.D. Zeuner Paul Scherrer Institut,
Villigen, Switzerland
L. Caminada48, K. Deiters, W. Erdmann, R. Horisberger, Q. Ingram, H.C.
Kaestli, D. Kotlinski, U. Langenegger, T. Rohe, S.A. Wiederkehr ETH Zurich -
Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland
M. Backhaus, P. Berger, N. Chernyavskaya, G. Dissertori, M. Dittmar, M.
Donegà, C. Dorfer, T.A. Gómez Espinosa, C. Grab, D. Hits, T. Klijnsma, W.
Lustermann, R.A. Manzoni, M. Marionneau, M.T. Meinhard, F. Micheli, P.
Musella, F. Nessi-Tedaldi, F. Pauss, G. Perrin, L. Perrozzi, S. Pigazzini, M.
Reichmann, C. Reissel, T. Reitenspiess, D. Ruini, D.A. Sanz Becerra, M.
Schönenberger, L. Shchutska, M.L. Vesterbacka Olsson, R. Wallny, D.H. Zhu
Universität Zürich, Zurich, Switzerland
T.K. Aarrestad, C. Amsler49, D. Brzhechko, M.F. Canelli, A. De Cosa, R. Del
Burgo, S. Donato, B. Kilminster, S. Leontsinis, V.M. Mikuni, I. Neutelings, G.
Rauco, P. Robmann, D. Salerno, K. Schweiger, C. Seitz, Y. Takahashi, S. Wertz,
A. Zucchetta National Central University, Chung-Li, Taiwan
T.H. Doan, C.M. Kuo, W. Lin, S.S. Yu National Taiwan University (NTU), Taipei,
Taiwan
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 Chulalongkorn University, Faculty of Science,
Department of Physics, Bangkok, Thailand
B. Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas, N. Suwonjandee, V.
Wachirapusitanand Çukurova University, Physics Department, Science and Art
Faculty, Adana, Turkey
A. Bat, F. Boran, S. Damarseckin50, Z.S. Demiroglu, F. Dolek, C. Dozen, I.
Dumanoglu, E. Eskut, G. Gokbulut, EmineGurpinar Guler51, Y. Guler, I. Hos52,
C. Isik, E.E. Kangal53, O. Kara, A. Kayis Topaksu, U. Kiminsu, M. Oglakci, G.
Onengut, K. Ozdemir54, A.E. Simsek, D. Sunar Cerci55, B. Tali55, U.G. Tok, S.
Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle East Technical University,
Physics Department, Ankara, Turkey
B. Isildak56, G. Karapinar57, M. Yalvac Bogazici University, Istanbul, Turkey
I.O. Atakisi, E. Gülmez, O. Kaya58, B. Kaynak, Ö. Özçelik, S. Ozkorucuklu59,
S. Tekten, E.A. Yetkin60 Istanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak, Y. Komurcu, S. Sen61 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
F. Ball, 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, S. Paramesvaran,
B. Penning, T. Sakuma, S. Seif El Nasr-Storey, D. Smith, V.J. Smith, J.
Taylor, A. Titterton Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev62, C. Brew, R.M. Brown, D. Cieri, D.J.A. Cockerill, J.A.
Coughlan, 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, W.J. Womersley Imperial College, London, United
Kingdom
R. Bainbridge, P. Bloch, J. Borg, S. Breeze, O. Buchmuller, A. Bundock,
GurpreetSingh CHAHAL63, D. Colling, P. Dauncey, G. Davies, M. Della Negra, R.
Di Maria, P. Everaerts, G. Hall, G. Iles, T. James, M. Komm, C. Laner, L.
Lyons, A.-M. Magnan, S. Malik, A. Martelli, V. Milosevic, J. Nash64, V.
Palladino, M. Pesaresi, D.M. Raymond, A. Richards, A. Rose, E. Scott, C. Seez,
A. Shtipliyski, M. Stoye, T. Strebler, S. Summers, A. Tapper, K. Uchida, T.
Virdee17, N. Wardle, D. Winterbottom, J. Wright, A.G. Zecchinelli, S.C. Zenz
Brunel University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, C.K. Mackay, A. Morton, I.D. Reid,
L. Teodorescu, S. Zahid Baylor University, Waco, USA
K. Call, 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 The University of Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, C. Henderson, P. Rumerio, C. West Boston University,
Boston, USA
D. Arcaro, T. Bose, Z. Demiragli, D. Gastler, S. Girgis, D. Pinna, C.
Richardson, J. Rohlf, D. Sperka, I. Suarez, L. Sulak, D. Zou Brown University,
Providence, USA
G. Benelli, B. Burkle, X. Coubez, D. Cutts, Y.t. Duh, M. Hadley, J. Hakala, U.
Heintz, J.M. Hogan65, K.H.M. Kwok, E. Laird, G. Landsberg, J. Lee, Z. Mao, M.
Narain, S. Sagir66, R. Syarif, E. Usai, D. Yu 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. Stolp,
D. Taylor, K. Tos, M. Tripathi, Z. Wang, F. Zhang University of California,
Los Angeles, USA
M. Bachtis, C. Bravo, R. Cousins, A. Dasgupta, A. Florent, J. Hauser, M.
Ignatenko, N. Mccoll, W.A. Nash, S. Regnard, D. Saltzberg, C. Schnaible, B.
Stone, V. Valuev University of California, Riverside, Riverside, USA
K. Burt, R. Clare, J.W. Gary, S.M.A. Ghiasi Shirazi, G. Hanson, G.
Karapostoli, E. Kennedy, O.R. Long, M. Olmedo Negrete, M.I. Paneva, W. Si, L.
Wang, H. Wei, S. Wimpenny, B.R. Yates, Y. Zhang University of California, San
Diego, La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, M. Derdzinski, 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. Würthwein, A. Yagil, G. Zevi Della Porta
University of California, Santa Barbara - Department of Physics, Santa
Barbara, USA
N. Amin, R. Bhandari, C. Campagnari, M. Citron, V. Dutta, M. Franco Sevilla,
L. Gouskos, J. Incandela, B. Marsh, H. Mei, A. Ovcharova, H. Qu, J. Richman,
U. Sarica, D. Stuart, S. Wang, J. Yoo 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, C. Wang, S.
Xie, Z. Zhang, R.Y. Zhu Carnegie Mellon University, Pittsburgh, USA
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, A. Johnson, E. MacDonald, T. Mulholland, R. Patel, A.
Perloff, K. Stenson, K.A. Ulmer, S.R. Wagner Cornell University, Ithaca, USA
J. Alexander, J. Chaves, Y. Cheng, J. Chu, A. Datta, A. Frankenthal, K.
Mcdermott, N. Mirman, J.R. Patterson, D. Quach, A. Rinkevicius67, A. Ryd, 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, J. Berryhill, P.C. Bhat, K. Burkett,
J.N. Butler, A. Canepa, G.B. Cerati, H.W.K. Cheung, F. Chlebana, M. Cremonesi,
J. Duarte, V.D. Elvira, J. Freeman, Z. Gecse, E. Gottschalk, L. Gray, D.
Green, S. Grünendahl, O. Gutsche, AllisonReinsvold Hall, J. Hanlon, R.M.
Harris, S. Hasegawa, R. Heller, J. Hirschauer, B. Jayatilaka, S. Jindariani,
M. Johnson, U. Joshi, B. Klima, M.J. Kortelainen, B. Kreis, S. Lammel, J.
Lewis, D. Lincoln, R. Lipton, M. Liu, T. Liu, J. Lykken, K. Maeshima, J.M.
Marraffino, D. Mason, P. McBride, P. Merkel, S. Mrenna, S. Nahn, V. O’Dell, V.
Papadimitriou, K. Pedro, C. Pena, G. Rakness, F. Ravera, L. Ristori, B.
Schneider, E. Sexton-Kennedy, N. Smith, A. Soha, W.J. Spalding, L. Spiegel, S.
Stoynev, J. Strait, N. Strobbe, L. Taylor, S. Tkaczyk, N.V. Tran, L. Uplegger,
E.W. Vaandering, C. Vernieri, M. Verzocchi, R. Vidal, M. Wang, H.A. Weber
University of Florida, Gainesville, USA
D. Acosta, P. Avery, P. Bortignon, D. Bourilkov, A. Brinkerhoff, L. Cadamuro,
A. Carnes, V. Cherepanov, D. Curry, F. Errico, R.D. Field, S.V. Gleyzer, B.M.
Joshi, M. Kim, J. Konigsberg, A. Korytov, K.H. Lo, P. Ma, 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, S. Hagopian, V. Hagopian, K.F. Johnson, R. Khurana, T.
Kolberg, G. Martinez, T. Perry, H. Prosper, C. Schiber, R. Yohay, J. Zhang
Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, V. Bhopatkar, M. Hohlmann, D. Noonan, M. Rahmani, M. Saunders,
F. Yumiceva University of Illinois at Chicago (UIC), Chicago, USA
M.R. Adams, L. Apanasevich, D. Berry, R.R. Betts, R. Cavanaugh, X. Chen, S.
Dittmer, O. Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, K. Jung, C.
Mills, T. Roy, M.B. Tonjes, N. Varelas, H. Wang, X. Wang, Z. Wu The University
of Iowa, Iowa City, USA
M. Alhusseini, B. Bilki51, W. Clarida, K. Dilsiz68, S. Durgut, R.P.
Gandrajula, M. Haytmyradov, V. Khristenko, O.K. Köseyan, J.-P. Merlo, A.
Mestvirishvili69, A. Moeller, J. Nachtman, H. Ogul70, Y. Onel, F. Ozok71, A.
Penzo, C. Snyder, E. Tiras, J. Wetzel Johns Hopkins University, Baltimore, USA
B. Blumenfeld, A. Cocoros, N. Eminizer, D. Fehling, L. Feng, A.V. Gritsan,
W.T. Hung, P. Maksimovic, J. Roskes, M. Swartz, M. Xiao The University of
Kansas, Lawrence, USA
C. Baldenegro Barrera, P. Baringer, A. Bean, S. Boren, J. Bowen, A. Bylinkin,
T. Isidori, S. Khalil, J. King, G. Krintiras, A. Kropivnitskaya, C. Lindsey,
D. Majumder, 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
A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, N.J. Hadley, S. Jabeen,
G.Y. Jeng, R.G. Kellogg, J. Kunkle, A.C. Mignerey, S. Nabili, F. Ricci-Tam, M.
Seidel, Y.H. Shin, A. Skuja, S.C. Tonwar, K. Wong Massachusetts Institute of
Technology, Cambridge, USA
D. Abercrombie, B. Allen, A. Baty, R. Bi, S. Brandt, W. Busza, I.A. Cali, M.
D’Alfonso, G. Gomez Ceballos, M. Goncharov, P. Harris, D. Hsu, M. Hu, M.
Klute, D. Kovalskyi, 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,
B. Wyslouch University of Minnesota, Minneapolis, USA
A.C. Benvenuti${}^{\textrm{\textdagger}}$, R.M. Chatterjee, A. Evans, S. Guts,
P. Hansen, J. Hiltbrand, Sh. Jain, S. Kalafut, Y. Kubota, Z. Lesko, J. Mans,
R. Rusack, M.A. Wadud University of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
K. Bloom, D.R. Claes, C. Fangmeier, L. Finco, F. Golf, R. Gonzalez Suarez, R.
Kamalieddin, I. Kravchenko, J.E. Siado, G.R. Snow, B. Stieger 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,
D.M. Morse, T. Orimoto, L. Skinnari, A. Tishelman-Charny, T. Wamorkar, B.
Wang, A. Wisecarver, D. Wood Northwestern University, Evanston, USA
S. Bhattacharya, J. Bueghly, T. Gunter, K.A. Hahn, N. Odell, M.H. Schmitt, K.
Sung, M. Trovato, 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, C. Mueller, Y. Musienko36, M. Planer, R. Ruchti, P. Siddireddy, G.
Smith, S. Taroni, M. Wayne, A. Wightman, M. Wolf, A. Woodard The Ohio State
University, Columbus, USA
J. Alimena, B. Bylsma, L.S. Durkin, S. Flowers, B. Francis, C. Hill, W. Ji, A.
Lefeld, T.Y. Ling, B.L. Winer Princeton University, Princeton, USA
S. Cooperstein, G. Dezoort, P. Elmer, J. Hardenbrook, N. Haubrich, S.
Higginbotham, A. Kalogeropoulos, S. Kwan, D. Lange, M.T. Lucchini, J. Luo, D.
Marlow, K. Mei, I. Ojalvo, J. Olsen, C. Palmer, P. Piroué, J. Salfeld-Nebgen,
D. Stickland, C. Tully, Z. Wang University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A. Barker, V.E. Barnes, S. Das, L. Gutay, M. Jones, A.W. Jung, A. Khatiwada,
B. Mahakud, D.H. Miller, G. Negro, N. Neumeister, C.C. Peng, S. Piperov, H.
Qiu, J.F. Schulte, J. Sun, F. Wang, R. Xiao, W. Xie Purdue University
Northwest, Hammond, USA
T. Cheng, J. Dolen, N. Parashar Rice University, Houston, USA
K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Kilpatrick, Arun 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, J. Han, O. Hindrichs, A. Khukhunaishvili, E.
Ranken, P. Tan, 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, S. Kyriacou, I. Laflotte, A. Lath, R.
Montalvo, K. Nash, M. Osherson, H. Saka, S. Salur, S. Schnetzer, D. Sheffield,
S. Somalwar, R. Stone, S. Thomas, P. Thomassen University of Tennessee,
Knoxville, USA
H. Acharya, A.G. Delannoy, J. Heideman, G. Riley, S. Spanier Texas A&M
University, College Station, USA
O. Bouhali72, A. Celik, M. Dalchenko, M. De Mattia, A. Delgado, S. Dildick, R.
Eusebi, J. Gilmore, T. Huang, T. Kamon73, S. Luo, D. Marley, R. Mueller, D.
Overton, L. Perniè, D. Rathjens, A. Safonov Texas Tech University, Lubbock,
USA
N. Akchurin, J. Damgov, F. De Guio, 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
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
M.W. Arenton, P. Barria, B. Cox, G. Cummings, R. Hirosky, M. Joyce, A.
Ledovskoy, C. Neu, B. Tannenwald, Y. Wang, E. Wolfe, F. Xia Wayne State
University, Detroit, USA
R. Harr, P.E. Karchin, N. Poudyal, J. Sturdy, P. Thapa, S. Zaleski University
of Wisconsin - Madison, Madison, WI, USA
J. Buchanan, C. Caillol, D. Carlsmith, S. Dasu, I. De Bruyn, L. Dodd, F.
Fiori, C. Galloni, B. Gomber74, M. Herndon, A. Hervé, U. Hussain, P. Klabbers,
A. Lanaro, A. Loeliger, K. Long, R. Loveless, J. Madhusudanan Sreekala, T.
Ruggles, A. Savin, V. Sharma, W.H. Smith, D. Teague, S. Trembath-reichert, N.
Woods †: Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
3: Also at Universidade Estadual de Campinas, Campinas, Brazil
4: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
5: Also at UFMS, Nova Andradina, Brazil
6: Also at Universidade Federal de Pelotas, Pelotas, Brazil
7: Also at Université Libre de Bruxelles, Bruxelles, Belgium
8: Also at University of Chinese Academy of Sciences, Beijing, China
9: Also at Institute for Theoretical and Experimental Physics named by A.I.
Alikhanov of NRC ‘Kurchatov Institute’, Moscow, Russia
10: Also at Joint Institute for Nuclear Research, Dubna, Russia
11: Also at Cairo University, Cairo, Egypt
12: Also at Helwan University, Cairo, Egypt
13: Now at Zewail City of Science and Technology, Zewail, Egypt
14: Also at Purdue University, West Lafayette, USA
15: Also at Université de Haute Alsace, Mulhouse, France
16: Also at Erzincan Binali Yildirim University, Erzincan, Turkey
17: Also at CERN, European Organization for Nuclear Research, Geneva,
Switzerland
18: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
19: Also at University of Hamburg, Hamburg, Germany
20: Also at Brandenburg University of Technology, Cottbus, Germany
21: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary,
Debrecen, Hungary
22: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
23: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary, Budapest, Hungary
24: Also at IIT Bhubaneswar, Bhubaneswar, India, Bhubaneswar, India
25: Also at Institute of Physics, Bhubaneswar, India
26: Also at Shoolini University, Solan, India
27: Also at University of Visva-Bharati, Santiniketan, India
28: Also at Isfahan University of Technology, Isfahan, Iran
29: Also at Italian National Agency for New Technologies, Energy and
Sustainable Economic Development, Bologna, Italy
30: Also at Centro Siciliano di Fisica Nucleare e di Struttura Della Materia,
Catania, Italy
31: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy
32: Also at Riga Technical University, Riga, Latvia, Riga, Latvia
33: Also at Malaysian Nuclear Agency, MOSTI, Kajang, Malaysia
34: Also at Consejo Nacional de Ciencia y Tecnología, Mexico City, Mexico
35: Also at Warsaw University of Technology, Institute of Electronic Systems,
Warsaw, Poland
36: Also at Institute for Nuclear Research, Moscow, Russia
37: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
38: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
39: Also at University of Florida, Gainesville, USA
40: Also at Imperial College, London, United Kingdom
41: Also at P.N. Lebedev Physical Institute, Moscow, Russia
42: Also at California Institute of Technology, Pasadena, USA
43: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia