TOP-19-008

# Measurement of the top quark Yukawa coupling from kinematic distributions in
the dilepton final state in proton-proton collisions at $\sqrt{s}=13\TeV$

###### Abstract

A measurement of the Higgs boson Yukawa coupling to the top quark is presented
using proton-proton collision data at $\sqrt{s}=13\TeV$, corresponding to an
integrated luminosity of 137, recorded with the CMS detector. The coupling
strength with respect to the standard model value, $Y_{\PQt}$, is determined
from kinematic distributions in final states containing ${\Pe}{\Pe}$,
${\Pgm}{\Pgm}$, or ${\Pe}{\Pgm}$ pairs. Variations of the Yukawa coupling
strength lead to modified distributions for production. In particular, the
distributions of the mass of the system and the rapidity difference of the top
quark and antiquark are sensitive to the value of $Y_{\PQt}$. The measurement
yields a best fit value of $Y_{\PQt}=1.16^{+0.24}_{-0.35}$, bounding
$Y_{\PQt}<1.54$ at a 95% confidence level.

##  0.1 Introduction

Since the discovery of the Higgs boson in 2012 [1, 2], one of the main goals
of the CERN LHC program has been to study in detail the properties of this new
particle. In the standard model (SM), all fermions acquire their mass through
the interaction with the Higgs field. More specifically, the mass of a given
fermion, $m_{\mathrm{f}}$, arises from a Yukawa interaction with coupling
strength $g_{\mathrm{f}}=\sqrt{2}m_{\mathrm{f}}/v$, where $v$ is the vacuum
expectation value of the Higgs field. Among all such couplings, the top quark
Yukawa coupling is of particular interest. It is not only the largest, but
also remarkably close to unity. Given the measured top quark mass [3, 4], the
mass-Yukawa coupling relation implies a value of the Yukawa coupling
$g_{\PQt}^{\mathrm{SM}}\approx 0.99$ when evaluated near the energy scale of
$m_{\PQt}$. Physics beyond the SM, such as two Higgs doublet and composite
Higgs boson models, introduce modified couplings that alter the interaction
between the top quark and the Higgs field [5, 6]. This makes the interaction
of the Higgs boson with the top quark one of the most interesting features of
the Higgs field to study at the LHC today, especially because it is
experimentally accessible through multiple avenues, both direct and indirect.

For the purpose of this measurement, we define for the top quark the parameter
$Y_{\PQt}=g_{\PQt}/g_{\PQt}^{\mathrm{SM}}$, which is equivalent to the
modifier $\kappa_{\PQt}$ introduced in the $\kappa$-framework [7]. We consider
only the case where $Y_{\PQt}\geq 0$, though certain specific techniques are
sensitive also to the sign of the Yukawa coupling (for example, Ref. [8]).
Recent efforts have had notable success in directly probing $g_{\PQt}$ via the
production of a Higgs boson in association with a top quark pair () [9, 10].
Currently, the most precise determination comes from the $\kappa$-framework
fit in Ref. [11], which yields $Y_{\PQt}=0.98\pm 0.14$ by combining
information from several Higgs boson production and decay channels. These
measurements, however, fold in assumptions of the SM branching fractions via
Higgs couplings to other particles. Another way to constrain $g_{\PQt}$, which
does not depend on these couplings, was presented in the search for four top
quark production in Ref. [12], yielding a limit of $Y_{\PQt}<1.7$ at a 95%
confidence level (). However, it is also possible to constrain $g_{\PQt}$
indirectly using the kinematic distributions of reconstructed pair events, a
technique that has been recently used by CMS to derive a similar limit of
$Y_{\PQt}<1.67$ at 95% in the lepton+jets decay channel [13]. The measurement
presented in this paper follows this last approach, but in the dilepton final
state.

Current commonly used Monte Carlo (MC) simulations of production include next-
to-leading-order (NLO) precision in perturbative quantum chromodynamics (QCD).
Subleading-order corrections arise from including electroweak (EW) terms in
the perturbative expansion of the strong coupling $\alpS$ and the EW coupling
$\alpha$. Such terms begin to noticeably affect the cross section only at
loop-induced order $\alpS^{2}\alpha$, and are typically not included in
current MC simulation. While these terms have a very small effect on the total
cross section, they can alter the shape of kinematic distributions to a
measurable extent. Such changes become more noticeable if the Yukawa coupling
affecting the loop correction (Fig. 1) is anomalously large. Therefore, these
corrections are of particular interest in deriving upper limits on $g_{\PQt}$.
For example, the distribution of the invariant mass of the system,
$M_{\ttbar}$, will be affected significantly by varying $Y_{\PQt}$. Doubling
the value of $Y_{\PQt}$ can alter the $M_{\ttbar}$ distribution by about 9%
near the production threshold, as described in Ref. [14]. Another variable
sensitive to the value of $Y_{\PQt}$ is the difference in rapidity between the
top quark and antiquark, $\Delta y_{\ttbar}=y({\PQt})-y(\overline{\PQt})$. In
production, $M_{\ttbar}$ and $\Delta y_{\ttbar}$ are proxies for the
Mandelstam kinematic variables $s$ and $t$, respectively, which span the event
phase space and can thus be used to include the EW corrections in previously
generated event samples via reweighting. The effects of these corrections are
shown for differential cross sections of $M_{\ttbar}$ and $\Delta y_{\ttbar}$
in Fig. 2. These are computed by reweighting simulated events at generator
level using predictions from the hathor software package [15].

Figure 1: Sample Feynman diagrams for EW contributions to gluon-induced and
quark-induced top quark pair production, where $\Gamma$ stands for neutral
vector and scalar bosons.

Figure 2: Effect of the EW corrections on differential kinematic distributions
for different values of $Y_{\PQt}$, after reweighting of simulated events. The
effect is shown on the distribution of the invariant mass, $M_{\ttbar}$
(left), and the difference in rapidity between the top quark and antiquark,
$\Delta y_{\ttbar}$ (right).

After calculating the dependence of these corrections on $Y_{\PQt}$, a
measurement is performed. We use events in the dilepton final state
(${\Pe}{\Pe}$, ${\Pgm}{\Pgm}$, or ${\Pe}{\Pgm}$), for which this type of
measurement has not yet been performed. While this decay channel has a smaller
branching fraction than the lepton+jets channel studied in Ref. [13], it has
lower backgrounds due to the presence of two final-state high-leptons.
However, two neutrinos are also expected in this final state, which escape
detection and pose challenges in the kinematic reconstruction. For this
reason, we do not perform a full kinematic reconstruction as was done in the
previous measurement in the lepton+jets channel. This measurement also
utilizes a much larger data set with an integrated luminosity of 137collected
during Run 2 at the LHC from 2016 to 2018, allowing us to achieve comparable
precision to that in Ref. [13] for a decay channel with a much lower branching
fraction.

In this paper, we will first briefly describe the CMS detector (Section 0.2),
and then discuss the data and MC samples (Section 0.3), followed by the
methods for event selection (Section 0.4) and reconstruction (Section 0.5). We
then present an outline of the measurement technique (Section 0.6) and the
contributing sources of uncertainty (Section 0.7), and conclude with the
results of the measurement (Section 0.8) and the summary (Section 0.9).

##  0.2 The CMS detector

The central feature of the CMS detector 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 (ECAL), and a brass and scintillator hadron
calorimeter (HCAL), each composed of a barrel and two endcap sections. Forward
calorimeters extend the coverage provided by the barrel and endcap detectors.
Muons are measured in gas-ionization detectors embedded in the steel flux-
return yoke outside the solenoid.

The particle-flow (PF) algorithm [16] aims to reconstruct and identify each
individual particle in an event, with an optimized combination of information
from the various elements of the CMS detector. The energy of photons is
obtained from the ECAL measurement. The energy of electrons is determined from
a combination of the electron momentum at the primary interaction vertex as
determined by the tracker, the energy of the corresponding ECAL cluster, and
the energy sum of all bremsstrahlung photons spatially compatible with
originating from the electron track. The energy of muons is obtained from the
curvature of the corresponding track. The energy of charged hadrons is
determined from a combination of their momentum measured in the tracker and
the matching ECAL and HCAL energy deposits, corrected for zero-suppression
effects and for the response function of the calorimeters to hadronic showers.
Finally, the energy of neutral hadrons is obtained from the corresponding
corrected ECAL and HCAL energies.

Events of interest are selected using a two-tiered trigger system [17]. The
first level (L1), composed of custom hardware processors, uses information
from the calorimeters and muon detectors to select events at a rate of around
100kHz within a time interval of less than 4. The second level, known as the
high-level trigger, consists of a farm of processors running a version of the
full event reconstruction software optimized for fast processing, and reduces
the event rate to around 1kHz before data storage.

A more detailed description of the CMS detector, together with a definition of
the coordinate system and relevant kinematical variables, can be found in Ref.
[18].

##  0.3 Simulation of top quark pair production and backgrounds

The production of events is simulated at the matrix-element (ME) level with
NLO QCD precision, using the 2.0 (hvq) generator [19, 20, 21, 22]. The
calculation is performed with the renormalization and factorization scales,
$\mu_{\mathrm{R}}$ and $\mu_{\mathrm{F}}$, set to the transverse top quark
mass, $\mT=\sqrt{\smash[b]{m_{\PQt}^{2}+\pt^{2}}}$, where is the transverse
momentum of the top quark and the quantity is evaluated in the rest frame. The
default value of $m_{\PQt}$ is set to 172.5. The ME calculations obtained from
are combined with the parton shower simulation from 8.219 [23], using the
underlying-event tune M2T4 [24] to simulate data taken in 2016, and 8.226
using the tune CP5 [25] to simulate data taken in 2017 and 2018. The parton
distribution function (PDF) set NNPDF3.0 at NLO [26] is used for 2016 and
updated to NNPDF3.1 [27] at next-to-NLO (NNLO) for 2017 and 2018. These
samples are normalized to a cross section calculated at NNLO in QCD including
resummation of next-to-next-to-leading logarithmic (NNLL) soft gluon terms
with Top++ 2.0 [28]. The calculation uses the PDF4LHC prescription [29] with
the MSTW2008 NNLO [30, 31], CT10 NNLO [32, 33] and NNPDF2.3 [34] PDF sets used
to generate an envelope of uncertainty with the midpoint of the envelope used
for the central predictions. The PDF uncertainty is then summed in quadrature
with the scale uncertainty to arrive at an overall uncertainty of $\approx$5%
on the nominal value of 832pb. The shape effects associated with the PDF
uncertainty are considered separately in Section 0.7.

A high purity of events can be obtained in the dilepton channel, as shown in
Section 0.4. A small contamination is expected to result from background
processes, which are modeled by simulation. In particular, we account for
dilepton production due to Drell–Yan type processes and single top quark
production. Other SM processes, such as boson production, were investigated
and found to have negligible contributions. Diboson production is also
included, although its expected contribution is minute due to the small total
cross section of the process.

About 1% of the events identified as dilepton decays are misidentified
lepton+jets decays. EW corrections are applied to all events, even
misidentified ones, so their kinematic distributions remain dependent on
$Y_{\PQt}$. Thus, these events are still considered as signal, even though
their contribution to the measurement sensitivity is greatly diminished
relative to dilepton events.

Single top quark events are simulated at NLO with in combination with , while
diboson events are simulated with at leading-order (LO) QCD precision.
Drell–Yan production is simulated at LO using version 2.2.2 for 2016 and
version 2.2.4 for 2017 onwards [35], with up to 4 additional partons,
interfaced to using the MLM matching algorithm [36, 37].

The detector response to all simulated events is modeled with the software
toolkit [38]. In addition, the effects of multiple proton-proton interactions
per event are included in simulations and the distribution of these pileup
interactions is reweighted to the vertex multiplicity distribution in the
data.

###  0.3.1 Simulation of electroweak corrections

Contributions to the top quark pair production arising from QCD+EW diagrams
are evaluated using the hathor package [15], which is used to compute a
double-differential cross section as a function of $M_{\ttbar}$ and $\Delta
y_{\ttbar}$ including LO QCD diagrams and certain EW diagrams of order
$\alpS^{2}\alpha$. These diagrams involve massive boson exchange and examples
are shown in Fig. 1. The contributions from photon-mediated interactions are
not included. Contributions from diagrams involving virtual photon exchange
should not be assessed individually, as they are partially cancelled not only
by real emission diagrams but also by contributions from $\Pg\PGg$ production
[39]. A complete assessment would require the modeling of photon content
within protons. This was not performed here, as the net effect is fairly
small. For example, Ref. [39] cites a 1% effect from photon-mediated
contributions to the cross section at the LHC with detector-based kinematic
cuts. Thus, we include only diagrams involving massive vector and scalar boson
interactions, which are the dominant EW diagrams at this order.

The ratio of this double-differential cross section is evaluated with respect
to the LO QCD computation, in order to obtain a multiplicative weight
correction $w(M_{\ttbar},\Delta y_{\ttbar})$. Applying this weight at parton
level to MC samples produced at NLO QCD approximates the inclusion of EW
corrections in the simulation. This multiplicative approach to including EW
corrections was used previously in Ref. [13], and has the benefit of
approximating the inclusion of diagrams at order
$\mathcal{O}(\alpS^{3}\alpha)$. Because EW corrections factorize in some
kinematic regimes, this is a better-motivated approach than the alternative
additive approach, in which one adds the fixed-order result at order
$\mathcal{O}(\alpS^{2}\alpha)$ while ignoring all potential contributions of
order $\mathcal{O}(\alpS^{3}\alpha)$ (see Ref. [40] for a more detailed
discussion). In other words, the additive approach applies the EW correction
factor only to the proportion of events present at LO QCD, while any interplay
between EW corrections and higher-order QCD simulation is ignored. Although
the multiplicative approach is clearly favored, neither approach can account
for the effects of two-loop contributions near the production threshold. To
account for the lack of knowledge of such terms, we take the difference
between the two predictions as a modeling uncertainty in this regime, as
suggested in Ref. [14]. The estimation of this uncertainty is discussed
further in Section 0.6.

The EW correction weights are calculated for discrete integer values of
$Y_{\PQt}=0,1,\ldots,5.$ Since the dependence of the production rate on
$Y_{\PQt}$ is exactly quadratic, these discrete values are sufficient to
parametrize event yields as a continuous function of $Y_{\PQt}$ (as discussed
in Section 0.6). This allows us to measure which value of $Y_{\PQt}$ best
describes the data.

##  0.4 Event and object selection

Events are selected using single-electron or single-muon triggers. Data taking
at the LHC was interrupted by technical stops at the end of each year, leading
to some changes in configuration and modeling between 2016, 2017, and 2018.
For events selected by the single-electron trigger, we require a trigger
threshold of 27with the exception of 2018, where a threshold of 32is used. In
the case of the single-muon trigger, we select events with a trigger threshold
of 24, which is raised to 27only for 2017 due to high event rates.

We ensure that all electrons and muons are within the silicon tracker coverage
by requiring a pseudorapidity $\abs{\eta}<2.4$. To operate well above the
trigger threshold, we then require at least one isolated electron or muon
reconstructed with $\pt>30$, except in 2018, where we require leading
electrons to have $\pt>34$in accordance with the trigger threshold. The same
lepton isolation criteria described in Ref. [41] are used. After selecting the
leading lepton, a second isolated electron or muon with $\pt>20\GeV$ is
required. Events with three or more isolated leptons with $\pt>15\GeV$ are
discarded.

Jets are clustered from PF objects via the anti-algorithm [42, 43] with a
distance parameter of 0.4. The jet momentum is calculated as the vectorial sum
of the momenta of its constituents. Corrections to the jet energy are derived
as a function of jet and $\eta$ in simulation and improved by measurements of
energy balance in data [44]. We select jets with $\abs{\eta}<2.4$ and
$\pt>30$.

Jets originating from quarks are identified using the DeepCSV algorithm [45].
The algorithm provides three working points: loose, medium, and tight, in
order of decreasing efficiency and increasing purity. The identification
efficiencies (and light quark misidentification rates) are 84 (11)%, 68
(1.1)%, and 50 (0.1)%, respectively. For an initial selection, we consider
events with a minimum of two jet candidates passing the loose working point of
the algorithm. When applied to simulated dilepton decays, we find that this
initial selection of jets will correctly include both jets originating from
top quark decays in 87% of events. In around 9% of simulated dilepton events
passing this initial selection, there are more than two jets passing the loose
working point, leading to an ambiguity in jet assignment. If such events have
exactly two jets passing a higher working point (medium or tight), then those
two jets are considered the viable candidates for jets originating from a top
quark decay, and the ambiguity is resolved without using kinematic properties
of the event. The small fraction of events with more than two viable jet
candidates, making up 4% of the initially selected dilepton events, are
discarded. After this selection procedure, each event remaining in the sample
has exactly two jet candidates, which together are correctly identified in 85%
of simulated dilepton events.

In order to remove Drell–Yan background events in the ${\Pe}{\Pe}$ and
${\Pgm}{\Pgm}$ channels, we reject events in which the two leptons have an
invariant mass below 50or within 10around the boson mass of 91.2.

The missing transverse momentum vector (), defined as the negative vector sum
of all transverse momenta, is generally of large magnitude in dilepton decays
because of the two undetected final-state neutrinos. To further aid in
removing Drell–Yan events, we impose an additional selection requirement on
the magnitude of the missing transverse momentum, requiring $\ptmiss>30$in all
events with ${\Pe}{\Pe}$ or ${\Pgm}{\Pgm}$ in the final state.

The breakdown of expected signal and background yields, summed over the three
channels (${\Pe}{\Pe}$, ${\Pgm}{\Pgm}$, ${\Pe}{\Pgm}$), is shown by year in
Table 0.4. The Drell–Yan background is estimated to be about 2%. Single top
quark production accounts for roughly another 2% of the estimated sample
composition.

Simulated signal, background, and data event yields for each of the three
years and their combination. The rightmost column shows the fraction of each
component relative to the total simulated sample yield across the full data
set. The statistical uncertainty in the simulated event counts is given. l x x
x x r Source & 2016 (36) 2017 (41) 2018 (60) All (137) % total MC
140 830, 130 170 550,100 259 620, 150 571 010, 220 96.2%
Drell–Yan 1920, 50 2690, 80 4960, 130 9840, 170 1.7%
Single 3020, 30 3520, 20 5830, 30 12 370, 50 2.1%
Diboson 140, 10 150, 10 250, 20 540, 20 0.1%
Total 145 940, 150 177 400, 120 270 660, 200 593 760, 280
Data 144 817 178 088 264 791 587 696

##  0.5 Event reconstruction

The EW corrections are calculated based on $M_{\ttbar}$ and $\Delta
y_{\ttbar}$. However, to evaluate these quantities it is necessary to
reconstruct the full kinematic properties of the system, including the two
undetected neutrinos. While it is possible to completely reconstruct the
neutrino momenta in the on-shell approximation, such a reconstruction is
highly sensitive to , which introduces large resolution effects and additional
systematic uncertainties. We observe that using the proxy variables
$M_{{\PQb}{\PQb}{\ell}{\ell}}=M({\PQb}+\overline{{\PQb}}+\ell+\overline{\ell})$
and $\abs{\Delta
y_{{\PQb}\ell{\PQb}\ell}}=\abs{y({\PQb}+\overline{\ell})-y(\overline{{\PQb}}+\ell)}$,
where $\ell$ represents a final-state electron or muon, results in a more
precise measurement.

Unlike $M_{{\PQb}{\PQb}{\ell}{\ell}}$, the accurate reconstruction of
$\abs{\Delta y_{{\PQb}\ell{\PQb}\ell}}$ requires that each of the two jets is
matched to the correct lepton, i.e., both originating from the same top quark
decay. In order to make this pairing, we utilize the information from the
kinematic constraints governing the neutrino momenta.

If one assumes the top quarks and bosons to be on-shell, the neutrino momenta
are constrained by a set of quadratic equations arising from the conservation
of four-momentum at each vertex. We refer to these kinematic equations,
collectively, as the mass constraint. The mass constraint for each top quark
decay results in a continuum of possible solutions for neutrino momenta, which
geometrically can be presented as an intersection of ellipsoids in three-
dimensional momentum-space [46]. For certain values of input momenta of jets
and leptons these ellipsoids do not intersect at all, such that the quadratic
equations have no real solution. In these scenarios, the mass constraint
cannot be satisfied.

In cases where the mass constraint can be satisfied, one could also constrain
in the event to equal the sum of the two undetected neutrinos. We call this
the constraint. This constraint reduces the remaining solutions to a discrete
set, containing either two or four possibilities that fully specify the
momenta of both neutrinos. Similar to the case of the mass constraint, there
are some values of the input parameters for which the constraint cannot be
satisfied.

When looking at simulated events where both jets are correctly reconstructed
and paired, we find that the mass constraint can be satisfied in 96% of all
cases, while the mass and constraints can be simultaneously satisfied in 55%
of cases. In contrast, if the jets are correctly reconstructed but incorrectly
paired to leptons, the mass constraint can be satisfied in only 23% of cases,
while both mass and constraints can be met in only 18% of cases.

Pairings with no solution to the mass constraint are thus frequently
incorrect. When the mass constraint can be satisfied, pairings with a solution
to the constraint are more likely to be correct. This information is used as
part of the pairing procedure, which has three steps.

  1. 1.

The mass constraint is checked for both possible pairings. If only one pairing
is found to satisfy the mass constraint, that pairing is used. If both
pairings fail to satisfy the mass constraint, the event is discarded. If both
pairings satisfy the mass constraint, we check the constraint.

  2. 2.

If only one pairing allows for the constraint while the other does not, the
pairing yielding an exact solution to the constraint is used.

  3. 3.

If the kinematic variables of the neutrinos do not suggest a clear pairing,
the jets, ${\PQb}_{1}$ and ${\PQb}_{2}$, are paired with the leptons ($\ell$,
$\overline{\ell}$) by minimizing the quantity

$\Sigma_{1(2)}=\Delta R({\PQb}_{1(2)},\ell)+\Delta
R({\PQb}_{2(1)},\overline{\ell})$

among the two possible pairings, where $\Delta
R({\PQb},\ell)=\sqrt{\smash[b]{(\eta_{\PQb}-\eta_{\ell})^{2}+(\varphi_{\PQb}-\varphi_{\ell})^{2}}}$
and $\varphi$ is the azimuthal angle in the transverse plane.

In simulation, this procedure discards 7% of the signal sample, targeting
events which generally involve an improperly assigned or misidentified jet (at
a rate of 72%). This raises the fraction of events that successfully identify
both jets from a top quark decay to 89% in simulation. After these steps, we
obtain the correct jet pairing in 82% of simulated dilepton events for which
both jets originating from top quark decays were correctly identified, and
thus 73% of simulated dilepton events overall.

Figure 3: The ratio of kinematic distributions with EW corrections (evaluated
for various values of $Y_{\PQt}$) to the SM kinematic distribution ($Y_{\PQt}$
=1) is shown, demonstrating the sensitivity of these distributions to the
Yukawa coupling. The plots on the left show the information at the generator
level, while the plots on the right are obtained from reconstructed events.
The axis scale is kept the same for the sake of comparison.

The sensitivity of our chosen kinematic variables to $Y_{\PQt}$, before and
after reconstruction, is shown in Fig. 3. We see that, in the chosen proxy
variables, not much sensitivity is lost in the reconstruction process. This is
especially true for the proxy mass observable, $M_{{\PQb}{\PQb}{\ell}{\ell}}$,
providing an advantage over $M_{\ttbar}$, which cannot be reconstructed as
accurately.

###  0.5.1 Comparison between data and simulation

Figure 4: Data-to-simulation comparisons for the jet multiplicity (upper
left), (upper right), lepton (lower left), and jet (lower right). The
uncertainty bands are derived by varying each uncertainty source up and down
by one standard deviation (as described in Section 0.7) and summing the
effects in quadrature. The signal simulation is divided into the following
categories: events with correctly identified leptons and jets in which jets
are correctly assigned (correct jets), events with correctly identified
leptons and jets in which jets are incorrectly assigned (swapped jets), events
with correctly identified leptons where the two jets originating from top
decays are not identified correctly (wrong jets), and lastly events where the
identified leptons are not those from boson decay vertices (wrong leptons).
The lower panels show the ratio of data to the simulated events in each bin,
with total uncertainty bands drawn around the nominal expected bin content.

Comparisons between data and simulation are shown in Fig. 4, where events are
broken into four categories: events with correctly identified leptons and jets
in which jets are correctly assigned (correct jets), events with correctly
identified leptons and jets in which jets are incorrectly assigned (swapped
jets), events with correctly identified leptons where the two jets originating
from top quark decays are not identified correctly (wrong jets), and lastly
events where the identified leptons are not those from decay vertices (wrong
leptons). The majority of events in the last category are dilepton decays
where a boson decay produces a $\tau$ lepton which itself decays leptonically,
with a small fraction being misidentified decays in the lepton+jets channel
(1% of the total signal). Though all events are subject to EW corrections and
thus considered as signal, the sensitivity of the reconstructed kinematic
variables is generally decreasing among the four categories.

Various observations can be made from Fig. 4. The agreement between data and
simulation appears generally to be within the total uncertainty (discussed
further in Section 0.7), and the small overall background rate is apparent.
Most events are seen to be associated with zero or one additional jet (beyond
the two jets). The effect of the selection requirement can be seen, removing
events in the ${\Pe}{\Pe}$ and ${\Pgm}{\Pgm}$ final states in a regime with
high Drell–Yan background rates. Single top quark production background rates
are seen to vary less steeply as a function of . Looking at the leading lepton
, we see that the additional use of a dilepton trigger would not yield a
substantial increase in sensitivity.

A slope is apparent in the ratio of data to the MC prediction in the
distributions of leptons and jets. The trends may be related to a previously
observed feature of the nominal +simulation, in which a harder top quark
distribution is observed than in data (as discussed, , in Ref. [41]). This
behavior is the subject of much discussion in the top quark physics community,
so we remark on it in this paper despite the fact that we are primarily
concerned with other kinematic variables. Fixed-order NNLO calculations are
available that generally show a softer top quark spectrum than in the
+simulation, which could be seen as evidence that the discrepancy arises from
mismodeling in simulation. However, the modeling of $M_{\ttbar}$ does not
appear to suffer such issues [41], and we see no evidence that the kinematic
variables used in this measurement are not well-described within the included
modeling uncertainties. Further discussion of the top quark spectrum in +and
its relation to fixed-order NNLO calculations can be found in Section 0.7.

##  0.6 Measurement strategy and statistical methods

Figure 5: The pre-fit agreement between data and MC simulation in the final
kinematic binning. The solid lines divide the three data-taking periods, while
the dashed lines divide the two $\abs{\Delta y_{{\PQb}\ell{\PQb}\ell}}$ bins
in each data-taking period, with $M_{{\PQb}{\PQb}{\ell}{\ell}}$ bin ranges
displayed on the $x$ axis. The lower panel shows the ratio of data to the
simulated events in each bin, with total uncertainty bands drawn around the
nominal expected bin content, obtained by summing the contributions of all
uncertainty sources in quadrature.

After reconstruction, events are binned coarsely in $\abs{\Delta
y_{{\PQb}\ell{\PQb}\ell}}$ and more finely in $M_{{\PQb}{\PQb}{\ell}{\ell}}$.
The binning is chosen to ensure each bin in each data-taking year contains at
least on the order of 10 000 events, as seen in Fig. 5, leading to a low
statistical uncertainty and improved uncertainty estimation.

In each bin, the expected yield is parametrized as a function of $Y_{\PQt}$.
The effect is exactly quadratic, as a consequence of the order at which EW
corrections are evaluated. We perform a quadratic fit to extrapolate the
effect of the EW corrections on a given bin as a continuous function of
$Y_{\PQt}$ (Fig. 6). This correction for each bin can be applied as a rate
parameter $R_{\mathrm{EW}}$ affecting the expected bin content.

We construct a likelihood function $\mathcal{L}$,

$\mathcal{L}=\left[\prod_{\text{bin}\in(M_{{\PQb}{\PQb}{\ell}{\ell}},\abs{\Delta
y_{{\PQb}\ell{\PQb}\ell}})}\mathcal{L}_{\text{bin}}\right]\,p(\phi)\,\prod_{i}p(\theta_{i}),$
(1)

where $\phi$ and $\\{\theta_{i}\\}$ are the suite of nuisance parameters
associated with individual sources of systematic uncertainty. The
distributions $p(\phi)$ and $p(\theta_{i})$ are penalty terms which assign
probability distributions that encode the likelihood the parameters vary from
their prior values, as discussed further below. Each bin has an individual
Poisson likelihood distribution,

$\mathcal{L}_{\text{bin}}=\mathrm{Poisson}\Big{[}n^{\text{bin}}_{\text{obs}}\Big{|}s^{\text{bin}}(\\{\theta_{i}\\})\,R_{\mathrm{EW}}^{\text{bin}}(Y_{\PQt},\phi)+b^{\text{bin}}(\\{\theta_{i}\\})\Big{]},$
(2)

describing the probability of a bin content to vary from statistical
fluctuations. Here $n^{\text{bin}}_{\text{obs}}$ is the total observed bin
count, with the expected bin count being the sum of the predicted signal yield
$s^{\text{bin}}$ and background yield $b^{\text{bin}}$. The number of expected
signal events is modified by the additional rate parameter $R_{\mathrm{EW}}$,
which depends on the Yukawa coupling ratio $Y_{\PQt}$ and a special nuisance
parameter $\phi$ that encodes the uncertainty associated with the
multiplicative application of EW corrections derived at order
$\mathcal{O}(\alpS^{2}\alpha)$. The full expression for the rate
$R_{\mathrm{EW}}^{\text{bin}}$, including this uncertainty term in the bins
near the production threshold, is given by

$R_{\mathrm{EW}}^{\text{bin}}(Y_{\PQt},\phi)=[1+\delta_{\mathrm{EW}}^{\text{bin}}(Y_{\PQt})]\,[1+\delta_{\mathrm{QCD}}^{\text{bin}}\delta_{\mathrm{EW}}^{\text{bin}}(Y_{\PQt})]^{\phi},$
(3)

where we have defined

$\delta_{\mathrm{\mathrm{EW}}}^{\text{bin}}=\frac{n^{\text{bin}}_{\mathrm{\textsc{hathor}}}-n^{\text{bin}}_{\mathrm{LO\,QCD}}}{n^{\text{bin}}_{\mathrm{LO\,QCD}}},\
\delta_{\mathrm{QCD}}^{\text{bin}}=\frac{n^{\text{bin}}_{\mathrm{\POWHEG}}-n^{\text{bin}}_{\mathrm{LO\,QCD}}}{n^{\text{bin}}_{\POWHEG}}.$
(4)

Figure 6: The EW correction rate modifier $R_{\mathrm{EW}}^{\text{bin}}$ in
two separate ($M_{{\PQb}{\PQb}{\ell}{\ell}},\Delta y_{{\PQb}\ell{\PQb}\ell}$)
bins from simulated 2017 data, demonstrating the quadratic dependence on
$Y_{\PQt}$. All bins have an increasing or decreasing quadratic yield
function, with the steepest dependence on $Y_{\PQt}$ found at lower values of
$M_{{\PQb}{\PQb}{\ell}{\ell}}$.

In the nominal case, we have
$R_{\mathrm{EW}}^{\text{bin}}(Y_{\PQt})=1+\delta_{\mathrm{EW}}(Y_{\PQt})$.
Intuitively, $\delta_{\mathrm{EW}}$ represents the marginal effect of EW
corrections included in hathor relative to the LO QCD calculation, while
$\delta_{\mathrm{QCD}}$ represents the marginal effect of higher-order terms
included in the sample relative to the LO QCD calculation. The multiplicative
approach to including EW corrections assumes that these two corrections
factorize. The quantity
$\delta_{\mathrm{QCD}}^{\text{bin}}\delta_{\mathrm{\mathrm{EW}}}^{\text{bin}}$
represents the cross term arising from the difference in multiplicative and
additive approaches. The Gaussian-distributed nuisance parameter $\phi$
modulates the uncertainty generated by this cross term, inducing a bin yield
which varies according to a log-normal distribution. We note that the
uncertainty in the EW corrections is unique because it depends on the value of
$Y_{\PQt}$ at which the EW corrections are evaluated. Thus, it is described by
its own term and nuisance parameter $\phi$, separate from other systematic
uncertainties. For bins away from the threshold where EW corrections decrease
as a function of $Y_{\PQt}$, we do not include this uncertainty. These bins do
not contribute much sensitivity to the measurement, and enter a kinematic
regime in which this method of uncertainty estimation is no longer meaningful.
At the large values of the Mandelstam variable $s$ that correspond to these
bins, the dominant terms contributing to $\delta_{\mathrm{EW}}$ are Sudakov
logarithms resulting from and boson exchange. These terms factorize well and
do not contribute to the uncertainty we wish to model [40].

Each nuisance parameter $\theta_{j}$ corresponding to an overall normalization
uncertainty, such as the uncertainty in the integrated luminosity or in cross
section values, is assumed to follow a log-normal distribution
$p(\theta_{j})$. Uncertainties with shape effects associated to nuisance
parameters $\\{\theta_{i}\\}$ are handled by generating up and down variations
of the bin content $s^{\text{bin}}$ for each $\theta_{i}$. These variations
result from changing the underlying theoretical/experimental sources, which
are outlined in Section 0.7, usually by one standard deviation ($\sigma$)
based on the uncertainty in our best estimates. These up and down variations
are then enforced to correspond to the bin modifiers associated with
$\theta_{i}=\pm 1$, while $\theta_{i}=0$ corresponds to the nominal estimate.
The nuisance parameter $\theta_{i}$ is then taken to follow a Gaussian
distribution $p(\theta_{i})$ with mean $\mu=0$ and variance $\sigma^{2}=1$ in
the likelihood. The collection of bin modifiers for these up and down
variations are referred to as templates, with examples shown in Section 0.8. A
vertical template morphing is applied to alter the shape as a function of the
underlying nuisance parameter $\theta_{i}$, where in each bin the modifier is
interpolated as a sixth-order polynomial spline for values of
$\theta_{i}\in[-1,1]$ and linearly outside of that region, assuring that
$s^{\text{bin}}(\theta_{i})$ remains continuous and twice differentiable.

The measurement of $Y_{\PQt}$ is then performed via a profile likelihood scan,
as described in Ref. [47]. By repeating a maximum likelihood fit over a fine
array of fixed values of $Y_{\PQt}$ and comparing to the likelihood at the
best fit value, we can use the properties of the maximum likelihood test
statistic to evaluate intervals at 68% and 95% around the best fit value.

##  0.7 Experimental and theoretical uncertainties

###  0.7.1 Sources of uncertainty

The list of uncertainties considered is very similar to that of the previous
measurement presented in Ref. [13]. The main differences are the lack of QCD
multijet background and the use of data from the full Run 2 data-taking
period. Full or partial correlations are imposed on the underlying uncertainty
sources between data-taking periods where appropriate, as discussed further in
Section 0.7.2. Uncertainties that do not alter the shape of the final
distribution are treated as normalization uncertainties, while all others are
treated as shape uncertainties on the binned data. Shape effects are
considered for the distributions of events only, as the contribution of
background events is small. Correlations of the uncertainties between
different data-taking periods are treated on a case-by-case basis. Because the
measurement is more sensitive to shape effects than normalization effects, the
uncertainties with the largest magnitude do not necessarily have the largest
impact on the measurement sensitivity. By repeating the measurement with any
given nuisance parameters frozen at their post-fit values, we are able to
evaluate what fraction of the measured uncertainty on $Y_{\PQt}$ is associated
to those nuisance parameters.

The dominant experimental uncertainty in this analysis comes from the
calibration of the detector jet energy response. Corrections to the
reconstructed jet energies are applied as a function of and $\eta$. We follow
the standard approach outlined in Ref. [44] to consider 26 separate
uncertainties that are typically involved in determining these calibrations.
In this approach, the uncertainty in the resolution of the jet reconstruction
is also considered in addition to the energy response. The effect of these
uncertainties is propagated to the reconstruction of . These effects account
for approximately $7\%$ of the total uncertainty on $Y_{\PQt}$ in the final
measurement.

Other experimental sources of uncertainty are comparatively minor. The overall
uncertainty in the integrated luminosity of 2.5, 2.3, and 2.5% is included as
a normalization uncertainty applied to all signal and background events in
2016, 2017, and 2018, respectively [48, 49, 50]. The uncertainty in the number
of pileup events included in simulation is assessed by varying the inelastic
cross section, 69.2mb, by 4.6% [51].

Efficiencies in jet identification and misidentification are corrected to
match data [45]. While this source is treated as a shape effect, the
uncertainty manifests approximately as an overall normalization effect on the
signal of around 3%, and contributes only about 1% of the final uncertainty on
$Y_{\PQt}$.

Similarly, scale factors are applied in bins of and $\eta$ to correct
simulated efficiencies of lepton reconstruction, identification, isolation,
and triggers to match data. These are derived from a fit using the tag-and-
probe method using boson decays [52, 53, 54]. This fit accounts for the
uncertainty from the limited number of events in the data sample as well as
differences in performance based on the jet multiplicity. Overall, the effect
is assessed to be below 2%.

As a standard technique to estimate the contributions of higher-order QCD
terms at the ME level, the renormalization scale $\mu_{\mathrm{R}}$ and
factorization scale $\mu_{\mathrm{F}}$ are each varied up and down in the
simulation by a factor of 2. Templates are generated for the individual
variation of $\mu_{\mathrm{R}}$ and $\mu_{\mathrm{F}}$, as well as an
additional template for the simultaneous variation of the two scales together
(up and down), leading to three separate shape uncertainties in total. Since
an NNLO cross section is already used to improve the normalization of the MC
simulation, the normalization effect induced by the scale variations is
overestimated. As we include a separate uncertainty on the cross section
normalization, the overall normalization effect is therefore removed entirely
from the scale variation templates, which are normalized to the nominal
sample. The resulting shape effects remain significant and these are among the
limiting uncertainties in the fit, contributing about 7% of the total
measurement uncertainty.

A 5% normalization uncertainty is assumed in the cross section, which covers
expected contributions from the higher-order terms not included in the
NNLO+NNLL cross section calculation [28], giving a more realistic
normalization uncertainty than the variation of $\mu_{\mathrm{R}}$ and
$\mu_{\mathrm{F}}$ in . The backgrounds in this analysis are small enough
($\approx$2% sample composition each) that we do not generate templates for
their response to individual systematic uncertainties. A 15% normalization
uncertainty is included on single top quark MC samples, which covers the
expected ME scale variation and the jet energy correction uncertainties
associated with these samples. The Drell–Yan and diboson MC samples are
assigned a 30% normalization uncertainty, to cover the larger ME scale
variation uncertainties associated with these LO simulations. The background
normalizations can alter slightly the expected shape of the data, but are not
among the most impactful uncertainties.

We include an uncertainty in the EW corrections, based on our methods for
generating and applying these additional terms, as outlined in Sections 0.3
and 0.6. Like the scale variations, this uncertainty is designed to cover
higher-order effects at the ME level, specifically those arising from diagrams
of order $\alpS^{3}\alpha$. It places an uncertainty on
$R_{\mathrm{EW}}^{\text{bin}}$ of 10–40% in the applicable bins, which
translates to a small overall uncertainty in bin rate unless the corrections
are evaluated at a value of $Y_{\PQt}$ far from the SM expectation. This helps
ensure that we do not fit an artificially high value of $Y_{\PQt}$ by ignoring
higher-order diagrams. This represents one of the most significant
uncertainties in the fit, accounting for approximately $8\%$ of the final
measurement uncertainty. It is also observed to primarily affect the lower
bound of the measurement, thus reducing our ability to distinguish between
values of $Y_{\PQt}<1$.

The uncertainty in modeling the initial- and final-state radiation in the
parton shower algorithm is assessed by varying the value of the
renormalization scales in the initial- and final-state radiation by a factor
of two. These are among the most limiting modeling uncertainties in the
measurement, contributing about $8\%$ of the total measurement uncertainty.
Uncertainties for other parameters in the parton shower description are
considered separately. The $h_{\mathrm{damp}}$ parameter, which controls the
ME to parton shower matching in +, is set to the nominal value of
$h_{\mathrm{damp}}=1.58\,m_{\PQt}$ (1.39 $m_{\PQt}$) in 2016 (2017–18).
Dedicated MC samples are generated with this parameter varied down to
$1\,m_{\PQt}$ ($0.874\,m_{\PQt}$) and up to $2.24\,m_{\PQt}$
($2.305\,m_{\PQt}$) in 2016 (2017–18), in order to estimate the effect of this
uncertainty. Dedicated MC samples are also generated with variations of the
underlying-event tune. The uncertainties due to the choice of
$h_{\mathrm{damp}}$ and the underlying-event tune are very minor compared to
the parton shower scale variations.

Dedicated MC samples are generated with the top quark mass varied up and down
by 1from the nominal value $m_{\PQt}=172.5\GeV$ to estimate the effect of the
uncertainty in the measured mass value. While this uncertainty has a
significant shape effect, it ultimately accounts for only about $1\%$ of the
total measurement uncertainty. It should be noted that, although the mass and
Yukawa coupling are generally treated as independent in this measurement,
varying the mass will slightly modify the definition of $Y_{\PQt}=1$. However,
this effect, which is below 1%, is much smaller than the sensitivity of the
measurement and can therefore be ignored.

The NNPDF sets [26] contain 100 individual variations as uncertainties.
Following the approach in Ref. [13], similar variations are combined to reduce
the number of variations to a more manageable set of 10 templates. The
variation of the strong coupling $\alpS$ used by NNPDF is treated separately
from the other PDF variations. The effect of uncertainties in the PDF set is
typically smaller than 1%, and together they account for roughly 2% of the
total measurement uncertainty.

The branching fraction of semileptonic hadron decays affects the jet response.
The effect of varying this quantity within its measured precision [55] is
included as an uncertainty, which has a small effect relative to other
modeling uncertainties.

The momentum transfer from quarks to hadrons is modeled with a transfer
function dependent on $x_{\PQb}=\pt(\PQb\text{-hadron})/\pt(\PQb\text{-jet})$.
To estimate the uncertainty, the transfer function is varied up and down
within uncertainty of the Bowler–Lund parameter [56] in . The resulting effect
is included by modifying event weights to reproduce the appropriate transfer
function. This has a noticeable shape effect of the order 4%, but was not
found to be a leading uncertainty in the fit.

In some measurements performed strictly in the context of the SM (for example,
in Ref. [57]), an additional uncertainty is included to account for an
observed difference in the top quark distribution between data and
+simulation. As the measurement presented here is sensitive to anomalously
high values of $Y_{\PQt}$, we do not want to include any additional
uncertainties which explicitly enforce agreement between SM simulation and the
data, as this could reduce our sensitivity to deviations from the SM.

With this in mind, studies were performed comparing different simulations to
assess whether top quark modelling disagreements necessitated the inclusion of
any additional uncertainties. Fixed-order calculations were studied for
production at NNLO, which generally show better agreement with the top quark
spectrum observed in data (see, for example, Refs. [58, 41]). Specifically,
differential cross sections in top quark and $M_{\ttbar}$ were studied using
publicly available fastNLO tables [59, 60], as well as multidifferential cross
sections [61]. Such NNLO calculations use a different choice of dynamical
scale in evaluating the top quark versus other kinematic variables, lending
them an edge in precision over full event simulation. We find that the
predictions from +samples are consistent with the differential and
multidifferential cross sections from Refs. [59, 60, 61] involving
$M_{\ttbar}$ and $\Delta y_{\ttbar}$, within modeling uncertainties. These
distributions appear consistent with the data as well, as previously observed
in Ref. [41]. By comparison, the top quark distribution evaluated at NNLO from
Refs. [59, 60] shows more substantial disagreement with +simulations. We
conclude that the variables relevant to our measurement technique appear
sufficiently well described by +simulations, and differences with relevant
NNLO calculations should be covered by the standard uncertainty estimation
techniques. However, analyses that are more specifically sensitive to the top
quark distribution should take care in addressing this discrepancy when using
+samples.

Information about the magnitudes and effects of significant uncertainties can
be found in Table 0.7.1.

The effect of all significant normalization (norm.) and shape uncertainties is
summarized. Uncertainties are grouped into categories based on their physical
origin, and the approximate effect on sample yield is stated. Additionally,
the fit is repeated with each category frozen to their post-fit values, in
order to assess the reduction of total fit uncertainty resulting from their
removal (rightmost column). Minor uncertainties with $<1\%$ effect on sample
yield are excluded from this summary. l c x x Uncertainty category type effect
on yield reduction in fit unc.
cross section norm. 5, ¡1,
background norm. norm. 0–1.5, ≈1,
luminosity norm. 2.3–2.5, ¡1,
jet energy corrections shape 0–4, 7.4,
EW correction unc. ($\phi$) shape (0–40,) δ_EW 7.6,
ME scales shape 0–5, 7.3,
parton shower scales shape 0–4, 7.7,
NNPDF uncertainties shape 0–3, 1.9,
top quark mass shape 0–2.5, 1.3,
tagging efficiency shape 2–2.5, ≈1,
mistagging efficiency shape 0–0.5, ¡1,
lepton scale factors shape 0–2, ≈1,
fragmentation shape 0–5, ¡1,
hadron branching frac. shape 1–2, ¡1,
pileup shape 0–0.5, ¡1,

###  0.7.2 Treatment of systematic uncertainties

Figure 7: The effect of the Yukawa parameter $Y_{\PQt}$ on reconstructed event
yield in the final binned distributions. The variation of $Y_{\PQt}$ induces a
shape distortion in the kinematic distributions. The marginal effect relative
to the standard model expectation $Y_{\PQt}$ =1 is visualized in the lower
panel.

In this analysis, the effect of the parameter of interest $Y_{\PQt}$ manifests
itself as a smooth shape distortion of the kinematic distributions, as shown
in Fig. 7. Although the nuisance parameters describing the sources of
uncertainty should induce smooth shape effects as well, their effects are
sometimes obscured by statistical noise or imprecise methods of estimation.
This is noticeable for the uncertainties associated with the jet energy scale,
jet energy resolution, parton shower modeling, pileup reweighting, and top
quark mass. For these templates only, we apply a one-iteration LOWESS
algorithm [62] to smooth the templates and remove fluctuations that may
disturb the fit. The underlying-event tune and $h_{\mathrm{damp}}$
uncertainties in the parton showering are small enough for their shapes to
disappear into statistical noise, and are therefore treated only as
normalization uncertainties.

Most templates are also symmetrized, by taking the larger effect of the up and
down variations in each bin and using this magnitude for both. This step helps
ensure a stable minimum in the likelihood fit, but is skipped for the
templates whose natural shape effect is notably asymmetric. In the few cases
where this may be an overly conservative approach, it nonetheless guarantees
the performance and reliability of the minimization procedure, and has little
effect on the final result.

Full or partial correlations between the 2016, 2017, and 2018 data analyses
are assumed for many uncertainties. In general, the theoretically motivated
uncertainties are considered fully correlated between years. Exceptions are
made in cases where modeling differed between years. The PDF uncertainties
cannot be correlated between 2016 and other data-taking periods, as the PDF
sets used for simulation were changed to a newer version. Due to changes in
the tune following 2016, the nominal scales used initial-state radiation and
final-state radiation differ after 2016, so those uncertainties are treated as
only partially correlated between 2016 and other data-taking periods. The
modeling of these uncertainties differs in the 2016 simulation, so the
associated nuisance parameter in this year is either partially or fully
decorrelated from those in the other years. Additionally, uncertainties whose
effects disappear into statistical noise due to limited MC sample size
(underlying-event tune and $h_{\mathrm{damp}}$) are converted to uncorrelated
normalization uncertainties.

Some experimental uncertainties can be broken into components, which are
either fully correlated or uncorrelated between years (large jet energy scale
contributions and integrated luminosity). The uncertainty in the number of
pileup events is considered fully correlated as it is evaluated by varying the
total inelastic cross section. For minor uncertainties from jet and lepton
scale factors, which have both correlated and statistical components, a 50%
correlation is assumed between years. Lastly, the jet energy resolution
uncertainties are treated as uncorrelated between years.

##  0.8 Results

We obtain a fit result of
$Y_{\PQt}=1.16^{+0.07}_{-0.08}\stat^{+0.23}_{-0.34}\syst$ and an approximate
upper limit at 95% of $Y_{\PQt}<1.54$, where the latter is determined from the
point at which $-2\ln(\mathcal{L}(Y_{\PQt}))$ increases by an amount of
$1.64^{2}$ relative to the minimum value. For comparison, the standard model
expectation based on simulated Asimov data [63] is
${Y_{\PQt}}=1\,^{+0.30}_{-0.57}(\text{tot})$ with $Y_{\PQt}<1.47$ at 95% CL.
The scan of the profile likelihood test statistic used to build these
intervals is shown in Fig. 8, along with a comparison to the expected behavior
based on simulated Asimov data sets. We also show the agreement of data and
simulation after performing the fit in Fig. 9. The minimum of the negative log
likelihood occurs at a configuration with good agreement between data and
simulation. The result is seen to be clearly limited by systematic
uncertainties rather than statistical uncertainty. The templates for the four
uncertainties with the greatest effect on the fit are shown in Fig. 10.

Figure 8: The result of a profile likelihood scan, performed by fixing the
value of $Y_{\PQt}$ at values over the interval $[0,3]$ and taking the ratio
of $-2\ln(\mathcal{L}(Y_{\PQt}))$ to the best fit value
$-2\ln(\mathcal{L}(\hat{Y}_{\PQt}))$. The expected curves from fits to
simulated Asimov data are shown produced for the SM value $Y_{\PQt}=1.0$
(dashed) and for the final best fit value of $Y_{\PQt}=1.16$ (dotted). Figure
9: The comparison between data and MC simulation at the best fit value of
$Y_{\PQt}=1.16$ after performing the likelihood maximization, with shaded
bands displaying the post-fit uncertainty. The solid lines separate the three
data-taking periods, while the dashed lines indicate the boundaries of the two
$\abs{\Delta y_{{\PQb}\ell{\PQb}\ell}}$ bins in each data-taking period, with
$M_{{\PQb}{\PQb}{\ell}{\ell}}$ bin ranges displayed on the $x$ axis. The lower
panel shows the ratio of data to the simulated events in each bin, with total
post-fit uncertainty bands drawn around the nominal expected bin content.

Figure 10: Templates are shown for the uncertainties associated with the
final-state radiation in (upper left), the jet energy corrections (upper
right), the factorization scale (lower left), and the renormalization scale
(lower right). Along with the intrinsic uncertainty in the EW corrections,
these are the limiting uncertainties in the fit. The shaded bars represent the
raw template information, while the lines show the shapes after smoothing and
symmetrization procedures have been applied. In the fit, the jet energy
corrections are split into 26 different components, but for brevity only the
total uncertainty is shown here. Variation between years is minimal for each
of these uncertainties, although they are treated separately in the fit.

This result is in agreement with the previously obtained measurement in the
lepton+jets final state in Ref. [13], while obtaining a slight increase in
sensitivity. Using a different decay channel and a larger data set provides a
measurement complementary to the previous result.

##  0.9 Summary

A measurement of the Higgs Yukawa coupling to the top quark is presented,
based on data from proton-proton collisions collected by the CMS experiment.
Data at a center-of-mass energy of 13is analyzed from the LHC Run 2, collected
in 2016–18 and corresponding to an integrated luminosity of 137. The resulting
best fit value of the top quark Yukawa coupling relative to the standard model
is given by $Y_{\PQt}=1.16^{+0.24}_{-0.35}$. This measurement uses the effects
of virtual Higgs boson exchange on kinematic properties to extract information
about the coupling from kinematic distributions. Although the sensitivity is
lower compared to constraints obtained from studying processes involving Higgs
boson production in Refs. [9] and [11], this measurement avoids dependence on
other Yukawa coupling values through additional branching assumptions, making
it a compelling independent measurement. This measurement also achieves a
slightly higher precision than the only other $Y_{\PQt}$ measurement that does
not make additional branching fraction assumptions, performed in the search
for production of four top quarks. The four top quark search places $Y_{\PQt}$
$<1.7$ at a 95% confidence level [12] while this measurement achieves an
approximate result of $Y_{\PQt}$ $<1.54$.

###### 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); RIF
(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 (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. Z191100007219010; the
Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the
Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy –
EXC 2121 “Quantum Universe” – 390833306; 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
Higher Education, project no. 02.a03.21.0005 (Russia); the Tomsk Polytechnic
University Competitiveness Enhancement Program; 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 Graduate Research Fellowship Program of
the National Science Foundation, grant no. DGE-141911; the Kavli Foundation;
the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation,
contract C-1845; and the Weston Havens Foundation (USA).

## References

  * [1] ATLAS Collaboration, “Observation of a new particle in the search for the standard model Higgs boson with the ATLAS detector at the LHC”,  Phys. Lett. B 716 (2012) 1, 10.1016/j.physletb.2012.08.020, arXiv:1207.7214.
  * [2] CMS Collaboration, “Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC”,  Phys. Lett. B 716 (2012) 30, 10.1016/j.physletb.2012.08.021, arXiv:1207.7235.
  * [3] ATLAS Collaboration, “Measurement of the top-quark mass in $t\bar{t}+1$-jet events collected with the ATLAS detector in $pp$ collisions at $\sqrt{s}=8$ TeV”,  JHEP 11 (2019) 150, 10.1007/JHEP11(2019)150, arXiv:1905.02302.
  * [4] CMS Collaboration, “Measurement of the top quark mass with lepton+jets final states using $\mathrm{p}$ $\mathrm{p}$ collisions at $\sqrt{s}=13\,\text{TeV}$”,  Eur. Phys. J. C 78 (2018) 891, 10.1140/epjc/s10052-018-6332-9, arXiv:1805.01428.
  * [5] G. C. Branco et al., “Theory and phenomenology of two-Higgs-doublet models”,  Phys. Rept. 516 (2012) 1, 10.1016/j.physrep.2012.02.002, arXiv:1106.0034.
  * [6] K. Agashe, R. Contino, and A. Pomarol, “The minimal composite Higgs model”,  Nucl. Phys. B 719 (2005) 165, 10.1016/j.nuclphysb.2005.04.035, arXiv:hep-ph/0412089.
  * [7] LHC Higgs Cross Section Working Group, “Handbook of LHC Higgs cross sections: 3. Higgs properties”,  CERN (2013) 10.5170/CERN-2013-004, arXiv:1307.1347.
  * [8] CMS Collaboration, “Search for associated production of a Higgs boson and a single top quark in proton-proton collisions at $\sqrt{s}=13$ TeV”,  Phys. Rev. D 99 (2019) 092005, 10.1103/PhysRevD.99.092005, arXiv:1811.09696.
  * [9] CMS Collaboration, “Observation of $\mathrm{t\overline{t}}$H production”,  Phys. Rev. Lett. 120 (2018) 231801, 10.1103/PhysRevLett.120.231801, arXiv:1804.02610.
  * [10] ATLAS Collaboration, “Observation of Higgs boson production in association with a top quark pair at the LHC with the ATLAS detector”,  Phys. Lett. B 784 (2018) 173, 10.1016/j.physletb.2018.07.035, arXiv:1806.00425.
  * [11] CMS Collaboration, “Combined measurements of Higgs boson couplings in proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  Eur. Phys. J. C 79 (2019) 421, 10.1140/epjc/s10052-019-6909-y, arXiv:1809.10733.
  * [12] CMS Collaboration, “Search for production of four top quarks in final states with same-sign or multiple leptons in proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  Eur. Phys. J. C 80 (2020) 75, 10.1140/epjc/s10052-019-7593-7, arXiv:1908.06463.
  * [13] CMS Collaboration, “Measurement of the top quark Yukawa coupling from $\mathrm{t\bar{t}}$ kinematic distributions in the lepton+jets final state in proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  Phys. Rev. D 100 (2019) 072007, 10.1103/PhysRevD.100.072007, arXiv:1907.01590.
  * [14] J. H. Kühn, A. Scharf, and P. Uwer, “Weak interactions in top-quark pair production at hadron colliders: an update”,  Phys. Rev. D 91 (2015) 014020, 10.1103/PhysRevD.91.014020, arXiv:1305.5773.
  * [15] M. Aliev et al., “HATHOR: HAdronic Top and Heavy quarks crOss section calculatoR”,  Comput. Phys. Commun. 182 (2011) 1034, 10.1016/j.cpc.2010.12.040, arXiv:1007.1327.
  * [16] CMS Collaboration, “Particle-flow reconstruction and global event description with the cms detector”,  JINST 12 (2017) P10003, 10.1088/1748-0221/12/10/P10003, arXiv:1706.04965.
  * [17] CMS Collaboration, “The CMS trigger system”,  JINST 12 (2017) P01020, 10.1088/1748-0221/12/01/P01020, arXiv:1609.02366.
  * [18] CMS Collaboration, “The CMS experiment at the CERN LHC”,  JINST 3 (2008) S08004, 10.1088/1748-0221/3/08/S08004.
  * [19] P. Nason, “A new method for combining NLO QCD with shower Monte Carlo algorithms”,  JHEP 11 (2004) 040, 10.1088/1126-6708/2004/11/040, arXiv:hep-ph/0409146.
  * [20] S. Frixione, P. Nason, and C. Oleari, “Matching NLO QCD computations with parton shower simulations: the POWHEG method”,  JHEP 11 (2007) 070, 10.1088/1126-6708/2007/11/070, arXiv:0709.2092.
  * [21] S. Alioli, P. Nason, C. Oleari, and E. Re, “A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX”,  JHEP 06 (2010) 043, 10.1007/JHEP06(2010)043, arXiv:1002.2581.
  * [22] S. Frixione, P. Nason, and G. Ridolfi, “A Positive-weight next-to-leading-order Monte Carlo for heavy flavour hadroproduction”,  JHEP 09 (2007) 126, 10.1088/1126-6708/2007/09/126, arXiv:0707.3088.
  * [23] T. Sjöstrand et al., “An introduction to PYTHIA 8.2”,  Comput. Phys. Commun. 191 (2015) 159, 10.1016/j.cpc.2015.01.024, arXiv:1410.3012.
  * [24] P. Skands, S. Carrazza, and J. Rojo, “Tuning PYTHIA 8.1: the Monash 2013 tune”,  Eur. Phys. J. C 74 (2014) 3024, 10.1140/epjc/s10052-014-3024-y, arXiv:1404.5630.
  * [25] CMS Collaboration, “Extraction and validation of a new set of CMS PYTHIA8 tunes from underlying-event measurements”,  Eur. Phys. J. C 80 (2020) 4, 10.1140/epjc/s10052-019-7499-4, arXiv:1903.12179.
  * [26] NNPDF Collaboration, “Parton distributions for the LHC Run II”,  JHEP 04 (2015) 040, 10.1007/JHEP04(2015)040, arXiv:1410.8849.
  * [27] NNPDF Collaboration, “Parton distributions from high-precision collider data”,  Eur. Phys. J. C 77 (2017) 663, 10.1140/epjc/s10052-017-5199-5, arXiv:1706.00428.
  * [28] M. Czakon and A. Mitov, “Top++: A program for the calculation of the top-pair cross-section at hadron colliders”,  Comput. Phys. Commun. 185 (2014) 2930, 10.1016/j.cpc.2014.06.021, arXiv:1112.5675.
  * [29] M. Botje et al., “The PDF4LHC working group interim recommendations”, (2011). arXiv:1101.0538.
  * [30] A. D. Martin, W. J. Stirling, R. S. Thorne, and G. Watt, “Parton distributions for the LHC”,  Eur. Phys. J. C 63 (2009) 189, 10.1140/epjc/s10052-009-1072-5, arXiv:0901.0002.
  * [31] A. D. Martin, W. J. Stirling, R. S. Thorne, and G. Watt, “Uncertainties on in global PDF analyses and implications for predicted hadronic cross sections”,  Eur. Phys. J. C 64 (2009) 653, 10.1140/epjc/s10052-009-1164-2, arXiv:0905.3531.
  * [32] H.-L. Lai et al., “New parton distributions for collider physics”,  Phys. Rev. D 82 (2010) 074024, 10.1103/PhysRevD.82.074024, arXiv:1007.2241.
  * [33] J. Gao et al., “CT10 next-to-next-to-leading order global analysis of QCD”,  Phys. Rev. D 89 (2014) 033009, 10.1103/PhysRevD.89.033009, arXiv:1302.6246.
  * [34] NNPDF Collaboration, “Parton distributions with LHC data”,  Nucl. Phys. B 867 (2013) 244, 10.1016/j.nuclphysb.2012.10.003, arXiv:1207.1303.
  * [35] J. Alwall et al., “The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations”,  JHEP 07 (2014) 079, 10.1007/JHEP07(2014)079, arXiv:1405.0301.
  * [36] M. L. Mangano, M. Moretti, F. Piccinini, and M. Treccani, “Matching matrix elements and shower evolution for top-quark production in hadronic collisions”,  JHEP 01 (2007) 013, 10.1088/1126-6708/2007/01/013, arXiv:hep-ph/0611129.
  * [37] J. Alwall et al., “Comparative study of various algorithms for the merging of parton showers and matrix elements in hadronic collisions”,  Eur. Phys. J. C 53 (2008) 473, 10.1140/epjc/s10052-007-0490-5, arXiv:0706.2569.
  * [38] GEANT4 Collaboration, “—a simulation toolkit”,  Nucl. Instrum. Meth. A 506 (2003) 250, 10.1016/S0168-9002(03)01368-8.
  * [39] W. Hollik and M. Kollar, “NLO QED contributions to top-pair production at hadron collider”,  Phys. Rev. D 77 (2008) 014008, 10.1103/PhysRevD.77.014008, arXiv:0708.1697.
  * [40] M. Czakon et al., “Top-pair production at the LHC through NNLO QCD and NLO EW”,  JHEP 10 (2017) 186, 10.1007/JHEP10(2017)186, arXiv:1705.04105.
  * [41] CMS Collaboration, “Measurement of differential cross sections for the production of top quark pairs and of additional jets in lepton+jets events from pp collisions at $\sqrt{s}=$ 13 TeV”,  Phys. Rev. D 97 (2018) 112003, 10.1103/PhysRevD.97.112003, arXiv:1803.08856.
  * [42] M. Cacciari, G. P. Salam, and G. Soyez, “The anti-jet clustering algorithm”,  JHEP 04 (2008) 063, 10.1088/1126-6708/2008/04/063, arXiv:0802.1189.
  * [43] M. Cacciari, G. P. Salam, and G. Soyez, “FastJet user manual”,  Eur. Phys. J. C 72 (2012) 1896, 10.1140/epjc/s10052-012-1896-2, arXiv:1111.6097.
  * [44] CMS Collaboration, “Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV”,  JINST 12 (2017) P02014, 10.1088/1748-0221/12/02/P02014, arXiv:1607.03663.
  * [45] CMS Collaboration, “Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV”,  JINST 13 (2018) P05011, 10.1088/1748-0221/13/05/P05011, arXiv:1712.07158.
  * [46] B. A. Betchart, R. Demina, and A. Harel, “Analytic solutions for neutrino momenta in decay of top quarks”,  Nucl. Instrum. Meth. A 736 (2014) 169, 10.1016/j.nima.2013.10.039, arXiv:1305.1878.
  * [47] CMS Collaboration, “Precise determination of the mass of the Higgs boson and tests of compatibility of its couplings with the standard model predictions using proton collisions at 7 and 8 TeV”,  Eur. Phys. J. C 75 (2015) 212, 10.1140/epjc/s10052-015-3351-7, arXiv:1412.8662.
  * [48] CMS Collaboration, “CMS Luminosity Measurements for the 2016 Data Taking Period”, CMS Physics Analysis Summary CMS-PAS-LUM-17-001, 2017.
  * [49] CMS Collaboration, “CMS luminosity measurement for the 2017 data-taking period at $\sqrt{s}=13~{}\mathrm{TeV}$”, CMS Physics Analysis Summary CMS-PAS-LUM-17-004, 2018.
  * [50] CMS Collaboration, “CMS luminosity measurement for the 2018 data-taking period at $\sqrt{s}=13~{}\mathrm{TeV}$”, CMS Physics Analysis Summary CMS-PAS-LUM-18-002, 2019.
  * [51] ATLAS Collaboration, “Measurement of the inelastic proton-proton cross section at $\sqrt{s}=13$with the ATLAS detector at the LHC”,  Phys. Rev. Lett. 117 (2016) 182002, 10.1103/PhysRevLett.117.182002, arXiv:1606.02625.
  * [52] CMS Collaboration, “Measurements of Inclusive $W$ and $Z$ Cross Sections in pp Collisions at $\sqrt{s}=7$ TeV”,  JHEP 01 (2011) 080, 10.1007/JHEP01(2011)080, arXiv:1012.2466.
  * [53] CMS Collaboration, “Performance of electron reconstruction and selection with the CMS detector in proton-proton collisions at $\sqrt{s}=8$”,  JINST 10 (2015) P06005, 10.1088/1748-0221/10/06/P06005, arXiv:1502.02701.
  * [54] CMS Collaboration, “Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  JINST 13 (2018) P06015, 10.1088/1748-0221/13/06/P06015, arXiv:1804.04528.
  * [55] Particle Data Group, M. Tanabashi et al., “Review of particle physics”,  Phys. Rev. D 98 (2018) 030001, 10.1103/PhysRevD.98.030001.
  * [56] M. G. Bowler, “${\rm e}^{+}{\rm e}^{-}$ Production of heavy quarks in the string model”,  Z. Phys. C 11 (1981) 169, 10.1007/BF01574001.
  * [57] CMS Collaboration, “Measurement of the $\mathrm{t}\overline{\mathrm{t}}$ production cross section, the top quark mass, and the strong coupling constant using dilepton events in pp collisions at $\sqrt{s}=$ 13 TeV”,  Eur. Phys. J. C 79 (2019) 368, 10.1140/epjc/s10052-019-6863-8, arXiv:1812.10505.
  * [58] M. Grazzini, S. Kallweit, and M. Wiesemann, “Fully differential NNLO computations with MATRIX”,  Eur. Phys. J. C 78 (2018) 537, 10.1140/epjc/s10052-018-5771-7, arXiv:1711.06631.
  * [59] M. Czakon, D. Heymes, and A. Mitov, “fastNLO tables for NNLO top-quark pair differential distributions”, (2017). arXiv:1704.08551.
  * [60] M. Czakon, D. Heymes, and A. Mitov, “High-precision differential predictions for top-quark pairs at the LHC”,  Phys. Rev. Lett. 116 (2016) 082003, 10.1103/PhysRevLett.116.082003, arXiv:1511.00549.
  * [61] M. Czakon et al., “A study of the impact of double-differential top distributions from CMS on parton distribution functions”, (2019). arXiv:1912.08801.
  * [62] W. S. Cleveland, “Robust locally weighted regression and smoothing scatterplots”,  J. Am. Stat. Assoc. 74 (1979) 829, 10.1080/01621459.1979.10481038.
  * [63] G. Cowan, K. Cranmer, E. Gross, and O. Vitells, “Asymptotic formulae for likelihood-based tests of new physics”,  Eur. Phys. J. C 71 (2011) 1554, 10.1140/epjc/s10052-011-1554-0, arXiv:1007.1727. [Erratum: 10.1140/epjc/s10052-013-2501-z].

##  .10 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, M. Dragicevic, J. Erö, A. Escalante Del
Valle, R. Frühwirth1, M. Jeitler1, N. Krammer, L. Lechner, D. Liko, T.
Madlener, I. Mikulec, F.M. Pitters, N. Rad, J. Schieck1, R. Schöfbeck, M.
Spanring, S. Templ, W. Waltenberger, C.-E. Wulz1, M. Zarucki Institute for
Nuclear Problems, Minsk, Belarus
V. Chekhovsky, A. Litomin, V. Makarenko, J. Suarez Gonzalez Universiteit
Antwerpen, Antwerpen, Belgium
M.R. Darwish2, E.A. De Wolf, D. Di Croce, X. Janssen, T. Kello3, 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, A. Morton, Q. Python, S. Tavernier, W.
Van Doninck, P. Van Mulders Université Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, B. Clerbaux, G. De Lentdecker, B. Dorney, L. Favart, A.
Grebenyuk, A.K. Kalsi, I. Makarenko, L. Moureaux, L. Pétré, A. Popov, N.
Postiau, E. Starling, L. Thomas, C. Vander Velde, P. Vanlaer, D. Vannerom, L.
Wezenbeek Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, M. Gruchala, I. Khvastunov4, M. Niedziela, C. Roskas,
K. Skovpen, M. Tytgat, W. Verbeke, B. Vermassen, M. Vit Université Catholique
de Louvain, Louvain-la-Neuve, Belgium
G. Bruno, F. Bury, C. Caputo, P. David, C. Delaere, M. Delcourt, I.S.
Donertas, A. Giammanco, V. Lemaitre, K. Mondal, J. Prisciandaro, A. Taliercio,
M. Teklishyn, P. Vischia, S. Wuyckens, J. Zobec Centro Brasileiro de Pesquisas
Fisicas, Rio de Janeiro, Brazil
G.A. Alves, G. Correia Silva, C. Hensel, A. Moraes Universidade do Estado do
Rio de Janeiro, Rio de Janeiro, Brazil
W.L. Aldá Júnior, E. Belchior Batista Das Chagas, H. BRANDAO MALBOUISSON, W.
Carvalho, J. Chinellato5, E. Coelho, E.M. Da Costa, G.G. Da Silveira6, D. De
Jesus Damiao, S. Fonseca De Souza, J. Martins7, D. Matos Figueiredo, M. Medina
Jaime8, M. Melo De Almeida, C. Mora Herrera, L. Mundim, H. Nogima, P. Rebello
Teles, L.J. Sanchez Rosas, A. Santoro, S.M. Silva Do Amaral, A. Sznajder, M.
Thiel, E.J. Tonelli Manganote5, F. Torres Da Silva De Araujo, A. Vilela
Pereira Universidade Estadual Paulista a, Universidade Federal do ABC b, São
Paulo, Brazil
C.A. Bernardesa, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M. Gregoresb,
D.S. Lemosa, P.G. Mercadanteb, S.F. Novaesa, Sandra S. Padulaa Institute for
Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia,
Bulgaria
A. Aleksandrov, G. Antchev, I. Atanasov, R. Hadjiiska, P. Iaydjiev, M.
Misheva, M. Rodozov, M. Shopova, G. Sultanov University of Sofia, Sofia,
Bulgaria
M. Bonchev, A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov, A. Petrov
Beihang University, Beijing, China
W. Fang3, Q. Guo, H. Wang, L. Yuan Department of Physics, Tsinghua University,
Beijing, China
M. Ahmad, Z. Hu, Y. Wang Institute of High Energy Physics, Beijing, China
E. Chapon, G.M. Chen9, H.S. Chen9, M. Chen, A. Kapoor, D. Leggat, H. Liao, Z.
Liu, R. Sharma, A. Spiezia, J. Tao, J. Thomas-wilsker, J. Wang, H. Zhang, S.
Zhang9, J. Zhao State Key Laboratory of Nuclear Physics and Technology, Peking
University, Beijing, China
A. Agapitos, Y. Ban, C. Chen, Q. Huang, A. Levin, Q. Li, M. Lu, X. Lyu, Y.
Mao, S.J. Qian, D. Wang, Q. Wang, J. Xiao Sun Yat-Sen University, Guangzhou,
China
Z. You Institute of Modern Physics and Key Laboratory of Nuclear Physics and
Ion-beam Application (MOE) - Fudan University, Shanghai, China
X. Gao3 Zhejiang University, Hangzhou, China
M. Xiao Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C. Florez, J. Fraga, A. Sarkar, M.A. Segura Delgado
Universidad de Antioquia, Medellin, Colombia
J. Jaramillo, J. Mejia Guisao, F. Ramirez, J.D. Ruiz Alvarez, C.A. Salazar
González, N. Vanegas Arbelaez University of Split, Faculty of Electrical
Engineering, Mechanical Engineering and Naval Architecture, Split, Croatia
D. Giljanovic, N. Godinovic, D. Lelas, I. Puljak, T. Sculac University of
Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, D. Majumder, M. Roguljic, A. Starodumov10, T. Susa
University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, E. Erodotou, A. Ioannou, G. Kole, M. Kolosova, S.
Konstantinou, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A.
Razis, H. Rykaczewski, H. Saka, D. Tsiakkouri Charles University, Prague,
Czech Republic
M. Finger11, M. Finger Jr.11, 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
A.A. Abdelalim12,13, S. Abu Zeid14, A. Ellithi Kamel15 Center for High Energy
Physics (CHEP-FU), Fayoum University, El-Fayoum, Egypt
A. Lotfy, Y. Mohammed16 National Institute of Chemical Physics and Biophysics,
Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, M. Raidal, C. Veelken Department of Physics, University of Helsinki,
Helsinki, Finland
P. Eerola, L. Forthomme, H. Kirschenmann, K. Osterberg, M. Voutilainen
Helsinki Institute of Physics, Helsinki, Finland
E. Brücken, F. Garcia, J. Havukainen, V. Karimäki, M.S. Kim, R. Kinnunen, T.
Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, H. Siikonen, E.
Tuominen, J. Tuominiemi Lappeenranta University of Technology, Lappeenranta,
Finland
P. Luukka, T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
C. Amendola, M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F.
Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
B. Lenzi, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin, A. Savoy-
Navarro17, M. Titov, G.B. Yu Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole
Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
S. Ahuja, F. Beaudette, M. Bonanomi, A. Buchot Perraguin, P. Busson, C.
Charlot, O. Davignon, B. Diab, G. Falmagne, R. Granier de Cassagnac, A.
Hakimi, I. Kucher, A. Lobanov, C. Martin Perez, M. Nguyen, C. Ochando, P.
Paganini, J. Rembser, R. Salerno, J.B. Sauvan, Y. Sirois, A. Zabi, A. Zghiche
Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France
J.-L. Agram18, J. Andrea, D. Bloch, G. Bourgatte, J.-M. Brom, E.C. Chabert, C.
Collard, J.-C. Fontaine18, D. Gelé, U. Goerlach, C. Grimault, A.-C. Le Bihan,
P. Van Hove Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
E. Asilar, S. Beauceron, C. Bernet, G. Boudoul, C. Camen, A. Carle, N. Chanon,
D. Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M. Gouzevitch, B.
Ille, Sa. Jain, I.B. Laktineh, H. Lattaud, A. Lesauvage, M. Lethuillier, L.
Mirabito, L. Torterotot, G. Touquet, M. Vander Donckt, S. Viret Georgian
Technical University, Tbilisi, Georgia
G. Adamov, Z. Tsamalaidze11 RWTH Aachen University, I. Physikalisches
Institut, Aachen, Germany
L. Feld, K. Klein, M. Lipinski, D. Meuser, A. Pauls, M. Preuten, M.P. Rauch,
J. Schulz, M. Teroerde RWTH Aachen University, III. Physikalisches Institut A,
Aachen, Germany
D. Eliseev, M. Erdmann, P. Fackeldey, B. Fischer, S. Ghosh, T. Hebbeker, K.
Hoepfner, H. Keller, L. Mastrolorenzo, M. Merschmeyer, A. Meyer, P. Millet, G.
Mocellin, S. Mondal, S. Mukherjee, D. Noll, A. Novak, T. Pook, A. Pozdnyakov,
T. Quast, M. Radziej, Y. Rath, H. Reithler, J. Roemer, A. Schmidt, S.C.
Schuler, A. Sharma, S. Wiedenbeck, S. Zaleski RWTH Aachen University, III.
Physikalisches Institut B, Aachen, Germany
C. Dziwok, G. Flügge, W. Haj Ahmad19, O. Hlushchenko, T. Kress, A. Nowack, C.
Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl20, T. Ziemons Deutsches
Elektronen-Synchrotron, Hamburg, Germany
H. Aarup Petersen, M. Aldaya Martin, P. Asmuss, I. Babounikau, S. Baxter, O.
Behnke, A. Bermúdez Martínez, A.A. Bin Anuar, K. Borras21, V. Botta, D.
Brunner, A. Campbell, A. Cardini, P. Connor, S. Consuegra Rodríguez, V.
Danilov, A. De Wit, M.M. Defranchis, L. Didukh, D. Domínguez Damiani, G.
Eckerlin, D. Eckstein, T. Eichhorn, L.I. Estevez Banos, E. Gallo22, A. Geiser,
A. Giraldi, A. Grohsjean, M. Guthoff, A. Harb, A. Jafari23, N.Z. Jomhari, H.
Jung, A. Kasem21, M. Kasemann, H. Kaveh, C. Kleinwort, J. Knolle, D. Krücker,
W. Lange, T. Lenz, J. Lidrych, K. Lipka, W. Lohmann24, R. Mankel, I.-A.
Melzer-Pellmann, J. Metwally, A.B. Meyer, M. Meyer, M. Missiroli, J. Mnich, A.
Mussgiller, V. Myronenko, Y. Otarid, D. Pérez Adán, S.K. Pflitsch, D. Pitzl,
A. Raspereza, A. Saggio, A. Saibel, M. Savitskyi, V. Scheurer, P. Schütze, C.
Schwanenberger, A. Singh, R.E. Sosa Ricardo, N. Tonon, O. Turkot, A.
Vagnerini, M. Van De Klundert, R. Walsh, D. Walter, Y. Wen, K. Wichmann, C.
Wissing, S. Wuchterl, O. Zenaiev, R. Zlebcik University of Hamburg, Hamburg,
Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, K. De Leo, T. Dreyer, A.
Ebrahimi, M. Eich, F. Feindt, A. Fröhlich, C. Garbers, E. Garutti, P.
Gunnellini, J. Haller, A. Hinzmann, A. Karavdina, G. Kasieczka, R. Klanner, R.
Kogler, V. Kutzner, J. Lange, T. Lange, A. Malara, C.E.N. Niemeyer, A.
Nigamova, K.J. Pena Rodriguez, O. Rieger, P. Schleper, S. Schumann, J.
Schwandt, D. Schwarz, J. Sonneveld, H. Stadie, G. Steinbrück, B. Vormwald, I.
Zoi Karlsruher Institut fuer Technologie, Karlsruhe, Germany
M. Baselga, S. Baur, J. Bechtel, T. Berger, E. Butz, R. Caspart, T. Chwalek,
W. De Boer, A. Dierlamm, A. Droll, K. El Morabit, N. Faltermann, K. Flöh, M.
Giffels, A. Gottmann, F. Hartmann20, C. Heidecker, U. Husemann, M.A. Iqbal, I.
Katkov25, P. Keicher, R. Koppenhöfer, S. Maier, M. Metzler, S. Mitra, D.
Müller, Th. Müller, M. Musich, G. Quast, K. Rabbertz, J. Rauser, D. Savoiu, D.
Schäfer, M. Schnepf, M. Schröder, D. Seith, I. Shvetsov, H.J. Simonis, R.
Ulrich, M. Wassmer, M. Weber, R. Wolf, S. Wozniewski Institute of Nuclear and
Particle Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, P. Asenov, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas,
G. Paspalaki, A. Stakia National and Kapodistrian University of Athens,
Athens, Greece
M. Diamantopoulou, D. Karasavvas, G. Karathanasis, P. Kontaxakis, C.K. Koraka,
A. Manousakis-katsikakis, A. Panagiotou, I. Papavergou, N. Saoulidou, K.
Theofilatos, K. Vellidis, E. Vourliotis National Technical University of
Athens, Athens, Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis, A. Zacharopoulou
University of Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, K.
Manitara, N. Manthos, I. Papadopoulos, J. Strologas MTA-ELTE Lendület CMS
Particle and Nuclear Physics Group, Eötvös Loránd University, Budapest,
Hungary
M. Bartók26, R. Chudasama, M. Csanad, M.M.A. Gadallah27, S. Lökös28, P. Major,
K. Mandal, A. Mehta, G. Pasztor, O. Surányi, G.I. Veres Wigner Research Centre
for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath29, F. Sikler, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary
S. Czellar, J. Karancsi26, J. Molnar, Z. Szillasi, D. Teyssier Institute of
Physics, University of Debrecen, Debrecen, Hungary
P. Raics, Z.L. Trocsanyi, B. Ujvari Eszterhazy Karoly University, Karoly
Robert Campus, Gyongyos, Hungary
T. Csorgo, F. Nemes, T. Novak Indian Institute of Science (IISc), Bangalore,
India
S. Choudhury, J.R. Komaragiri, D. Kumar, L. Panwar, P.C. Tiwari National
Institute of Science Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati30, D. Dash, C. Kar, P. Mal, T. Mishra, V.K. Muraleedharan Nair
Bindhu, A. Nayak31, D.K. Sahoo30, N. Sur, S.K. Swain Panjab University,
Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, N. Dhingra32, R. Gupta, A.
Kaur, S. Kaur, P. Kumari, M. Lohan, M. Meena, K. Sandeep, S. Sharma, J.B.
Singh, A.K. Virdi University of Delhi, Delhi, India
A. Ahmed, A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, A.
Kumar, M. Naimuddin, P. Priyanka, K. Ranjan, A. Shah Saha Institute of Nuclear
Physics, HBNI, Kolkata, India
M. Bharti33, R. Bhattacharya, S. Bhattacharya, D. Bhowmik, S. Dutta, S. Ghosh,
B. Gomber34, M. Maity35, S. Nandan, P. Palit, A. Purohit, P.K. Rout, G. Saha,
S. Sarkar, M. Sharan, B. Singh33, S. Thakur33 Indian Institute of Technology
Madras, Madras, India
P.K. Behera, S.C. Behera, P. Kalbhor, A. Muhammad, R. Pradhan, P.R. Pujahari,
A. Sharma, A.K. Sikdar Bhabha Atomic Research Centre, Mumbai, India
D. Dutta, V. Kumar, K. Naskar36, P.K. Netrakanti, L.M. Pant, P. Shukla Tata
Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, R. Kumar Verma, G.B. Mohanty, U. Sarkar Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, M. Guchait, S. Karmakar, S.
Kumar, G. Majumder, K. Mazumdar, S. Mukherjee, D. Roy, N. Sahoo Indian
Institute of Science Education and Research (IISER), Pune, India
S. Dube, B. Kansal, K. Kothekar, S. Pandey, A. Rane, A. Rastogi, S. Sharma
Department of Physics, Isfahan University of Technology, Isfahan, Iran
H. Bakhshiansohi37 Institute for Research in Fundamental Sciences (IPM),
Tehran, Iran
S. Chenarani38, S.M. Etesami, M. Khakzad, M. Mohammadi Najafabadi 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, R. Alya,b,39, C. Arutaa,b, A. Colaleoa, D. Creanzaa,c, N. De
Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, A. Di Pilatoa,b, W.
Elmetenaweea,b, L. Fiorea, A. Gelmia,b, M. Gula, G. Iasellia,c, M. Incea,b, S.
Lezkia,b, G. Maggia,c, M. Maggia, I. Margjekaa,b, V. Mastrapasquaa,b, J.A.
Merlina, S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, A. Ranieria, G.
Selvaggia,b, L. Silvestrisa, F.M. Simonea,b, 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, L. Brigliadoria,b, R. Campaninia,b, P. Capiluppia,b, A.
Castroa,b, F.R. Cavalloa, M. Cuffiania,b, G.M. Dallavallea, T. Diotalevia,b,
F. Fabbria, A. Fanfania,b, E. Fontanesia,b, P. Giacomellia, L. Giommia,b, C.
Grandia, L. Guiduccia,b, F. Iemmia,b, S. Lo Meoa,40, S. Marcellinia, G.
Masettia, F.L. Navarriaa,b, A. Perrottaa, F. Primaveraa,b, T. Rovellia,b, G.P.
Sirolia,b, N. Tosia INFN Sezione di Catania a, Università di Catania b,
Catania, Italy
S. Albergoa,b,41, S. Costaa,b, A. Di Mattiaa, R. Potenzaa,b, A. Tricomia,b,41,
C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b, Firenze, Italy
G. Barbaglia, A. Cassesea, R. Ceccarellia,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, F. Fioria, E. Focardia,b, G. Latinoa,b, P. Lenzia,b, M.
Lizzoa,b, M. Meschinia, S. Paolettia, R. Seiditaa,b, G. Sguazzonia, 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, F. Cetorellia,b, V. Cirioloa,b,20,
F. De Guioa,b, M.E. Dinardoa,b, P. Dinia, S. Gennaia, A. Ghezzia,b, P.
Govonia,b, L. Guzzia,b, M. Malbertia, S. Malvezzia, D. Menascea, F. Montia,b,
L. Moronia, M. Paganonia,b, D. Pedrinia, S. Ragazzia,b, T. Tabarelli de
Fatisa,b, D. Valsecchia,b,20, 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, F. Fabozzia,c, F. Fiengaa,
A.O.M. Iorioa,b, L. Listaa,b, S. Meolaa,d,20, P. Paoluccia,20, 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, D. Biselloa,b, A. Bolettia,b, A. Bragagnoloa,b, R.
Carlina,b, P. Checchiaa, P. De Castro Manzanoa, T. Dorigoa, F. Gasparinia,b,
U. Gasparinia,b, S.Y. Hoha,b, L. Layera, M. Margonia,b, A.T. Meneguzzoa,b, M.
Presillab, P. Ronchesea,b, R. Rossina,b, F. Simonettoa,b, G. Strong, A. Tikoa,
M. Tosia,b, H. YARARa,b, M. Zanettia,b, P. Zottoa,b, A. Zucchettaa,b, G.
Zumerlea,b INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
C. Aime‘a,b, A. Braghieria, S. Calzaferria,b, D. Fiorinaa,b, P. Montagnaa,b,
S.P. Rattia,b, V. Rea, M. Ressegottia,b, C. Riccardia,b, P. Salvinia, I. Vaia,
P. Vituloa,b INFN Sezione di Perugia a, Università di Perugia b, Perugia,
Italy
M. Biasinia,b, G.M. Bileia, D. Ciangottinia,b, L. Fanòa,b, P. Laricciaa,b, G.
Mantovania,b, V. Mariania,b, M. Menichellia, F. Moscatellia, A. Piccinellia,b,
A. Rossia,b, A. Santocchiaa,b, D. Spigaa, T. Tedeschia,b 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, M.R. Di Domenicoa,b, S.
Donatoa, L. Gianninia,c, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c,
G. Mandorlia,c, A. Messineoa,b, F. Pallaa, G. Ramirez-Sancheza,c, A. Rizzia,b,
G. Rolandia,c, S. Roy Chowdhurya,c, A. Scribanoa, N. Shafieia,b, P. Spagnoloa,
R. Tenchinia, G. Tonellia,b, N. Turinia, 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, M. Diemoza, E.
Longoa,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, R.
Tramontanoa,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, A. Belloraa,b, C. Biinoa, A. Cappatia,b, N. Cartigliaa, S.
Comettia, M. Costaa,b, R. Covarellia,b, N. Demariaa, B. Kiania,b, F. Leggera,
C. Mariottia, S. Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M.
Montenoa, M.M. Obertinoa,b, G. Ortonaa, L. Pachera,b, N. Pastronea, M.
Pelliccionia, G.L. Pinna Angionia,b, M. Ruspaa,c, R. Salvaticoa,b, F.
Sivieroa,b, V. Solaa, A. Solanoa,b, D. Soldia,b, A. Staianoa, D. Trocinoa,b
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 Kyungpook National University, Daegu, Korea
S. Dogra, C. Huh, B. Kim, D.H. Kim, G.N. Kim, J. Lee, S.W. Lee, C.S. Moon,
Y.D. Oh, S.I. Pak, B.C. Radburn-Smith, S. Sekmen, Y.C. Yang Chonnam National
University, Institute for Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon Hanyang University, Seoul, Korea
B. Francois, T.J. Kim, J. Park Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, S. Ha, B. Hong, K. Lee, K.S. Lee, J. Lim, J. Park,
S.K. Park, J. Yoo Kyung Hee University, Department of Physics, Seoul, Republic
of Korea
J. Goh, A. Gurtu Sejong University, Seoul, Korea
H.S. Kim, Y. Kim Seoul National University, Seoul, Korea
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, S. Ko, H. Kwon, H.
Lee, K. Lee, S. Lee, K. Nam, B.H. Oh, M. Oh, S.B. Oh, H. Seo, U.K. Yang, I.
Yoon University of Seoul, Seoul, Korea
D. Jeon, J.H. Kim, B. Ko, J.S.H. Lee, I.C. Park, Y. Roh, D. Song, I.J. Watson
Yonsei University, Department of Physics, Seoul, Korea
H.D. Yoo Sungkyunkwan University, Suwon, Korea
Y. Choi, C. Hwang, Y. Jeong, H. Lee, Y. Lee, I. Yu College of Engineering and
Technology, American University of the Middle East (AUM), Kuwait
Y. Maghrbi Riga Technical University, Riga, Latvia
V. Veckalns42 Vilnius University, Vilnius, Lithuania
A. Juodagalvis, A. Rinkevicius, G. Tamulaitis National Centre for Particle
Physics, Universiti Malaya, Kuala Lumpur, Malaysia
W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli Universidad de Sonora (UNISON),
Hermosillo, Mexico
J.F. Benitez, A. Castaneda Hernandez, J.A. Murillo Quijada, L. Valencia Palomo
Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
G. Ayala, H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz43,
R. Lopez-Fernandez, D.A. Perez Navarro, A. Sanchez-Hernandez Universidad
Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, M. Ramirez-Garcia, F. Vazquez Valencia
Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
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
J. Mijuskovic4, N. Raicevic University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M.I. Asghar, M.I.M. Awan, 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, T. Frueboes, M. Górski, M. Kazana, M.
Szleper, P. Traczyk, P. Zalewski Institute of Experimental Physics, Faculty of
Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk44, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Olszewski, M. Walczak Laboratório de Instrumentação e Física
Experimental de Partículas, Lisboa, Portugal
M. Araujo, P. Bargassa, D. Bastos, P. Faccioli, M. Gallinaro, J. Hollar, N.
Leonardo, T. Niknejad, J. Seixas, K. Shchelina, O. Toldaiev, J. Varela Joint
Institute for Nuclear Research, Dubna, Russia
V. Alexakhin, P. Bunin, Y. Ershov, I. Golutvin, I. Gorbunov, V. Karjavine, A.
Lanev, A. Malakhov, V. Matveev45,46, V.V. Mitsyn, P. Moisenz, V. Palichik, V.
Perelygin, M. Savina, V. Shalaev, S. Shmatov, V. Smirnov, O. Teryaev, V.
Trofimov, N. Voytishin, B.S. Yuldashev47, A. Zarubin, I. Zhizhin Petersburg
Nuclear Physics Institute, Gatchina (St. Petersburg), Russia
G. Gavrilov, V. Golovtcov, Y. Ivanov, V. Kim48, E. Kuznetsova49, V. Murzin, V.
Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Volkov, A. Vorobyev
Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, G. Pivovarov, D.
Tlisov${}^{\textrm{\textdagger}}$, 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. Nikitenko50, V. Popov, G.
Safronov, A. Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A. Zhokin Moscow
Institute of Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
M. Chadeeva51, R. Chistov51, A. Oskin, P. Parygin, S. Polikarpov51 P.N.
Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin, M. Kirakosyan, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia
A. Belyaev, E. Boos, V. Bunichev, M. Dubinin52, L. Dudko, A. Ershov, V.
Klyukhin, N. Korneeva, I. Lokhtin, S. Obraztsov, M. Perfilov, V. Savrin, P.
Volkov Novosibirsk State University (NSU), Novosibirsk, Russia
V. Blinov53, T. Dimova53, L. Kardapoltsev53, I. Ovtin53, Y. Skovpen53
Institute for High Energy Physics of National Research Centre ‘Kurchatov
Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, V. Kachanov, A. Kalinin, D. Konstantinov, V. Petrov,
R. Ryutin, A. Sobol, S. Troshin, N. Tyurin, A. Uzunian, A. Volkov National
Research Tomsk Polytechnic University, Tomsk, Russia
A. Babaev, A. Iuzhakov, V. Okhotnikov, L. Sukhikh Tomsk State University,
Tomsk, Russia
V. Borchsh, V. Ivanchenko, E. Tcherniaev University of Belgrade: Faculty of
Physics and VINCA Institute of Nuclear Sciences, Belgrade, Serbia
P. Adzic54, P. Cirkovic, M. Dordevic, P. Milenovic, J. Milosevic Centro de
Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid,
Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M.
Barrio Luna, Cristina F. Bedoya, J.A. Brochero Cifuentes, C.A. Carrillo
Montoya, M. Cepeda, M. Cerrada, N. Colino, B. De La Cruz, A. Delgado Peris,
J.P. Fernández Ramos, J. Flix, M.C. Fouz, A. García Alonso, O. Gonzalez Lopez,
S. Goy Lopez, J.M. Hernandez, M.I. Josa, J. León Holgado, D. Moran, Á. Navarro
Tobar, A. Pérez-Calero Yzquierdo, J. Puerta Pelayo, I. Redondo, L. Romero, S.
Sánchez Navas, M.S. Soares, A. Triossi, L. Urda Gómez, C. Willmott Universidad
Autónoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz, R. Reyes-Almanza 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, E. Palencia Cortezon, C. Ramón Álvarez, J. Ripoll Sau,
V. Rodríguez Bouza, S. Sanchez Cruz, A. Trapote 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, G. Gomez, C. Martinez Rivero, P. Martinez
Ruiz del Arbol, F. Matorras, J. Piedra Gomez, C. Prieels, F. Ricci-Tam, T.
Rodrigo, A. Ruiz-Jimeno, L. Scodellaro, I. Vila, J.M. Vizan Garcia University
of Colombo, Colombo, Sri Lanka
MK Jayananda, B. Kailasapathy55, D.U.J. Sonnadara, DDC Wickramarathna
University of Ruhuna, Department of Physics, Matara, Sri Lanka
W.G.D. Dharmaratna, K. Liyanage, N. Perera, N. Wickramage CERN, European
Organization for Nuclear Research, Geneva, Switzerland
T.K. Aarrestad, D. Abbaneo, B. Akgun, E. Auffray, G. Auzinger, J. Baechler, P.
Baillon, A.H. Ball, D. Barney, J. Bendavid, N. Beni, M. Bianco, A. Bocci, P.
Bortignon, E. Bossini, E. Brondolin, T. Camporesi, G. Cerminara, L. Cristella,
D. d’Enterria, A. Dabrowski, N. Daci, V. Daponte, A. David, A. De Roeck, M.
Deile, R. Di Maria, M. Dobson, M. Dünser, N. Dupont, A. Elliott-Peisert, N.
Emriskova, F. Fallavollita56, D. Fasanella, S. Fiorendi, A. Florent, G.
Franzoni, J. Fulcher, W. Funk, S. Giani, D. Gigi, K. Gill, F. Glege, L.
Gouskos, M. Guilbaud, D. Gulhan, M. Haranko, J. Hegeman, Y. Iiyama, V.
Innocente, T. James, P. Janot, J. Kaspar, J. Kieseler, M. Komm, N. Kratochwil,
C. Lange, P. Lecoq, K. Long, C. Lourenço, L. Malgeri, M. Mannelli, A.
Massironi, F. Meijers, S. Mersi, E. Meschi, F. Moortgat, M. Mulders, J.
Ngadiuba, J. Niedziela, S. Orfanelli, L. Orsini, F. Pantaleo20, L. Pape, E.
Perez, M. Peruzzi, A. Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, D.
Rabady, A. Racz, M. Rieger, M. Rovere, H. Sakulin, J. Salfeld-Nebgen, S.
Scarfi, C. Schäfer, C. Schwick, M. Selvaggi, A. Sharma, P. Silva, W. Snoeys,
P. Sphicas57, J. Steggemann, S. Summers, V.R. Tavolaro, D. Treille, A. Tsirou,
G.P. Van Onsem, A. Vartak, M. Verzetti, K.A. Wozniak, W.D. Zeuner Paul
Scherrer Institut, Villigen, Switzerland
L. Caminada58, W. Erdmann, R. Horisberger, Q. Ingram, H.C. Kaestli, D.
Kotlinski, U. Langenegger, T. Rohe ETH Zurich - Institute for Particle Physics
and Astrophysics (IPA), Zurich, Switzerland
M. Backhaus, P. Berger, A. Calandri, N. Chernyavskaya, A. De Cosa, G.
Dissertori, M. Dittmar, M. Donegà, C. Dorfer, T. Gadek, T.A. Gómez Espinosa,
C. Grab, D. Hits, W. Lustermann, A.-M. Lyon, R.A. Manzoni, M.T. Meinhard, F.
Micheli, F. Nessi-Tedaldi, F. Pauss, V. Perovic, G. Perrin, L. Perrozzi, S.
Pigazzini, M.G. Ratti, M. Reichmann, C. Reissel, T. Reitenspiess, B. Ristic,
D. Ruini, D.A. Sanz Becerra, M. Schönenberger, V. Stampf, M.L. Vesterbacka
Olsson, R. Wallny, D.H. Zhu Universität Zürich, Zurich, Switzerland
C. Amsler59, C. Botta, D. Brzhechko, M.F. Canelli, R. Del Burgo, J.K.
Heikkilä, M. Huwiler, A. Jofrehei, B. Kilminster, S. Leontsinis, A. Macchiolo,
P. Meiring, V.M. Mikuni, U. Molinatti, I. Neutelings, G. Rauco, A. Reimers, P.
Robmann, K. Schweiger, Y. Takahashi, S. Wertz National Central University,
Chung-Li, Taiwan
C. Adloff60, C.M. Kuo, W. Lin, A. Roy, T. Sarkar35, S.S. Yu National Taiwan
University (NTU), Taipei, Taiwan
L. Ceard, P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.y. Li, R.-S.
Lu, E. Paganis, A. Psallidas, A. Steen, E. Yazgan Chulalongkorn University,
Faculty of Science, Department of Physics, Bangkok, Thailand
B. Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas Çukurova University,
Physics Department, Science and Art Faculty, Adana, Turkey
F. Boran, S. Damarseckin61, Z.S. Demiroglu, F. Dolek, C. Dozen62, I.
Dumanoglu63, E. Eskut, G. Gokbulut, Y. Guler, E. Gurpinar Guler64, I. Hos65,
C. Isik, E.E. Kangal66, O. Kara, A. Kayis Topaksu, U. Kiminsu, G. Onengut, K.
Ozdemir67, A. Polatoz, A.E. Simsek, B. Tali68, U.G. Tok, S. Turkcapar, I.S.
Zorbakir, C. Zorbilmez Middle East Technical University, Physics Department,
Ankara, Turkey
B. Isildak69, G. Karapinar70, K. Ocalan71, M. Yalvac72 Bogazici University,
Istanbul, Turkey
I.O. Atakisi, E. Gülmez, M. Kaya73, O. Kaya74, Ö. Özçelik, S. Tekten75, E.A.
Yetkin76 Istanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak63, Y. Komurcu, S. Sen77 Istanbul University, Istanbul,
Turkey
F. Aydogmus Sen, S. Cerci68, B. Kaynak, S. Ozkorucuklu, D. Sunar Cerci68
Institute for Scintillation Materials of National Academy of Science of
Ukraine, Kharkov, Ukraine
B. Grynyov National Scientific Center, Kharkov Institute of Physics and
Technology, Kharkov, Ukraine
L. Levchuk University of Bristol, Bristol, United Kingdom
E. Bhal, S. Bologna, J.J. Brooke, E. Clement, D. Cussans, H. Flacher, J.
Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, B. Krikler, S. Paramesvaran, T.
Sakuma, S. Seif El Nasr-Storey, V.J. Smith, J. Taylor, A. Titterton Rutherford
Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev78, C. Brew, R.M. Brown, D.J.A. Cockerill, K.V. Ellis, K.
Harder, S. Harper, J. Linacre, K. Manolopoulos, D.M. Newbold, E. Olaiya, D.
Petyt, T. Reis, T. Schuh, C.H. Shepherd-Themistocleous, A. Thea, I.R. Tomalin,
T. Williams Imperial College, London, United Kingdom
R. Bainbridge, P. Bloch, S. Bonomally, J. Borg, S. Breeze, O. Buchmuller, A.
Bundock, V. Cepaitis, G.S. Chahal79, D. Colling, P. Dauncey, G. Davies, M.
Della Negra, G. Fedi, G. Hall, G. Iles, J. Langford, L. Lyons, A.-M. Magnan,
S. Malik, A. Martelli, V. Milosevic, J. Nash80, V. Palladino, M. Pesaresi,
D.M. Raymond, A. Richards, A. Rose, E. Scott, C. Seez, A. Shtipliyski, M.
Stoye, A. Tapper, K. Uchida, T. Virdee20, N. Wardle, S.N. Webb, D.
Winterbottom, A.G. Zecchinelli Brunel University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, C.K. Mackay, I.D. Reid, L.
Teodorescu, S. Zahid Baylor University, Waco, USA
A. Brinkerhoff, K. Call, B. Caraway, J. Dittmann, K. Hatakeyama, A.R.
Kanuganti, C. Madrid, B. McMaster, N. Pastika, S. Sawant, C. Smith, J. Wilson
Catholic University of America, Washington, DC, USA
R. Bartek, A. Dominguez, R. Uniyal, A.M. Vargas Hernandez The University of
Alabama, Tuscaloosa, USA
A. Buccilli, O. Charaf, S.I. Cooper, S.V. Gleyzer, C. Henderson, P. Rumerio,
C. West Boston University, Boston, USA
A. Akpinar, A. Albert, D. Arcaro, C. Cosby, Z. Demiragli, D. Gastler, C.
Richardson, J. Rohlf, K. Salyer, D. Sperka, D. Spitzbart, I. Suarez, S. Yuan,
D. Zou Brown University, Providence, USA
G. Benelli, B. Burkle, X. Coubez21, D. Cutts, Y.t. Duh, M. Hadley, U. Heintz,
J.M. Hogan81, K.H.M. Kwok, E. Laird, G. Landsberg, K.T. Lau, J. Lee, M.
Narain, S. Sagir82, R. Syarif, E. Usai, W.Y. Wong, D. Yu, W. Zhang University
of California, Davis, Davis, USA
R. Band, C. Brainerd, R. Breedon, M. Calderon De La Barca Sanchez, M. Chertok,
J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk, F. Jensen, W.
Ko${}^{\textrm{\textdagger}}$, O. Kukral, R. Lander, M. Mulhearn, D. Pellett,
J. Pilot, M. Shi, D. Taylor, K. Tos, M. Tripathi, Y. Yao, F. Zhang University
of California, Los Angeles, USA
M. Bachtis, R. Cousins, A. Dasgupta, D. Hamilton, J. Hauser, M. Ignatenko, T.
Lam, N. Mccoll, W.A. Nash, S. Regnard, D. Saltzberg, C. Schnaible, B. Stone,
V. Valuev University of California, Riverside, Riverside, USA
K. Burt, Y. Chen, R. Clare, J.W. Gary, S.M.A. Ghiasi Shirazi, G. Hanson, G.
Karapostoli, O.R. Long, N. Manganelli, M. Olmedo Negrete, M.I. Paneva, W. Si,
S. Wimpenny, Y. Zhang University of California, San Diego, La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, S. Cooperstein, N. Deelen, M. Derdzinski,
J. Duarte, R. Gerosa, D. Gilbert, B. Hashemi, V. Krutelyov, J. Letts, M.
Masciovecchio, S. May, S. Padhi, M. Pieri, V. Sharma, M. Tadel, F. Würthwein,
A. Yagil University of California, Santa Barbara - Department of Physics,
Santa Barbara, USA
N. Amin, C. Campagnari, M. Citron, A. Dorsett, V. Dutta, J. Incandela, B.
Marsh, H. Mei, A. Ovcharova, H. Qu, M. Quinnan, J. Richman, U. Sarica, D.
Stuart, S. Wang California Institute of Technology, Pasadena, USA
D. Anderson, A. Bornheim, O. Cerri, I. Dutta, J.M. Lawhorn, N. Lu, J. Mao,
H.B. Newman, T.Q. Nguyen, J. Pata, M. Spiropulu, J.R. Vlimant, S. Xie, Z.
Zhang, R.Y. Zhu Carnegie Mellon University, Pittsburgh, USA
J. Alison, M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, M. Sun, I.
Vorobiev University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, E. MacDonald, T. Mulholland, R. Patel, A. Perloff, K.
Stenson, K.A. Ulmer, S.R. Wagner Cornell University, Ithaca, USA
J. Alexander, Y. Cheng, J. Chu, D.J. Cranshaw, A. Datta, A. Frankenthal, K.
Mcdermott, J. Monroy, J.R. Patterson, D. Quach, A. Ryd, W. Sun, S.M. Tan, Z.
Tao, J. Thom, P. Wittich, M. Zientek Fermi National Accelerator Laboratory,
Batavia, USA
S. Abdullin, M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S.
Banerjee, L.A.T. Bauerdick, A. Beretvas, D. Berry, J. Berryhill, P.C. Bhat, K.
Burkett, J.N. Butler, A. Canepa, G.B. Cerati, H.W.K. Cheung, F. Chlebana, M.
Cremonesi, V.D. Elvira, J. Freeman, Z. Gecse, E. Gottschalk, L. Gray, D.
Green, S. Grünendahl, O. Gutsche, R.M. Harris, S. Hasegawa, R. Heller, T.C.
Herwig, J. Hirschauer, B. Jayatilaka, S. Jindariani, M. Johnson, U. Joshi, P.
Klabbers, T. Klijnsma, B. Klima, M.J. Kortelainen, S. Lammel, D. Lincoln, R.
Lipton, M. Liu, T. Liu, J. Lykken, K. Maeshima, D. Mason, P. McBride, P.
Merkel, S. Mrenna, S. Nahn, V. O’Dell, V. Papadimitriou, K. Pedro, C. Pena52,
O. Prokofyev, F. Ravera, A. Reinsvold Hall, L. Ristori, B. Schneider, E.
Sexton-Kennedy, N. Smith, A. Soha, W.J. Spalding, L. Spiegel, S. Stoynev, J.
Strait, L. Taylor, S. Tkaczyk, N.V. Tran, L. Uplegger, E.W. Vaandering, H.A.
Weber, A. Woodard University of Florida, Gainesville, USA
D. Acosta, P. Avery, D. Bourilkov, L. Cadamuro, V. Cherepanov, F. Errico, R.D.
Field, D. Guerrero, B.M. Joshi, M. Kim, J. Konigsberg, A. Korytov, K.H. Lo, K.
Matchev, N. Menendez, G. Mitselmakher, D. Rosenzweig, K. Shi, J. Wang, S.
Wang, X. Zuo Florida State University, Tallahassee, USA
T. Adams, A. Askew, D. Diaz, R. Habibullah, S. Hagopian, V. Hagopian, K.F.
Johnson, R. Khurana, T. Kolberg, G. Martinez, H. Prosper, C. Schiber, R.
Yohay, J. Zhang Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, S. Butalla, T. Elkafrawy14, M. Hohlmann, D. Noonan, M. Rahmani,
M. Saunders, F. Yumiceva University of Illinois at Chicago (UIC), Chicago, USA
M.R. Adams, L. Apanasevich, H. Becerril Gonzalez, R. Cavanaugh, X. Chen, S.
Dittmer, O. Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, C. Mills, G. Oh,
T. Roy, M.B. Tonjes, N. Varelas, J. Viinikainen, X. Wang, Z. Wu The University
of Iowa, Iowa City, USA
M. Alhusseini, K. Dilsiz83, S. Durgut, R.P. Gandrajula, M. Haytmyradov, V.
Khristenko, O.K. Köseyan, J.-P. Merlo, A. Mestvirishvili84, A. Moeller, J.
Nachtman, H. Ogul85, Y. Onel, F. Ozok86, A. Penzo, C. Snyder, E. Tiras, J.
Wetzel, K. Yi87 Johns Hopkins University, Baltimore, USA
O. Amram, B. Blumenfeld, L. Corcodilos, M. Eminizer, A.V. Gritsan, S.
Kyriacou, P. Maksimovic, C. Mantilla, J. Roskes, M. Swartz, T.Á. Vámi The
University of Kansas, Lawrence, USA
C. Baldenegro Barrera, P. Baringer, A. Bean, A. Bylinkin, T. Isidori, S.
Khalil, J. King, G. Krintiras, A. Kropivnitskaya, C. Lindsey, N. Minafra, M.
Murray, C. Rogan, C. Royon, S. Sanders, E. Schmitz, J.D. Tapia Takaki, Q.
Wang, J. Williams, G. Wilson Kansas State University, Manhattan, USA
S. Duric, A. Ivanov, K. Kaadze, D. Kim, Y. Maravin, T. Mitchell, A. Modak, A.
Mohammadi Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
E. Adams, A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, N.J. Hadley, S.
Jabeen, G.Y. Jeng, R.G. Kellogg, T. Koeth, A.C. Mignerey, S. Nabili, M.
Seidel, A. Skuja, S.C. Tonwar, L. Wang, K. Wong Massachusetts Institute of
Technology, Cambridge, USA
D. Abercrombie, B. Allen, R. Bi, S. Brandt, W. Busza, I.A. Cali, Y. Chen, M.
D’Alfonso, G. Gomez Ceballos, M. Goncharov, P. Harris, D. Hsu, M. Hu, M.
Klute, D. Kovalskyi, J. Krupa, Y.-J. Lee, P.D. Luckey, B. Maier, A.C. Marini,
C. Mcginn, C. Mironov, S. Narayanan, X. Niu, C. Paus, D. Rankin, C. Roland, G.
Roland, Z. Shi, G.S.F. Stephans, K. Sumorok, K. Tatar, D. Velicanu, J. Wang,
T.W. Wang, Z. Wang, B. Wyslouch University of Minnesota, Minneapolis, USA
R.M. Chatterjee, A. Evans, S. Guts${}^{\textrm{\textdagger}}$, P. Hansen, J.
Hiltbrand, Sh. Jain, M. Krohn, Y. Kubota, Z. Lesko, J. Mans, M. Revering, R.
Rusack, R. Saradhy, N. Schroeder, N. Strobbe, M.A. Wadud University of
Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
K. Bloom, S. Chauhan, D.R. Claes, C. Fangmeier, L. Finco, F. Golf, J.R.
González Fernández, I. Kravchenko, J.E. Siado, G.R.
Snow${}^{\textrm{\textdagger}}$, B. Stieger, W. Tabb, F. Yan State University
of New York at Buffalo, Buffalo, USA
G. Agarwal, H. Bandyopadhyay, C. Harrington, L. Hay, I. Iashvili, A.
Kharchilava, C. McLean, D. Nguyen, J. Pekkanen, S. Rappoccio, B. Roozbahani
Northeastern University, Boston, USA
G. Alverson, E. Barberis, C. Freer, Y. Haddad, A. Hortiangtham, J. Li, G.
Madigan, B. Marzocchi, D.M. Morse, V. Nguyen, T. Orimoto, A. Parker, L.
Skinnari, A. Tishelman-Charny, T. Wamorkar, B. Wang, A. Wisecarver, D. Wood
Northwestern University, Evanston, USA
S. Bhattacharya, J. Bueghly, Z. Chen, A. Gilbert, T. Gunter, K.A. Hahn, N.
Odell, M.H. Schmitt, K. Sung, M. Velasco University of Notre Dame, Notre Dame,
USA
R. Bucci, N. Dev, R. Goldouzian, M. Hildreth, K. Hurtado Anampa, C. Jessop,
D.J. Karmgard, K. Lannon, W. Li, N. Loukas, N. Marinelli, I. Mcalister, F.
Meng, K. Mohrman, Y. Musienko45, R. Ruchti, P. Siddireddy, S. Taroni, M.
Wayne, A. Wightman, M. Wolf, L. Zygala The Ohio State University, Columbus,
USA
J. Alimena, B. Bylsma, B. Cardwell, L.S. Durkin, B. Francis, C. Hill, A.
Lefeld, B.L. Winer, B.R. Yates Princeton University, Princeton, USA
P. Das, G. Dezoort, P. Elmer, B. Greenberg, N. Haubrich, S. Higginbotham, A.
Kalogeropoulos, G. Kopp, S. Kwan, D. Lange, M.T. Lucchini, J. Luo, D. Marlow,
K. Mei, I. Ojalvo, J. Olsen, C. Palmer, P. Piroué, D. Stickland, C. Tully
University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
V.E. Barnes, R. Chawla, S. Das, L. Gutay, M. Jones, A.W. Jung, B. Mahakud, G.
Negro, N. Neumeister, C.C. Peng, S. Piperov, H. Qiu, J.F. Schulte, M.
Stojanovic17, N. Trevisani, F. Wang, R. Xiao, W. Xie Purdue University
Northwest, Hammond, USA
T. Cheng, J. Dolen, N. Parashar Rice University, Houston, USA
A. Baty, S. Dildick, K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Kilpatrick, A.
Kumar, W. Li, B.P. Padley, R. Redjimi, J. Roberts${}^{\textrm{\textdagger}}$,
J. Rorie, W. Shi, A.G. Stahl Leiton University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, J.L. Dulemba, C. Fallon, T. Ferbel, M.
Galanti, A. Garcia-Bellido, O. Hindrichs, A. Khukhunaishvili, E. Ranken, R.
Taus Rutgers, The State University of New Jersey, Piscataway, USA
B. Chiarito, J.P. Chou, A. Gandrakota, Y. Gershtein, E. Halkiadakis, A. Hart,
M. Heindl, E. Hughes, S. Kaplan, O. Karacheban24, I. Laflotte, A. Lath, R.
Montalvo, K. Nash, M. Osherson, S. Salur, S. Schnetzer, S. Somalwar, R. Stone,
S.A. Thayil, S. Thomas, H. Wang University of Tennessee, Knoxville, USA
H. Acharya, A.G. Delannoy, S. Spanier Texas A&M University, College Station,
USA
O. Bouhali88, M. Dalchenko, A. Delgado, R. Eusebi, J. Gilmore, T. Huang, T.
Kamon89, H. Kim, S. Luo, S. Malhotra, R. Mueller, D. Overton, L. Perniè, D.
Rathjens, A. Safonov, J. Sturdy Texas Tech University, Lubbock, USA
N. Akchurin, J. Damgov, V. Hegde, S. Kunori, K. Lamichhane, S.W. Lee, T.
Mengke, S. Muthumuni, T. Peltola, S. Undleeb, I. Volobouev, Z. Wang, A.
Whitbeck Vanderbilt University, Nashville, USA
E. Appelt, S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H.
Ni, K. Padeken, F. Romeo, P. Sheldon, S. Tuo, J. Velkovska, M. Verweij
University of Virginia, Charlottesville, USA
M.W. Arenton, B. Cox, G. Cummings, J. Hakala, R. Hirosky, M. Joyce, A.
Ledovskoy, A. Li, C. Neu, B. Tannenwald, Y. Wang, E. Wolfe, F. Xia Wayne State
University, Detroit, USA
P.E. Karchin, N. Poudyal, P. Thapa University of Wisconsin - Madison, Madison,
WI, USA
K. Black, T. Bose, J. Buchanan, C. Caillol, S. Dasu, I. De Bruyn, P.
Everaerts, C. Galloni, H. He, M. Herndon, A. Hervé, U. Hussain, A. Lanaro, A.
Loeliger, R. Loveless, J. Madhusudanan Sreekala, A. Mallampalli, D. Pinna, T.
Ruggles, A. Savin, V. Shang, V. Sharma, W.H. Smith, D. Teague, S. Trembath-
reichert, W. Vetens †: Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at Institute of Basic and Applied Sciences, Faculty of Engineering,
Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt
3: Also at Université Libre de Bruxelles, Bruxelles, Belgium
4: Also at IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
5: Also at Universidade Estadual de Campinas, Campinas, Brazil
6: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
7: Also at UFMS, Nova Andradina, Brazil
8: Also at Universidade Federal de Pelotas, Pelotas, Brazil
9: Also at University of Chinese Academy of Sciences, Beijing, China
10: Also at Institute for Theoretical and Experimental Physics named by A.I.
Alikhanov of NRC ‘Kurchatov Institute’, Moscow, Russia
11: Also at Joint Institute for Nuclear Research, Dubna, Russia
12: Also at Helwan University, Cairo, Egypt
13: Now at Zewail City of Science and Technology, Zewail, Egypt
14: Also at Ain Shams University, Cairo, Egypt
15: Now at Cairo University, Cairo, Egypt
16: Now at Fayoum University, El-Fayoum, Egypt
17: Also at Purdue University, West Lafayette, USA
18: Also at Université de Haute Alsace, Mulhouse, France
19: Also at Erzincan Binali Yildirim University, Erzincan, Turkey
20: Also at CERN, European Organization for Nuclear Research, Geneva,
Switzerland
21: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
22: Also at University of Hamburg, Hamburg, Germany
23: Also at Department of Physics, Isfahan University of Technology, Isfahan,
Iran, Isfahan, Iran
24: Also at Brandenburg University of Technology, Cottbus, Germany
25: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
26: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary,
Debrecen, Hungary
27: Also at Physics Department, Faculty of Science, Assiut University, Assiut,
Egypt
28: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary, Budapest, Hungary
29: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
30: Also at IIT Bhubaneswar, Bhubaneswar, India, Bhubaneswar, India
31: Also at Institute of Physics, Bhubaneswar, India
32: Also at G.H.G. Khalsa College, Punjab, India
33: Also at Shoolini University, Solan, India
34: Also at University of Hyderabad, Hyderabad, India
35: Also at University of Visva-Bharati, Santiniketan, India
36: Also at Indian Institute of Technology (IIT), Mumbai, India
37: Also at Deutsches Elektronen-Synchrotron, Hamburg, Germany
38: Also at Department of Physics, University of Science and Technology of
Mazandaran, Behshahr, Iran
39: Now at INFN Sezione di Bari a, Università di Bari b, Politecnico di Bari
c, Bari, Italy
40: Also at Italian National Agency for New Technologies, Energy and
Sustainable Economic Development, Bologna, Italy
41: Also at Centro Siciliano di Fisica Nucleare e di Struttura Della Materia,
Catania, Italy
42: Also at Riga Technical University, Riga, Latvia, Riga, Latvia
43: Also at Consejo Nacional de Ciencia y Tecnología, Mexico City, Mexico
44: Also at Warsaw University of Technology, Institute of Electronic Systems,
Warsaw, Poland
45: Also at Institute for Nuclear Research, Moscow, Russia
46: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
47: Also at Institute of Nuclear Physics of the Uzbekistan Academy of
Sciences, Tashkent, Uzbekistan
48: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
49: Also at University of Florida, Gainesville, USA
50: Also at Imperial College, London, United Kingdom
51: Also at P.N. Lebedev Physical Institute, Moscow, Russia
52: Also at California Institute of Technology, Pasadena, USA
53: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
54: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
55: Also at Trincomalee Campus, Eastern University, Sri Lanka, Nilaveli, Sri
Lanka
56: Also at INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy,
Pavia, Italy
57: Also at National and Kapodistrian University of Athens, Athens, Greece
58: Also at Universität Zürich, Zurich, Switzerland
59: Also at Stefan Meyer Institute for Subatomic Physics, Vienna, Austria,
Vienna, Austria
60: Also at Laboratoire d’Annecy-le-Vieux de Physique des Particules,
IN2P3-CNRS, Annecy-le-Vieux, France
61: Also at Şırnak University, Sirnak, Turkey
62: Also at Department of Physics, Tsinghua University, Beijing, China,
Beijing, China
63: Also at Near East University, Research Center of Experimental Health
Science, Nicosia, Turkey
64: Also at Beykent University, Istanbul, Turkey, Istanbul, Turkey
65: Also at Istanbul Aydin University, Application and Research Center for
Advanced Studies (App. & Res. Cent. for Advanced Studies), Istanbul, Turkey
66: Also at Mersin University, Mersin, Turkey
67: Also at Piri Reis University, Istanbul, Turkey
68: Also at Adiyaman University, Adiyaman, Turkey
69: Also at Ozyegin University, Istanbul, Turkey
70: Also at Izmir Institute of Technology, Izmir, Turkey
71: Also at Necmettin Erbakan University, Konya, Turkey
72: Also at Bozok Universitetesi Rektörlügü, Yozgat, Turkey
73: Also at Marmara University, Istanbul, Turkey
74: Also at Milli Savunma University, Istanbul, Turkey
75: Also at Kafkas University, Kars, Turkey
76: Also at Istanbul Bilgi University, Istanbul, Turkey
77: Also at Hacettepe University, Ankara, Turkey
78: Also at School of Physics and Astronomy, University of Southampton,
Southampton, United Kingdom
79: Also at IPPP Durham University, Durham, United Kingdom
80: Also at Monash University, Faculty of Science, Clayton, Australia
81: Also at Bethel University, St. Paul, Minneapolis, USA, St. Paul, USA
82: Also at Karamanoğlu Mehmetbey University, Karaman, Turkey
83: Also at Bingol University, Bingol, Turkey
84: Also at Georgian Technical University, Tbilisi, Georgia
85: Also at Sinop University, Sinop, Turkey
86: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey
87: Also at Nanjing Normal University Department of Physics, Nanjing, China
88: Also at Texas A&M University at Qatar, Doha, Qatar
89: Also at Kyungpook National University, Daegu, Korea, Daegu, Korea