TOP-15-018

TOP-15-018

# Measurement of the top quark forward-backward production asymmetry and the
anomalous chromoelectric and chromomagnetic moments in collisions at
$\sqrt{s}=13\TeV$

###### Abstract

The parton-level top quark () forward-backward asymmetry and the anomalous
chromoelectric ($\hat{d}_{\PQt}$) and chromomagnetic ($\hat{\mu}_{\PQt}$)
moments have been measured using LHC collisions at a center-of-mass energy of
13, collected in the CMS detector in a data sample corresponding to an
integrated luminosity of 35.9. The linearized variable $A^{(1)}_{\mathrm{FB}}$
is used to approximate the asymmetry. Candidate events decaying to a muon or
electron and jets in final states with low and high Lorentz boosts are
selected and reconstructed using a fit of the kinematic distributions of the
decay products to those expected for final states. The values found for the
parameters are
$A^{(1)}_{\mathrm{FB}}=0.048^{+0.095}_{-0.087}\stat^{+0.020}_{-0.029}\syst$,
$\hat{\mu}_{\PQt}=-0.024^{+0.013}_{-0.009}\stat^{+0.016}_{-0.011}\syst$, and a
limit is placed on the magnitude of $\abs{\hat{d}_{\PQt}}<0.03$ at 95%
confidence level.

##  0.1 Introduction

The top quark () is the most massive of the known fundamental particles, with
a mass ($m_{\PQt}$) that is close to the electroweak scale. The Yukawa
coupling of the top quark to the Higgs field is close to unity [1], which
suggests that the top quark may play a role in electroweak symmetry breaking.
The top quark is also the only color-triplet fermion that decays before
forming color-singlet bound states. This provides a way to study its
fundamental interactions with gauge bosons without the complications caused by
hadronization.

At the CERN LHC, according to the standard model (SM) of particle physics, top
quarks are produced predominantly in pairs via the strong interaction, as
described by quantum chromodynamics (QCD). Feynman diagrams for leading-order
(LO) quark-antiquark () and gluon-gluon () initiated subprocesses are shown in
Fig. 1(a), and example diagrams for next-to-leading-order (NLO) quark-gluon ()
initiated subprocess are shown in Fig. 1(b). The NLO QCD calculations predict
approximately 6% and 69% production of top quark pairs at a center-of-mass
energy of 13.

Figure 1: (a) Feynman diagrams for LO - and -initiated subprocesses, and (b)
example diagrams for the NLO -initiated subprocess.

We describe here a search for anomalies in the angular distribution of
produced pairs. Those anomalies can be caused by modifications of the top
quark-antiquark-gluon () vertex or by the presence of heavy states coupled to
top quarks [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]. They are
characterized through their impacts on the distribution of
$c^{*}=\cos\theta^{*}$, where $\theta^{*}$ is the production angle of the top
quark relative to the direction of the initial-state parton in the center-of-
mass frame, as shown in Fig. 2 for a $\qqbar\to\ttbar$ event.

Figure 2:  Production angle $\theta^{*}$ in a $\qqbar\to\ttbar$ event defined
in the center-of-mass frame.

When subprocesses contain an initial-state quark or antiquark, the sign of
$c^{*}$ follows from the relative directions of the initial-state quark and
the top quark (or the initial-state antiquark and the top antiquark). We
search separately for anomalies in the $c^{*}$-antisymmetric (linearly
dependent on $c^{*}$) and the $c^{*}$-symmetric (dependent only on $c^{*2}$)
distribution functions, using the former to measure the top quark forward-
backward asymmetry () and the latter to measure the anomalous chromoelectric
($\hat{d}_{\PQt}$) and chromomagnetic ($\hat{\mu}_{\PQt}$) dipole moments of
the vertex.

The parton-level forward-backward asymmetry is defined as

$\AFB=\frac{\sigma(c^{*}>0)-\sigma(c^{*}<0)}{\sigma(c^{*}>0)+\sigma(c^{*}<0)},$
(1)

where $\sigma(c^{*}>0)$ and $\sigma(c^{*}<0)$ represent the cross sections for
production of the top quark in the forward and backward hemispheres,
respectively, relative to the incident quark direction. The NLO effects
generate positive values for in -initiated subprocesses [15, 16, 17, 18].

A related quantity was measured by the CDF and D0 experiments at the Fermilab
Tevatron, a proton-antiproton collider that operated at a center-of-mass
energy $\sqrt{s}=1.96$, where the subprocess dominates production. Because the
quantity measured at the Tevatron includes and initial states, it is expected
to be smaller than the quantity defined in Eq. (1) by a factor of $\approx
0.85$ (about 18% smaller). Initial measurements [19, 20] were somewhat larger,
especially at CDF, than expected at the NLO level; however, more recent
results [21, 22, 23] are consistent with the SM. Previous LHC measurements
sensitive to the top quark angular production asymmetry performed by the ATLAS
[24, 25] and CMS [26, 27, 28, 29] Collaborations focused on the top quark
charge asymmetry $A_{\mathrm{C}}$, which does not separate the initial states
from the and initial states, and therefore uses only part of the available
information.

This work represents a different approach that adopts a simplified model for
the production mechanism using a likelihood analysis to separate the
subprocess from the and subprocesses as well as from other backgrounds. The
model provides an LO description of several possible “beyond the SM processes”
[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], and a reasonable approximation
for expected NLO QCD effects [15]. The differential cross section for
$\qqbar\to\ttbar$ can be expressed as a linear combination of symmetric and
antisymmetric functions of the production angle, with the antisymmetric
function approximated as a linear function of $c^{*}$:

$\frac{\rd\sigma}{\rd c^{*}}(\qqbar)\approx
f_{\text{sym}}(c^{*})+\left[\int_{-1}^{1}f_{\text{sym}}(x)\rd{}x\right]c^{*}A^{(1)}_{\mathrm{FB}}(m_{\ttbar}).$
(2)

The symmetric function $f_{\text{sym}}$ depends only on kinematic properties
of the event and $A^{(1)}_{\mathrm{FB}}$ is a parameter that depends upon
$m_{\ttbar}$, the invariant mass of the system. Using Eq. (2) to evaluate Eq.
(1), we find that $\AFB\approx A^{(1)}_{\mathrm{FB}}$, which defines the
linearized forward-backward asymmetry. Equation (2) describes the LO exchange
and interference terms expected from $s$-channel resonances with chiral
couplings. Because the statistical power of the sample is not sufficient to
measure $A^{(1)}_{\mathrm{FB}}$ as a function of $m_{\ttbar}$, we measure only
an average value over the entire sample. This approximation is similar to the
mass-averaged leading term of a Legendre polynomial used by the CDF
Collaboration in 2013 [30] to characterize the angular distribution of their
data.

The validity of this approximation is verified by fitting the linearized
function given in Eq. (4) to data simulated with the NLO generator [31] and
comparing the resulting $A^{(1)}_{\mathrm{FB}}$ values with the $\AFB$ values
determined from counting events with positive and negative $c^{*}$. The
results of this study are presented in Table 0.1. Samples of simulated and
events at $\sqrt{s}=1.96\TeV$ and $\sqrt{s}=13\TeV$, respectively, are also
subdivided into two $m_{\ttbar}$ regions to perform the analysis. Comparing
the results in the full samples and in the $m_{\ttbar}$ regions, we conclude
that the linear approximation is accurate to within a few percent of measured
asymmetries and that it reflects the average asymmetry of each sample. We
further note that smaller values of the asymmetry at $\sqrt{s}=13\TeV$ than
the asymmetry at $\sqrt{s}=1.96\TeV$ appear to be due to the more copious
production of events with energetic extra jets which contribute negatively to
the average asymmetry. It is clear that our LHC measurements are not directly
comparable with those from the Tevatron experiments.

The accuracy of the linear approximation used to define
$A^{(1)}_{\mathrm{FB}}$ is assessed by comparing the results of linear fitting
to determine $A^{(1)}_{\mathrm{FB}}$ and of event counting to determine
$\AFB$. The and data samples are simulated with the NLO generator [31] at
center-of-mass (CM) energies of 1.96 and 13, respectively.  Initial State CM
Energy Mass Range $\AFB$ (counting) $A^{(1)}_{\mathrm{FB}}$ (fitting) 1.96
$m_{\ttbar}<500\GeV$ 0.0535$\pm$0.0012 0.0546$\pm$0.0011 1.96
$m_{\ttbar}>500\GeV$ 0.0998$\pm$0.0024 0.1044$\pm$0.0022 1.96 all
0.0626$\pm$0.0011 0.0639$\pm$0.0010 13 $m_{\ttbar}<500\GeV$ 0.0249$\pm$0.0016
0.0251$\pm$0.0014 13 $m_{\ttbar}>500\GeV$ 0.0414$\pm$0.0022 0.0414$\pm$0.0020
13 all 0.0306$\pm$0.0013 0.0305$\pm$0.0012

Several authors have considered the impact of possible top quark anomalous
chromoelectric and chromomagnetic dipole moments on the production of a system
at hadron colliders [32, 33, 34, 35]. We follow the conventions and results of
Ref. [34], and define $\hat{d}_{\PQt}$ and $\hat{\mu}_{\PQt}$ in terms of an
effective Lagrangian of the kind

${\cal
L}_{\ttbar\Pg}=-g_{s}\,\left[\PAQt\gamma^{\mu}G_{\mu}\PQt+i\frac{\hat{d}_{\PQt}}{2m_{\PQt}}\;\PAQt\sigma^{\mu\nu}\gamma_{5}G_{\mu\nu}\PQt+\frac{\hat{\mu}_{\PQt}}{2m_{\PQt}}\;\PAQt\sigma^{\mu\nu}G_{\mu\nu}\PQt\right],$
(3)

where $\sigma^{\mu\nu}=\frac{i}{2}[\gamma^{\mu},\gamma^{\nu}]$,
$G_{\mu}=G_{\mu}^{a}T^{a}$; where the $G_{\mu}^{a}$ are the gluon fields and
the $SU(3)_{C}$ generators are $T^{a}=\frac{1}{2}\lambda^{a}$ ($a$=1, …, 8);
the $G_{\mu\nu}=G_{\mu\nu}^{a}T^{a}$, with the gluon field-strength tensors
being
$G_{\mu\nu}^{a}=\partial_{\mu}G_{\nu}^{a}-\partial_{\nu}G_{\mu}^{a}-g_{s}f_{abc}G_{\mu}^{b}G_{\nu}^{c}$,
and the $f_{abc}$ being $SU(3)$ group structure constants.

This paper discusses our measurements of $A^{(1)}_{\mathrm{FB}}$,
$\hat{d}_{\PQt}$, and $\hat{\mu}_{\PQt}$ in collisions at the CERN LHC. The
analysis uses theoretical models of the cross section as functions of
$A^{(1)}_{\mathrm{FB}}$, $\hat{d}_{\PQt}$, and $\hat{\mu}_{\PQt}$ to describe
the angular distributions observed in a sample of -enriched events collected
at the CMS experiment. The analysis is based on final states containing a
single lepton (muon or electron) and several jets, usually referred to as
lepton+jets events. Our measurement of the $A^{(1)}_{\mathrm{FB}}$ of top
quarks in initial states is the first of its kind at the LHC. Values of
$\hat{d}_{\PQt}$ and $\hat{\mu}_{\PQt}$ have previously been extracted from
spin correlations [36], but this is the first measurement that relies on
differential distributions in events.

This paper is organized as follows. Section 0.2 details the strategies used in
extracting parameters of interest from the observed cross section. Section 0.3
briefly describes the CMS detector and the final-state objects used in the
analysis. Section 0.4 discusses the recorded and simulated events. Section 0.5
describes the event selection and kinematic reconstruction of the events.
Section 0.6 discusses the fitting method and the techniques used to estimate
the backgrounds. Section 0.7 describes the systematic uncertainties associated
with the analysis. Section 0.8 reviews the results of the fits to the data,
and Section 0.9 provides a brief summary.

##  0.2 Analysis strategy

Measuring the top quark forward-backward asymmetry at the LHC is considerably
more challenging than at the Tevatron, where the cross section is dominated by
the subprocess and the incident quark and antiquark directions are well
defined by the proton and antiproton beams. At the LHC, however, production is
dominated by the subprocess, from which no asymmetry arises, thus complicating
the extraction of the asymmetry in the $\qqbar\to\ttbar$ subprocess. To
separate the from the and subprocesses, and from other backgrounds, we use the
quantities $m_{\ttbar}$, $x_{\text{F}}$, and $c^{*}$ to describe events,
where: $x_{\text{F}}=2p_{\mathrm{L}}/\sqrt{s}$ is the scaled longitudinal
momentum $p_{\mathrm{L}}$ of the system in the laboratory frame; and $c^{*}$,
$m_{\ttbar}$ were defined in Section 0.1. The use of $x_{\text{F}}$ to
separate events with different initial states is a technique that is well-
established in the literature, and its application to a measurement of in top
quark pair production was proposed in [37].

The distributions in $m_{\ttbar}$, $x_{\text{F}}$, and $c^{*}$ for the , , and
initial states for events simulated using the NLO v2 [38, 39, 40] Monte Carlo
(MC) generator for collisions at $\sqrt{s}=13\TeV$ are shown in Fig. 3; the
and distributions are quite similar. Because of this similarity, and because
the SM asymmetry for events is expected to be approximately two orders of
magnitude smaller than for events [15], the and subprocesses are combined into
a single distribution function in our analysis. The $m_{\ttbar}$ distribution
for events is somewhat narrower than for the other processes. The $c^{*}$
distribution for events is more isotropic than that for the and processes due
to the $t$-channel pole that dominates their cross sections. Of key importance
is that the $x_{\text{F}}$ distribution for the events has a longer tail that
helps to discriminate them and to correctly identify the incident-quark
direction. Because the gluon and antiquark parton distribution functions
(PDFs) of the proton have a lower average transverse momentum than those of
the quark parton, the direction of the system $p_{\mathrm{L}}$ is correlated
with the initial quark direction in -initiated events. This provides a
reasonable choice for the initial parton direction as the direction of the
Collins–Soper frame [41]. The result of taking the longitudinal direction of
the pair in the lab frame as the quark direction is shown in Fig. 3: defining
$N_{\mathrm{C}}$ as the number of correct assignments and $N_{\mathrm{I}}$ as
the number of incorrect assignments, the dilution factor
$D=(N_{\mathrm{C}}-N_{\mathrm{I}})/(N_{\mathrm{C}}+N_{\mathrm{I}})$ becomes
large in the enriched region of large $\abs{x_{\text{F}}}$. The reduced $D$ in
the largest $\abs{x_{\text{F}}}$ bins appears to be an artifact of the
NNPDF3.0 PDF set [42] used to generate the plot, and it does not affect this
analysis as no data events are observed with such large values of
$\abs{x_{\text{F}}}$.

Figure 3: The generator-level $c^{*}$ (upper left), $\abs{x_{\text{F}}}$
(upper right), and $m_{\ttbar}$ (lower left) distributions normalized to unity
for the subprocesses , , and ${\Pg}{\Pg}\to\ttbar$. The dilution factor, when
taking the longitudinal direction of the pair in the lab frame as the quark
direction for events, is shown in the lower right plot as a function of
$\abs{x_{\text{F}}}$.

The differential cross section for $\qqbar\to\ttbar$ can be written in the
center-of-mass frame as

$\frac{\rd\sigma}{\rd
c^{*}}(\qqbar)=K\frac{\pi\alpS^{2}}{9m_{\ttbar}^{2}}\beta\left\\{2-\beta^{2}+\beta^{2}c^{*2}+\alpha\left(1-\beta^{2}c^{*2}\right)+2\left[2-\frac{2}{3}\beta^{2}+\alpha\left(1-\frac{1}{3}\beta^{2}\right)\right]A^{(1)}_{\mathrm{FB}}c^{*}\right\\},$
(4)

where $K$ is an NLO normalization factor, $\alpS=g_{s}^{2}/4\pi$ is the strong
coupling constant, $\beta=\sqrt{\smash[b]{1-4m_{\PQt}^{2}/m_{\ttbar}^{2}}}$ is
the velocity of the top quark in the center-of-mass frame, and $\alpha(\beta)$
is the longitudinal polarization of the exchanged gluon. This parameterization
describes the process to NLO precision, using a linear approximation for the
NLO forward-backward asymmetry that is also an LO description of the effects
of possible $s$-channel vector resonances in extensions of the standard model
[8, 14].

At LO, the presence of the $\hat{d}_{\PQt}$ and $\hat{\mu}_{\PQt}$ terms
modify both the and contributions to the $\to$ cross section. As in the case
of $A^{(1)}_{\mathrm{FB}}$, we use a framework that approximates SM NLO
contributions with good accuracy, and provides possible anomalous
contributions at LO. The cross section can then be expressed as follows [34]:

$\displaystyle\frac{\rd\sigma}{\rd c^{*}}(\qqbar)$
$\displaystyle=K\frac{\pi\alpS^{2}}{9m_{\ttbar}^{2}}\beta\biggl{\\{}2-\beta^{2}+\beta^{2}c^{*2}+\alpha\left(1-\beta^{2}c^{*2}\right)$
(5)
$\displaystyle+4(2\hat{\mu}_{\PQt}+\hat{\mu}_{\PQt}^{2}-\hat{d}_{\PQt}^{2})+4\left(\hat{\mu}_{\PQt}^{2}+\hat{d}_{\PQt}^{2}\right)\frac{1-\beta^{2}c^{*2}}{1-\beta^{2}}\biggr{\\}},$

where the NLO asymmetry described in Eq. (4) has been suppressed because the
anomalous moments affect only the symmetric part of the cross section. Note
that the last term is strongly enhanced at large values of $m_{\ttbar}$ (as
$\beta\to 1$). The cross section can be expressed as [34]:

$\displaystyle\frac{\rd\sigma}{\rd
c^{*}}({\Pg}{\Pg})=K\frac{\pi\alpS^{2}\beta}{48m_{\ttbar}^{2}}\biggl{\\{}$
$\displaystyle\frac{7+9\beta^{2}c^{*2}}{1-\beta^{2}c^{*2}}\left[\frac{1-\beta^{4}c^{*4}+2\beta^{2}(1-\beta^{2})(1-c^{*2})}{2(1-\beta^{2}c^{*2})}\left(1+\varepsilon\beta^{2}c^{*2}\right)+\hat{\mu}_{\PQt}(1+\hat{\mu}_{\PQt})\right]$
$\displaystyle+8(\hat{\mu}_{\PQt}^{2}+\hat{d}_{\PQt}^{2})\left(\frac{7(1+\hat{\mu}_{\PQt})}{1-\beta^{2}}+\frac{1-5\hat{\mu}_{\PQt}}{2(1-\beta^{2}c^{*2})}\right)$
$\displaystyle+8(\hat{\mu}_{\PQt}^{2}+\hat{d}_{\PQt}^{2})^{2}\left(\frac{1}{1-\beta^{2}c^{*2}}+\frac{1}{1-\beta^{2}}+\frac{4(1-\beta^{2}c^{*2})}{(1-\beta^{2})^{2}}\right)\biggr{\\}},$

where the SM NLO contributions are parameterized through the factor $K$ and
the empirical $\varepsilon=\varepsilon(\beta)$ function. As for , the effects
from anomalous moments are most significant as $\beta\to 1$.

Because the effects from as yet undiscovered massive particles and from the
anomalous moments $\hat{d}_{\PQt}$ and $\hat{\mu}_{\PQt}$ are most noticeable
at large $m_{\ttbar}$, and because the fraction of events increases with top
quark-pair momentum, the first of the three decay topologies we consider,
called “type-1,” comprises events with high Lorentz boost in which the decay
products of the hadronic top quark are fully merged into a single jet. The
second topology, called “type-2,” consists of events that have a large, high-
momentum, high-mass jet that indicates either a partially- or fully-merged
hadronic top quark decay, but which lack a single large jet definitively
identified as originating from a merged top quark decay. These type-2 events
are included to bridge the gap between events where the top quark decay
products are fully merged or fully resolved. The type-1 and -2 topologies are
collectively referred to as “boosted” because their decaying top quarks have
high . The third and most populated category, called “type-3,” includes events
with low-mass jets; type-3 events are also called “resolved” events because
all decay products are individually distinguishable. The fully selected event
sample comprises approximately 2.2% type-1 (7195 +jets and 3108 +jets), 11.6%
type-2 (50311 +jets and 3735 +jets), and 86.2% type-3 (234839 +jets and 166213
+jets events) events.

As detailed in Section 0.6, linear combinations of normalized three-
dimensional distributions (templates) in $m_{\ttbar}$, $x_{\text{F}}$, and
$c^{*}$ reconstructed from simulated SM events generated at NLO are used in a
simultaneous likelihood fit to the observed differential cross section in 12
total channels defined by the decay topology, lepton charge, and lepton
flavor. Generator-level information is used to separate from and events and to
reweight them using Eqs. (4)–(0.2) to produce the parameter-independent
templates. Other MC contributions and events in data are used to construct
templates representing the background. A total general linear combination of
these templates is fitted to data to independently extract the three values of
$A^{(1)}_{\mathrm{FB}}$, $\hat{d}_{\PQt}$, and $\hat{\mu}_{\PQt}$.

##  0.3 The CMS detector and physics objects

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. Within the solenoid
volume are a silicon pixel and strip tracker, a lead tungstate crystal
electromagnetic calorimeter (ECAL), and a brass and scintillator hadron
calorimeter (HCAL), each composed of a barrel and two endcap sections. Forward
calorimeters extend the pseudorapidity coverage provided by the barrel and
endcap detectors. Muons are detected in gas-ionization chambers embedded in
the steel flux-return yoke outside the solenoid. A more detailed description
of the CMS detector, together with a definition of the coordinate system used
and the relevant kinematic variables, can be found in Ref. [43].

Events of interest are selected using a two-tiered trigger system [44]. The
first level, 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.

The particle-flow (PF) algorithm [45] aims to reconstruct and identify each
individual particle in an event using an optimized combination of information
from the various elements of the CMS detector. Muons are identified in the
range $\abs{\eta}<2.4$, with detection planes made using three technologies:
drift tubes, cathode strip chambers, and resistive plate chambers. The energy
of each muon is obtained from the curvature of the corresponding track. The
energy of each electron is determined from a combination of the electron
momentum at the primary interaction vertex (PV) 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 each charged hadron is determined from a combination of
its 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 each neutral
hadron is obtained from the corresponding corrected ECAL and HCAL energy.

The candidate vertex with the largest value of summed physics-object $\pt^{2}$
is taken to be the primary $\Pp\Pp$ interaction vertex. The physics objects
are the jets, clustered using the anti- (AK) jet finding algorithm [46, 47]
with the tracks assigned to candidate vertices as inputs, and the associated
missing transverse momentum. The missing transverse momentum vector in an
event is defined as the negative vector sum of the transverse momenta of all
the reconstructed PF objects. Its magnitude is denoted as [48]. The is
modified to account for corrections to the energy scale of the reconstructed
jets in the event. Anomalous high- events can be due to a variety of
reconstruction failures, detector malfunctions or non collisions backgrounds.
Such events are rejected by event filters that are designed to identify more
than 85–90% of the spurious high- events with a misidentification rate less
than 0.1% [48].

Jets called “AK4” (“AK8”) are clustered from the PF objects in an event using
the anti- algorithm with a distance parameter of 0.4 (0.8). Jet momentum is
determined as the vectorial sum of all PF object momenta in the jet, and is
found to be, on average, within 5 to 10% of the true momentum over the whole
spectrum and detector acceptance, based on simulation. Additional interactions
within the same or nearby bunch crossings (pileup) can contribute extra tracks
and calorimetric energy depositions, increasing the apparent jet momentum. To
mitigate this effect, tracks originating from pileup vertices are discarded,
and an offset correction is applied to account for remaining contributions.
Jet energy corrections are derived from simulation studies [49] so the average
measured response of jets becomes identical to that of particle-level jets. In
situ measurements of the momentum balance in dijet, photon+jet, +jet, and
multijet events are used to detect any residual differences between the jet
energy scale in data and in simulation, and appropriate corrections are made.
Additional selection criteria are applied to each jet to identify jets
potentially dominated by instrumental effects or reconstruction failures [50].

To prevent double counting of lepton momentum in high- (“boosted”) leptonic
top quark decays for which the final state lepton and quark jet are not
expected to be well separated, leptons reconstructed and identified within
jets are not taken into account in the jet momentum computation. Muons are
removed from jets using a key-based algorithm, wherein a muon four-vector is
subtracted from that of a reconstructed jet if the muon’s PF candidate appears
in the list of jet PF candidates, and electrons are removed from jets by
subtracting their four-vectors from those of any reconstructed jets within
equal to the distance parameter of the jet clustering algorithm.

The “soft drop” (SD) algorithm [51] with angular exponent $\beta=0$, soft
cutoff threshold $z_{\text{cut}}<0.1$, and characteristic radius
$R_{\mathrm{0}}=0.8$ [52], is applied to AK8 jets to remove soft, wide-angle
radiation. In this algorithm the constituents of the AK8 jets are reclustered
using the Cambridge–Aachen algorithm [53, 54]. Application of the SD algorithm
improves discrimination between signal events, which contain jets from boson
or top quark decays, and background events, which consist of jets produced via
the strong interaction, referred to as “QCD multijet” events. This improves
the jet mass resolution, reducing the mass of single light quark and gluon
jets, and mitigating the effects of pileup [55]. The subjets identified by the
algorithm are considered as individual objects themselves and are used in the
reconstruction of the decaying top quark pair, as described in Section 0.5.

##  0.4 Data and simulation

This analysis is performed using the data collected by the CMS detector in
2016 at $\sqrt{s}=13\TeV$, corresponding to an integrated luminosity of
$35.9\pm 0.9\fbinv$ [56]. Events in the type-1 and -2 channels pass a single-
muon trigger requiring a muon with $\pt>50\GeV$, a single-electron trigger
requiring an electron with $\pt>115\GeV$, or an electron+jets trigger with 50
and 165thresholds on the electron and the leading-jet , respectively. Events
in the type-3 channels must pass a single-muon trigger requiring an isolated
muon with $\pt>24\GeV$, or a single-electron trigger for electrons with
$\pt>27\GeV$. The average trigger efficiencies for events passing offline
selection are approximately 95 (97)% in the boosted (resolved) +jets channels
and approximately 98 (74)% in the boosted (resolved) +jets channels.

The MC generator [31] is used to simulate the signal at NLO using the 8.219
parton-shower generator [57] and the CUETP8M2T4 tune [58] assuming
$m_{\PQt}=172.5\GeV$. These MC events are also reweighted [59] to model the
dependence of the distributions on renormalization and factorization scales
$\mu_{\mathrm{R}}=\mu_{\mathrm{F}}=\sqrt{\smash[b]{m_{\PQt}^{2}+\pt^{2}}}$
[60, 61], and the choice of the NNPDF3.0 PDFs [42], with $\alpS=0.118$.

The and generators using the same tune are also used to simulate contributions
to the background from single top quark processes at NLO, both in the $t$
channel [62] using and madspin [63] and in the channel with v1 [64].
Background contributions from single top quark processes in the $s$ channel
are simulated at NLO using the MC generator [65] matched to parton showers;
and contributions from Drell–Yan ( or +jets) and +jets production are
simulated at LO using the 5 MC generator [65], matched to parton showers and
the CUETP8M1 tune through the MLM prescription [66].

All MC events are processed through a full simulation of the CMS detector in
[67]. The distributions for MC events are normalized to their predicted cross
sections at NLO for single top quark production in the $s$ and $t$ channels
[68]; at NLO using additional next-to-next-to-leading logarithms (NNLL) soft
gluon correction for single top quark production in the channel [69]; at next-
to-next-to-leading order (NNLO) for +jets and /+jets [70, 71, 72]; and at
NNLO+NNLL for the signal [73]. All MC samples are corrected to bring their
generated pileup distributions into agreement with those observed in data.
Identical selection criteria and reconstruction procedures are otherwise
applied to all simulated events and data.

##  0.5 Event reconstruction and selection

The selection criteria are designed to identify events in which one of the two
top quarks decays to a charged lepton, a neutrino, and a single quark jet, and
the other decays to only jets. The leptonically decaying top quark can yield a
muon or electron in one of the three topologies, leading to six mutually
exclusive categories: $\Pgm+$jets and $\Pe+$jets, each of type-1, type-2, and
type-3.

Muon (electron) candidates are required to have $\pt>50$ (80)in the boosted
topologies, and $\pt>30\GeV$ in the resolved topology. Only lepton candidates
in the range of $\abs{\eta}<2.4$ are considered, and any electron candidates
in the transition region between barrel and endcap calorimeters, corresponding
to $1.44<\abs{\eta}<1.57$, are rejected. To suppress misidentified leptons
resulting from the products of hadronization, accepted leptons are required to
be isolated from nearby hadron activity in the event. Lepton isolation is
determined in part with a “2D selection” applied as a logical “or” of two
independent selections. The first is a requirement on the component of the
lepton momentum that is transverse to the axis of the nearest AK4 jet, called
$\pt^{\text{rel}}$, and the second is the distance
$\DR=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}$ with respect to the
nearest AK4 jet. In both cases, the nearest AK4 jet must have $\pt>15\GeV$ and
$\abs{\eta}<3.0$. To be considered isolated in the boosted topologies, a
lepton must have $\pt^{\text{rel}}>30\GeV$ or $\DR>0.4$. In the resolved
topology, lepton isolation is additionally determined according to the sum of
the scalar of the neutral and charged hadron PF candidates located within a
cone of size $\DR=0.4$ and $0.3$ for muons and electrons, respectively. This
sum is required to be less than 15 (6)% of the muon (electron) , and only
leptons passing both the 2D selection and these PF isolation criteria are
considered isolated in the resolved topology. Any event containing more than
one isolated lepton is rejected to suppress background from two-lepton decays
in which each top quark decays to leptons. Requirements are also imposed on
the in each event to suppress background from multijet events containing a
muon or electron. Events in the boosted muon+jets or electron+jets channels
are required to have $\ptmiss>50$ or 100, respectively, and resolved events of
both lepton flavors are required to have $\ptmiss>40\GeV$. Events failing
requirements or lepton isolation requirements are used in the estimation of
QCD multijet background contribution from data, as described in Section 0.6.

All AK4 and AK8 jets are required to have $\pt>30$ and $\pt>200\GeV$,
respectively, and to be in the range of $\abs{\eta}<2.4$. The AK8 jets are
also required to have at least two subjets identified through the SD
clustering algorithm. In type-1 events, at least one AK8 jet must be present
and be identified (“tagged”) as originating from the merged decay of a top
quark. These identified top quark (-tagged) jets are selected using
simultaneous criteria on jet ; the jet mass after application of the SD
algorithm; and the ungroomed $N$-subjettiness [74] substructure discriminant
variable $\tau_{32}=\tau_{3}/\tau_{2}$, for which smaller values indicate a
greater likelihood that the jet is composed of three rather than two subjets.
An AK8 jet is considered -tagged if it has $\pt>400\GeV$, an SD mass in the
range $105<m_{\text{AK8}}^{\text{SD}}<220\GeV$, and $\tau_{32}<0.81$. By
contrast, type-2 events are required to have no -tagged AK8 jets, but must
contain at least one AK8 jet with $m_{\text{AK8}}^{\text{SD}}>40\GeV$, and at
least four AK4 jets. Type-1 and -2 muon (electron) events require the highest
momentum AK4 jet to have $\pt>150$ (250)and the second-highest momentum AK4
jet to have $\pt>50$ (70). Type-3 events are required to contain zero selected
AK8 jets, and at least four selected AK4 jets.

To help discriminate the signal from /+jets and +jets backgrounds, AK4 jets
originating from decays of quarks are identified using an algorithm that
combines lifetime information from tracks and secondary vertices [75]. Type-1
and -2 events are required to have at least one AK4 jet that is identified as
a quark (-tagged) at the “loose” working point of the algorithm, which has an
83% efficiency of correctly identifying a quark jet and a 9% probability of
misidentifying a gluon, quark, or light quark jet as a quark jet (defined as
the mistag rate). Type-3 events are required to have at least two AK4 jets
tagged at the “medium” working point of the algorithm with a tagging
efficiency of 63% and a 1% mistag rate. In all cases, tagging algorithms are
applied before removal of any double-counted leptons within AK4 jets.

The kinematic quantities of the top quark pair are reconstructed from their
constituent decay products using a maximum-likelihood fit [76, 77], which
varies the momenta of the decay products within their resolutions and iterates
over all possible assignments of jets to determine the most likely value of
the unknown longitudinal momentum of the neutrino and the best possible
assignment of jets that conforms with the decay hypothesis. In type-1 events,
the top quark that decays to all-jets is assumed to be the -tagged AK8 jet,
and the lepton side of the decay is reconstructed from all combinations of the
lepton, , and any -tagged AK4 jets that result in a lepton+jets top quark
candidate (lepton + + AK4 jet) separated from the -tagged AK8 jet by
$\DR(\PQt_{\text{jets}},\PQt_{\text{lep}})>2$. Type-2 and -3 events ignore AK8
jets entirely in their reconstruction, and instead their possible jet
configurations comprise all assignments of the four or five highest AK4 jets
to either the lepton or all-jets side of the decay; events containing five AK4
jets consider additional configurations in which any of the non--tagged jets
may be disregarded as external to the top quark pair decay.

For each event, the hypothesis with the smallest value of $-2\ln{L}$ (where
$L$ is the likelihood) is chosen to represent the complete top quark pair
decay, and the lepton and all-jets top quark four-vectors are reconstructed as
the vector sums of their rescaled particle four-momenta. This kinematic
fitting procedure is highly effective, returning the correct hypotheses in 98,
80, and 73% of type-1, -2, and -3 lepton+jets MC events, respectively, for
which an MC-based particle-matched hypothesis exists.

After reconstruction, type-1 and type-2 events are further required to have
top quark masses $m_{\PQt,\text{lep}}^{\text{reco}}<210\GeV$ for decays
containing one lepton, and $-2\ln{L}<-15$. The inversion of either of these
criteria defines a control region used to constrain the cross section of the
+jets background process as described in Section 0.6. From applying the
selection criteria to simulated events, we expect about $6210\pm 80$ ($3110\pm
60$), $39100\pm 200$ ($2490\pm 50$), and $188500\pm 400$ ($134500\pm 400$)
lepton+jets events in the type-1, -2, and -3 ()+jets signal regions,
respectively. The relatively poor efficiency for type-2 electrons as compared
with that for type-1 electrons results from the more stringent and lepton and
jet criteria applied to electron events in those regions and the lower
$m_{\ttbar}$ of events in the type-2 signal region.

Figures 4–6 show comparisons of the reconstructed variables, called
$c^{*}_{\mathrm{r}}$, $x_{\mathrm{r}}$, and $m_{\mathrm{r}}$ to distinguish
them from the corresponding parton-level quantities defined in Section 0.2,
for selected MC events and data for type-1, -2, and -3 regions, including the
QCD multijet background estimated from data, as discussed in Section 0.6. The
data-based method used to determine the multijet background contributions
tends to overestimate the observed data, particularly in the type-3 electron
sample; also, the disagreements visible in the $x_{\mathrm{r}}$ distributions
are due to the choice of the PDFs. Both of these systematic effects are
corrected by the fit to data.

Figure 4:  Reconstructed $c^{*}_{\mathrm{r}}$ (upper), $\abs{x_{\mathrm{r}}}$
(middle), and $m_{\mathrm{r}}$ (lower) for events passing type-1 +jets (left
column) and +jets (right column) selection criteria. The uncertainty pictured
in the hatched bands represents just the statistical contributions. The
multijet background is estimated from data, as discussed in Section 0.6. The
lower panels in each figure show the ratio of data to MC expectation in each
bin, and the last bins of the $\abs{x_{\mathrm{r}}}$ and $m_{\mathrm{r}}$
plots include overflow.

Figure 5: Data/MC comparison of reconstructed $c^{*}_{\mathrm{r}}$ (upper),
$\abs{x_{\mathrm{r}}}$ (middle), and $m_{\mathrm{r}}$ (lower) for events
passing type-2 +jets (left column) and +jets (right column) selection
criteria. The MC signal and background show their nominal predictions, with
the MC uncertainty pictured in the hatched bands representing statistical
uncertainties. The contribution from multijet background is estimated from
data, as discussed in Section 0.6. The lower panels of each figure show the
ratio of the observed data to MC expectation in each bin, and the last bins of
the $\abs{x_{\mathrm{r}}}$ and $m_{\mathrm{r}}$ plots include overflow.

Figure 6: Data/MC comparison of reconstructed $c^{*}_{\mathrm{r}}$ (upper),
$\abs{x_{\mathrm{r}}}$ (middle), and $m_{\mathrm{r}}$ (lower) for events
passing type-3 +jets (left column) and +jets (right column) selection
criteria. The MC signal and background show their nominal predictions, with
the MC uncertainty pictured in the hatched bands representing statistical
uncertainties. The contribution from multijet background is estimated from
data, as discussed in Section 0.6. The lower panels of each figure show the
ratio of the observed data to MC expectation in each bin, and the last bins of
the $\abs{x_{\mathrm{r}}}$ and $m_{\mathrm{r}}$ plots include overflow.

##  0.6 Constructing templates and estimating background

To measure $A^{(1)}_{\mathrm{FB}}$, the detector is assumed to have the same
efficiency for reconstructing tracks of positively and negatively charged
particles in the same configuration, implying a charge-parity symmetry where
the acceptance for an event with a positively charged lepton and angles
($c^{*}$, $c^{*}_{\mathrm{r}}$) is the same as that for a negatively charged
lepton with angles ($-c^{*}$, $-c^{*}_{\mathrm{r}}$). Counting data events
reconstructed with positively- and negatively-charged leptons shows that this
assumption is justified, with a maximum impact of about $10^{-5}$ on the
quantity.

To exploit this symmetry, a fourth reconstructed quantity, the lepton charge
$Q$, is used to describe each event. The four-dimensional distribution
function $f(\vec{y})$ of the reconstructed variables
$\vec{y}=(m_{\mathrm{r}},c^{*}_{\mathrm{r}},x_{\mathrm{r}},Q)$ is determined
from fully simulated and reconstructed events and from empirically determined
reconstructed background,

$f(\vec{y})=\sum_{n}R_{\text{bk},n}f_{\text{bk},n}(\vec{y})+\left\\{\left(1-R_{\qqbar}\right)f_{{\Pg}{\Pg}}(\vec{y})+R_{\qqbar}\left[f_{\text{qs}}(\vec{y})+A^{(1)}_{\mathrm{FB}}f_{\text{qa}}(\vec{y})\right]\right\\},$
(6)

where the $f_{\text{bk},n}(\vec{y})$ represent normalized distribution
functions for several backgrounds; $R_{\text{bk},n}$ are matching background-
fraction scale factors; $R_{\qqbar}$ is the fraction of events that are
categorized as initiated; $f_{{\Pg}{\Pg}}(\vec{y})$ is the normalized
distribution function for , , , , and $\Pq_{i}\Paq_{j}$ (where $i$, $j$ label
flavors and $i\neq j$) events; $f_{\text{qs}}(\vec{y})$ is the symmetrized
distribution for events, and $f_{\text{qa}}(\vec{y})$ is the antisymmetrized
linearized distribution for events. The symmetrized function is created by
increasing the event counts in the bins at ($c^{*}_{\mathrm{r}}$, $Q$) and
($-c^{*}_{\mathrm{r}}$, $-Q$) by 0.5 for each generated event. The
antisymmetrized, linearized function in Eq. (4) is constructed by adding half
the weight

$w_{\text{qa}}(m_{\ttbar}^{2},c^{*})=\frac{2\left[2-\frac{2}{3}\beta^{2}+\alpha(1-\frac{1}{3}\beta^{2})\right]c^{*}}{2-\beta^{2}+\beta^{2}c^{*2}+\alpha\left(1-\beta^{2}c^{*2}\right)}$
(7)

to the bin at ($c^{*}_{\mathrm{r}}$, $Q$) and half the weight
$w_{\text{qa}}(m_{\ttbar}^{2},-c^{*})$ to the mirror bin at
($-c^{*}_{\mathrm{r}}$, $-Q$). The values of the function $\alpha$ used in the
weighting procedure are determined from fits to generator level data in 10
bins of $\beta$. The resulting values of $\alpha$ are small, $\approx 0.12$,
except near the threshold.

These templates are independent of any of the parameters to be determined.
This technique determines the average parton-level linearized asymmetry and
accounts for resolution, dilution, migration, and acceptance effects, as long
as they are modeled in the simulation. The templates modeling well-simulated
background contributions can likewise be populated using simulated events.

To measure $\hat{d}_{\PQt}$ and $\hat{\mu}_{\PQt}$, Eqs. (5) and (0.2) are
used to define a 4D distribution function in terms of eight parameter-
independent template functions,

$\displaystyle f(\vec{y})=$
$\displaystyle\sum_{n}R_{\text{bk},n}f_{\text{bk},n}(\vec{y})+\biggl{\\{}\frac{(1-R_{\qqbar})}{F_{{\Pg}{\Pg}}(\hat{\mu}_{\PQt},\hat{d}_{\PQt})}\Bigl{[}f_{\mathrm{g}0}(\vec{y})+\hat{\mu}_{\PQt}(1+\hat{\mu}_{\PQt})f_{\mathrm{g}1}(\vec{y})$
(8)
$\displaystyle+(\hat{\mu}_{\PQt}^{2}+\hat{d}_{\PQt}^{2})(1+\hat{\mu}_{\PQt})f_{\mathrm{g}2}(\vec{y})+(\hat{\mu}_{\PQt}^{2}+\hat{d}_{\PQt}^{2})(1-5\hat{\mu}_{\PQt})f_{\mathrm{g}3}(\vec{y})+(\hat{\mu}_{\PQt}^{2}+\hat{d}_{\PQt}^{2})^{2}f_{\mathrm{g}4}(\vec{y})\Bigr{]}$
$\displaystyle+\frac{R_{\qqbar}}{F_{\qqbar}(\hat{\mu}_{\PQt},\hat{d}_{\PQt})}\Bigl{[}f_{\mathrm{q}0}(\vec{y})+\left(2\hat{\mu}_{\PQt}+\hat{\mu}_{\PQt}^{2}-\hat{d}_{\PQt}^{2}\right)f_{\mathrm{q}1}(\vec{y})+\left(\hat{\mu}_{\PQt}^{2}+\hat{d}_{\PQt}^{2}\right))f_{\mathrm{q}2}(\vec{y})\Bigr{]}\biggr{\\}},$

where the template functions $f_{\text{qi}}(\vec{y})$ and
$f_{\text{gi}}(\vec{y})$ are constructed from simulated events using weights,
and the functions $F_{\qqbar}$ and $F_{{\Pg}{\Pg}}$ maintain normalization of
the and contributions independent of the parameters $\hat{d}_{\PQt}$ and
$\hat{\mu}_{\PQt}$, as described in Appendix .10.

The distribution functions in Eqs. (6) and (8) are fitted to data as templates
in three-dimensional histograms in $m_{\mathrm{r}}$, $x_{\mathrm{r}}$, and
$c^{*}_{\mathrm{r}}$ for each reconstructed top quark pair, separated
according to lepton charge $Q$. The histograms are binned differently in each
channel, using variable bin widths in each dimension. Because the general
analyzing power of any particular event is most dependent on its value of
$\abs{x_{\mathrm{r}}}$, a one-dimensional binning is first chosen in this
quantity alone. A two dimensional binning in $c^{*}_{\mathrm{r}}$ and
$m_{\mathrm{r}}$ is then determined in each bin of $\abs{x_{\mathrm{r}}}$. To
maintain analyzing power for the $A^{(1)}_{\mathrm{FB}}$ parameter of
interest, all areas of the phase space have a minimum of two bins depending on
the sign of $c^{*}_{\mathrm{r}}$. Each template is stored as a list of two-
dimensional histograms indexed by bin in $\abs{x_{\mathrm{r}}}$, and is
unrolled into a one dimensional histogram for fitting. Tables .11–.11 in
Appendix .11 list the edges of the bins in $\abs{x_{\mathrm{r}}}$ and their
corresponding two dimensional $c^{*}_{\mathrm{r}}$ and $m_{\mathrm{r}}$
binning schemes for each topology and lepton flavor.

The $f_{\text{qs}}(\vec{y})$, $f_{\text{qa}}(\vec{y})$,
$f_{{\Pg}{\Pg}}(\vec{y})$, $f_{\text{qi}}(\vec{y})$, and
$f_{\text{gi}}(\vec{y})$ templates are constructed by accumulating lepton+jets
MC events in each bin, reweighted to factorize out parameters of interest (,
through the weight in Eq. (7) used for $f_{\text{qa}}(\vec{y})$, and the
weights in Appendix .10 used for $f_{\text{qi}}(\vec{y})$ and
$f_{\text{gi}}(\vec{y})$). These templates, along with
$f_{\text{bk},n}(\vec{y})$ describing background contributions, are summed in
a linear combination to estimate the total observed cross section.

The backgrounds contributing to this analysis are from dilepton and all-jet ,
single top quark, Drell–Yan /+jets and +jets, and multijet processes.
Background contributions other than multijet events are estimated using MC
predictions after applying corrections to account for differences in
identification and selection efficiency between simulation and data. The top
quark and /+jets backgrounds are considered together as one set of background
templates $f_{\text{bk},1}(\vec{y})$, which are expected to contribute
approximately $670\pm 30$ ($270\pm 20$), $5300\pm 70$ ($400\pm 20$), and
$36800\pm 200$ ($26900\pm 200$) events to the type-1, -2, and -3 ()+jets
signal regions, respectively.

The +jets background process is expected from simulation to contribute
approximately $690\pm 30$ ($260\pm 20$), $6170\pm 80$ ($570\pm 20$), and
$273200\pm 500$ ($1940\pm 40$) events to the type-1, -2, and -3 signal
regions, respectively. The contribution to the background from +jets processes
is modeled using a dedicated set of background templates,
$f_{\text{bk},2}(\vec{y})$, because its production cross section is less
precisely known from theory. For type-1 and -2 events, which expect larger
relative +jets contributions, the amount of +jets background is constrained by
performing a simultaneous fit to data in orthogonal control regions enriched
in +jets events. These control regions are populated by events that are
otherwise selected as described in Section 0.5, but which fail either of the
requirements on $m_{\PQt,\text{lep}}^{\text{reco}}$ or $-2\ln{L}$, ,
$m_{\PQt,\text{lep}}^{\text{reco}}\geq 210\GeV$ or $-2\ln{L}\geq-15$. This
selection is chosen by comparing simulated semileptonic and +jets events;
+jets events consistently exhibit larger values of the kinematic fit and have
leptonic top quark masses that are further away from the true top quark mass
than semileptonic events.

The QCD multijet background contribution represents a small fraction of the
total hadronic final state cross section and may not be well described in
simulation. This background is estimated from data using a matrix method
similar to that of Ref. [78]. The distribution in $\vec{y}$ of the multijet
background is estimated in each channel using orthogonal selection sidebands,
which contain events whose lepton candidates fail isolation requirements. The
distributions are initially normalized by applying transfer factors,
calculated as the ratios of the numbers of events that satisfy lepton
isolation requirements to those that do not, measured in sidebands containing
events that fail requirements. The expected residual contamination from
background processes other than multijets is estimated from simulation and
subtracted from the control samples. The transfer factors are approximately
0.004 (0.46), 0.003 (0.35), and 0.37 (1.44), resulting in nominal expectations
of approximately $140\pm 10$ ($170\pm 10$), $1460\pm 40$ ($330\pm 20$), and
$10200\pm 100$ ($8610\pm 90$) events, in the type-1, -2, and -3 signal region
()+jets channels, respectively. The final normalization of the multijet
background templates $f_{\text{bk},3}(\vec{y})$ is determined by the fit to
data. Comparisons between the multijet background estimated from data and
simulation show good agreement to within statistical uncertainties.

##  0.7 Systematic uncertainties

Systematic uncertainties in the normalization and shapes of the template
distributions arise from a variety of sources and are accounted for through
“nuisance” parameters that include their statistical limitations, and their
constraints by the prior distributions described below. These sources and the
methods of their accounting are listed below and summarized in Table 0.7.

Jet energy corrections: As described in Section 0.3, jet energies and their
resolutions are corrected in the simulation to agree with observations in
data. The jet energy corrections are achieved through the application of a
scale factor that depends on the jet , $\eta$, area $A$, and the event pileup
$n_{\text{PV}}$. The jet energy resolution is corrected by the application of
a Gaussian smearing that depends on the jet $\abs{\eta}$. The jet energy scale
and resolution factors are independently shifted up and down by one standard
deviation ($\sigma$) as they are applied to AK4 and AK8 jets simultaneously,
including the propagation of the corrections to the observed . The up and down
variations are then used to construct sets of up and down templates for use in
the fitting procedure. The resulting templates are smoothed to reduce bias
from statistical noise before fitting.

Pileup: Uncertainties in the procedure used for reweighting the pileup
distribution in the simulation are included by varying the inelastic cross
section ${\pm}4.6\%$ [79].

Trigger and lepton identification and isolation efficiencies: Trigger
efficiencies in data and simulation are determined using multiple independent
methods, each resulting in a lepton - and $\eta$-dependent scale factor
applied to simulated events. The different algorithms for determining lepton
identification and isolation provide their own sets of scale factors dependent
on pileup, lepton , and lepton $\eta$. The average values of the scale factors
are ${\approx}0.94$ and 0.96 for boosted and resolved +jets events,
respectively, and ${\approx}0.98$ for boosted and resolved +jets events. The
precision of the boosted +jets trigger efficiency measurement is limited by
the number of events passing selection criteria. These scale factors are
independently shifted up and down by one $\sigma$ to provide sets of templates
for interpolation.

tagging efficiency: Scale factors dependent on jet flavor, , and $\eta$ are
applied to simulated events to correct for differences in tagging efficiency
and mistag rates between data and simulation. Their uncertainties are
propagated to templates independently for each jet flavor and working point of
the tagging algorithm.

Top tagging efficiency: Three data/MC efficiency scale factors with associated
systematic uncertainties are applied to all simulated events selected with
-tagged jets to correct for the differences in tagging efficiency between data
and simulation. The three factors applied depend on the jet according to
whether the particular jet is fully, partially, or not merged, as determined
by an MC matching procedure.

Top quark reweighting: Recent NNLO QCD + electroweak calculations of top quark
pair production [80] describe how NNLO effects impact the top quark and
antiquark spectra in ways that NLO generators cannot reproduce. Scale factors
dependent on NLO-generated top quark and antiquark are calculated and applied
to bring the generated distributions into agreement with those predictions,
and the uncertainties in the calculated scale factors are considered as
systematic uncertainties [81].

PDFs and strong coupling: The systematic uncertainty from the choice of the
NNPDF3.0 PDFs used in generating MC events is included using the
${\pm}1\sigma$ deviations observed in 100 per-event weights representing the
uncertainties in the PDFs [42]. Changes in are included by recalculating the
generated event weight with $\alpS=0.118\pm 0.0015$ [82]. This uncertainty is
combined in quadrature with the overall PDF uncertainty to provide one set of
up/down templates describing the simultaneous variation of PDF and [83, 84].

Renormalization and factorization scales: Modeling uncertainties on the
renormalization ($\mu_{\mathrm{R}}$) and factorization ($\mu_{\mathrm{F}}$)
scales used in the matrix element generation process are included by
reweighting simulated events to match alternate scenarios with
$\mu_{\mathrm{R}}$ and $\mu_{\mathrm{F}}$ shifted up and down by a factor of
two, both independently and simultaneously, resulting in three nuisance
parameters describing $\mu_{\mathrm{R}}$, $\mu_{\mathrm{F}}$, and combined
$\mu_{\mathrm{R}}$ and $\mu_{\mathrm{F}}$ scales [60, 61].

Parton shower radiation, matching scales, and the underlying event:
Uncertainties in initial-state radiation (ISR) and final-state radiation
(FSR), and matrix element to parton shower (ME-PS) matching, as well as
uncertainties from the choice of CUETP8M2T4 tune in the simulation, are
included using templates constructed from independent MC samples generated
with parameters shifted by their uncertainties [58]. The resulting templates
are smoothed to reduce bias from statistical noise before fitting.

Modeling of color reconnection: Systematic uncertainties from the choice of
modeling color reconnection are included using a single set of up/down changes
in templates constructed as an envelope of the average fractional shifts
observed in each bin of templates constructed from independent MC samples
corresponding to three different color reconnection hypotheses [57, 85, 86].
These templates are also smoothed before fitting.

quark fragmentation and hadron semileptonic branching fraction: Uncertainties
in quark fragmentation and the hadron semileptonic branching fractions are
included using per-event scale factors that depend on the generator-level
transfer function $x_{\PQb}=\pt(\text{\PB})/\pt(\PQb-\text{jet})$, where
$\pt(\PB)$ represents the transverse momentum of the hadron, for fragmentation
uncertainties [87], and on the ratios of measured to simulated branching
fractions for branching fraction uncertainties [1].

Integrated luminosity: The total integrated luminosity has an uncertainty of
${\pm}2.5\%$ [56] that corresponds to a single nuisance parameter represented
by a log-normal prior correlated across all analysis channels.

Process yields: A ${\pm}1\%$ uncertainty in the fraction of lepton+jets top
quark-pair production caused by quark-antiquark annihilation is included as a
nuisance parameter $R_{\qqbar}$ affecting the distributions in templates. A
${\pm}10\%$ uncertainty on the cross section of +jets is included as a
nuisance parameter $R_{\PW+\text{jets}}$, affecting the normalization of all
$f_{\text{bk},2}(\vec{y})$ templates. The scales of these uncertainties are
chosen to characterize variations between different simulations. Their optimal
values as determined by the fitting procedure are smaller, reflecting the
constraints imposed by the observed data.

Transfer factors for multijet background estimated from data: The estimated
statistical uncertainties in the transfer factors for each multijet background
estimate are modeled by several independent variation nuisances
$R_{\text{QCD}}^{t/C/R}$, one for each estimate that is made. The scales of
these uncertainties are approximately 30 and 20% for type-1 +jets events with
positively and negatively charged muons, respectively, and approximately 10%
for type-1 +jets events of both electron charges. For type-2 and -3 events of
any lepton flavor and charge the uncertainties are on the order of a few
percent.

Finite MC sample event count: The statistical fluctuations in MC predictions
are included through the “Barlow–Beeston light” method [88], which adds a
Poisson uncertainty reflecting the number of events accumulated in each
template bin.

List of nuisance parameters considered in fits to data. The “N” stands for
“normalization,” and “S” for “shape” of the distribution in the “Type” column.
The “Size” column lists the absolute value of the associated fractional shifts
averaged over all affected template bins. The quantities
$R_{\text{QCD}}^{t/C/R}$ indicate that the QCD multijet yield uncertainties
are independent in each topology, channel, and region.  Source Uncertainty in
Type Size Affects Jet energy scale ${\pm}1\sigma(\pt,\eta,A)$ N & S 7.6% All
Jet energy resolution ${\pm}1\sigma(\abs{\eta})$ N & S 3.2% All Pileup
${\pm}1\sigma(n_{\text{PV}})$ N & S 2.9% All Boosted +jets trigger eff.
${\pm}1\sigma(\pt,\eta)$ N & S 0.4% Type-1/2 +jets Resolved +jets trigger eff.
${\pm}1\sigma(\pt,\eta)$ N & S 0.1% Type-3 +jets Boosted +jets trigger eff.
${\pm}1\sigma(\pt,\abs{\eta})$ N & S 18.6% Type-1/2 +jets Resolved +jets
trigger eff. ${\pm}1\sigma(\pt,\eta)$ N & S 2.5% Type-3 +jets Muon ident. eff.
${\pm}1\sigma(\pt,\abs{\eta},n_{\text{PV}})$ N & S 0.4% All +jets Muon PF
isolation eff. ${\pm}1\sigma(\pt,\abs{\eta},n_{\text{PV}})$ N & S 0.2% Type-3
+jets Electron ident. eff. ${\pm}1\sigma(\pt,\abs{\eta})$ N & S 1.0% All +jets
tag eff., jets (loose) ${\pm}1\sigma(\pt,\eta)$ N & S 2.5% Type-1/2 tag eff.,
jets (loose) ${\pm}1\sigma(\pt,\eta)$ N & S 1.2% Type-1/2 tag eff., light jets
(loose) ${\pm}1\sigma(\pt,\eta)$ N & S 6.3% Type-1/2 tag eff., jets (medium)
${\pm}1\sigma(\pt,\eta)$ N & S 1.9% Type-3 tag eff., jets (medium)
${\pm}1\sigma(\pt,\eta)$ N & S 0.8% Type-3 tag eff., light jets (medium)
${\pm}1\sigma(\pt,\eta)$ N & S 1.2% Type-3 tag eff. (merged)
${\pm}1\sigma(\pt)$ N & S 1.6% Type-1 tag eff. (semimerged)
${\pm}1\sigma(\pt)$ N & S 2.2% Type-1 tag eff. (not merged)
${\pm}1\sigma(\pt)$ N & S 2.8% Type-1 ISR scale ${\pm}1\sigma$ N & S 2.2% FSR
scale ${\pm}1\sigma$ N & S 2.6% ME-PS matching ($h_{\text{damp}}$)
${\pm}1\sigma$ N & S 2.5% CUETP8M2T4 tune ${\pm}1\sigma$ N & S 2.4% Color
reconnection ${\pm}1\sigma$ S 2.8% fragmentation ${\pm}1\sigma(x_{\PQb})$ N &
S 3.7% branching fraction ${\pm}1\sigma$ N & S 1.0% Top quark reweighting
${\pm}1\sigma(\pt^{\text{gen},\PQt},\pt^{\text{gen},\PAQt})$ S 2.5%
PDF/variation NNPDF 3.0 S 1.5% Renormalization scale $\mu_{\mathrm{R}}$
$\frac{1}{2}\mu_{\mathrm{R}}\to 2\mu_{\mathrm{R}}$ S 2.6% Factorization scale
$\mu_{\mathrm{F}}$ $\frac{1}{2}\mu_{\mathrm{F}}\to 2\mu_{\mathrm{F}}$ S 1.5%
Combined $\mu_{\mathrm{R}}$/$\mu_{\mathrm{F}}$ scale $\frac{1}{2}\to
2(\mu_{\mathrm{R}}\text{ and }\mu_{\mathrm{F}})$ S 3.8% MC Integrated
luminosity ${\pm}2.5\%$ N All $R_{\qqbar}$ ${\pm}1\%$ N & S All
$f_{\text{qp}*}/f_{\text{qm}*}$ $R_{\PW+\text{jets}}$ ${\pm}10\%$ N All +jets
MC $R_{\text{QCD}}^{t/C/R}$ (20 params total) ${\pm}1\sigma\stat$ N Multijet

A template-based, binned likelihood, combining the three-dimensional
observable space in all channels and regions, is constructed to compare the
data with expectation using a Poisson probability in each independent template
bin as a function of the fit parameters. Systematic uncertainties are defined
through nuisance parameters in the likelihood. Shape uncertainties are
incorporated by interpolation between the nominal and shifted templates,
constrained with a Gaussian prior. The interpolation is calculated with a
sixth-order polynomial for shifts smaller than one $\sigma$, and with a linear
function for shifts beyond one $\sigma$. Some shifted templates are smoothed
before fitting by merging bins in $m_{\mathrm{r}}$ and $c^{*}_{\mathrm{r}}$
and applying the average shifts. Normalization uncertainties are included
using log-normal priors. The hierarchies of the most impactful systematic
uncertainties are different for each parameter of interest, but one persistent
observation is that the PDF/ variation tends to be a large effect, correcting
the $x_{\text{F}}$ distribution to better agree with that observed in data.

These nuisance parameters and their corresponding functions are incorporated
into three different likelihood fits to determine the three parameters of
interest. A fit based on Eq. (6) is used to estimate $A^{(1)}_{\mathrm{FB}}$
and therefore assumes that $\hat{d}_{\PQt}=\hat{\mu}_{\PQt}=0$. Fits based on
Eq. (8) are used to separately extract $\hat{d}_{\PQt}$ (with
$\hat{\mu}_{\PQt}=0$) and $\hat{\mu}_{\PQt}$ (with $\hat{d}_{\PQt}=0$) where
we assume that $\AFB=0.036$ (the value from the MC). The resulting estimates
are not sensitive to this assumption because $\hat{d}_{\PQt}$ and
$\hat{\mu}_{\PQt}$ affect only the $c^{*}$-symmetric part of the cross
section.

Final values of $A^{(1)}_{\mathrm{FB}}$ and $\hat{\mu}_{\PQt}$ are determined
from a Neyman construction [89] in which 1000 pseudo-data sets are generated
from the template models with input values of parameters of interest and then
fitted. The median and ${\pm}1\sigma$ contours are plotted, and the value of
the parameter of interest is the input value in the pseudo-experiments whose
fitted median is the value returned by the fit to data, interpolating linearly
between points if needed. The uncertainty intervals are constructed similarly
from the ${\pm}1\sigma$ curves. When calculating the total intervals
(statistical + systematic), the pseudo-data sets are generated using values of
the nuisance parameters which are randomly sampled from their prior
distributions. The intervals from which the statistical uncertainties are
derived are instead obtained using pseudo-data sets in which nuisance
parameters are fixed to their nominal values in both the toy generation and
the fit. The final 95% confidence limit on $\abs{\hat{d}_{\PQt}}$ is
determined using a one-dimensional likelihood profile, because of the
unresolved sign ambiguity of $\hat{d}_{\PQt}$ in Eq. (8), and because of the
small observed value of the $\hat{d}_{\PQt}$ parameter. The upper limit at 95%
confidence level, computed using the asymptotic formulae for the distribution
of the test statistic $\tilde{t}(\alpha)$ of Ref. [90], corresponds to a shift
in the negative log likelihood of $-2\Delta\ln{L}=3.84$.

##  0.8 Results

The application of the fitting procedure to the data yields
$A^{(1)}_{\mathrm{FB}}=0.048^{+0.095}_{-0.087}\stat^{+0.020}_{-0.029}\syst$
and $\hat{\mu}_{\PQt}=-0.024^{+0.013}_{-0.009}\stat^{+0.016}_{-0.011}\syst$.
Figure 7 shows the Neyman construction plots corresponding to these final
values. The profile likelihood shown in Fig. 8 results in a 95% confidence
limit $\abs{\hat{d}_{\PQt}}<0.03$. Figures 9 and 10 show comparisons of
representative fit functions for the $A^{(1)}_{\mathrm{FB}}$ analysis in each
signal region (SR) channel, summed over lepton charge. Overall, we observe
good agreement, and results from the $\hat{d}_{\PQt}$ and $\hat{\mu}_{\PQt}$
analyses are comparable.

Figure 7:  Neyman constructions for the $A^{(1)}_{\mathrm{FB}}$ (left) and
$\hat{\mu}_{\PQt}$ (right) parameters of interest in groups of 1000 pseudo-
experiments generated with systematic uncertainty nuisance parameters allowed
to vary. The horizontal dotted lines indicate the values of the parameters
determined from the fits and the vertical dotted lines indicate where these
values intersect with the central value and uncertainty contours from the
pseudo-experiment groups. Figure 8:  One-dimensional likelihood profile of the
$\hat{d}_{\PQt}$ parameter, where the change in the minimum log likelihood
from its global minimum ($-2\Delta\ln{L}$) is shown on the $y$-axis as a
function of $\abs{\hat{d}_{\PQt}}$. The dashed and solid lines show the
intersections at $-2\Delta\ln{L}=1.0$ and $3.84$, corresponding to the one-
sided limits on $\abs{\hat{d}_{\PQt}}$ at the 68 and 95% confidence limits,
respectively.

Figure 9:  Comparisons of fitted data and MC expectations as a function of
template bin number for the $A^{(1)}_{\mathrm{FB}}$ parameter extraction. The
plots show events in the type-1 (upper row) and type-2 (lower row) +jets (left
column) and +jets (right column) channels, all summed over lepton charge. The
MC uncertainty pictured in the hatched bands represents the total (statistical
and systematic) uncertainty. The vertical solid, dashed, and dot-dashed lines
indicate the edges of the bins in $x_{\mathrm{r}}$ and $m_{\mathrm{r}}$ and
the midpoints of the $c^{*}$ distributions, respectively, corresponding to the
binning schemes listed in Tables .11–.11 in Appendix .11.

Figure 10:  Comparisons of fitted data and MC expectations as a function of
template bin number for the $A^{(1)}_{\mathrm{FB}}$ parameter extraction. The
plots show events in the type-3 +jets (upper) and +jets (lower) channels, all
summed over lepton charge. The MC uncertainty pictured in the hatched bands
represents the total (statistical and systematic) uncertainty. The vertical
solid, dashed, and dot-dashed lines indicate the edges of the bins in
$x_{\mathrm{r}}$ and $m_{\mathrm{r}}$ and the midpoints of the $c^{*}$
distributions, respectively, corresponding to the binning schemes listed in
Tables .11 and .11 in Appendix .11.

The goodness of fit is evaluated using a $\chi^{2}$ test statistic optimized
for Poisson-distributed data [91]. Comparisons of the statistic observed for
each fit to data with those observed in fits to samples of pseudo-data yield
$p$-values that quantify the fraction of pseudo-experiments with poorer fits.
The $p$-values for the fits extracting $A^{(1)}_{\mathrm{FB}}$,
$\hat{d}_{\PQt}$, and $\hat{\mu}_{\PQt}$ are 0.206, 0.233, and 0.274,
respectively, suggesting that the fitting models reasonably describe the
observed data.

The measured $A^{(1)}_{\mathrm{FB}}$ is consistent with the SM expectation.
The measured anomalous chromoelectric and chromomagnetic moments are
consistent with SM expectations ($\hat{\mu}_{\PQt}=\hat{d}_{\PQt}=0$), and
with the most recent results extracted from top quark spin correlation
measurements performed by CMS at 13 [92]. The spin correlation measurements,
which are more precise by a factor of approximately two, use samples of
dileptonic final states and are independent of the lepton+jets based
measurements presented in this paper.

##  0.9 Summary

The linearized parton-level top quark forward-backward asymmetry
($A^{(1)}_{\mathrm{FB}}$) and anomalous chromoelectric ($\hat{d}_{\PQt}$) and
chromomagnetic ($\hat{\mu}_{\PQt}$) moments have been measured using LHC
proton-proton collisions at a center-of-mass energy of 13, corresponding to an
integrated luminosity of 35.9. Candidate events decaying to lepton+jets final
states with “resolved” (low-energy) and “boosted” (high-energy) topologies
were selected and reconstructed through a kinematic fit of the decay products
to hypotheses. The parameters were extracted from independent template-based
likelihood fits to the data based on differential models of extensions to
leading-order tree-level cross sections for quark-antiquark and gluon-gluon
initial states, yielding
$A^{(1)}_{\mathrm{FB}}=0.048^{+0.095}_{-0.087}\stat^{+0.020}_{-0.029}\syst$,
$\hat{\mu}_{\PQt}=-0.024^{+0.013}_{-0.009}\stat^{+0.016}_{-0.011}\syst$, and
$\abs{\hat{d}_{\PQt}}<0.03$ at 95% confidence level.

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing centers and personnel of the Worldwide LHC Computing Grid for
delivering so effectively the computing infrastructure essential to our
analyses. Finally, we acknowledge the enduring support for the construction
and operation of the LHC and the CMS detector provided by the following
funding agencies: BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq,
CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST,
and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF
(Cyprus); SENESCYT (Ecuador); MoER, ERC IUT, PUT and ERDF (Estonia); Academy
of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG,
and HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM
(Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES
(Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT,
LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC
(Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom,
RAS, RFBR, and NRC KI (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER
(Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST
(Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK
(Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA).
Individuals have received support from the Marie-Curie program and the
European Research Council and Horizon 2020 Grant, contract Nos. 675440,
752730, and 765710 (European Union); the Leventis Foundation; the A.P. Sloan
Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science
Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et
dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door
Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium)
under the “Excellence of Science – EOS” – be.h project n. 30820817; the
Beijing Municipal Science & Technology Commission, No. Z181100004218003; the
Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the
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
Education, grant no. 14.W03.31.0026 (Russia); the Programa Estatal de Fomento
de la Investigación Científica y Técnica de Excelencia María de Maeztu, grant
MDM-2015-0509 and the Programa Severo Ochoa del Principado de Asturias; the
Thalis and Aristeia programs cofinanced by EU-ESF and the Greek NSRF; the
Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University
and the Chulalongkorn Academic into Its 2nd Century Project Advancement
Project (Thailand); the Nvidia Corporation; the Welch Foundation, contract
C-1845; and the Weston Havens Foundation (USA).

## References

  * [1] Particle Data Group, M. Tanabashi et al., “Review of particle physics”,  Phys. Rev. D 98 (2018) 030001, 10.1103/PhysRevD.98.030001.
  * [2] A. Djouadi, G. Moreau, F. Richard, and R. K. Singh, “The forward-backward asymmetry of top quark production at the Tevatron in warped extra dimensional models”,  Phys. Rev. D 82 (2010) 071702, 10.1103/PhysRevD.82.071702, arXiv:0906.0604.
  * [3] P. H. Frampton, J. Shu, and K. Wang, “Axigluon as possible explanation for ${\Pp}$ anti-${\Pp}$ ${\to}$ ${\PQt}$ anti-${\PQt}$ forward-backward asymmetry”,  Phys. Lett. B 683 (2010) 294, 10.1016/j.physletb.2009.12.043, arXiv:0911.2955.
  * [4] P. Ferrario and G. Rodrigo, “Heavy colored resonances in ${\ttbar}$ \+ jet at the LHC”,  JHEP 02 (2010) 051, 10.1007/JHEP02(2010)051, arXiv:0912.0687.
  * [5] D.-W. Jung, P. Ko, J. S. Lee, and S.-h. Nam, “Model independent analysis of the forward-backward asymmetry of top quark production at the Tevatron”,  Phys. Lett. B 691 (2010) 238, 10.1016/j.physletb.2010.06.040, arXiv:0912.1105.
  * [6] J. Cao, Z. Heng, L. Wu, and J. M. Yang, “Top quark forward-backward asymmetry at the Tevatron: a comparative study in different new physics models”,  Phys. Rev. D 81 (2010) 014016, 10.1103/PhysRevD.81.014016, arXiv:0912.1447.
  * [7] V. Barger, W.-Y. Keung, and C.-T. Yu, “Asymmetric left-right model and the top pair forward-backward asymmetry”,  Phys. Rev. D 81 (2010) 113009, 10.1103/PhysRevD.81.113009, arXiv:1002.1048.
  * [8] Q.-H. Cao et al., “Forward-backward asymmetry of top quark pair production”,  Phys. Rev. D 81 (2010) 114004, 10.1103/PhysRevD.81.114004, arXiv:1003.3461.
  * [9] M. Bauer et al., “Top quark forward-backward asymmetry in Randall-Sundrum models beyond the leading order”,  JHEP 11 (2010) 039, 10.1007/JHEP11(2010)039, arXiv:1008.0742.
  * [10] E. Alvarez, L. Da Rold, and A. Szynkman, “A composite Higgs model analysis of forward-backward asymmetries in the production of tops at Tevatron and bottoms at LEP and SLC”,  JHEP 05 (2011) 070, 10.1007/JHEP05(2011)070, arXiv:1011.6557.
  * [11] C.-H. Chen, G. Cvetic, and C. S. Kim, “Forward-backward asymmetry of top quark in unparticle physics”,  Phys. Lett. B 694 (2011) 393, 10.1016/j.physletb.2010.10.030, arXiv:1009.4165.
  * [12] Y. Bai, J. L. Hewett, J. Kaplan, and T. G. Rizzo, “LHC Predictions from a Tevatron anomaly in the top quark forward-backward asymmetry”,  JHEP 03 (2011) 003, 10.1007/JHEP03(2011)003, arXiv:1101.5203.
  * [13] A. R. Zerwekh, “The axigluon, a four-site model and the top quark forward-backward asymmetry at the Tevatron”,  Phys. Lett. B 704 (2011) 62, 10.1016/j.physletb.2011.08.064, arXiv:1103.0956.
  * [14] M. I. Gresham, I.-W. Kim, and K. M. Zurek, “On models of new physics for the Tevatron top ”,  Phys. Rev. D 83 (2011) 114027, 10.1103/PhysRevD.83.114027, arXiv:1103.3501.
  * [15] J. H. Kühn and G. Rodrigo, “Charge asymmetry of heavy quarks at hadron colliders”,  Phys. Rev. D 59 (1999) 054017, 10.1103/PhysRevD.59.054017, arXiv:hep-ph/9807420.
  * [16] J. H. Kühn and G. Rodrigo, “Charge asymmetries of top quarks at hadron colliders revisited”,  JHEP 01 (2012) 063, 10.1007/JHEP01(2012)063, arXiv:1109.6830.
  * [17] J. A. Aguilar-Saavedra, W. Bernreuther, and Z. G. Si, “Collider-independent top quark forward-backward asymmetries: standard model predictions”,  Phys. Rev. D 86 (2012) 115020, 10.1103/PhysRevD.86.115020, arXiv:1209.6352.
  * [18] M. Czakon, P. Fiedler, and A. Mitov, “Resolving the Tevatron top quark forward-backward asymmetry puzzle: fully differential next-to-next-to-leading-order calculation”,  Phys. Rev. Lett. 115 (2015) 052001, 10.1103/PhysRevLett.115.052001, arXiv:1411.3007.
  * [19] D0 Collaboration, “First measurement of the forward-backward charge asymmetry in top quark pair production”,  Phys. Rev. Lett. 100 (2008) 142002, 10.1103/PhysRevLett.100.142002, arXiv:0712.0851.
  * [20] CDF Collaboration, “Evidence for a mass dependent forward-backward asymmetry in top quark pair production”,  Phys. Rev. D 83 (2011) 112003, 10.1103/PhysRevD.83.112003, arXiv:1101.0034.
  * [21] D0 Collaboration, “Measurement of the forward-backward asymmetry in top quark-antiquark production in ${\Pp}\bar{{\Pp}}$ collisions using the lepton+jets channel”,  Phys. Rev. D 90 (2014) 072011, 10.1103/PhysRevD.90.072011, arXiv:1405.0421.
  * [22] CDF Collaboration, “Measurement of the forward-backward asymmetry of top-quark and antiquark pairs using the full CDF Run II data set”,  Phys. Rev. D 93 (2016) 112005, 10.1103/PhysRevD.93.112005, arXiv:1602.09015.
  * [23] CDF and D0 Collaborations, “Combined forward-backward asymmetry measurements in top-antitop quark production at the Tevatron”,  Phys. Rev. Lett. 120 (2018) 042001, 10.1103/PhysRevLett.120.042001, arXiv:1709.04894.
  * [24] ATLAS Collaboration, “Measurement of the charge asymmetry in top quark pair production in ${\Pp}{\Pp}$ collisions at $\sqrt{s}=7$ TeV using the ATLAS detector”,  Eur. Phys. J. C 72 (2012) 2039, 10.1140/epjc/s10052-012-2039-5, arXiv:1203.4211.
  * [25] ATLAS Collaboration, “Measurement of the charge asymmetry in highly boosted top-quark pair production in $\sqrt{s}=$ 8 TeV collision data collected by the ATLAS experiment”,  Phys. Lett. B 756 (2016) 52, 10.1016/j.physletb.2016.02.055, arXiv:1512.06092.
  * [26] CMS Collaboration, “Measurement of the charge asymmetry in top-quark pair production in proton-proton collisions at $\sqrt{s}=7$ TeV”,  Phys. Lett. B 709 (2012) 28, 10.1016/j.physletb.2012.01.078, arXiv:1112.5100.
  * [27] CMS Collaboration, “Measurements of the charge asymmetry using the dilepton decay channel in collisions at $\sqrt{s}=$ 7 TeV”,  JHEP 04 (2014) 191, 10.1007/JHEP04(2014)191, arXiv:1402.3803.
  * [28] CMS Collaboration, “Measurement of the charge asymmetry in top quark pair production in pp collisions at $\sqrt{s}=$ 8 TeV using a template method”,  Phys. Rev. D 93 (2016) 034014, 10.1103/PhysRevD.93.034014, arXiv:1508.03862.
  * [29] CMS Collaboration, “Measurements of charge asymmetry using dilepton final states in collisions at $\sqrt{s}=8$ TeV”,  Phys. Lett. B 760 (2016) 365, 10.1016/j.physletb.2016.07.006, arXiv:1603.06221.
  * [30] CDF Collaboration, “Measurement of the differential cross section $d{\sigma}/d(\cos{\theta}t)$ for top quark pair production in ${\Pp}$-${\bar{\Pp}}$ collisions at $\sqrt{s}=1.96\TeV$”,  Phys. Rev. Lett. 111 (2013), no. 18, 182002, 10.1103/PhysRevLett.111.182002, arXiv:1306.2357.
  * [31] 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.
  * [32] D. Atwood, A. Aeppli, and A. Soni, “Extracting anomalous gluon-top effective couplings at the supercolliders”,  Phys. Rev. Lett. 69 (1992) 2754, 10.1103/PhysRevLett.69.2754.
  * [33] D. Atwood, A. Kagan, and T. G. Rizzo, “Constraining anomalous top quark couplings at the Tevatron”,  Phys. Rev. D 52 (1995) 6264, 10.1103/PhysRevD.52.6264, arXiv:hep-ph/9407408.
  * [34] P. Haberl, O. Nachtmann, and A. Wilch, “Top production in hadron-hadron collisions and anomalous top-gluon couplings”,  Phys. Rev. D 53 (1996) 4875, 10.1103/PhysRevD.53.4875, arXiv:hep-ph/9505409.
  * [35] K.-M. Cheung, “Probing the chromoelectric and chromomagnetic dipole moments of the top quark at hadronic colliders”,  Phys. Rev. D 53 (1996) 3604, 10.1103/PhysRevD.53.3604, arXiv:hep-ph/9511260.
  * [36] CMS Collaboration, “Measurements of spin correlations and top quark polarization using dilepton final states in pp collisions at $\sqrt{s}=8$ TeV”,  Phys. Rev. D 93 (2016) 052007, 10.1103/PhysRevD.93.052007, arXiv:1601.01107.
  * [37] J. A. Aguilar-Saavedra and A. Juste, “Collider-independent ${\ttbar}$ forward-backward asymmetries”,  Phys. Rev. Lett. 109 (2012) 211804, 10.1103/PhysRevLett.109.211804, arXiv:1205.1898.
  * [38] 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.
  * [39] S. Frixione, P. Nason, and C. Oleari, “Matching NLO QCD computations with parton shower simulations: the method”,  JHEP 11 (2007) 070, 10.1088/1126-6708/2007/11/070, arXiv:0709.2092.
  * [40] S. Alioli, P. Nason, C. Oleari, and E. Re, “A general framework for implementing NLO calculations in shower Monte Carlo programs: the BOX”,  JHEP 06 (2010) 043, 10.1007/JHEP06(2010)043, arXiv:1002.2581.
  * [41] J. C. Collins and D. E. Soper, “Angular distribution of dileptons in high-energy hadron collisions”,  Phys. Rev. D 16 (1977) 2219, 10.1103/PhysRevD.16.2219.
  * [42] NNPDF Collaboration, “Parton distributions for the LHC Run II”,  JHEP 04 (2015) 040, 10.1007/JHEP04(2015)040, arXiv:1410.8849.
  * [43] CMS Collaboration, “The CMS experiment at the CERN LHC”,  JINST 3 (2008) S08004, 10.1088/1748-0221/3/08/S08004.
  * [44] CMS Collaboration, “The CMS trigger system”,  JINST 12 (2017) P01020, 10.1088/1748-0221/12/01/P01020, arXiv:1609.02366.
  * [45] 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.
  * [46] 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.
  * [47] 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.
  * [48] CMS Collaboration, “Performance of missing transverse momentum reconstruction in proton-proton collisions at $\sqrt{s}=13$ TeV using the CMS detector”,  JINST 14 (2019) P07004, 10.1088/1748-0221/14/07/P07004, arXiv:1903.06078.
  * [49] 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.
  * [50] CMS Collaboration, “Jet performance in pp collisions at $\sqrt{s}=7$ TeV”, CMS Physics Analysis Summary, CMS-PAS-JME-10-003, 2010.
  * [51] M. Dasgupta, A. Fregoso, S. Marzani, and G. P. Salam, “Towards an understanding of jet substructure”,  JHEP 09 (2013) 029, 10.1007/JHEP09(2013)029, arXiv:1307.0007.
  * [52] A. J. Larkoski, S. Marzani, G. Soyez, and J. Thaler, “Soft drop”,  JHEP 05 (2014) 146, 10.1007/JHEP05(2014)146, arXiv:1402.2657.
  * [53] Y. L. Dokshitzer, G. D. Leder, S. Moretti, and B. R. Webber, “Better jet clustering algorithms”,  JHEP 08 (1997) 001, 10.1088/1126-6708/1997/08/001, arXiv:hep-ph/9707323.
  * [54] M. Wobisch and T. Wengler, “Hadronization corrections to jet cross-sections in deep inelastic scattering”, in  Proceedings of the Workshop on Monte Carlo Generators for HERA Physics, Hamburg, Germany, p. 270.  1998\.  arXiv:hep-ph/9907280.
  * [55] CMS Collaboration, “Jet algorithms performance in 13 TeV data”, CMS Physics Analysis Summary, CMS-PAS-JME-16-003, 2017.
  * [56] CMS Collaboration, “CMS luminosity measurements for the 2016 data taking period”, CMS Physics Analysis Summary, CMS-PAS-LUM-17-001, 2017.
  * [57] T. Sjöstrand et al., “An introduction to 8.2”,  Comput. Phys. Commun. 191 (2015) 159, 10.1016/j.cpc.2015.01.024, arXiv:1410.3012.
  * [58] CMS Collaboration, “Investigations of the impact of the parton shower tuning in 8 in the modelling of $\mathrm{t\overline{t}}$ at $\sqrt{s}=8$ and 13 TeV”, CMS Physics Analysis Summary, CMS-PAS-TOP-16-021, 2016.
  * [59] A. Kalogeropoulos and J. Alwall, “The SysCalc code: A tool to derive theoretical systematic uncertainties”, (2018). arXiv:1801.08401.
  * [60] M. Cacciari et al., “The cross-section at 1.8 TeV and 1.96 TeV: A study of the systematics due to parton densities and scale dependence”,  JHEP 04 (2004) 068, 10.1088/1126-6708/2004/04/068, arXiv:hep-ph/0303085.
  * [61] S. Catani, D. de Florian, M. Grazzini, and P. Nason, “Soft gluon resummation for Higgs boson production at hadron colliders”,  JHEP 07 (2003) 028, 10.1088/1126-6708/2003/07/028, arXiv:hep-ph/0306211.
  * [62] S. Alioli, P. Nason, C. Oleari, and E. Re, “NLO single-top production matched with shower in POWHEG: $s$\- and $t$-channel contributions”,  JHEP 09 (2009) 111, 10.1088/1126-6708/2009/09/111, arXiv:0907.4076. [Erratum: 10.1007/JHEP02(2010)011].
  * [63] P. Artoisenet, R. Frederix, O. Mattelaer, and R. Rietkerk, “Automatic spin-entangled decays of heavy resonances in Monte Carlo simulations”,  JHEP 03 (2013) 015, 10.1007/JHEP03(2013)015, arXiv:1212.3460.
  * [64] E. Re, “Single-top Wt-channel production matched with parton showers using the method”,  Eur. Phys. J. C 71 (2011) 1547, 10.1140/epjc/s10052-011-1547-z, arXiv:1009.2450.
  * [65] 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.
  * [66] 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.
  * [67] GEANT4 Collaboration, “—a simulation toolkit”,  Nucl. Instrum. Meth. A 506 (2003) 250, 10.1016/S0168-9002(03)01368-8.
  * [68] P. Kant et al., “HatHor for single top-quark production: updated predictions and uncertainty estimates for single top-quark production in hadronic collisions”,  Comput. Phys. Commun. 191 (2015) 74, 10.1016/j.cpc.2015.02.001, arXiv:1406.4403.
  * [69] N. Kidonakis, “Two-loop soft anomalous dimensions for single top quark associated production with a W or H”,  Phys. Rev. D 82 (2010) 054018, 10.1103/PhysRevD.82.054018, arXiv:1005.4451.
  * [70] R. Gavin, Y. Li, F. Petriello, and S. Quackenbush, “W Physics at the LHC with FEWZ 2.1”,  Comput. Phys. Commun. 184 (2013) 208, 10.1016/j.cpc.2012.09.005, arXiv:1201.5896.
  * [71] R. Gavin, Y. Li, F. Petriello, and S. Quackenbush, “FEWZ 2.0: A code for hadronic Z production at next-to-next-to-leading order”,  Comput. Phys. Commun. 182 (2011) 2388, 10.1016/j.cpc.2011.06.008, arXiv:1011.3540.
  * [72] Y. Li and F. Petriello, “Combining QCD and electroweak corrections to dilepton production in FEWZ”,  Phys. Rev. D 86 (2012) 094034, 10.1103/PhysRevD.86.094034, arXiv:1208.5967.
  * [73] 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.
  * [74] J. Thaler and K. Van Tilburg, “Identifying boosted objects with N-subjettiness”,  JHEP 03 (2011) 015, 10.1007/JHEP03(2011)015, arXiv:1011.2268.
  * [75] 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.
  * [76] J. Erdmann et al., “A likelihood-based reconstruction algorithm for top-quark pairs and the KLFitter framework”,  Nucl. Instrum. Meth. A 748 (2014) 18, 10.1016/j.nima.2014.02.029, arXiv:1312.5595.
  * [77] F. James and M. Roos, “Minuit: a system for function minimization and analysis of the parameter errors and correlations”,  Comput. Phys. Commun. 10 (1975) 343, 10.1016/0010-4655(75)90039-9.
  * [78] CMS Collaboration, “Search for resonant production in lepton+jets events in collisions at $\sqrt{s}=7\TeV$”,  JHEP 12 (2012) 015, 10.1007/JHEP12(2012)015, arXiv:1209.4397.
  * [79] CMS Collaboration, “Measurement of the inelastic proton-proton cross section at $\sqrt{s}=13$ TeV”,  JHEP 07 (2018) 161, 10.1007/JHEP07(2018)161, arXiv:1802.02613.
  * [80] 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.
  * [81] CMS Collaboration, “Search for new physics in top quark production in dilepton final states in proton-proton collisions at $\sqrt{s}$ = 13 TeV”,  Eur. Phys. J. C 79 (2019) 886, 10.1140/epjc/s10052-019-7387-y, arXiv:1903.11144.
  * [82] J. Butterworth et al., “PDF4LHC recommendations for LHC Run II”,  J. Phys. G 43 (2016) 023001, 10.1088/0954-3899/43/2/023001, arXiv:1510.03865.
  * [83] J. Rojo et al., “The PDF4LHC report on PDFs and LHC data: results from run I and preparation for run II”,  J. Phys. G 42 (2015) 103103, 10.1088/0954-3899/42/10/103103, arXiv:1507.00556.
  * [84] A. Accardi et al., “A critical appraisal and evaluation of modern PDFs”,  Eur. Phys. J. C 76 (2016) 471, 10.1140/epjc/s10052-016-4285-4, arXiv:1603.08906.
  * [85] S. Argyropoulos and T. Sjöstrand, “Effects of color reconnection on $\ttbar$ final states at the LHC”,  JHEP 11 (2014) 043, 10.1007/JHEP11(2014)043, arXiv:1407.6653.
  * [86] J. R. Christiansen and P. Z. Skands, “String formation beyond leading colour”,  JHEP 08 (2015) 003, 10.1007/JHEP08(2015)003, arXiv:1505.01681.
  * [87] H. Landsman, “b-quark fragmentation and gluon splitting to b anti-b (LEP)”, in  Proceedings, 11th International Workshop on Deep Inelastic Scattering (DIS 2003): St. Petersburg, Russia, April 23-27, 2003.
  * [88] J. S. Conway, “Incorporating nuisance parameters in likelihoods for multisource spectra”, in  Proceedings, PHYSTAT 2011 Workshop on Statistical Issues Related to Discovery Claims in Search Experiments and Unfolding, CERN, Geneva, Switzerland, January 17-20, 2011.  arXiv:1103.0354.  10.5170/CERN-2011-006.115.
  * [89] J. Neyman, “Outline of a theory of statistical estimation based on the classical theory of probability”,  Philos. Trans. Royal Soc. A 236 (1937) 333, 10.1098/rsta.1937.0005.
  * [90] 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].
  * [91] S. Baker and R. D. Cousins, “Clarification of the use of chi-square and likelihood functions in fits to histograms”,  Nucl. Instrum. Methods Phys. Res 221 (1984) 437, 10.1016/0167-5087(84)90016-4.
  * [92] CMS Collaboration, “Measurement of the top quark polarization and $\mathrm{t\bar{t}}$ spin correlations using dilepton final states in proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  Phys. Rev. D 100 (2019) 072002, 10.1103/PhysRevD.100.072002, arXiv:1907.03729.

##  .10 Appendix 1: Reweighting factors

The five parameter-independent template functions $f_{\text{gn}}(\vec{y})$
defined in Eq. (8) are constructed from fully simulated events at NLO using
the following weights [34],

$\displaystyle w_{\mathrm{g}0}(m_{\ttbar}^{2},c^{*})$ $\displaystyle=1,$ (9)
$\displaystyle w_{\mathrm{g}1}(m_{\ttbar}^{2},c^{*})$
$\displaystyle=\frac{1}{B(c^{*},\beta)\left(1+\varepsilon\beta^{2}c^{*2}\right)},$
$\displaystyle w_{\mathrm{g}2}(m_{\ttbar}^{2},c^{*})$
$\displaystyle=\frac{56}{\left(1-\beta^{2}\right)A(c^{*},\beta)B(c^{*},\beta)\left(1+\varepsilon\beta^{2}c^{*2}\right)},$
$\displaystyle w_{\mathrm{g}3}(m_{\ttbar}^{2},c^{*})$
$\displaystyle=\frac{4}{\left(1-\beta^{2}c^{2}_{*}\right)A(c^{*},\beta)B(c^{*},\beta)\left(1+\varepsilon\beta^{2}c^{*2}\right)},$
$\displaystyle w_{\mathrm{g}4}(m_{\ttbar}^{2},c^{*})$
$\displaystyle=\frac{8}{A(c^{*},\beta)B(c^{*},\beta)\left(1+\varepsilon\beta^{2}c^{*2}\right)}\left[\frac{1}{1-\beta^{2}c^{2}_{*}}+\frac{1}{1-\beta^{2}}+\frac{4(1-\beta^{2}c^{2}_{*})}{(1-\beta^{2})^{2}}\right],$

where the $\varepsilon$ function is determined from fits to NLO generator
level data in 10 bins of $\beta$ and the functions $A$ and $B$ are defined as

$\displaystyle A(c^{*},\beta)=\frac{7+9\beta^{2}c^{*2}}{1-\beta^{2}c^{*2}},$
(10) $\displaystyle
B(c^{*},\beta)=\frac{1-\beta^{4}c^{*4}+2\beta^{2}(1-\beta^{2})(1-c^{*2})}{2(1-\beta^{2}c^{*2})}.$

The three parameter-independent template functions $f_{\text{qn}}(\vec{y})$
defined in Eq. (8) are constructed from fully simulated initiated events at
NLO using the following weights,

$\displaystyle w_{\mathrm{q}0}(m_{\ttbar}^{2},c^{*})$ $\displaystyle=1,$ (11)
$\displaystyle w_{\mathrm{q}1}(m_{\ttbar}^{2},c^{*})$
$\displaystyle=\frac{4}{1+\beta^{2}c^{*2}+\left(1-\beta^{2}\right)+\alpha\left(1-\beta^{2}c^{*2}\right)},$
$\displaystyle w_{\mathrm{q}2}(m_{\ttbar}^{2},c^{*})$
$\displaystyle=\frac{4}{1+\beta^{2}c^{*2}+\left(1-\beta^{2}\right)+\alpha\left(1-\beta^{2}c^{*2}\right)}\frac{1-\beta^{2}c^{*2}}{1-\beta^{2}}.$

The distribution functions are normalized as follows,

$\displaystyle\sum_{Q}\int\rd x_{\mathrm{r}}\rd m_{\mathrm{r}}\rd
c^{*}_{\mathrm{r}}f_{\text{bk}}(x_{\mathrm{r}},m_{\mathrm{r}},c^{*}_{\mathrm{r}},Q)=1,$
(12) $\displaystyle\sum_{Q}\int\rd x_{\mathrm{r}}\rd m_{\mathrm{r}}\rd
c^{*}_{\mathrm{r}}f_{\mathrm{g}0}(x_{\mathrm{r}},m_{\mathrm{r}},c^{*}_{\mathrm{r}},Q)=1,$
$\displaystyle\sum_{Q}\int\rd x_{\mathrm{r}}\rd m_{\mathrm{r}}\rd
c^{*}_{\mathrm{r}}f_{\mathrm{q}0}(x_{\mathrm{r}},m_{\mathrm{r}},c^{*}_{\mathrm{r}},Q)=1,$

and the functions $F_{\text{gn}}$ and $F_{\text{qn}}$ are the integrals of the
reweighted functions $f_{\text{gn}}$ and $f_{\text{qn}}$ for $\text{n}>0$,

$\displaystyle F_{\text{gn}}=\sum_{Q}\int\rd x_{\mathrm{r}}\rd
m_{\mathrm{r}}\rd
c^{*}_{\mathrm{r}}f_{\text{gn}}(x_{\mathrm{r}},m_{\mathrm{r}},c^{*}_{\mathrm{r}},Q),$
(13) $\displaystyle F_{\text{qn}}=\sum_{Q}\int\rd x_{\mathrm{r}}\rd
m_{\mathrm{r}}\rd
c^{*}_{\mathrm{r}}f_{\text{qn}}(x_{\mathrm{r}},m_{\mathrm{r}},c^{*}_{\mathrm{r}},Q).$

These are then used to define the normalization factors in Eq. (8),

$\displaystyle F_{{\Pg}{\Pg}}(\hat{\mu}_{\PQt},\hat{d}_{\PQt})=$
$\displaystyle
1+\hat{\mu}_{\PQt}(1+\hat{\mu}_{\PQt})F_{\mathrm{g}1}+(\hat{\mu}_{\PQt}^{2}+\hat{d}_{\PQt}^{2})(1+\hat{\mu}_{\PQt})F_{\mathrm{g}2}$
(14)
$\displaystyle+(\hat{\mu}_{\PQt}^{2}+\hat{d}_{\PQt}^{2})(1-5\hat{\mu}_{\PQt})F_{\mathrm{g}3}+(\hat{\mu}_{\PQt}^{2}+\hat{d}_{\PQt}^{2})^{2}F_{\mathrm{g}4},$
$\displaystyle F_{\qqbar}(\hat{\mu}_{\PQt},\hat{d}_{\PQt})=$ $\displaystyle
1+\left(2\hat{\mu}_{\PQt}+\hat{\mu}_{\PQt}^{2}-\hat{d}_{\PQt}^{2}\right)F_{\mathrm{q}1}+\left(\hat{\mu}_{\PQt}^{2}+\hat{d}_{\PQt}^{2}\right)F_{\mathrm{q}2}.$

##  .11 Appendix 2: Template binning

Template binning in the type-1 +jets signal regions (each template has 12
total bins)  $\abs{x_{\mathrm{r}}}$ bin range $m_{\mathrm{r}}$ bin edges ()
$c^{*}_{\mathrm{r}}$ bin edges 0.00 $\to$ 0.10 350 $\to$ 5550 $-1.00$,
$-0.50$, $-0.25$, 0.00, 0.25, 0.50, 1.00 0.10 $\to$ 0.24 350, 1100, 5550 $-1$,
0, 1 0.24 $\to$ 1.00 350 $\to$ 5550 $-1$, 0, 1

Template binning in the type-1 +jets signal regions (each template has 8 total
bins)  $\abs{x_{\mathrm{r}}}$ bin range $m_{\mathrm{r}}$ bin edges ()
$c^{*}_{\mathrm{r}}$ bin edges 0.00 $\to$ 0.10 350 $\to$ 5550 4 even-width
bins 0.10 $\to$ 0.24 350 $\to$ 5550 $-1$, 0, 1 0.24 $\to$ 1.00 350 $\to$ 5550
$-1$, 0, 1

Template binning in the type-2 +jets signal regions (each template has 46
total bins)  $\abs{x_{\mathrm{r}}}$ bin range $m_{\mathrm{r}}$ bin edges ()
$c^{*}_{\mathrm{r}}$ bin edges 0.00 $\to$ 0.04 350, 730, 860, 5650 $-1.00$,
$-0.50$, $-0.25$, 0.00, 0.25, 0.50, 1.00 0.04 $\to$ 0.09 350, 730, 5650
$-1.00$, $-0.50$, $-0.25$, 0.00, 0.25, 0.50, 1.00 0.09 $\to$ 0.15 350 $\to$
5650 $-1.00$, $-0.50$, $-0.25$, 0.00, 0.25, 0.50, 1.00 0.15 $\to$ 0.24 350,
730, 860, 5650 $-1$, 0, 1 0.24 $\to$ 1.00 350, 730, 5650 $-1$, 0, 1

Template binning in the type-2 +jets signal regions (each template has 6 total
bins)  $\abs{x_{\mathrm{r}}}$ bin range $m_{\mathrm{r}}$ bin edges ()
$c^{*}_{\mathrm{r}}$ bin edges 0.00 $\to$ 0.15 350 $\to$ 5650 4 even-width
bins 0.15 $\to$ 1.00 350 $\to$ 5650 $-1$, 0, 1

Template binning in the type-3 +jets signal regions (each template has 222
total bins)  $\abs{x_{\mathrm{r}}}$ bin range $m_{\mathrm{r}}$ bin edges ()
$c^{*}_{\mathrm{r}}$ bin edges 0.000 $\to$ 0.020 350, 425, 500, 2650 20 even-
width bins 0.020 $\to$ 0.040 350, 400, 450, 500, 600, 2650 10 even-width bins
0.040 $\to$ 0.060 350, 475, 2650 16 even-width bins 0.060 $\to$ 0.080 350,
475, 2650 16 even-width bins 0.080 $\to$ 0.100 350, 475, 2650 $-1.0$, $-0.6$,
$-0.4$, $-0.2$, 0.0, 0.2, 0.4, 0.6, 1.0 0.100 $\to$ 0.115 350, 475, 2650 4
even-width bins 0.115 $\to$ 0.130 350, 475, 2650 4 even-width bins 0.130 $\to$
0.150 350 $\to$ 2650 $-1.00$, $-0.50$, $-0.25$, 0.00, 0.25, 0.50, 1.00 0.150
$\to$ 0.180 350 $\to$ 2650 $-1.00$, $-0.50$, $-0.25$, 0.00, 0.25, 0.50, 1.00
0.180 $\to$ 1.000 350 $\to$ 2650 4 even-width bins

Template binning in the type-3 +jets signal regions (each template has 164
total bins)  $\abs{x_{\mathrm{r}}}$ bin range $m_{\mathrm{r}}$ bin edges ()
$c^{*}_{\mathrm{r}}$ bin edges 0.000 $\to$ 0.025 350, 400, 450, 500, 600, 2650
10 even-width bins 0.025 $\to$ 0.050 350, 475, 2650 20 even-width bins 0.050
$\to$ 0.075 350, 475, 2650 16 even-width bins 0.075 $\to$ 0.100 350, 420, 475,
550, 2650 $-1.00$, $-0.50$, $-0.25$, 0.00, 0.25, 0.50, 1.00 0.100 $\to$ 0.125
350, 475, 2650 4 even-width bins 0.125 $\to$ 0.155 350 $\to$ 2650 $-1.00$,
$-0.50$, $-0.25$, 0.00, 0.25, 0.50, 1.00 0.155 $\to$ 1.000 350 $\to$ 2650 4
even-width bins

##  .12 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, M. Flechl, R. Frühwirth1, M. Jeitler1, N. Krammer, I. Krätschmer, D.
Liko, T. Madlener, I. Mikulec, N. Rad, J. Schieck1, R. Schöfbeck, M. Spanring,
W. Waltenberger, C.-E. Wulz1, M. Zarucki Institute for Nuclear Problems,
Minsk, Belarus
V. Drugakov, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
M.R. Darwish, E.A. De Wolf, D. Di Croce, X. Janssen, A. Lelek, M. Pieters, H.
Rejeb Sfar, H. Van Haevermaet, P. Van Mechelen, S. Van Putte, N. Van Remortel
Vrije Universiteit Brussel, Brussel, Belgium
F. Blekman, E.S. Bols, S.S. Chhibra, J. D’Hondt, J. De Clercq, D. Lontkovskyi,
S. Lowette, I. Marchesini, S. Moortgat, Q. Python, S. Tavernier, W. Van
Doninck, P. Van Mulders Université Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, B. Clerbaux, G. De Lentdecker, H. Delannoy, B. Dorney, L.
Favart, A. Grebenyuk, A.K. Kalsi, L. Moureaux, A. Popov, N. Postiau, E.
Starling, L. Thomas, C. Vander Velde, P. Vanlaer, D. Vannerom Ghent
University, Ghent, Belgium
T. Cornelis, D. Dobur, I. Khvastunov2, M. Niedziela, C. Roskas, K. Skovpen, M.
Tytgat, W. Verbeke, B. Vermassen, M. Vit Université Catholique de Louvain,
Louvain-la-Neuve, Belgium
O. Bondu, G. Bruno, C. Caputo, P. David, C. Delaere, M. Delcourt, A.
Giammanco, V. Lemaitre, J. Prisciandaro, A. Saggio, M. Vidal Marono, P.
Vischia, 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
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, C. De Oliveira Martins, S.
Fonseca De Souza, L.M. Huertas Guativa, H. Malbouisson, J. Martins5, D. Matos
Figueiredo, M. Medina Jaime6, M. Melo De Almeida, C. Mora Herrera, L. Mundim,
H. Nogima, W.L. Prado Da Silva, P. Rebello Teles, L.J. Sanchez Rosas, A.
Santoro, A. Sznajder, M. Thiel, E.J. Tonelli Manganote3, F. Torres Da Silva De
Araujo, A. Vilela Pereira Universidade Estadual Paulista a, Universidade
Federal do ABC b, São Paulo, Brazil
C.A. Bernardesa, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M. Gregoresb,
D.S. Lemos, P.G. Mercadanteb, S.F. Novaesa, SandraS. Padulaa Institute for
Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia,
Bulgaria
A. Aleksandrov, G. Antchev, R. Hadjiiska, P. Iaydjiev, 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. Fang7, X. Gao7, L. Yuan Department of Physics, Tsinghua University,
Beijing, China
M. Ahmad, Z. Hu, Y. Wang Institute of High Energy Physics, Beijing, China
G.M. Chen8, H.S. Chen8, M. Chen, C.H. Jiang, D. Leggat, H. Liao, Z. Liu, A.
Spiezia, J. Tao, E. Yazgan, H. Zhang, S. Zhang8, J. Zhao State Key Laboratory
of Nuclear Physics and Technology, Peking University, Beijing, China
A. Agapitos, Y. Ban, G. Chen, A. Levin, J. Li, L. Li, Q. Li, Y. Mao, S.J.
Qian, D. Wang, Q. Wang Zhejiang University, Hangzhou, China
M. Xiao Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C. Florez, C.F. González Hernández, M.A. Segura Delgado
Universidad de Antioquia, Medellin, Colombia
J. Mejia Guisao, J.D. Ruiz Alvarez, C.A. Salazar González, N. Vanegas Arbelaez
University of Split, Faculty of Electrical Engineering, Mechanical Engineering
and Naval Architecture, Split, Croatia
D. Giljanović, N. Godinovic, D. Lelas, I. Puljak, T. Sculac University of
Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, K. Kadija, B. Mesic, M. Roguljic, A. Starodumov9,
T. Susa University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, E. Erodotou, A. Ioannou, M. Kolosova, S. Konstantinou,
G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H.
Rykaczewski, H. Saka, D. Tsiakkouri Charles University, Prague, Czech Republic
M. Finger10, M. Finger Jr.10, A. Kveton, J. Tomsa Escuela Politecnica
Nacional, Quito, Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
A.A. Abdelalim11,12, S. Abu Zeid National Institute of Chemical Physics and
Biophysics, Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, M. Raidal, C. Veelken Department of Physics, University of Helsinki,
Helsinki, Finland
P. Eerola, L. Forthomme, H. Kirschenmann, K. Osterberg, M. Voutilainen
Helsinki Institute of Physics, Helsinki, Finland
F. Garcia, J. Havukainen, J.K. Heikkilä, 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
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, B. Fabbro, J.L. Faure, F.
Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
C. Leloup, B. Lenzi, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin,
A. Savoy-Navarro13, M. Titov, G.B. Yu Laboratoire Leprince-Ringuet,
CNRS/IN2P3, Ecole Polytechnique, Institut Polytechnique de Paris
S. Ahuja, C. Amendola, F. Beaudette, P. Busson, C. Charlot, B. Diab, G.
Falmagne, R. Granier de Cassagnac, I. Kucher, A. Lobanov, C. Martin Perez, M.
Nguyen, C. Ochando, P. Paganini, J. Rembser, R. Salerno, J.B. Sauvan, Y.
Sirois, A. Zabi, A. Zghiche Université de Strasbourg, CNRS, IPHC UMR 7178,
Strasbourg, France
J.-L. Agram14, J. Andrea, D. Bloch, G. Bourgatte, J.-M. Brom, E.C. Chabert, C.
Collard, E. Conte14, J.-C. Fontaine14, D. Gelé, U. Goerlach, M. Jansová, A.-C.
Le Bihan, N. Tonon, P. Van Hove Centre de Calcul de l’Institut National de
Physique Nucleaire et de Physique des Particules, CNRS/IN2P3, Villeurbanne,
France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, C. Camen, A. Carle, N. Chanon, R.
Chierici, D. Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M.
Gouzevitch, B. Ille, Sa. Jain, I.B. Laktineh, H. Lattaud, A. Lesauvage, M.
Lethuillier, L. Mirabito, S. Perries, V. Sordini, L. Torterotot, G. Touquet,
M. Vander Donckt, S. Viret Georgian Technical University, Tbilisi, Georgia
T. Toriashvili15 Tbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze10 RWTH Aachen University, I. Physikalisches Institut, Aachen,
Germany
C. Autermann, 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
M. Erdmann, 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
G. Flügge, W. Haj Ahmad16, O. Hlushchenko, T. Kress, T. Müller, A. Nowack, C.
Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl17 Deutsches Elektronen-
Synchrotron, Hamburg, Germany
M. Aldaya Martin, P. Asmuss, I. Babounikau, H. Bakhshiansohi, K. Beernaert, O.
Behnke, A. Bermúdez Martínez, A.A. Bin Anuar, K. Borras18, V. Botta, A.
Campbell, A. Cardini, P. Connor, S. Consuegra Rodríguez, C. Contreras-Campana,
V. Danilov, A. De Wit, M.M. Defranchis, C. Diez Pardos, D. Domínguez Damiani,
G. Eckerlin, D. Eckstein, T. Eichhorn, A. Elwood, E. Eren, E. Gallo19, A.
Geiser, A. Grohsjean, M. Guthoff, M. Haranko, A. Harb, A. Jafari, N.Z.
Jomhari, H. Jung, A. Kasem18, M. Kasemann, H. Kaveh, J. Keaveney, C.
Kleinwort, J. Knolle, D. Krücker, W. Lange, T. Lenz, J. Lidrych, K. Lipka, W.
Lohmann20, R. Mankel, I.-A. Melzer-Pellmann, A.B. Meyer, M. Meyer, M.
Missiroli, J. Mnich, A. Mussgiller, V. Myronenko, D. Pérez Adán, S.K.
Pflitsch, D. Pitzl, A. Raspereza, A. Saibel, M. Savitskyi, V. Scheurer, P.
Schütze, C. Schwanenberger, R. Shevchenko, A. Singh, R.E. Sosa Ricardo, H.
Tholen, O. Turkot, A. Vagnerini, M. Van De Klundert, R. Walsh, Y. Wen, K.
Wichmann, C. Wissing, O. Zenaiev, R. Zlebcik University of Hamburg, Hamburg,
Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, T. Dreyer, A. Ebrahimi, F.
Feindt, A. Fröhlich, C. Garbers, E. Garutti, D. Gonzalez, P. Gunnellini, J.
Haller, A. Hinzmann, A. Karavdina, G. Kasieczka, R. Klanner, R. Kogler, N.
Kovalchuk, S. Kurz, V. Kutzner, J. Lange, T. Lange, A. Malara, J. Multhaup,
C.E.N. Niemeyer, A. Reimers, O. Rieger, P. Schleper, S. Schumann, J. Schwandt,
J. Sonneveld, H. Stadie, G. Steinbrück, B. Vormwald, I. Zoi Karlsruher
Institut fuer Technologie, Karlsruhe, Germany
M. Akbiyik, M. Baselga, S. Baur, T. Berger, E. Butz, R. Caspart, T. Chwalek,
W. De Boer, A. Dierlamm, K. El Morabit, N. Faltermann, M. Giffels, A.
Gottmann, M.A. Harrendorf, F. Hartmann17, C. Heidecker, U. Husemann, S.
Kudella, S. Maier, S. Mitra, M.U. Mozer, D. Müller, Th. Müller, M. Musich, A.
Nürnberg, G. Quast, K. Rabbertz, D. Schäfer, M. Schröder, I. Shvetsov, H.J.
Simonis, R. Ulrich, M. Wassmer, M. Weber, C. Wöhrmann, 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 National and Kapodistrian University of Athens, Athens, Greece
M. Diamantopoulou, G. Karathanasis, P. Kontaxakis, A. Manousakis-katsikakis,
A. Panagiotou, I. Papavergou, N. Saoulidou, A. Stakia, K. Theofilatos, K.
Vellidis, E. Vourliotis National Technical University of Athens, Athens,
Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis University of
Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, K.
Manitara, N. Manthos, I. Papadopoulos, J. Strologas, F.A. Triantis, D.
Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
M. Bartók21, R. Chudasama, M. Csanad, 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. Horvath22, F. Sikler, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi21, J. Molnar, Z. Szillasi Institute of
Physics, University of Debrecen, Debrecen, Hungary
P. Raics, D. Teyssier, Z.L. Trocsanyi, B. Ujvari Eszterhazy Karoly University,
Karoly Robert Campus, Gyongyos, Hungary
T. Csorgo, W.J. Metzger, F. Nemes, T. Novak Indian Institute of Science
(IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri, P.C. Tiwari National Institute of Science
Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati24, C. Kar, G. Kole, P. Mal, V.K. Muraleedharan Nair Bindhu, A.
Nayak25, D.K. Sahoo24, S.K. Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, N. Dhingra26, R. Gupta, A.
Kaur, M. Kaur, S. Kaur, P. Kumari, M. Lohan, M. Meena, K. Sandeep, S. Sharma,
J.B. Singh, A.K. Virdi, G. Walia University of Delhi, Delhi, India
A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, Ashok Kumar, M.
Naimuddin, P. Priyanka, K. Ranjan, Aashaq Shah, R. Sharma Saha Institute of
Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj27, M. Bharti27, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep27,
D. Bhowmik, S. Dutta, S. Ghosh, B. Gomber28, M. Maity29, K. Mondal, S. Nandan,
A. Purohit, P.K. Rout, G. Saha, S. Sarkar, T. Sarkar29, M. Sharan, B. Singh27,
S. Thakur27 Indian Institute of Technology Madras, Madras, India
P.K. Behera, P. Kalbhor, A. Muhammad, P.R. Pujahari, A. Sharma, A.K. Sikdar
Bhabha Atomic Research Centre, Mumbai, India
D. Dutta, V. Jha, D.K. Mishra, P.K. Netrakanti, L.M. Pant, P. Shukla Tata
Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, G.B. Mohanty, N. Sur, RavindraKumar Verma Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, S. Karmakar,
S. Kumar, G. Majumder, K. Mazumdar, N. Sahoo, S. Sawant Indian Institute of
Science Education and Research (IISER), Pune, India
S. Dube, B. Kansal, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, A. Rastogi, S.
Sharma Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
S. Chenarani, S.M. Etesami, M. Khakzad, M. Mohammadi Najafabadi, M. Naseri, F.
Rezaei Hosseinabadi University College Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, R. Alya,b,30, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L.
Cristellaa,b, N. De Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, W.
Elmetenaweea,b, L. Fiorea, A. Gelmia,b, G. Iasellia,c, M. Incea,b, S.
Lezkia,b, G. Maggia,c, M. Maggia, J.A. Merlina, G. Minielloa,b, S. Mya,b, S.
Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, R. Radognaa, 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, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
C. Cioccaa, G. Codispotia,b, M. Cuffiania,b, G.M. Dallavallea, F. Fabbria, A.
Fanfania,b, E. Fontanesia,b, P. Giacomellia, C. Grandia, L. Guiduccia,b, F.
Iemmia,b, S. Lo Meoa,31, S. Marcellinia, G. Masettia, F.L. Navarriaa,b, A.
Perrottaa, F. Primaveraa,b, A.M. Rossia,b, T. Rovellia,b, G.P. Sirolia,b, N.
Tosia INFN Sezione di Catania a, Università di Catania b, Catania, Italy
S. Albergoa,b,32, S. Costaa,b, A. Di Mattiaa, R. Potenzaa,b, A. Tricomia,b,32,
C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b, Firenze, Italy
G. Barbaglia, A. Cassese, R. Ceccarelli, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, F. Fioria,c, E. Focardia,b, G. Latinoa,b, P. Lenzia,b, M.
Meschinia, S. Paolettia, 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, V. Cirioloa,b,17, 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, D.
Zuoloa,b INFN Sezione di Napoli a, Università di Napoli ’Federico II’ b,
Napoli, Italy, Università della Basilicata c, Potenza, Italy, Università G.
Marconi d, Roma, Italy
S. Buontempoa, N. Cavalloa,c, A. De Iorioa,b, A. Di Crescenzoa,b, F.
Fabozzia,c, F. Fiengaa, G. Galatia, A.O.M. Iorioa,b, L. Layera,b, L. Listaa,b,
S. Meolaa,d,17, P. Paoluccia,17, B. Rossia, C. Sciaccaa,b, E. Voevodinaa,b
INFN Sezione di Padova a, Università di Padova b, Padova, Italy, Università di
Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, D. Biselloa,b, A. Bolettia,b, A. Bragagnoloa,b, R.
Carlina,b, P. Checchiaa, P. De Castro Manzanoa, T. Dorigoa, U. Dossellia, F.
Gasparinia,b, U. Gasparinia,b, A. Gozzelinoa, S.Y. Hoha,b, M. Margonia,b, A.T.
Meneguzzoa,b, J. Pazzinia,b, M. Presillab, P. Ronchesea,b, R. Rossina,b, F.
Simonettoa,b, A. Tikoa, M. Tosia,b, M. Zanettia,b, P. Zottoa,b, G. Zumerlea,b
INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
A. Braghieria, 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, R.
Leonardia,b, E. Manonia, G. Mantovania,b, V. Mariania,b, M. Menichellia, A.
Rossia,b, A. Santocchiaa,b, D. Spigaa INFN Sezione di Pisa a, Università di
Pisa b, Scuola Normale Superiore di Pisa c, Pisa, Italy
K. Androsova, P. Azzurria, G. Bagliesia, V. Bertacchia,c, L. Bianchinia, T.
Boccalia, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, S. Donatoa, L.
Gianninia,c, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c, G.
Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, G. Rolandi33, S. Roy
Chowdhury, A. Scribanoa, P. Spagnoloa, R. Tenchinia, G. Tonellia,b, N. Turini,
A. Venturia, P.G. Verdinia INFN Sezione di Roma a, Sapienza Università di Roma
b, Rome, Italy
F. Cavallaria, M. Cipriania,b, D. Del Rea,b, E. Di Marcoa, 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
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. Bellora, C. Biinoa, A. Cappatia,b, N. Cartigliaa, S.
Comettia, M. Costaa,b, R. Covarellia,b, N. Demariaa, B. Kiania,b, F. Legger,
C. Mariottia, S. Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M.
Montenoa, M.M. Obertinoa,b, G. Ortonaa,b, L. Pachera,b, N. Pastronea, M.
Pelliccionia, G.L. Pinna Angionia,b, A. Romeroa,b, M. Ruspaa,c, R.
Salvaticoa,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, A. Zanettia Kyungpook National University,
Daegu, Korea
B. Kim, D.H. Kim, G.N. Kim, J. Lee, S.W. Lee, C.S. Moon, Y.D. Oh, S.I. Pak, S.
Sekmen, D.C. Son, Y.C. Yang Chonnam National University, Institute for
Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon, G. Oh Hanyang University, Seoul, Korea
B. Francois, T.J. Kim, J. Park Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, S. Ha, B. Hong, K. Lee, K.S. Lee, J. Lim, J. Park,
S.K. Park, Y. Roh, J. Yoo Kyung Hee University, Department of Physics
J. Goh Sejong University, Seoul, Korea
H.S. Kim Seoul National University, Seoul, Korea
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, H. Lee, K. Lee, S.
Lee, K. Nam, M. Oh, S.B. Oh, B.C. Radburn-Smith, U.K. Yang, H.D. Yoo, I. Yoon
University of Seoul, Seoul, Korea
D. Jeon, J.H. Kim, J.S.H. Lee, I.C. Park, I.J Watson Sungkyunkwan University,
Suwon, Korea
Y. Choi, C. Hwang, Y. Jeong, J. Lee, Y. Lee, I. Yu Riga Technical University,
Riga, Latvia
V. Veckalns34 Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, A. Rinkevicius, G. Tamulaitis, J. Vaitkus National
Centre for Particle Physics, Universiti Malaya, Kuala Lumpur, Malaysia
Z.A. Ibrahim, F. Mohamad Idris35, W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli
Universidad de Sonora (UNISON), Hermosillo, Mexico
J.F. Benitez, A. Castaneda Hernandez, J.A. Murillo Quijada, L. Valencia Palomo
Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz36, R. Lopez-
Fernandez, A. Sanchez-Hernandez Universidad Iberoamericana, Mexico City,
Mexico
S. Carrillo Moreno, C. Oropeza Barrera, M. Ramirez-Garcia, F. Vazquez Valencia
Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Montenegro, Podgorica, Montenegro
J. Mijuskovic2, N. Raicevic University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler, P. Lujan National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M. Ahmad, M.I.M. Awan, Q. Hassan, H.R. Hoorani, W.A. Khan, M.A.
Shah, M. Shoaib, M. Waqas AGH University of Science and Technology Faculty of
Computer Science, Electronics and Telecommunications, Krakow, Poland
V. Avati, L. Grzanka, M. Malawski National Centre for Nuclear Research,
Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, M. Górski, M. Kazana, M. Szleper, P.
Zalewski Institute of Experimental Physics, Faculty of Physics, University of
Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk37, 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, A. Di Francesco, P. Faccioli, B. Galinhas,
M. Gallinaro, J. Hollar, N. Leonardo, T. Niknejad, J. Seixas, K. Shchelina, G.
Strong, O. Toldaiev, J. Varela Joint Institute for Nuclear Research, Dubna,
Russia
S. Afanasiev, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov, A. Kamenev,
V. Karjavine, A. Lanev, A. Malakhov, V. Matveev38,39, P. Moisenz, V. Palichik,
V. Perelygin, M. Savina, S. Shmatov, S. Shulha, N. Skatchkov, V. Smirnov, N.
Voytishin, A. Zarubin Petersburg Nuclear Physics Institute, Gatchina (St.
Petersburg), Russia
L. Chtchipounov, V. Golovtcov, Y. Ivanov, V. Kim40, E. Kuznetsova41, P.
Levchenko, V. Murzin, V. Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L.
Uvarov, A. Vorobyev Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, D. Tlisov, A. Toropin Institute for Theoretical
and Experimental Physics named by A.I. Alikhanov of NRC ‘Kurchatov Institute’,
Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, A. Nikitenko42, V. Popov, I.
Pozdnyakov, G. Safronov, A. Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A.
Zhokin Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
R. Chistov43, M. Danilov43, D. Philippov, S. Polikarpov43, E. Tarkovskii P.N.
Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin, M. Kirakosyan, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia
A. Baskakov, A. Belyaev, E. Boos, V. Bunichev, M. Dubinin44, L. Dudko, V.
Klyukhin, N. Korneeva, I. Lokhtin, S. Obraztsov, M. Perfilov, V. Savrin, P.
Volkov Novosibirsk State University (NSU), Novosibirsk, Russia
A. Barnyakov45, V. Blinov45, T. Dimova45, L. Kardapoltsev45, Y. Skovpen45
Institute for High Energy Physics of National Research Centre ‘Kurchatov
Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, V. Kachanov, D. Konstantinov, P.
Mandrik, V. Petrov, R. Ryutin, S. Slabospitskii, A. Sobol, S. Troshin, N.
Tyurin, A. Uzunian, A. Volkov National Research Tomsk Polytechnic University,
Tomsk, Russia
A. Babaev, A. Iuzhakov, V. Okhotnikov Tomsk State University, Tomsk, Russia
V. Borchsh, V. Ivanchenko, E. Tcherniaev University of Belgrade: Faculty of
Physics and VINCA Institute of Nuclear Sciences
P. Adzic46, P. Cirkovic, M. Dordevic, P. Milenovic, J. Milosevic, M.
Stojanovic Centro de Investigaciones Energéticas Medioambientales y
Tecnológicas (CIEMAT), Madrid, Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M.
Barrio Luna, CristinaF. 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, O. Gonzalez Lopez, S. Goy Lopez,
J.M. Hernandez, M.I. Josa, D. Moran, Á. Navarro Tobar, A. Pérez-Calero
Yzquierdo, J. Puerta Pelayo, I. Redondo, L. Romero, S. Sánchez Navas, M.S.
Soares, A. Triossi, C. Willmott Universidad Autónoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz, 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, J.R. González Fernández, E. Palencia Cortezon, V.
Rodríguez Bouza, S. Sanchez Cruz Instituto de Física de Cantabria (IFCA),
CSIC-Universidad de Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, J. Duarte Campderros, M.
Fernandez, P.J. Fernández Manteca, A. García Alonso, G. Gomez, C. Martinez
Rivero, P. Martinez Ruiz del Arbol, F. Matorras, J. Piedra Gomez, C. Prieels,
T. Rodrigo, A. Ruiz-Jimeno, L. Russo47, L. Scodellaro, I. Vila, J.M. Vizan
Garcia University of Colombo, Colombo, Sri Lanka
K. Malagalage University of Ruhuna, Department of Physics, Matara, Sri Lanka
W.G.D. Dharmaratna, N. Wickramage CERN, European Organization for Nuclear
Research, Geneva, Switzerland
D. Abbaneo, B. Akgun, E. Auffray, G. Auzinger, J. Baechler, P. Baillon, A.H.
Ball, D. Barney, J. Bendavid, M. Bianco, A. Bocci, P. Bortignon, E. Bossini,
E. Brondolin, T. Camporesi, A. Caratelli, G. Cerminara, E. Chapon, G.
Cucciati, D. d’Enterria, A. Dabrowski, N. Daci, V. Daponte, A. David, O.
Davignon, A. De Roeck, M. Deile, R. Di Maria, M. Dobson, M. Dünser, N. Dupont,
A. Elliott-Peisert, N. Emriskova, F. Fallavollita48, D. Fasanella, S.
Fiorendi, G. Franzoni, J. Fulcher, W. Funk, S. Giani, D. Gigi, K. Gill, F.
Glege, L. Gouskos, M. Gruchala, M. Guilbaud, D. Gulhan, J. Hegeman, C.
Heidegger, Y. Iiyama, V. Innocente, T. James, P. Janot, O. Karacheban20, J.
Kaspar, J. Kieseler, M. Krammer1, N. Kratochwil, C. Lange, P. Lecoq, C.
Lourenço, L. Malgeri, M. Mannelli, A. Massironi, F. Meijers, S. Mersi, E.
Meschi, F. Moortgat, M. Mulders, J. Ngadiuba, J. Niedziela, S. Nourbakhsh, S.
Orfanelli, L. Orsini, F. Pantaleo17, L. Pape, E. Perez, M. Peruzzi, A.
Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, F.M. Pitters, D. Rabady, A.
Racz, M. Rieger, M. Rovere, H. Sakulin, J. Salfeld-Nebgen, S. Scarfi, C.
Schäfer, C. Schwick, M. Selvaggi, A. Sharma, P. Silva, W. Snoeys, P.
Sphicas49, J. Steggemann, S. Summers, V.R. Tavolaro, D. Treille, A. Tsirou,
G.P. Van Onsem, A. Vartak, M. Verzetti, W.D. Zeuner Paul Scherrer Institut,
Villigen, Switzerland
L. Caminada50, K. Deiters, 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, N. Chernyavskaya, G. Dissertori, M. Dittmar, M.
Donegà, C. Dorfer, T.A. Gómez Espinosa, C. Grab, D. Hits, W. Lustermann, R.A.
Manzoni, M.T. Meinhard, F. Micheli, P. Musella, F. Nessi-Tedaldi, F. Pauss, 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, L.
Shchutska, M.L. Vesterbacka Olsson, R. Wallny, D.H. Zhu Universität Zürich,
Zurich, Switzerland
T.K. Aarrestad, C. Amsler51, C. Botta, D. Brzhechko, M.F. Canelli, A. De Cosa,
R. Del Burgo, B. Kilminster, S. Leontsinis, V.M. Mikuni, I. Neutelings, G.
Rauco, P. Robmann, K. Schweiger, C. Seitz, Y. Takahashi, S. Wertz, A.
Zucchetta National Central University, Chung-Li, Taiwan
C.M. Kuo, W. Lin, A. Roy, S.S. Yu National Taiwan University (NTU), Taipei,
Taiwan
P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.y. Li, R.-S. Lu, E.
Paganis, A. Psallidas, A. Steen Chulalongkorn University, Faculty of Science,
Department of Physics, Bangkok, Thailand
B. Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas, N. Suwonjandee Çukurova
University, Physics Department, Science and Art Faculty, Adana, Turkey
A. Bat, F. Boran, A. Celik52, S. Damarseckin53, Z.S. Demiroglu, F. Dolek, C.
Dozen54, I. Dumanoglu, G. Gokbulut, EmineGurpinar Guler55, Y. Guler, I. Hos56,
C. Isik, E.E. Kangal57, O. Kara, A. Kayis Topaksu, U. Kiminsu, G. Onengut, K.
Ozdemir58, S. Ozturk59, A.E. Simsek, U.G. Tok, S. Turkcapar, I.S. Zorbakir, C.
Zorbilmez Middle East Technical University, Physics Department, Ankara, Turkey
B. Isildak60, G. Karapinar61, M. Yalvac Bogazici University, Istanbul, Turkey
I.O. Atakisi, E. Gülmez, M. Kaya62, O. Kaya63, Ö. Özçelik, S. Tekten, E.A.
Yetkin64 Istanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak65, Y. Komurcu, S. Sen66 Istanbul University, Istanbul,
Turkey
S. Cerci67, B. Kaynak, S. Ozkorucuklu, D. Sunar Cerci67 Institute for
Scintillation Materials of National Academy of Science of Ukraine, Kharkov,
Ukraine
B. Grynyov National Scientific Center, Kharkov Institute of Physics and
Technology, Kharkov, Ukraine
L. Levchuk University of Bristol, Bristol, United Kingdom
E. Bhal, S. Bologna, J.J. Brooke, D. Burns68, 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. Belyaev69, C. Brew, R.M. Brown, D.J.A. Cockerill, J.A. Coughlan,
K. Harder, S. Harper, J. Linacre, K. Manolopoulos, D.M. Newbold, E. Olaiya, D.
Petyt, T. Reis, T. Schuh, C.H. Shepherd-Themistocleous, A. Thea, I.R. Tomalin,
T. Williams Imperial College, London, United Kingdom
R. Bainbridge, P. Bloch, J. Borg, S. Breeze, O. Buchmuller, A. Bundock,
GurpreetSingh CHAHAL70, D. Colling, P. Dauncey, G. Davies, M. Della Negra, P.
Everaerts, G. Hall, G. Iles, M. Komm, L. Lyons, A.-M. Magnan, S. Malik, A.
Martelli, V. Milosevic, A. Morton, J. Nash71, V. Palladino, M. Pesaresi, D.M.
Raymond, A. Richards, A. Rose, E. Scott, C. Seez, A. Shtipliyski, M. Stoye, T.
Strebler, A. Tapper, K. Uchida, T. Virdee17, N. Wardle, D. Winterbottom, A.G.
Zecchinelli, S.C. Zenz Brunel University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, C.K. Mackay, I.D. Reid, L.
Teodorescu, S. Zahid Baylor University, Waco, USA
A. Brinkerhoff, K. Call, B. Caraway, J. Dittmann, K. Hatakeyama, C. Madrid, B.
McMaster, N. Pastika, C. Smith Catholic University of America, Washington, DC,
USA
R. Bartek, A. Dominguez, R. Uniyal, A.M. Vargas Hernandez The University of
Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, S.V. Gleyzer, C. Henderson, P. Rumerio, C. West
Boston University, Boston, USA
A. Albert, D. Arcaro, Z. Demiragli, D. Gastler, C. Richardson, J. Rohlf, D.
Sperka, D. Spitzbart, I. Suarez, L. Sulak, D. Zou Brown University,
Providence, USA
G. Benelli, B. Burkle, X. Coubez18, D. Cutts, Y.t. Duh, M. Hadley, U. Heintz,
J.M. Hogan72, K.H.M. Kwok, E. Laird, G. Landsberg, K.T. Lau, J. Lee, M.
Narain, S. Sagir73, 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, Z. Wang, F. Zhang University
of California, Los Angeles, USA
M. Bachtis, C. Bravo, R. Cousins, A. Dasgupta, A. Florent, J. Hauser, M.
Ignatenko, N. Mccoll, W.A. Nash, S. Regnard, D. Saltzberg, C. Schnaible, B.
Stone, V. Valuev University of California, Riverside, Riverside, USA
K. Burt, Y. Chen, R. Clare, J.W. Gary, S.M.A. Ghiasi Shirazi, G. Hanson, G.
Karapostoli, O.R. Long, M. Olmedo Negrete, M.I. Paneva, W. Si, L. Wang, S.
Wimpenny, B.R. Yates, Y. Zhang University of California, San Diego, La Jolla,
USA
J.G. Branson, P. Chang, S. Cittolin, S. Cooperstein, N. Deelen, M. Derdzinski,
J. Duarte, R. Gerosa, D. Gilbert, B. Hashemi, D. Klein, V. Krutelyov, J.
Letts, M. Masciovecchio, S. May, S. Padhi, M. Pieri, V. Sharma, M. Tadel, F.