MUO-17-001

$HeadURL$ $Id$

MUO-17-001

# Performance of the reconstruction and identification of high-momentum muons
in proton-proton collisions at $\sqrt{s}=13\TeV$

###### Abstract

The CMS detector at the LHC has recorded events from proton-proton collisions,
with muon momenta reaching up to 1.8in the collected dimuon samples. These
high-momentum muons allow direct access to new regimes in physics beyond the
standard model. Because the physics and reconstruction of these muons are
different from those of their lower-momentum counterparts, this paper presents
for the first time dedicated studies of efficiencies, momentum assignment,
resolution, scale, and showering of very high momentum muons produced at the
LHC. These studies are performed using the 2016 and 2017 data sets of proton-
proton collisions at $\sqrt{s}=13\TeV$ with integrated luminosities of 36.3
and 42.1, respectively.

###### Contents

  1. 0.1 Introduction
  2. 0.2 The CMS detector
  3. 0.3 Data samples and simulation
  4. 0.4 High-$\pt$ muon reconstruction overview
    1. 0.4.1 Reconstruction
    2. 0.4.2 Muon radiative energy losses: showering
    3. 0.4.3 Identification
  5. 0.5 Efficiency measurements
    1. 0.5.1 High-$\pt$ muon identification efficiency
    2. 0.5.2 Reconstruction efficiency
    3. 0.5.3 Combined L1 and HLT efficiency
    4. 0.5.4 The L1 trigger efficiency
  6. 0.6 Momentum assignment performance
    1. 0.6.1 Momentum performance in simulation
    2. 0.6.2 Momentum resolution from cosmic ray muons and collision events
    3. 0.6.3 Momentum scale from collision events
  7. 0.7 Summary
  8. .8 The CMS Collaboration

##  0.1 Introduction

One of the main tasks of the CMS experiment is to search for new phenomena in
proton-proton () collisions delivered by the CERN LHC. Good identification and
precise measurement of muons, electrons, photons, and jets over a large energy
range and at high instantaneous luminosities are necessary for these searches
to be effective. In particular, searches for heavy gauge bosons such as the
$\PZpr$ [1, 2] and $\PWpr$ [3] rely on precise reconstruction of muons up to
very high momentum. With the data recorded from collisions in Run 2 at
$\sqrt{s}=13\TeV$, corresponding to integrated luminosities of 36.3in 2016 and
42.1in 2017, the CMS detector has recorded a sufficiently large sample of
higher-energy muons to allow the first detailed studies of such muons at the
LHC, presented here. For some analyses that require an independent data set
with all CMS subdetectors activated, the luminosities recorded are slightly
lower with 35.9in 2016 and 41.5in 2017.

Previously published studies of the CMS muon detectors [4] and muon
reconstruction [5] were based on data from collisions recorded during Run 1 in
2010 at $\sqrt{s}=7\TeV$, as well as on data recorded in 2015 and 2016 at 13
[6]. An extensive description of the performance of the muon detector and the
muon reconstruction software is given in Refs. [4, 5], while Ref. [6] focuses
on significant improvements made to the muon system during the long shutdown
period in 2013–2014 between LHC Runs 1 and 2. These changes resulted in
reconstruction software and the high-level trigger (HLT) that were shown to
have similar or better performance than in 2010, despite the higher
instantaneous luminosity.

In this paper, we present performance measurements of the muon triggering,
reconstruction, identification, and momentum assignment, for muons with high
transverse momentum $\pt>200\GeV$. Above this threshold, the effects of
radiative energy losses in the steel flux-return yoke of the solenoid due to
pair production, bremsstrahlung, and photonuclear interactions, as well as
detector alignment, become significant enough to motivate dedicated studies.

Various sources of high-momentum muons are used to ensure significant and
meaningful results. We include muons from the decays of high-mass off-shell
standard model (SM) vector bosons, denoted as high-mass Drell–Yan events (DY),
and muons from the decay of on-shell or bosons recoiling against jets, denoted
as ()+jets events. In addition, we study high-momentum muons originating from
cosmic rays, recorded during both the collisions and dedicated periods with no
beam.

##  0.2 The CMS detector

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. Within the solenoid
volume are a silicon pixel and strip tracker, a lead tungstate crystal
electromagnetic calorimeter (ECAL), and a brass and scintillator hadron
calorimeter (HCAL), each composed of a barrel and two endcap sections. Forward
calorimeters extend the coverage in pseudorapidity $\eta$ provided by the
barrel and endcap detectors. Muons are detected in gas-ionization chambers
embedded in the steel flux-return yoke outside the solenoid.

Events of interest are selected using a two-tiered trigger system [7]. The
first level (L1), composed of custom hardware processors, uses information
from the calorimeters and muon detectors to select events at a rate of around
100kHz within a fixed time interval of less than 4. The second, high-level
trigger (HLT) 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.

Muons are measured in the range $\abs{\eta}<2.4$ with detection planes made
using three technologies: drift tubes (DTs), cathode strip chambers (CSCs),
and resistive plate chambers (RPCs). The single-muon trigger efficiency with
respect to reconstructed muons exceeds 90% over the full $\eta$ range with
respect to reconstructed muons, and the efficiency to reconstruct and identify
muons that pass the trigger requirements is greater than 96%. Matching muons
to tracks measured in the silicon tracker results in a relative resolution of
1% in the barrel and 3% in the endcaps, for muons with up to 100. The
resolution in the barrel is better than 7% for muons with up to 1 [6].

At the end of the 2016 LHC running period, an additional pixel layer was added
to the tracker; the HLT sequences were modified to sustain a higher rate due
to the increase of the number of interactions in the same or adjacent bunch
crossings, referred to as pileup; and the detector was opened and the
alignment conditions were consequently changed. These modifications could
impact several studies performed in this paper; whenever it appears to be the
case, it is explicitly mentioned.

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. [8].

##  0.3 Data samples and simulation

The studies described in this paper are mostly based on data recorded using
single-muon triggers. In addition, for the trigger studies, we use data
samples recorded with single-electron triggers and missing transverse momentum
() triggers, referred to as independent data sets, since they provide unbiased
samples of muons suitable for studies of muon triggers. (We follow common
usage in defining as the magnitude of the projection onto the plane
perpendicular to the beam axis of the vector sum of the momenta of all
reconstructed objects in an event.) To maximize the sample size at high
momentum, the muon data sets from 2016 and 2017 are merged when the
performance under study is independent of the detector and software changes
from one year to another; otherwise, the results are presented for the two
years separately. The results in this paper are obtained from selected data
samples consisting of events with a pair of reconstructed muons, or with a
single reconstructed muon for the trigger study using independent data sets;
throughout, muon $\pt>53\GeV$ is required, in order to be above trigger turn-
on effects at the trigger threshold of 50. Further event criteria are applied,
depending on the study, and are described in detail below. Cosmic ray muon
data, recorded in the absence of LHC beams or in gaps between collisions, are
used to provide complementary studies on the muon momentum resolution and
charge assignment.

The selected data events are compared with simulations from several event
generators that use the Monte Carlo (MC) method. The DY
$\PZ/\gamma^{\star}\to\mu^{+}\mu^{-}$ signal samples are generated with v2 [9,
10, 11] at next-to-leading order (NLO) in both QCD and electroweak
corrections, and cover a mass range from 50up to 5. For the studies that use
exclusively the peak ($60<m_{\mu\mu}<120\GeV$) and explore the high-momentum
muons produced from boosted bosons, we use samples enriched in $+$jets
generated with v2.2.2 [12]. Finally, the $\PW^{*}\to\mu\nu$ signal samples,
used to validate the single-muon trigger efficiency, are generated at leading-
order (LO) with 8.212 (8.230) [13] for 2016 (2017) studies.

The dominant backgrounds over the full dimuon mass range are, in order of
importance, , t, and ; they are simulated at NLO with . The cross section is
calculated at next-to-NLO (NNLO) with Top$++$ v2.0 [14]. Other electroweak
backgrounds, such as and , are generated with .

For all simulated samples mentioned above, the fragmentation and parton
showering is modeled with 8.212 with the CUETP8M1 [15] underlying event tune
for the 2016 studies or with 8.230 with CP5 [16] tune for 2017 studies. The
NNPDF3.0 [17] and NNPDF3.1 [18] parton distribution function sets are used for
the 2016 and 2017 samples, respectively. The simulation of the CMS detector
response is based on [19]; the events are then reconstructed with the same
algorithms as used for data. Pileup is also simulated, except for studies
where it is explicitly stated that this is not the case.

##  0.4 High-$\boldsymbol{\pt}$ muon reconstruction overview

Most of the muons produced in CMS originate in processes such as semileptonic
decays of top quarks or heavy-flavor hadrons, or in leptonic decays of on-
shell vector bosons (, ). Such muons typically have $\pt<200\GeV$, and are
referred to as low-$\pt$ muons. On the other hand, high-muons are produced in
rare processes such as off-shell production of high-mass or on-shell
production of high-$\pt$ ⋆ and ${}^{\star}/\gamma$ bosons, and could be
produced from the decay of beyond the standard model (BSM) particles with TeV-
scale mass (, or bosons).

Experimentally, the main differences between high- and low-muons can be
understood as follows. As the muon momentum increases, the resolution of the
reconstructed track degrades. In the part of the orbit in near-uniform
magnetic field $B$, the measurement of depends on $B$, and the radius of
curvature, $R$, of the track [20]:

$\pt[\text{GeV}]=\abs{0.3B[\text{T}]R[\text{m}]}.$ (1)

The magnetic field is monitored with high precision and is roughly uniform at
3.8T in the tracker volume inside the solenoid. The radius of curvature is
related to the arc length $L$ and sagitta $s$ of the track via

$R[\text{m}]\approx L[\text{m}]^{2}/8s[\text{m}],$ (2)

where the approximation is valid for $L/R\ll 1$. Assigning arithmetic signs
consistently to $R$, $s$, and the charge $q$ (in units of proton charge)
yields

$s[\text{m}]\approx(0.3B[\text{T}]L[\text{m}]^{2}/8)(q/\pt[\GeVns])=(0.3BL^{2}/8)\kappa,$
(3)

where $\kappa=q/\pt$ is referred to as the (signed) curvature of the muon
track. Because $s$ is linearly related to the measurement of hit positions in
the detector (which have approximately symmetric uncertainties), the
uncertainty in $\kappa$ (rather than in $\pt$) from the cumulative effect of
hit uncertainties is (approximately) Gaussian. Hence $\kappa$ is the more
natural variable for use in muon momentum resolution and scale studies, as
discussed in Section 0.6. As the increases and the sagitta in the tracker
decreases, the muon momentum measurement can be improved by using the large
$BL^{2}$ between the tracker and the muon system (and within the muon system),
if the $\pt$ is large enough that multiple Coulomb scattering in the
calorimeters and in the steel flux-return yoke of the solenoid does not spoil
the measurement. Thus high-$\pt$ muon track reconstruction and muon momentum
measurement rely on matching tracks reconstructed in the inner tracker and the
muon system, separated by more than 3 meters and forming a global track, as
explained in Section 0.4.1. However, because of the smallness of the sagitta
(or more precisely, the generalizations of sagitta in nonuniform $B$) in the
TeV regime, the muon resolution is sensitive to alignment of the hits used to
reconstruct the muon track. The impact of the detector alignment on the
momenta resolution is discussed in Section 0.6.

If a muon traveling through the steel of the magnet flux-return yoke has
sufficiently large momentum, radiative energy losses (bremsstrahlung with
inner and outer e+e- pair production, photonuclear interactions) are no longer
negligible compared to ionization energy losses. The muon critical energy for
iron, $E^{\text{iron}}_{\mathrm{c}}$, at which the ionization energy losses
are equal to the sum of all radiative losses, is around 300[20]. As a
consequence, the main source of energy loss for muons above
$E^{\text{iron}}_{\mathrm{c}}$ propagating through the steel between the muon
subdetectors is radiative energy losses. This radiation creates cascades of
particles (electromagnetic showers) and can lead to extra hits being
reconstructed in the muon detectors. These showers can have a strong impact on
the muon performance (, triggering, reconstruction, or measurement). The muon
showering primarily depends on the total muon momentum, as opposed to the
transverse component that is commonly used in physics analyses. Depending on
the longitudinal component of momentum, muons with $\pt>200\GeV$ can have
energies above $E^{\text{iron}}_{\mathrm{c}}$. The potential presence of
showers around the muon track is what motivates the choice of $\pt>200\GeV$ to
define a high-muon in the paper. Dedicated algorithms for momentum assignment
have been developed and are discussed in Section 0.4.1. In addition, in order
to understand the behavior of high-muons and the impact of showering along the
CMS detection sequence, we parameterize the showering and then confront
simulation with data on the various muon performance aspects. This shower
tagging is discussed in Section 0.4.2, whereas the results of the muon
performance as a function of muon showering are shown in Sections 0.5 and 0.6.

Some BSM searches, involving high-muons, probe processes with small cross
sections for which negligible backgrounds from SM processes are expected. High
efficiency for measuring TeV muons is particularly important for obtaining a
high sensitivity in such searches. For example, the current upper limit [21]
on the product of production cross section and branching fraction for a boson
with a mass of 2, $\sigma(\zp){\mathcal{B}}(\zp\to\mu\mu)$, is
${\mathcal{B}}(10^{-7})$ smaller than that of the SM boson,
$\sigma(\PZ){\mathcal{B}}(\PZ\to\mu\mu)$. In such analyses, the signal
efficiencies are derived with simulated samples. While the simulations can be
validated in some kinematic regions using boson events in data, the lack of
signal at higher masses forces the analysis strategy to extrapolate into the
highest regions. Therefore, it is important to have uniform reconstruction,
identification, and triggering efficiencies as a function of the muon $p$ and
$\pt$, and to ensure that any sensitivity to muon showering is understood.
Dedicated high-muon identification criteria have been developed and further
improved during LHC Run 2 in order to provide robustness with increasing muon
; they are detailed in Section 0.4.3. The level of agreement between the
performance in data and simulation is quantified in terms of data-to-
simulation efficiency ratios called scale factors (SF).

###  0.4.1 Reconstruction

In the standard CMS reconstruction procedure for collisions, muon tracks are
first reconstructed independently in the inner tracker and in the muon systems
[22]. In the latter, tracks called “standalone muons” are reconstructed by
using information from DT, CSC, and RPC detectors along a muon trajectory
using the Kalman filter technique [23]. In both the barrel and endcap regions,
the muon detectors reside in four “stations”, which are typically separated by
23 to 63of steel. The steel thickness prevents an electromagnetic shower from
propagating across more than one station. Within each station, there are
multiple planes of detectors, from which “hits” are recorded. The hits within
a station are combined into local “segments”, which are in turn combined into
standalone muons.

Matching standalone-muon tracks with tracks reconstructed in the inner tracker
yields combined tracks referred to as “global muons”. If the momentum,
direction, and position in the transverse plane of the inner and standalone
tracks are compatible, then the global track is fit by combining hits from the
tracker track and standalone-muon track in a common fit.

Global muons are complemented by objects referred to as “tracker muons” that
are built by propagating the inner tracker tracks to the muon system with
loose geometrical matching to DT or CSC segments. If at least one muon segment
matches the extrapolated track, the track is qualified as a tracker muon.
Tracker muons have higher efficiency than global muons in regions of the CMS
detector with less instrumentation and for muons with low-$\pt$.

The momentum of a muon reconstructed as a global muon can be extracted from
the combined tracker-plus-standalone trajectory. For high-muons, however,
extra particles produced in electromagnetic showers can contaminate the muon
detectors, yielding extra reconstructed hits and segments. These extra
segments can be picked up by the trajectory building algorithm instead of the
correct muon track segment, or even make the reconstruction of the muon track
in a chamber impossible. The high-case thus requires careful treatment of the
information from the muon system. A set of specially developed TeV-muon track
refits has been developed to address this issue: the “tracker-plus-first-muon-
station” (TPFMS) fit, the “Picky” fit, and the “dynamic truncation” (DYT) fit.
The momentum assignment is finally performed by the “TuneP” algorithm, which
chooses the best muon reconstruction among the tracker-only track, TPFMS, DYT,
and Picky fits.

The TPFMS fit is historically the first alternative to the global muon fit
(which is based on all the trajectory measurements). It only uses hits from
the tracker and the innermost muon station containing hits, thus taking
advantage of a large $BL^{2}$, while neglecting the stations that are farther
along the muon’s trajectory, thus reducing potential contamination from
showers. Even with this omission, by making a judicious track-by-track choice
between the tracker-only fit and the TPFMS fit, the resolution at high $\pt$
can be improved with respect to both the tracker-only fit and the global fit
[6].

Other strategies for improvement have also been developed. If a shower in one
muon station corrupts the position measurement in that specific station, thus
the thickness of the steel layer will absorb the shower and prevent it from
leaking into the next station. Then, in principle, if it is possible to
identify a station where a shower occurs, then it can be discarded from the
muon global fit instead of rejecting most stations, as is done with TPFMS. The
Picky algorithm was developed with this approach in mind. It identifies
stations containing showers based on the hit multiplicity, and for each of
them, it imposes extra requirements on hit compatibility with the muon
trajectory. If hits in a station with showering fail these requirements, that
station is removed from the trajectory fit.

The DYT fit approach is based on the observation that in some cases, when a
muon loses a large fraction of its energy, its orbit can change and the
segments (or hits) in subsequent stations may no longer be consistent with the
initial trajectory. In other cases, where the energy loss is less severe, only
hits in one station appear incompatible, while the rest of the trajectory is
negligibly changed and can be used in the fit. The DYT algorithm starts from
the tracker track and proceeds outwards, iteratively adding to the fit muon
hits compatible with the extrapolated track trajectory. When incompatible hits
are found it ignores them or stops the fit entirely, depending on the degree
of incompatibility.

Thus, the algorithm for choosing between the tracker-only fit and TPFMS has
evolved into a more general algorithm, known as the TuneP algorithm, for
choosing among the various refits on a track-by-track basis. It uses the track
fit $\chi^{2}/$dof tail probability and the relative measurement uncertainty
$\sigma_{\pt}/\pt$, where $\sigma_{\pt}$ is the uncertainty in , as determined
by the Kalman filter. The algorithm starts its search for the best track fit
choice by initially considering the Picky hypothesis and comparing its
$\sigma_{\pt}/\pt$ with the value estimated for the corresponding track but
refitted by the DYT algorithm. The refit with the smallest uncertainty in is
then compared to the tracker-only fit, and the track with the lower
$\chi^{2}/$dof tail probability value is kept, to be finally compared with the
TPFMS refitter algorithm. The final best track is chosen after the last
comparison according to the $\chi^{2}$/dof tail probability. In the rare cases
where there is no convergence in the Picky algorithm refit, or in the other
refits tried consecutively, the global fit is kept.

In cases where the final candidate track has a lower than 200, the tracker-
only fit is used. Figure 1 presents the fractions for each choice of TuneP
among DYT, Picky, and any of the other fits (TPFMS, global, or tracker-only),
as a function of the muon , separately for the barrel and endcap regions. The
selected muons come from dimuon events and are required to pass the high-
identification described in Section 0.5.1, and to have $\pt>200\GeV$. To
simulate the data events, we add to DY simulation all the other electroweak
processes that arise in data and that mimic DY events (diboson, , single top
quark, etc.). We do not add the background from SM events comprised uniquely
of jets produced through the strong interaction, because this background is
negligible above $200\GeV$. The simulation reproduces well what is observed in
data: similar fractions in the choice among the refits across the full
spectrum, with a preference for Picky in the barrel (${\approx}$60%) while
similar fractions for DYT and Picky are found in the endcaps (${\approx}$50%).
When DYT was first developed, its performance was studied integrated over muon
$\eta$ and in consequence found to be driven by the endcap region where most
of the showering takes place. The high level of agreement between data and
simulation is an indication that the impact of showering on momentum
assignment is well reproduced by simulation.

Figure 1: Fraction of choices of different refit algorithms chosen by TuneP,
comparing 2016+2017 data and DY simulation for five $\pt$ ranges and for two
$\eta$ categories: (left) barrel with $\abs{\eta}<1.2$ and (right) endcap with
$1.2<\abs{\eta}<2.4$. The central value in each bin is obtained from the
average of the distribution within the bin.

###  0.4.2 Muon radiative energy losses: showering

In order to understand the effect of showers on the various aspects of muon
reconstruction and measurements (including triggering) we have developed
empirical definitions to identify (“tag”) and characterize showers in the muon
systems. Both data and simulation samples are used to converge on this
definition of a “shower” and are compared to study the adequacy of the shower
modeling in simulation.

The “extended tag-and-probe” technique (Section 0.5) is used to study showers
in simulated high-mass DY samples and in dimuon events from the single-muon
primary data sets (Section 0.3). Definitions for tags, probes, and dimuon
pairs are the same as those used to measure muon reconstruction efficiency,
and are described in detail in Section 0.5.2. In addition, single-muon (or
antiparallel double-muon) samples uniform in $\eta$ and $p$ in the range
between 5 and 2500are generated without simulating pileup. In this case, the
muon candidates used in the analysis are required to satisfy only the
selection criteria used for probes, except that the muons are not required to
come from the primary vertex, since it is problematic to accurately
reconstruct a vertex with only two tracks that are nearly antiparallel.

The multiplicity of segments reconstructed within a single DT or CSC station
can be used as a proxy to identify showers. The tracker track of the selected
probes is extrapolated to the different station layers of the muon detectors.
Segments belonging to the chambers traversed by the propagated track are
counted if they lie within $\abs{\Delta x}<25\cm$ from the extrapolated track
position, with $\Delta x$ computed in local chamber coordinates and
representing the bending direction of the track. If the extrapolated track
crosses a given station layer close to the border between chambers, or if
different chambers overlap, segments satisfying the requirement on $\Delta x$
in all potentially crossed chambers are counted. Finally, the number of track-
segment matches, provided by the tracker muon identification for all the
chambers involved in the computation, is also counted and subtracted from the
total sum. The result of this logic is the number of extra segments (, the
number of segments in addition to those belonging to a muon track), computed
independently for each station crossed by a muon. It is referred to as
$N_{\text{seg}}$.

The DT and CSC local reconstruction can generate “ghosts”, , reconstructed
tracks with no corresponding genuine track, in cases of multiple track
segments traversing a single chamber. For example, in the case of DTs, the
segment fitting is first performed independently in the $\phi$ and $\theta$
views of a chamber and pairs of such “2D segments” are then combined only at a
later step of the segment reconstruction to provide a three-dimensional
object. Combinations are built out of all possible permutations of
$\phi$–$\theta$ 2D segments, leaving to the standalone track reconstruction
the burden of the disambiguation. A similar phenomenon happens for CSCs,
though with different logic due to a different approach to the segment
building.

The value of $N_{\text{seg}}$ above which a station is considered to have a
shower was chosen after considering several possibilities. The probability to
have at least one station with a shower increases with the muon momentum,
while for very low momentum it should be close to zero. The slope of
dependence is larger for a looser requirement on $N_{\text{seg}}$. However,
when requiring $N_{\text{seg}}\geq 1$, the shower probability for very low
momentum is still ${\approx}$20–30%, which suggests a large contribution from
ghosts. This falls to ${\approx}$5–10% for $N_{\text{seg}}\geq 2$;
consequently, the requirement $N_{\text{seg}}\geq 2$ is chosen as the working
point for shower tagging, because this is the most sensitive definition having
acceptably small mistagging of showers at low momentum.

The probabilities of finding a shower in each of the four muon stations are
computed separately and are compatible, except in the first muon station in
the endcap, where the shower probability is higher than in the remaining
endcap stations by ${\approx}$20%. We attribute this to hadronic punch-through
hadrons from other collisions in the bunch crossing, wrongly tagged as muon-
induced showers; this effect is not present in the single-muon simulated
sample, which does not include pileup. For the purpose of the studies in this
paper, we use a simple picture with one number characterizing the probability
of tagging a shower in any station. Figure 2 shows the resulting probability
$P_{\text{shower}}(p)$ to tag a shower in at least one of the four muon
stations as a function of the muon momentum.

Figure 2: The probability $P_{\text{shower}}(p)$ to tag at least one shower in
any of the four stations, as a function of the incoming muon momentum, for
(upper left) DTs; (upper right) CSCs with muon $\abs{\eta}<1.8$; and (lower)
CSCs with muon $\abs{\eta}>1.8$ . Results are evaluated for the shower tagging
definition requiring $N_{\text{seg}}\geq 2$. Different colors refer to: data
(black), DY simulation (red), and single muons simulated with a uniform $p$
distribution (blue).

Results from data are compared with those from the simulated high-mass DY and
single-muon samples, in the barrel and endcap regions separately. The endcaps
are further split above and below $\abs{\eta}=1.8$ to isolate the forward
endcap region that has the highest shower probability. Below 1000there is good
agreement between data and simulation, thus validating the modeling of showers
in simulation.

###  0.4.3 Identification

High-momentum muons are produced in rare processes with low cross sections and
backgrounds. Often in searches the muon identification performance is measured
using simulation in TeV signal regions that is validated only with
extrapolations from measurements at lower momenta. In order to make this
procedure more robust, the muon identification efficiency is designed to be
uniformly high as a function of muon $p$ and $\pt$. For this purpose a
dedicated high-muon identification was designed during Run 1 [24] (“Run 1
high-ID”), targeting topologies involving high-muons; it was further improved
during Run 2 (“Run 2 high-ID”).

In the Run 1 high-ID, muons are required to be global muons with at least two
segments reconstructed in two muon stations that match the inner track. This
selection suppresses punch-through and accidental track-to-segment matches.
The main source of inefficiency is due to the gaps between the muon chambers
and is more prominent in the barrel region, where CMS has two pathways
(“chimneys”) for services located around $\abs{\eta}=0.3$. In contrast,
chambers in the endcaps overlap with each other, which provides continuous
coverage. The main update of this selection for Run 2 is to consider global
muons that have only one segment matching the inner track, but only when the
extrapolation from the tracker muon to the muon system predicts that they pass
through the muon system gaps. In that case, only zero or one segment is
expected to match the inner track. This change in the Run 2 high-ID raises the
signal efficiency by 1 to 2$\%$ at high and improves agreement between the
data and simulation. The efficiency gain affects high-muons slightly more than
lower-muons because of a kinematic correlation: high-muons are mostly produced
from high-mass states that have low absolute rapidity and hence their muon
decay products are more likely to be in the barrel region.

To guarantee that the muon system information is also used in the final
momentum assignment, the Run 1 high-ID requires that at least one valid muon
system hit be retained in the global muon fit, which removes the outlier hits.
The global muon valid hit collection is inherited from the parent standalone
muon and the hits are qualified as valid when their addition to the global
muon fit does not degrade the $\chi^{2}$. However, in the presence of showers,
the hit multiplicity increases and the $\chi^{2}$ of the standalone fit gets
worse when trying to include them in the trajectory fit. The TuneP algorithm
that has been developed to optimize the muon refit (Section 0.4.1) can result
in a hit collection used for the final momentum assignment that differs from
the global hits collection; furthermore, if $\pt<200\GeV$, the TuneP algorithm
chooses the fit using only tracker hits. Hence, the second change from the Run
1 high-ID to the Run 2 high-ID consists in requiring that either the global
muon fit or the fit chosen by TuneP use at least one valid muon system hit.
This change raises the signal efficiency by 1% for muons with $\pt>500\GeV$,
mostly affecting the endcap region where showering (which scales with $p$, not
) is more abundant.

Figure 3 displays the Run 1 high-ID efficiency as a function of muon $\eta$
and $\pt$, with comparison to the Run 2 high-ID efficiency. They are obtained
from DY simulations and from dimuon events in data when combining the full
2016 and 2017 data sets. The method to compute these efficiencies as well as
more details and results concerning the Run 2 high-ID efficiency are discussed
in Section 0.5.1.

The other selection criteria of the Run 2 high-ID are the same as for the Run
1 high-ID, with notably tight requirements on the track part of the global
muon. A minimal number of pixel hits and tracker layers is required in order
to ensure that the muon originates from the center of the primary interaction,
to suppress cosmic ray muons and muons produced from meson decays in flight,
and to ensure good momentum measurement resolution. Finally, a muon is
required to have a reliable $\pt$ assignment to perform the analysis; thus
only global muons with a TuneP relative measurement uncertainty,
$\sigma_{\pt}/\pt$, smaller than 30$\%$ are considered.

Figure 3: Comparison between the efficiency of Run 2 and Run 1 high-ID, as a
function of (left) $\eta$ and (right) . The efficiencies are obtained from
dimuon events with a mass greater than 120to further select the high-mass DY
process. The top panel shows the data to simulation efficiency ratio obtained
for the Run 1 (blue squares) and for the Run 2 high-ID (black circles). The
bottom panel shows the Run 2 to Run 1 high-ID efficiency ratio obtained from
the data (black circles) and from simulation (red triangles). The central
value in each bin is obtained from the average of the distribution within the
bin.

##  0.5 Efficiency measurements

The tag-and-probe method [5] is a standard technique for measuring
efficiencies for prompt muons coming from boson decays. The method provides an
unbiased estimation of the total muon efficiency $\epsilon_{\mu}$ at the
various stages of muon trigger, offline muon tracking reconstruction, and muon
identification. Each component of $\epsilon_{\mu}$ is determined individually
and factorized according to:

$\epsilon_{\mu}=\epsilon_{\mathrm{track}}\epsilon_{\mathrm{ID}}\epsilon_{\mathrm{reco}}\epsilon_{\mathrm{trig}}.$
(4)

The efficiency $\epsilon_{\mathrm{track}}$ of the tracker track reconstruction
appears independent of the muon momentum and does not require dedicated study
at high momentum [25]. All other components of $\epsilon_{\mu}$ rely on the
performance of the muon system and can potentially be affected by muon
showering as well as by the biases in the muon system alignment. Such features
would lead to a dependence of efficiency on muon $\pt$ and $\eta$. The
individual components $\epsilon_{\mathrm{ID}}$, $\epsilon_{\mathrm{reco}}$,
and $\epsilon_{\mathrm{trig}}$ are scrutinized and computed as functions of
these kinematic variables in Sections 0.5.1-0.5.3, respectively. In addition,
in order to understand the impact of muon showering on the efficiency and to
establish if the simulation models data accurately, the various efficiency
components are studied as a function of showering, using the shower tagging
method described in Section 0.4.2. A slight difference with respect to the
usual tag-and-probe method concerns $\epsilon_{\mathrm{reco}}$, where the
probe is a tracker muon instead of a track. Starting from a track allows
probing of the entire muon system reconstruction, whereas for the tracker muon
requirement, there is already the assumption that at least two segments are
reconstructed in the muon chambers and that they are aligned with the track.
We have checked that this difference has a negligible impact and no $p$
dependence. To gain further insight into the combined L1 and HLT efficiency of
Section 0.5.3, separate L1 efficiency studies are presented in Section 0.5.4.

In order to compute $\epsilon_{\mu}$ up to of 1, the standard tag-and-probe
method has been augmented. In this “extended tag-and-probe” method, we aim to
collect as many prompt high-muons from the DY process as possible with maximal
suppression of backgrounds. Therefore, we do not restrict the invariant mass
of the tag and probe muons to the boson mass window. For background rejection,
we impose very tight isolation requirements on both tag and probe muons. The
isolation requirements rely exclusively on the energy measured in the tracker,
in a cone centered on the muon track and with a radius $\Delta
R=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}$ smaller than 0.3. No
inputs from the calorimeters are considered in the computation of isolation,
to avoid including radiation emitted by the muon that could bias the shower
studies. Only muons with total energy in the cone smaller than 30and not more
than $5\%$ of their are kept. In addition to the isolation selection,
kinematical criteria can be applied, such as requiring back-to-back events in
the transverse plane, or a balance between the $\pt$ vectors of the two muons.
This last set of criteria can be used to reduce the background contribution
from events; when they are not part of the pair selection, they are at least
used to cross-check the results. The tag muon is required to pass the full Run
2 high-ID described in Section 0.4.3. After applying the probe selection,
which depends on the efficiency under study, no further background subtraction
is needed; the efficiency is calculated by counting passing and failing probe
muons.

###  0.5.1 High-$\boldsymbol{\pt}$ muon identification efficiency

The Run 2 high-ID efficiency is measured using the extended tag-and-probe
method on muons that are reconstructed as global muons. The results are
presented in Fig. 4 for the combined 2016 and 2017 data sets and for simulated
DY samples. The efficiency as a function of is shown separately in four $\eta$
regions with different detector composition and characteristics:
$\abs{\eta}<0.9$, only composed of DTs; $0.9<\abs{\eta}<1.2$, composed of both
DTs and CSCs; $1.2<\abs{\eta}<2.1$, only composed of CSCs; and
$2.1<\abs{\eta}<2.4$, the very forward region composed of CSCs but very
sensitive to pileup, punch through, and showering.

Figure 4: High-ID efficiency for 2016 and 2017 data, and corresponding DY
simulation, as a function of for (upper left) $\abs{\eta}<0.9$, (upper right)
$0.9<\abs{\eta}<1.2$, (lower left) $1.2<\abs{\eta}<2.1$, and (lower right)
$2.1<\abs{\eta}<2.4$. The black circles represent data; the red triangles
represent DY simulation. The data-to-simulation ratio, also called the data-
to-simulation scale factor (SF), is displayed in the lower panels. The central
value in each bin is obtained from the average of the distribution within the
bin.

A very high identification efficiency, mostly above 98%, is found over the
full detector acceptance. No -dependent inefficiency is found for either 2016
or 2017 data. The DY simulation predicts slightly higher efficiency than
observed in data, but the data-to-simulation agreement is uniform with
increasing . The “$N-1$ efficiencies” for each ID requirement are individually
tested by dividing the number of probe muons passing a given selection
criterion by the number of probe muons passing all other criteria. Figure 5
shows the results for each criterion that are obtained for muon $\pt>53\GeV$
and binned in $\eta$. Although the matching criteria between the muon system
segments and the inner tracker part of the global muon were updated between
Run 1 and Run 2 (Section 0.4.3), this selection is still responsible for the
slight discrepancy between simulation and data in the barrel region. In the
endcaps ($\abs{\eta}>1.2$), we observe a slight inefficiency in both 2016 and
2017 data with respect to the rest of the detector and to simulation, due to
the requirement of a valid muon detector hit in the final momentum fit.
Finally, we observe a small efficiency gain in 2017 (${+}$0.5%) with respect
to 2016 in the barrel region, which can be traced back to the tracker part of
the muon Run 2 high-ID that links the improvement with the new pixel detector
installed in CMS between the 2016 and 2017 data taking periods.

Figure 5: The $N-1$ efficiencies, for $\pt>53\GeV$ and binned in $\eta$,
comparison between 2016 and 2017 data sets and for the corresponding DY
simulations, for (upper left) $\abs{\eta}<0.9$, (upper right)
$0.9<\abs{\eta}<1.2$, (lower left) $1.2<\abs{\eta}<2.1$, and (lower right)
$2.1<\abs{\eta}<2.4$. The black circles represent 2016 data; the blue squares
represent 2017 data. The lower panels display the ratio of $N-1$ efficiencies
obtained for each of the criteria, between 2017 and 2016 data sets, and
between data and their corresponding simulations for both years.

The Run 2 high-ID efficiency is very high and no trend is observed with
increasing $\pt$. The results are also provided as a function of the muon $p$
in Fig. 6. The 2016 and 2017 data sets are combined in order to reach higher
sensitivity. The efficiencies are further split into two categories, whether
or not a shower is tagged, given a muon. The overlap region
($0.9<\abs{\eta}<1.2$) is not included, to avoid double counting from CSC and
DT segment-overlap that biases the shower tagging definition. No effect due to
showering can be seen in the endcap region (upper right and lower plots), but
a slight decrease in the efficiency of 1% is visible over the full momentum
spectrum in the barrel region (left plot) for muons with an associated shower.
This inefficiency is due to requirements on the matching of the inner track to
the segments in the muon system, which are responsible for most of the
inefficiency in the barrel region. In most of the cases, the muon is failing
these identification criteria because it fails to be reconstructed as a
tracker muon, despite the fact that the global reconstruction is successful.
It appears likely that those muons are emitting showers in the calorimeters,
which cause a change in trajectory before entering the muon system, so that
the tracker-track extrapolation does not match the segments.

Figure 6: High-ID efficiency for 2016+2017 data, and corresponding DY
simulation, as a function of $p$ for (upper left) $\abs{\eta}<0.9$, (upper
right) $1.2<\abs{\eta}<2.1$, and (lower) $2.1<\abs{\eta}<2.4$. The blue
squares show efficiency for muons in data with no showers tagged; the green
inverted triangles show the same for muons in DY simulation. The black circles
correspond to muons in data with at least one shower tag, while the red
triangles are the same for muons in DY simulation. The central value in each
bin is obtained from the average of the distribution within the bin.

###  0.5.2 Reconstruction efficiency

The standalone and global muon reconstruction efficiencies are studied as a
function of muon $\eta$ and $p$ using the extended tag-and-probe method. The
selected probe muons are required to be good quality tracker muons, and the
efficiency to reconstruct either standalone or global muons is calculated with
respect to these probes. Figure 7 shows the 2016 and 2017 standalone muon
reconstruction efficiency as a function of muon $\eta$ for muons with
$\pt>53\GeV$. The efficiency is above 99% in the barrel region and up to
$\abs{\eta}=1.6$, both for data and simulation, and for both data sets. For
$\abs{\eta}>1.6$, the simulation does not reproduce the slight inefficiency
observed in data.

Figure 7: Standalone muon reconstruction efficiency as a function of muon
$\eta$ for the (left) 2016 and (right) 2017 data sets. The blue points
represent the data, while the red empty squares represent the simulation.

Figure 8: Standalone muon reconstruction efficiency as a function of muon
momentum in two different $\abs{\eta}$ regions: (left) $\abs{\eta}<1.6$, and
(right) forward endcaps from, $1.6<\abs{\eta}<2.4$. The upper row shows the
2016 results, with blue points representing data and red empty squares
representing simulation. The lower row shows the 2017 results. The lower
panels of the plots show the ratio of data to simulation. The central value in
each bin is obtained from the average of the distribution within the bin.

To characterize the inefficiency seen in the forward part of the detector and
in both data sets, Fig. 8 shows the standalone muon reconstruction efficiency
as a function of $p$ for $\abs{\eta}<1.6$ and for the forward endcaps
$(1.6<\abs{\eta}<2.4)$. The measured efficiency in the $\abs{\eta}<1.6$ region
is uniform in $p$ up to approximately 2in both data and simulation. In the
region $1.6<\abs{\eta}<2.4$, a decreasing trend as a function of $p$ is
observed in both data and simulation, although it is more pronounced in data
by approximately 2%. In order to separate out the possible effect of pileup
(in particular since the forward part of the detector suffers from the dense
track activity), Fig. 9 compares the standalone reconstruction efficiency
obtained in data with DY simulation for events with low pileup environment
(defined as having less than 15 reconstructed primary vertices) and for events
with higher pileup environment. In addition, since the muons crossing the
forward region of the detector have a higher probability to shower (Fig. 2),
the results are then further split between events where at least one shower is
tagged from events without any showering detected.

Figure 9: Standalone muon reconstruction efficiency as a function of muon $p$
for muons with $1.6<\abs{\eta}<2.4$. The left plots are for low pileup (up to
15 vertices) while the right plots are for higher pileup. The upper plots are
obtained with events without any showers; the lower ones contain events with
at least one shower. The blue points represent data and the red empty squares
represent simulation. The lower panels of the plots show the ratio of data to
simulation. The central value in each bin is obtained from the average of the
distribution within the bin.

For the low-pileup environment and events without tagged showers, the
efficiency measured both in simulation and in data is mostly uniform across
the momentum spectrum and is almost 100 (99)% in simulation (data). It starts
to show a decreasing trend for higher pileup activity with the efficiency
going down to 98 (96)% for muons with momentum of a few TeV in simulation
(data). Although the simulation results show a dependence on the level of
pileup, they do not reproduce the data trend when there are more than 15
vertices. When no shower is found, the decreasing trend seen in simulation,
and more pronounced in data, is due to pileup. In the presence of showers, the
inefficiency trend is enhanced in both data and simulation, and in particular
for events inside the high pileup environment, where the lowest efficiency
value is 95 (93)% for muons of few TeV in simulation (data). The data vs.
simulation discrepancy is slightly enhanced in the presence of showering for
events recorded in both low- and high-pileup environments. We conclude that
muon showering and dense track activity interfere within the muon
reconstruction, and lead to the momentum dependence of up to 5% in the
inefficiency when both effects are combined.

The scrutiny of DY events from simulation shows that approximately half of the
events responsible for the reconstruction inefficiency do have a standalone
muon, but it is not associated with its tracker part. Despite the fact that
the tracker part and the standalone muon share common segments, the
extrapolation of the standalone muon to the tracker volume is not succeeding.
Hence the standalone muon and the global muon formed from it (if any) both
exist, but the momentum assignment is wrong. The other half of the events are
again good tracker muons, with associated muon segments in several muon
chambers, but in these cases no standalone muon is reconstructed. Still,
several segments are found across the entire muon system (over the 4 stations)
and they match the tracker part. This observation indicates a reconstruction
failure at the muon system level, namely the inability to reconstruct the
standalone trajectory out of the detected segments.

The global reconstruction efficiency is computed for probe muons that are also
standalone muons and is displayed as a function of the muon momentum in Fig.
10. The results are integrated over muon $\eta$ but split according to the
(left) absence or (right) presence of showers. The efficiency is almost 100%
over the full momentum spectrum when the events do not contain showering
muons. A slight decreasing trend is observed in the presence of muon
showering, although the global reconstruction efficiency remains greater than
99%.

Figure 10: Global muon reconstruction efficiency as a function of muon
momentum. The left plot is obtained with events without any showers, while the
right one contains events with at least one shower. The blue points represent
data and the red empty squares represent simulation. The lower panels of the
plots show the ratio of data to simulation. The central value in each bin is
obtained from the average of the distribution within the bin.

###  0.5.3 Combined L1 and HLT efficiency

Figure 11: The combined HLT+L1 efficiency with respect to the offline
selection, and the ratio of data to simulation for different methods, as
functions of , for (left) 2016 data and (right) 2017 data. The red triangles
are measured using an independent data set collected with a trigger; the black
circles are measured by the extended tag-and-probe method in which selected
events have $m_{\mu\mu}>120\GeV$. The central value in each bin is obtained
from the average of the distribution within the bin.

The overall trigger efficiency (combined L1 and HLT) is measured using the
extended tag-and-probe method, as well as using events selected by a set of
triggers without muon requirements. The events selected in these independent
data sets contain a high-energy electron or large . This second approach leads
to a sample enriched in +jets and $\ttbar$ events that could be used to probe
muon triggers.

Figure 11 shows the trigger efficiency measurement using the extended tag-and-
probe (black), and independent data set (red) methods as a function of the
muon for 2016 and 2017 data. The two methods are compatible with each other,
reinforcing the robustness of the results. The measured trigger efficiency in
2016 and 2017 data shows a slight decreasing trend as a function of the muon
with a value of 90 (85)% at 60(1). The SF between the trigger efficiencies in
data and simulation ranges between 0.95 and 0.9.

The 2016 and 2017 trigger efficiencies obtained with the extended tag-and-
probe method are computed separately for the barrel and overlap regions, and
compared to simulation in Fig. 12. In both data sets, the efficiency trend as
a function of is seen in the barrel but even more pronounced in the overlap
region. In the barrel, the ratio of data to simulation is 0.98 (0.97) for 2016
(2017) data and is uniform with in both data sets. The residual efficiency
dependence of the results is caused by the L1 component, due to the lower
efficiency of the L1 muon trigger for muons with shower tags, as discussed in
Section 0.5.4. In the overlap region, the inefficiency trend is much more
severe in data than in simulation, and the SF are increasing with . They range
from 0.95 at 60and down to 0.85in the highest bin in 2016 data (and 0.9 in
2017 data). Hence, though the efficiency trend is visible in both the barrel
and overlap regions, the dependence of the SF is coming exclusively from the
overlap region. This effect has been tracked down to the L1 trigger and the
causes are attributed to a nonoptimal arbitration between the DT and CSC
segments that are both present in the overlap region. Even though the muon
identification relies equally on CSC and on DT segments, the momentum
assignment will be more accurate if the estimated value comes from the DT. A
fix was implemented in 2018 so that the DT estimated muon assignment is used
in these cases.

Figure 12: The combined HLT+L1 efficiency with respect to the offline
selection, and the ratio of data to simulation, as a function of , for (upper)
2016 data and (lower) 2017 data and simulation. The left plots are for the
barrel region and the right plots are for the overlap region. The red
triangles represent the simulation while the black dots are the data. The
lower panels display the ratio of efficiencies in data and simulation. The
central value in each bin is obtained from the average of the distribution
within the bin.

###  0.5.4 The L1 trigger efficiency

The L1 component of the overall muon trigger efficiency at high is
parameterized separately for the two cases when an associated shower is, or is
not, tagged. From Fig. 11, it can be seen that above the initial turn-on
curve, the trigger efficiency is mostly uniform, but appears to be slowly
deteriorating as the muon momentum increases. It is important to quantify the
size of this effect from L1, because it can impact all high-physics
measurements.

The approach used here relies on assuming that the inefficiency appearing at
high is due to showering in the muon detectors, and that the momentum
dependence arises because the probability of showering in a station increases
with increasing momentum. That is, the efficiency under study can be
parameterized as a function of the number of showers
$\epsilon(N_{\text{shower}})$, which should be independent of the momentum.

The validity of the shower-based approach is verified by studying the L1 muon
efficiency as a function of the number of showers for different muon momentum
slices. We observe that, within a momentum slice, the trigger efficiency does
correlate with the number of showers. Furthermore, the dependence on
$N_{\text{shower}}$ is the same or similar across the compared $p$ ranges.

The shower probability shown in Fig. 2 is parameterized as a function of muon
momentum as $P_{\mathrm{shower}}(p)$. The parameterization is performed by
fitting the distribution in the region up to 1, separately for the data and
for the DY simulation, with a linear function. The upper end of the range is
dictated by the lack of a sufficient number of muons in data above $p\approx
1\TeV$.

The L1 efficiency can thus be calculated as a function of $p$ according to:

$\epsilon_{\mathrm{L1}}(p)=\sum\limits_{N_{\text{shower}}=0}^{4}\epsilon(N_{\text{shower}})P_{\mathrm{Nshower}}(p),$
(5)

where $P_{\mathrm{Nshower}}(p)$ is the probability for a muon of momentum $p$
to produce the number of showers given by $N_{\text{shower}}$, which can be
calculated from $P_{\mathrm{shower}}(p)$ using standard combinatorial
formulas. The maximum number of showers is 4 since there are 4 muon stations.

We extract $\epsilon(N_{\text{shower}})$ from simulated DY events and from the
2016 and 2017 data sets recorded with the trigger. An event selection is
applied to remove cosmic ray muons from the data and to select only well-
reconstructed isolated muons passing the high-identification criteria. Regions
in the barrel ($\abs{\eta}<0.9$) and endcap ($\abs{\eta}>1.2$) are analyzed
separately. The overlap region where muons can have hits in both DT and CSC is
not considered in this study.

For each muon reconstructed offline, the L1 muon candidates close to the
extrapolated muon trajectory are stored. The candidate with the highest and in
time with the collision is taken as the L1 candidate assigned to this muon.
The L1 efficiency for the muon is defined based on whether an L1 candidate
with above the L1 threshold (22) is found or not.

The final efficiency measurement is extracted from a combination of 2016 and
2017 data sets, which maximizes the sample size. The resulting L1 efficiency
for muons with different numbers of showers is shown in Table 1.

Figure 13: The L1 efficiency in three $\eta$ regions: (upper left) barrel; (upper right) for muon with $1.2<\abs{\eta}<1.8$; and (lower) endcap with muon $\abs{\eta}>1.8$. The plots show a comparison between directly determining the efficiency from simulation (blue dots) and with data (black triangles) with respect to calculating it from shower multiplicity, both in 2016+2017 combined data (black line) and 2017 simulation (dashed blue line). The shaded bands include the statistical uncertainties of the measurements and the systematic uncertainty of the showering probability determination. Table 1: The L1 trigger efficiency for barrel and endcap muons measured as a function of the number of showers in the muon stations. The endcap was split into near ($\abs{\eta}<1.8$) and far ($\abs{\eta}>1.8$) sections. $N_{\text{shower}}$ | Barrel L1 efficiency | Near endcap L1 efficiency | Far endcap L1 efficiency
---|---|---|---
| Data | Simulation | Data | Simulation | Data | Simulation
0 | 93.9$\pm$0.1% | 94.4$\pm$0.1% | 89.3$\pm$0.1% | 91.4$\pm$0.1% | 88.3$\pm$0.1% | 90.0$\pm$0.1%
1 | 82.2$\pm$0.1% | 82.7$\pm$0.1% | 84.9$\pm$0.2% | 87.2$\pm$0.1% | 83.5$\pm$0.1% | 85.9$\pm$0.1%
2 | 67.1$\pm$0.7% | 67.3$\pm$0.3% | 78.9$\pm$0.5% | 81.9$\pm$0.3% | 77.0$\pm$0.2% | 79.8$\pm$0.3%
3 | 49.8$\pm$3.4% | 50.1$\pm$1.4% | 76.9$\pm$2.0% | 76.0$\pm$1.0% | 70.2$\pm$0.8% | 72.7$\pm$0.9%
4 | 40$\pm$15% | 36$\pm$9% | 68$\pm$11% | 75$\pm$5% | 63$\pm$3% | 60$\pm$4%

These numbers are combined with the parameterization of the number of showers
versus $p$, as described above, yielding the L1 efficiency, as a function of
$p$, shown in Fig. 13. The results shown as a black line in the plot were
derived using the shower-based approach described above, taking both the
shower probability and the L1 efficiency from data. The shaded bands represent
the statistical and systematic uncertainties of the shower probability
determination. They are dominated by the small number of events at high
momentum, particularly in the barrel region (cf. Fig. 2). The efficiency
calculated directly from the data events is shown as black points. The two
methods give comparable results, indicating that the presence of showers
contributes to the L1 inefficiency at high momentum. The L1 efficiency
measured in the simulated DY sample is shown for comparison, as blue points
and lines. The two methods agree well, with a decreasing efficiency trend
similar to that observed in data.

##  0.6 Momentum assignment performance

At low and intermediate momenta, below 100, the muon resolution is dominated
by the hits measured in the silicon tracker. In contrast, hits and segments
measured in the muon chambers are significantly affected by multiple
scattering of the muon trajectory while passing through the calorimeters and
the flux-return yoke. This multiple scattering is reduced with increasing
momentum, and above 200the muon chamber measurements start to improve on the
measured . The ultimate performance at high is then determined by the
precision of the muon chamber measurements and by the alignment of muon
chambers relative to each other and to the inner tracker.

The alignment of the silicon tracker is a challenging task, achieving a
statistical accuracy better than 10on the position of individual detector
modules [26, 27]. To these small remaining alignment uncertainties one has to
add the intrinsic resolution of silicon hits (typical 10–30). The intrinsic
precision of the muon DT chambers in the barrel region and the CSC in the
endcap region, is of the order of 100–200, to which the possible chamber
misalignment is added in quadrature [4].

Until 2015, the CMS muon reconstruction neglected the alignment uncertainties
of the muon chambers, referred to as alignment position errors (APE).
Resultant shortcomings were evident, as observed deviations were larger than
expected uncertainties for the muon segment parameters with respect to the
extrapolated track from the inner tracker. The best possible reconstruction
for a high-muon track can be reached by a correct relative weighting of
tracker and muon detector hits. In the high-momentum regime this balance
requires including appropriate muon alignment uncertainties in the Kalman
filter. From the beginning of Run 2, the muon reconstruction has been using
nonzero muon APEs [28]. Muon APEs have been introduced for local reconstructed
segments in each station for all six segment degrees of freedom (three local
positions $x$, $y$, and $z$; and three local angles $\phi_{x}$, $\phi_{y}$,
and $\phi_{z}$), chamber-by-chamber, for both DT and CSC chambers; they are
taken as uncorrelated, as a first approximation.

The muon momentum resolution and the closely related charge assignment are
studied in detail using simulation in Section 0.6.1. These studies span the
entire momentum spectrum with high precision and provide estimates of the
impact of different detector alignment conditions, with and without the APEs.
The performance of the momentum resolution and scale measurements are then
assessed in data from both cosmic ray muons and collisions, and compared to
simulation, in Sections 0.6.2 and 0.6.3, respectively.

###  0.6.1 Momentum performance in simulation

The momentum resolution of highly energetic muons can be measured in simulated
events, where the true muon momentum is known. The resolution can be extracted
from the distribution of the relative residual in $q/p$:

$R_{\text{reco-
gen}}=\frac{(q/p)^{\mathrm{reco}}-(q/p)^{\mathrm{gen}}}{(q/p)^{\mathrm{gen}}},$
(6)

where $q/p$ is the charge sign divided by the momentum of the muon. The
expectations for various alignment scenarios have been tested in simulation on
back-to-back dimuons with distributions uniform in $\eta$, $\phi$, and $p$,
within the range $5\GeV<p<2.5\TeV$. For smaller intervals of momentum within
that range, the standard deviation $\sigma$ of a fit to a Gaussian function of
the distribution for the TuneP algorithm is shown in Fig. 14, as a function of
the momentum. The performance of the tracker-only fit is also given for
comparison.

Figure 14: Muon momentum resolution (standard deviation $\sigma$ of the fit of
the core distributions to a Gaussian function) in (left) the barrel region
$\abs{\eta}<0.9$ and (right) the endcap region $1.2<\abs{\eta}<2.4$, for the
TuneP algorithm, as a function of muon momentum, for the various misalignment
scenarios with and without APEs. A comparison with the ideal scenario is also
given. The performance of the tracker-only fit is shown for comparison.

Figure 15:  Comparison of the TuneP and global reconstruction algorithms for
simulated muons in the (upper) barrel and (lower) endcap, for the cases with
and without the presence of tagged showers in any muon station. The left plots
show the momentum resolution (Gaussian $\sigma$); the right plots show the
tail fraction with $\abs{\delta k/k}>20\%$, as a function of muon momentum.

Startup and asymptotic scenarios with and without the corresponding APEs have
been simulated, together with the ideal scenario with no misalignment (APEs
set to zero). The startup scenario corresponds to the preliminary alignment at
the beginning of a data-taking period. The startup performance is expected to
be suboptimal, in particular because of alignment after the opening and
closure of the detector during the LHC shutdown periods. The final alignment
of the individual muon chambers (both DT and CSC), also called asymptotic, is
determined starting from the aligned silicon tracker geometry, by
extrapolating selected muon tracks from the inner tracker to the muon chambers
[29]. The alignment algorithm can use both cosmic ray muons and muons from
collisions, selected with high purity and $\pt$ above a minimum threshold to
limit the multiple scattering ($\pt>30\GeV$ for collision muons in 2016 data
taking). Significant improvements are found with the inclusion of APEs in the
startup scenario. In the endcaps, the startup performance is worse than
tracker-only, but gets recovered with APEs. In the barrel region, the
performance gets closer to asymptotic by including the APEs. Overall, there
are also small improvements for the asymptotic scenario due to the inclusion
of APEs.

To further assess the performance of the TuneP algorithm, it is important to
study not only the Gaussian core resolution, but also the tails of the
residual distribution that are sensitive to muon showering. We characterize
the tails by the fraction of muons with relative momentum residual
$\abs{\delta k/k}>20\%$ (with $k=q/p$), as a function of the muon momentum.
The comparisons of the momentum resolution and the tails between the global
muon fit and the TuneP choice are shown in Fig. 15 for the asymptotic
conditions of alignment and APEs. Two cases are defined by whether or not at
least one shower was found in the muon system.

With TuneP, the momentum resolution $\sigma$ is about 2% for muons with
$p<200\GeV$ in the barrel, whereas it is slightly above that value in the
endcap. At 2, the resolution reaches about 6% in the barrel and 8% in the
endcap. A clear advantage of the strategy to remove contaminated muon stations
from the trajectory fit is seen by comparing the resolution tails of the
global muon fit and TuneP. The TuneP assignment is mostly independent of
showering. This does not come at the expense of the core resolution, which
does not degrade with respect to the global muon fit, but rather is also
slightly improved.

Finally, the TuneP momentum assignment provides a reliable determination of
the muon charge sign up to very high momenta. Several studies made on DY and
single muon simulations predict a charge misassignment probability varying
from $10^{-5}$ to $10^{-4}$ for muon momenta from 100to 2. Cosmic ray muons
can be used to partially validate these probabilities [30]. During Run 2, we
collected 20 000 cosmic ray muons crossing the tracker volume of CMS with
$\pt>30\GeV$. Only one cosmic ray muon appears to have a wrong charge
assignment, with apparent $\pt=640\GeV$ (estimated from the lower CMS
hemisphere).

###  0.6.2 Momentum resolution from cosmic ray muons and collision events

In addition to the muons produced from heavy-boson decays, high-muons from
cosmic ray interactions and decays in the atmosphere [31] provide an excellent
source of clean events that the CMS detector can measure. As the muons
traverse the CMS detector close to vertically, two reconstructed legs (upper
and lower) provide independent measurements of the momentum for a single
physical muon. The muon momentum scale and resolution can then be assessed.
For each selected event, we require two global muons, one in each hemisphere
of the detector, with good tracker track quality to further ensure that the
track is crossing well within the tracker volume, similarly to muons produced
in collisions.

The two global muon tracks belong to the same cosmic ray muon trajectory and
should then have similar momentum. It is then possible to extract the relative
$q/\pt$ residual, $R_{\text{cosmic}}(q/\pt)$, defined as:

$R_{\text{cosmic}}(q/\pt)=\frac{{(q/\pt)}^{\text{Upper}}-{(q/\pt)}^{\text{Lower}}}{\sqrt{2}(q/\pt)^{\text{Lower}}},$
(7)

where ${(q/\pt)}^{\text{Upper}}$ and ${(q/\pt)}^{\text{Lower}}$ are the charge
sign divided by $\pt$ for the upper and the lower muon tracks, respectively.
The factor of $\sqrt{2}$ accounts for the fact that the $q/\pt$ measurements
of the two tracks are independent.

Figure 16 compares the $\pt$ resolution from $R_{\text{cosmic}}$ measured with
the cosmic ray muons collected in 2016 and 2017, crossing the barrel
($\abs{\eta}<1.2$) and the endcap ($1.2<\abs{\eta}<1.6$) regions. The fits use
the TuneP algorithm and the resolution obtained from simulated DY events,
$R_{\text{reco-gen}}$, is defined in Eq. (6). One third of the cosmic ray muon
sample was collected during collisions using the same single-muon trigger used
to record high-momentum muons from heavy-boson decays in order to guarantee
the same detection environment; the remainder was collected during dedicated
cosmic ray muon runs with no LHC beams. The full cosmic ray muon sample has
the same reconstruction procedure as that used for collisions. Good agreement
is found between the cosmic ray muon data and the simulated DY events. The
uncertainties in the highest bins are dominated by the small number of cosmic
ray muons recorded (only 247 events with $\pt>500\GeV$).

Figure 16: Gaussian $\sigma$ of fits to $q/\pt$ relative residuals for TuneP
cosmic ray muons collected in 2016 and 2017 for (left) the barrel
($\abs{\eta}<1.2$) and (right) the endcap ($1.2<\abs{\eta}<1.6$) regions,
compared to the resolution extracted from DY simulation.

Figure 17: Dimuon mass resolution (Gaussian $\sigma$), as a function of muon
TuneP in the BB category. Each dimuon event is counted twice since each muon
($\mu^{+}$ and $\mu^{-}$) in the event is filling the histograms. Results for
(left) 2016 and (right) 2017 are shown. Data are shown in black while the
resolution obtained from simulation is shown in red. The lower panels of the
plots show the ratio of data to simulation; the blue boxes represent the
statistical uncertainties. The central value in each bin is obtained from the
average of the distribution within the bin.

The coverage in $\eta$ is limited with cosmic ray muons, which are
predominantly close to vertical. Hence the momentum resolution performance is
measured best for $\abs{\eta}<1.6$. To overcome this limitation, events in the
boson peak from collisions can be used to assess the dimuon mass resolution,
as a function of the of the individual muons in a dimuon pair. The mass
resolution function is the convolution of a Breit–Wigner distribution that
models the intrinsic decay width of the boson (both mean and width set to the
PDG values [20]) with a double Crystal Ball function [32, 33] that models the
detector effects. The boson peak is fit in a mass range
$75<m_{\mu\mu}<105\GeV$. Each muon in the event is counted separately when
filling the histograms, according to muon , so that each event is counted
twice. The resulting dimuon mass resolution as a function of is shown in Figs.
17 and 18 for events having both muons in the barrel (BB) or at least one of
the two muons in the endcap (BE+EE), respectively. The BE+EE results are
further split to isolate the forward endcap part (at least one of the two
muons with $\abs{\eta}>1.6$) in the lower plot in Fig. 18.

Figure 18: Dimuon mass resolution (Gaussian $\sigma$), as a function of muon
TuneP in the BE+EE category. Each dimuon event is counted twice since each
muon ($\mu^{+}$ and $\mu^{-}$) in the event is filling the histograms. Results
for (upper left) 2016 and (upper right) 2017 are shown. The lower plot is for
2017 data where the BE+EE category is defined with at least one of the two
muons with $\abs{\eta}>1.6$. Data are shown in black while the resolution
obtained from simulation is shown in red. The central value in each bin is
obtained from the average of the distribution within the bin.

For the BB events, the mass resolution in data agrees with simulation for both
2016 and 2017 data. These results confirm what is observed with cosmic ray
muons. Above 300the 2017 resolution is slightly better than that in 2016. This
is due to changes in the muon system alignment and improved values of APEs.
For the BE+EE events, an offset of about 15% can be seen over the entire range
in the 2017 data. The discrepancy is localized in the forward endcap region,
as can be seen in the bottom plot of Fig. 18 that restricts the BE+EE category
to events with only one of the two muons with $\abs{\eta}>1.6$. The results
are presented as a function of the leading muon ; the shift in the resolution
between data and simulation is seen only when the events have at least one
high-muon. This endcap region is known to have a tracker alignment bias, as
can also be seen in the scale results in Fig. 21.

###  0.6.3 Momentum scale from collision events

The scale of the muon $\pt$ is sensitive to three effects that can potentially
introduce biases: muon energy losses, detector misalignments, and magnetic
field variations. The calibration of the momentum scale is performed by
modifying the curvature of the muon, while taking into account these three
physics effects. The detector alignment biases result in an additive
correction $k_{\mathrm{b}}$ (to the (signed) muon curvature $\kappa$) that has
the same sign for both positively and negatively charged muons, resulting in
an increase in the measured $p$ for one sign charge, and a decrease in
measured $p$ for the other sign. The variations from the magnetic field lead
to a multiplicative correction factor to the curvature. The energy loss is
taken into account with an additive term, that increases the muon momentum
independent of the muon charge.

For intermediate- and low-muons, two different methods are used for Run 2 data
to estimate the additive and multiplicative correction factors to the muon
curvature. The first method selects muons from boson decays and derives the
corrections from the mean value of the distribution of $q/\pt$, with further
tuning performed using the mean of the dimuon invariant mass spectrum [34].
The second method selects muons from boson, , and 1S resonances, and
determines corrections using a Kalman filter. The corrections are provided as
a function of the muon $\eta$ and $\phi$ in both methods and as a function of
the muon in the second method. In Run 2 data, the dominant source of scale
bias is coming from the detector alignment.

For high-muons, the reconstruction of the muon relies both on the tracker and
on the muon system inputs. Thus, the derived corrections from the two previous
methods that focus only on the tracker information, are not directly
applicable. In addition, the intrinsic alignment of the muon chambers within
the muon system, and their alignment respectively to the tracker, are sources
of potential scale bias. The generalized endpoint (GE) method [6] quantifies
biases in the determination relying on muons produced from DY events. The
method consists of comparing the muon curvature distribution between data and
simulated events, modifying the simulated values by a constant additive bias
term ($k_{\mathrm{b}}$), such that the distribution is distorted as
$\kappa\to\kappa+k_{\mathrm{b}}$. A $\chi^{2}$ test is performed between the
curvature distribution in data and in simulation, as a function of the
injected bias $k_{\mathrm{b}}$, in order to find the minimum of the
distribution. Such a distortion reproduces a potential detector alignment bias
that changes $p$ in opposite directions for positively and negatively charged
muons. The muon curvature without any additive bias in simulation is shown in
Fig. 19.

Figure 19: Data to simulation comparison of the curvature distribution in
$\PZ\to\mu^{+}\mu^{-}$ events, for 2017 data with muon $\pt>200\GeV$.

The estimated additive biases measured with GE are presented as a function of
$\eta$ and $\phi$, for the 2016 data in Fig. 20 and for the 2017 data in Fig.
21. For each year, the results are obtained using both the tracker and the
TuneP assignment. The sample size of high-momentum muons is limited, but it is
visible that the detector parts that are most affected by the misalignment are
the endcaps, in both years, with an estimated bias $k_{\mathrm{b}}\approx
0.15/\TeV$ in 2016 data and a maximum $k_{\mathrm{b}}\approx 0.5/\TeV$ in 2017
data localized in the forward positive endcap and in a given $\phi$ sector
($-60^{\circ}<\phi<60^{\circ}$). No significant differences are found when
comparing the bias values obtained with TuneP and tracker . Thus the
misalignment is mostly coming from the tracker component while the muon system
alignment does not contribute significantly. The local tracker misalignment
found in 2017 data is suspected to be caused by radiation effects that impact
the pixel and strip detector calibration throughout the run period. In
addition, for that specific year the alignment procedure was limited by the
sample size of muons from Z decays and from cosmic rays, which are needed to
ensure the right muon mixture input when performing the detector calibration.
These results are in agreement with those from Ref. [34].

Figure 20: Measurement of the scale bias for muons above 200with 2016 data. On
the left the corresponds to TuneP, while on the right it corresponds to the
tracker-only assignment.

Figure 21: Measurement of the scale bias for muons above 200with 2017 data. On
the left the corresponds to the TuneP, while on the right it corresponds to
the tracker-only assignment.

##  0.7 Summary

The performance of muon reconstruction, identification, trigger, and momentum
assignment has been studied in a sample enriched in high-momentum muons using
proton-proton collisions at $\sqrt{s}=13\TeV$, collected by the CMS experiment
at the LHC in 2016-2017, and corresponding to the integrated luminosity of
78.4. Depending on the longitudinal component of the momentum, muons with
transverse momentum $\pt>200\GeV$ can have radiative energy losses in steel
that are no longer negligible compared to ionization energy losses. Dedicated
methods have been developed to study the performance impact of the detector
alignment and electromagnetic showers along the muon track. Overall, the
measurements are described accurately by the simulation and their reach in
momentum is limited by the statistical uncertainties. The largest discrepancy
between data and simulation is found at the trigger level with a 10%
efficiency difference for muons with around 1. Representative figures of merit
that illustrate the muon performance at high momentum are listed below.

  * •

The identification efficiency measured in data is $>$98% over the full
spectrum, up to $1000\GeV$ for a pseudorapidity magnitude $\abs{\eta}<2.4$. No
dependence on the momentum p is observed. The ratio of data to simulation is
within 1% of unity for $0<\abs{\eta}<0.9$ and $2.1<\abs{\eta}<2.4$, and within
0.5% for $0.9<\abs{\eta}<2.1$.

  * •

The standalone reconstruction efficiency measured in data is $>$98% over the
full spectrum and up to $1500\GeV$ for $\abs{\eta}<1.6$. No dependence on the
momentum p is observed. The ratio of data to simulation is uniform and equal
to 0.99. In the forward detector region ($\abs{\eta}>1.6$), an inefficiency
trend starting at $p=200\GeV$ is observed both in simulation and in data,
although it is slightly more pronounced in the latter. The muon showering and
the dense track activities for muon momentum around $1000\GeV$ interfere
within the muon reconstruction, and lead to a momentum dependence with up to
5% inefficiency when both effects are combined.

  * •

The total trigger efficiency measured in data shows a decreasing trend from
92% at $\pt=100\GeV$ down to 80% at $\pt=1000\GeV$, integrated over muon
$\eta$. The simulation does not reproduce the severity of the slope and the
ratio of data to simulation deviates from unity at the level of 10%. This
discrepancy is driven by the first level (L1) trigger and is localized in the
overlap region ($0.9<\abs{\eta}<1.2$) because of a nonideal interplay between
DT and CSC signals. This was improved in 2018.

  * •

The L1 efficiency suffers from showering effects. Direct measurements from
data and simulation are compared to a parameterization derived from showering
inputs. The trend as a function of $p$ is reproduced by the parameterization
within the uncertainties. The biggest impact of showering is seen in the
barrel region ($\abs{\eta}<0.9$), both in simulation and in data. The
simulation does not fully reproduce the slope seen in data in both barrel and
endcap regions, thus indicating a slight underestimation of the showering
effect at L1 in simulation.

  * •

TuneP momentum assignment and performance is robust against the presence of
showers. The simulation reproduces the choice of TuneP among the different TeV
refitters in data.

  * •

The boson mass resolution is $<$3for events with the leading muon up to
450over the full $\eta$ range. The boson mass resolution is very similar
between simulation and data, except in the endcap region for the 2017 data,
where a tracker alignment bias degrades the resolution by 20% for events with
the leading muon above 150.

  * •

The trajectory curvature bias $k_{\mathrm{b}}$ is compatible with zero in the
barrel region, but is as large as $0.5\TeV^{-1}$ for $\abs{\eta}>2.1$ in 2017
data.

These results show that the performance of the CMS detector is outstanding for
high energy muons and is largely well described by simulation.

###### 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: the Austrian Federal Ministry of Education, Science and
Research and the Austrian Science Fund; the Belgian Fonds de la Recherche
Scientifique, and Fonds voor Wetenschappelijk Onderzoek; the Brazilian Funding
Agencies (CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP); the Bulgarian Ministry of
Education and Science; CERN; the Chinese Academy of Sciences, Ministry of
Science and Technology, and National Natural Science Foundation of China; the
Colombian Funding Agency (COLCIENCIAS); the Croatian Ministry of Science,
Education and Sport, and the Croatian Science Foundation; the Research
Promotion Foundation, Cyprus; the Secretariat for Higher Education, Science,
Technology and Innovation, Ecuador; the Ministry of Education and Research,
Estonian Research Council via IUT23-4, IUT23-6 and PRG445 and European
Regional Development Fund, Estonia; the Academy of Finland, Finnish Ministry
of Education and Culture, and Helsinki Institute of Physics; the Institut
National de Physique Nucléaire et de Physique des Particules / CNRS, and
Commissariat à l’Énergie Atomique et aux Énergies Alternatives / CEA, France;
the Bundesministerium für Bildung und Forschung, the Deutsche
Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy – EXC 2121
“Quantum Universe” – 390833306, and Helmholtz-Gemeinschaft Deutscher
Forschungszentren, Germany; the General Secretariat for Research and
Technology, Greece; the National Research, Development and Innovation Fund,
Hungary; the Department of Atomic Energy and the Department of Science and
Technology, India; the Institute for Studies in Theoretical Physics and
Mathematics, Iran; the Science Foundation, Ireland; the Istituto Nazionale di
Fisica Nucleare, Italy; the Ministry of Science, ICT and Future Planning, and
National Research Foundation (NRF), Republic of Korea; the Ministry of
Education and Science of the Republic of Latvia; the Lithuanian Academy of
Sciences; the Ministry of Education, and University of Malaya (Malaysia); the
Ministry of Science of Montenegro; the Mexican Funding Agencies (BUAP,
CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI); the Ministry of Business,
Innovation and Employment, New Zealand; the Pakistan Atomic Energy Commission;
the Ministry of Science and Higher Education and the National Science Centre,
Poland; the Fundação para a Ciência e a Tecnologia, Portugal; JINR, Dubna; the
Ministry of Education and Science of the Russian Federation, the Federal
Agency of Atomic Energy of the Russian Federation, Russian Academy of
Sciences, the Russian Foundation for Basic Research, and the National Research
Center “Kurchatov Institute”; the Ministry of Education, Science and
Technological Development of Serbia; the Secretaría de Estado de
Investigación, Desarrollo e Innovación, Programa Consolider-Ingenio 2010, Plan
Estatal de Investigación Científica y Técnica y de Innovación 2017–2020,
research project IDI-2018-000174 del Principado de Asturias, and Fondo Europeo
de Desarrollo Regional, Spain; the Ministry of Science, Technology and
Research, Sri Lanka; the Swiss Funding Agencies (ETH Board, ETH Zurich, PSI,
SNF, UniZH, Canton Zurich, and SER); the Ministry of Science and Technology,
Taipei; the Thailand Center of Excellence in Physics, the Institute for the
Promotion of Teaching Science and Technology of Thailand, Special Task Force
for Activating Research and the National Science and Technology Development
Agency of Thailand; the Scientific and Technical Research Council of Turkey,
and Turkish Atomic Energy Authority; the National Academy of Sciences of
Ukraine; the Science and Technology Facilities Council, UK; the US Department
of Energy, and the US National Science Foundation. Individuals have received
support from the Marie-Curie program and the European Research Council and
Horizon 2020 Grant, contract Nos. 675440, 752730, and 765710 (European Union);
the Leventis Foundation; the A.P. Sloan Foundation; the Alexander von Humboldt
Foundation; the Belgian Federal Science Policy Office; the Fonds pour la
Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA-
Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-
Belgium); the F.R.S.-FNRS and FWO (Belgium) under the “Excellence of Science –
EOS” – be.h project n. 30820817; the Beijing Municipal Science & Technology
Commission, No. Z181100004218003; the Ministry of Education, Youth and Sports
(MEYS) of the Czech Republic; the Lendület (“Momentum”) Program and the János
Bolyai Research Scholarship of the Hungarian Academy of Sciences, the New
National Excellence Program ÚNKP, the NKFIA research grants 123842, 123959,
124845, 124850, 125105, 128713, 128786, and 129058 (Hungary); the Council of
Scientific and Industrial Research, India; the HOMING PLUS program of the
Foundation for Polish Science, cofinanced from European Union, Regional
Development Fund, the Mobility Plus program of the Ministry of Science and
Higher Education, the National Science Center (Poland), contracts Harmonia
2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and
2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406; the National Priorities
Research Program by Qatar National Research Fund; the Ministry of Science and
Education, grant no. 3.2989.2017 (Russia); the Programa de Excelencia María de
Maeztu, 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] A. Leike, “The phenomenology of extra neutral gauge bosons”,  Phys. Rept. 317 (1999) 143, 10.1016/S0370-1573(98)00133-1, arXiv:hep-ph/9805494.
  * [2] P. Langacker, “The physics of heavy $Z^{\prime}$ gauge bosons”,  Rev. Mod. Phys. 81 (2009) 1199, 10.1103/RevModPhys.81.1199, arXiv:0801.1345.
  * [3] K. Hsieh, K. Schmitz, J.-H. Yu, and C. P. Yuan, “Global analysis of general SU(2) x SU(2) x U(1) models with precision data”,  Phys. Rev. D 82 (2010) 035011, 10.1103/PhysRevD.82.035011, arXiv:1003.3482.
  * [4] CMS Collaboration, “The performance of the CMS muon detector in proton-proton collisions at $\sqrt{s}=7$ TeV at the LHC”,  JINST 8 (2013) P11002, 10.1088/1748-0221/8/11/P11002, arXiv:1306.6905.
  * [5] CMS Collaboration, “Performance of CMS muon reconstruction in pp collision events at $\sqrt{s}=7$ TeV”,  JINST 7 (2012) P10002, 10.1088/1748-0221/7/10/P10002, arXiv:1206.4071.
  * [6] CMS Collaboration, “Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  JINST 13 (2018) P06015, 10.1088/1748-0221/13/06/P06015, arXiv:1804.04528.
  * [7] CMS Collaboration, “The CMS trigger system”,  JINST 12 (2017) P01020, 10.1088/1748-0221/12/01/P01020, arXiv:1609.02366.
  * [8] CMS Collaboration, “The CMS experiment at the CERN LHC”,  JINST 3 (2008) S08004, 10.1088/1748-0221/3/08/S08004.
  * [9] 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.
  * [10] S. Frixione, P. Nason, and C. Oleari, “Matching NLO QCD computations with parton shower simulations: the POWHEG method”,  JHEP 11 (2007) 070, 10.1088/1126-6708/2007/11/070, arXiv:0709.2092.
  * [11] S. Alioli, P. Nason, C. Oleari, and E. Re, “A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX”,  JHEP 06 (2010) 043, 10.1007/JHEP06(2010)043, arXiv:1002.2581.
  * [12] 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.
  * [13] T. Sjöstrand et al., “An introduction to PYTHIA 8.2”,  Comput. Phys. Commun. 191 (2015) 159, 10.1016/j.cpc.2015.01.024, arXiv:1410.3012.
  * [14] 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.
  * [15] CMS Collaboration, “Underlying event tunes and double parton scattering”, CMS Physics Analysis Summary CMS-PAS-GEN-14-001, 2014.
  * [16] CMS Collaboration, “Extraction and validation of a new set of CMS PYTHIA8 tunes from underlying-event measurements”, CMS Physics Analysis Summary CMS-PAS-GEN-17-001, 2018.  arXiv:1903.12179.
  * [17] NNPDF Collaboration, “Parton distributions for the LHC Run II”,  JHEP 04 (2015) 040, 10.1007/JHEP04(2015)040, arXiv:1410.8849.
  * [18] NNPDF Collaboration, “Parton distributions from high-precision collider data”,  Eur. Phys. J. C 77 (2017) 663, 10.1140/epjc/s10052-017-5199-5, arXiv:1706.00428.
  * [19] GEANT4 Collaboration, “—a simulation toolkit”,  Nucl. Instrum. Meth. A 506 (2003) 250, 10.1016/S0168-9002(03)01368-8.
  * [20] Particle Data Group, M. Tanabashi et al., “Review of particle physics”,  Phys. Rev. D 98 (2018) 030001, 10.1103/PhysRevD.98.030001.
  * [21] CMS Collaboration, “Search for high-mass resonances in dilepton final states in proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  JHEP 06 (2018) 120, 10.1007/JHEP06(2018)120, arXiv:1803.06292.
  * [22] W. Adam, B. Mangano, T. Speer, and T. Todorov, “Track reconstruction in the CMS tracker”, Technical Report CMS-NOTE-2006-041, 2006.
  * [23] R. Frühwirth, “Application of Kalman filtering to track and vertex fitting”,  Nucl. Instrum. Meth. A 262 (1987) 444, 10.1016/0168-9002(87)90887-4.
  * [24] CMS Collaboration, “Search for physics beyond the standard model in dilepton mass spectra in proton-proton collisions at $\sqrt{s}=8$ TeV”,  JHEP 04 (2015) 025, 10.1007/JHEP04(2015)025, arXiv:1412.6302.
  * [25] CMS Collaboration, “Description and performance of track and primary-vertex reconstruction with the CMS tracker”,  JINST 9 (2014) P10009, 10.1088/1748-0221/9/10/P10009, arXiv:1405.6569.
  * [26] CMS Collaboration, “Alignment of the CMS silicon tracker during commissioning with cosmic rays”,  JINST 5 (2010) T03009, 10.1088/1748-0221/5/03/T03009, arXiv:0910.2505.
  * [27] CMS Collaboration, “Alignment of the CMS tracker with LHC and cosmic ray data”,  JINST 9 (2014) P06009, 10.1088/1748-0221/9/06/P06009, arXiv:1403.2286.
  * [28] CMS Collaboration, “Performance of muon reconstruction including alignment position errors for 2016 collision data”, CMS Detector Performance Summary CMS-DP-2016-067, 2016.
  * [29] CMS Collaboration, “Alignment of the CMS muon system with cosmic-ray and beam-halo muons”,  JINST 5 (2010) T03020, 10.1088/1748-0221/5/03/T03020, arXiv:0911.4022.
  * [30] CMS Collaboration, “Performance of CMS muon reconstruction in cosmic-ray events”,  JINST 5 (2010) T03022, 10.1088/1748-0221/5/03/T03022, arXiv:0911.4994.
  * [31] LVD Collaboration, “Measurement of cosmic muon charge ratio with the Large Volume Detector”, (2013). arXiv:1311.6995.
  * [32] M. J. Oreglia, “A study of the reactions $\psi^{\prime}\to\gamma\gamma\psi$”.  PhD thesis, Stanford University, 1980.  SLAC Report SLAC-R-236, see Appendix D.
  * [33] J. E. Gaiser, “Charmonium spectroscopy from radiative decays of the J/$\psi$ and $\psi^{\prime}$”.  PhD thesis, Stanford University, 1982.  SLAC Report SLAC-R-255.
  * [34] A. Bodek et al., “Extracting muon momentum scale corrections for hadron collider experiments”,  Eur. Phys. J. C 72 (2012) 2194, 10.1140/epjc/s10052-012-2194-8, arXiv:1208.3710.

##  .8 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan${}^{\textrm{\textdagger}}$, A. Tumasyan Institut für
Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, T. Bergauer, M. Dragicevic, J. Erö, A. Escalante Del
Valle, M. Flechl, R. Frühwirth1, M. Jeitler1, N. Krammer, I. Krätschmer, D.
Liko, T. Madlener, I. Mikulec, N. Rad, J. Schieck1, R. Schöfbeck, M. Spanring,
D. Spitzbart, W. Waltenberger, C.-E. Wulz1, M. Zarucki Institute for Nuclear
Problems, Minsk, Belarus
V. Drugakov, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
M.R. Darwish, E.A. De Wolf, D. Di Croce, X. Janssen, 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, K. Skovpen, 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, 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, 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, S. Wuyckens, J. Zobec Centro Brasileiro de Pesquisas Fisicas, Rio de
Janeiro, Brazil
F.L. Alves, G.A. Alves, G. Correia Silva, C. Hensel, A. Moraes, P. Rebello
Teles Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, C. De Oliveira Martins, S.
Fonseca De Souza, L.M. Huertas Guativa, H. Malbouisson, J. Martins5, D. Matos
Figueiredo, M. Medina Jaime6, M. Melo De Almeida, C. Mora Herrera, L. Mundim,
H. Nogima, W.L. Prado Da Silva, L.J. Sanchez Rosas, A. Santoro, A. Sznajder,
M. Thiel, E.J. Tonelli Manganote3, F. Torres Da Silva De Araujo, A. Vilela
Pereira Universidade Estadual Paulista a, Universidade Federal do ABC b, São
Paulo, Brazil
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 Beihang
University, Beijing, China
W. Fang7, X. Gao7, L. Yuan Institute of High Energy Physics, Beijing, China
G.M. Chen, H.S. Chen, 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 Tsinghua University, Beijing, China
M. Ahmad, Z. Hu, Y. 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, D. Tsiakkouri Charles University, Prague, Czech Republic
M. Finger10, M. Finger Jr.10, A. Kveton, J. Tomsa Escuela Politecnica
Nacional, Quito, Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
H. Abdalla11, S. Elgammal12 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, P. Luukka, T.
Mäenpää, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta University of
Technology, Lappeenranta, Finland
T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, B. Fabbro, J.L. Faure, F.
Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
C. Leloup, 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, F. Lagarde, 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, B. Wittmer 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, D. Bertsche, A.A. Bin Anuar, K. Borras18, V.
Botta, A. Campbell, A. Cardini, P. Connor, S. Consuegra Rodríguez, C.
Contreras-Campana, V. Danilov, A. De Wit, M.M. Defranchis, C. Diez Pardos, D.
Domínguez Damiani, G. Eckerlin, D. Eckstein, T. Eichhorn, A. Elwood, E. Eren,
E. Gallo19, A. Geiser, 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, 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, V. Blobel, 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. Perieanu, A. Reimers, O. Rieger, C. Scharf, P.
Schleper, S. Schumann, J. Schwandt, J. Sonneveld, H. Stadie, G. Steinbrück,
F.M. Stober, B. Vormwald, I. Zoi Karlsruher Institut fuer Technologie,
Karlsruhe, Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, T. Berger, E. Butz, R. Caspart, T.
Chwalek, W. De Boer, A. Dierlamm, K. El Morabit, N. Faltermann, M. Giffels, P.
Goldenzweig, A. Gottmann, M.A. Harrendorf, F. Hartmann17, U. Husemann, S.
Kudella, S. Mitra, M.U. Mozer, D. Müller, Th. Müller, M. Musich, A. Nürnberg,
G. Quast, K. Rabbertz, M. Schröder, I. Shvetsov, H.J. Simonis, R. Ulrich, M.
Wassmer, M. Weber, C. Wöhrmann, R. Wolf Institute of Nuclear and Particle
Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, P. Asenov, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas,
G. Paspalaki National and Kapodistrian University of Athens, Athens, Greece
M. Diamantopoulou, G. Karathanasis, P. Kontaxakis, A. 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, M.I.
Nagy, G. Pasztor, O. Surányi, G.I. Veres Wigner Research Centre for Physics,
Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath22, F. Sikler, T.Á. Vámi, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi21, J. Molnar, Z. Szillasi Institute of
Physics, University of Debrecen, Debrecen, Hungary
P. Raics, D. Teyssier, Z.L. Trocsanyi, B. Ujvari Eszterhazy Karoly University,
Karoly Robert Campus, Gyongyos, Hungary
T. Csorgo, W.J. Metzger, F. Nemes, T. Novak Indian Institute of Science
(IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri, P.C. Tiwari National Institute of Science
Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati24, C. Kar, G. Kole, P. Mal, V.K. Muraleedharan Nair Bindhu, A.
Nayak25, D.K. Sahoo24, S.K. Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, R. Chawla, N. Dhingra, R.
Gupta, A. Kaur, M. Kaur, S. Kaur, P. Kumari, M. Lohan, M. Meena, K. Sandeep,
S. Sharma, J.B. Singh, A.K. Virdi, G. Walia University of Delhi, Delhi, India
A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, Ashok Kumar, M.
Naimuddin, P. Priyanka, K. Ranjan, Aashaq Shah, R. Sharma Saha Institute of
Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj26, M. Bharti26, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep26,
D. Bhowmik, S. Dutta, S. Ghosh, B. Gomber27, M. Maity28, K. Mondal, S. Nandan,
A. Purohit, P.K. Rout, G. Saha, S. Sarkar, T. Sarkar28, M. Sharan, B. Singh26,
S. Thakur26 Indian Institute of Technology Madras, Madras, India
P.K. Behera, P. Kalbhor, A. Muhammad, P.R. Pujahari, A. Sharma, A.K. Sikdar
Bhabha Atomic Research Centre, Mumbai, India
D. Dutta, V. Jha, V. Kumar, D.K. Mishra, P.K. Netrakanti, L.M. Pant, P. Shukla
Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, G.B. Mohanty, N. Sur, RavindraKumar Verma Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, S. Karmakar,
S. Kumar, G. Majumder, K. Mazumdar, N. Sahoo, S. Sawant Indian Institute of
Science Education and Research (IISER), Pune, India
S. 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. Chenarani29, E. Eskandari Tadavani, S.M. Etesami29, 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. Merlin, 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. 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, P. Lujana, 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, G. Fedia,
L. Gianninia,c, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c, G.
Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, G. 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 National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M. Ahmad, Q. Hassan, H.R. Hoorani, W.A. Khan, S. Qazi, M.A. Shah, 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.
Traczyk, 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
M. Chadeeva43, P. Parygin, D. Philippov, E. Popova, V. Rusinov P.N. Lebedev
Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin, M. Kirakosyan, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia
A. Belyaev, E. Boos, M. Dubinin44, L. Dudko, A. Ershov, A. Gribushin, A.
Kaminskiy45, V. Klyukhin, O. Kodolova, I. Lokhtin, S. Obraztsov, S.
Petrushanko, V. Savrin Novosibirsk State University (NSU), Novosibirsk, Russia
A. Barnyakov46, V. Blinov46, T. Dimova46, L. Kardapoltsev46, Y. Skovpen46
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. Adzic47, 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. Russo48, 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, M. Dobson, M. Dünser, N. Dupont, A. Elliott-
Peisert, N. Emriskova, F. Fallavollita49, 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, C. Schäfer, C. Schwick, M.
Selvaggi, A. Sharma, P. Silva, W. Snoeys, P. Sphicas50, 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. Caminada51, K. Deiters, W. Erdmann, R. Horisberger, Q. Ingram, H.C.
Kaestli, D. Kotlinski, U. Langenegger, T. Rohe, S.A. Wiederkehr ETH Zurich -
Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland
M. Backhaus, P. Berger, N. Chernyavskaya, G. Dissertori, M. Dittmar, M.
Donegà, C. Dorfer, T.A. Gómez Espinosa, C. Grab, D. Hits, 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. Amsler52, 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
T.H. Doan, 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. Celik53, S. Damarseckin54, Z.S. Demiroglu, F. Dolek, C.
Dozen55, I. Dumanoglu, G. Gokbulut, EmineGurpinar Guler56, Y. Guler, I. Hos57,
C. Isik, E.E. Kangal58, O. Kara, A. Kayis Topaksu, U. Kiminsu, G. Onengut, K.
Ozdemir59, S. Ozturk60, A.E. Simsek, U.G. Tok, S. Turkcapar, I.S. Zorbakir, C.
Zorbilmez Middle East Technical University, Physics Department, Ankara, Turkey
B. Isildak61, G. Karapinar62, M. Yalvac Bogazici University, Istanbul, Turkey
I.O. Atakisi, E. Gülmez, M. Kaya63, O. Kaya64, Ö. Özçelik, S. Tekten, E.A.
Yetkin65 Istanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak, 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, B. Penning, 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, R.
Di Maria, 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
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, C. Henderson, P. Rumerio, C. West Boston University,
Boston, USA
A. Albert, D. Arcaro, Z. Demiragli, D. Gastler, C. Richardson, J. Rohlf, D.
Sperka, 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,
R. Gerosa, D. Gilbert, B. Hashemi, D. Klein, V. Krutelyov, J. Letts, M.
Masciovecchio, S. May, S. Padhi, M. Pieri, V. Sharma, M. Tadel, F. Würthwein,
A. Yagil, G. Zevi Della Porta University of California, Santa Barbara -
Department of Physics, Santa Barbara, USA
N. Amin, R. Bhandari, C. Campagnari, M. Citron, V. Dutta, M. Franco Sevilla,
J. Incandela, B. Marsh, H. Mei, A. Ovcharova, H. Qu, J. Richman, U. Sarica, D.
Stuart, S. Wang California Institute of Technology, Pasadena, USA
D. Anderson, A. Bornheim, O. Cerri, I. Dutta, J.M. Lawhorn, N. Lu, J. Mao,
H.B. Newman, T.Q. Nguyen, J. Pata, M. Spiropulu, J.R. Vlimant, S. Xie, Z.
Zhang, R.Y. Zhu Carnegie Mellon University, Pittsburgh, USA
M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, M. Sun, I. Vorobiev, M.
Weinberg University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, E. MacDonald, T. Mulholland, R. Patel, A. Perloff, K.
Stenson, K.A. Ulmer, S.R. Wagner Cornell University, Ithaca, USA
J. Alexander, Y. Cheng, J. Chu, A. Datta, A. Frankenthal, K. Mcdermott, J.R.
Patterson, D. Quach, A. Ryd, S.M. Tan, Z. Tao, J. Thom, P. Wittich, M. Zientek
Fermi National Accelerator Laboratory, Batavia, USA
S. Abdullin, M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S.
Banerjee, L.A.T. Bauerdick, A. Beretvas, D. Berry, J. Berryhill, P.C. Bhat, K.
Burkett, J.N. Butler, A. Canepa, G.B. Cerati, H.W.K. Cheung, F. Chlebana, M.
Cremonesi, J. Duarte, V.D. Elvira, J. Freeman, Z. Gecse, E. Gottschalk, L.
Gray, D. Green, S. Grünendahl, O. Gutsche, J. Hanlon, R.M. Harris, S.
Hasegawa, R. Heller, J. Hirschauer, B. Jayatilaka, S. Jindariani, M. Johnson,
U. Joshi, T. Klijnsma, B. Klima, M.J. Kortelainen, B. Kreis, S. Lammel, J.
Lewis, D. Lincoln, R. Lipton, M. Liu, T. Liu, J. Lykken, K. Maeshima, J.M.
Marraffino, D. Mason, P. McBride, P. Merkel, S. Mrenna, S. Nahn, V. O’Dell, V.
Papadimitriou, K. Pedro, C. Pena, G. Rakness, F. Ravera, A. Reinsvold Hall, L.
Ristori, B. Schneider, E. Sexton-Kennedy, N. Smith, A. Soha, W.J. Spalding, L.
Spiegel, S. Stoynev, J. Strait, N. Strobbe, L. Taylor, S. Tkaczyk, N.V. Tran,
L. Uplegger, E.W. Vaandering, C. Vernieri, R. Vidal, M. Wang, H.A. Weber
University of Florida, Gainesville, USA
D. Acosta, P. Avery, D. Bourilkov, A. Brinkerhoff, L. Cadamuro, V. Cherepanov,
F. Errico, R.D. Field, S.V. Gleyzer, D. Guerrero, B.M. Joshi, M. Kim, J.
Konigsberg, A. Korytov, K.H. Lo, K. Matchev, N. Menendez, G. Mitselmakher, D.
Rosenzweig, K. Shi, J. Wang, S. Wang, X. Zuo Florida International University,
Miami, USA
Y.R. Joshi Florida State University, Tallahassee, USA
T. Adams, A. Askew, S. Hagopian, V. Hagopian, K.F. Johnson, R. Khurana, T.
Kolberg, G. Martinez, T. Perry, H. Prosper, C. Schiber, R. Yohay, J. Zhang
Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, M. Hohlmann, D. Noonan, M. Rahmani, M. Saunders, F. Yumiceva