MUO-19-001

MUO-19-001

# Performance of the CMS muon trigger system in proton-proton collisions at
$\sqrt{s}=13\TeV$

###### Abstract

The muon trigger system of the CMS experiment uses a combination of hardware
and software to identify events containing a muon. During Run 2 (covering
2015–2018) the LHC achieved instantaneous luminosities as high as $2\times
10^{34}\percms$ while delivering proton-proton collisions at
$\sqrt{s}=13\TeV$. The challenge for the trigger system of the CMS experiment
is to reduce the registered event rate from about 40MHz to about 1kHz.
Significant improvements important for the success of the CMS physics program
have been made to the muon trigger system via improved muon reconstruction and
identification algorithms since the end of Run 1 and throughout the Run 2
data-taking period. The new algorithms maintain the acceptance of the muon
triggers at the same or even lower rate throughout the data-taking period
despite the increasing number of additional proton-proton interactions in each
LHC bunch crossing. In this paper, the algorithms used in 2015 and 2016 and
their improvements throughout 2017 and 2018 are described. Measurements of the
CMS muon trigger performance for this data-taking period are presented,
including efficiencies, transverse momentum resolution, trigger rates, and the
purity of the selected muon sample. This paper focuses on the single- and
double-muon triggers with the lowest sustainable transverse momentum
thresholds used by CMS. The efficiency is measured in a transverse momentum
range from 8 to several hundred.

##  0.1 Introduction

Muons feature prominently in many of the most important signatures studied at
the CERN LHC, including final states from hadron, vector boson, and Higgs
boson decays, as well as in searches for new physics beyond the standard
model. Since they are the only detectable particles to traverse the whole
detector without significant loss of energy, muons are ideal probes to
identify interesting interactions among the large number of proton-proton
($\Pp\Pp$) collisions provided by the LHC. The ability to reconstruct and
identify muons with high efficiency and precision while maintaining low
misidentification rates is therefore crucial to the success of the CMS physics
program.

In the $\Pp\Pp$ collisions, muons originate from a variety of processes. The
dominant source is decay in flight of pions and kaons, followed by
semileptonic decays of b and c hadrons. Muons from the decay of vector or
Higgs bosons contribute less than 1% to the overall muon sample [1].

In Run 2 (years 2015–2018), the center-of-mass energy ($\sqrt{s}$) of the
$\Pp\Pp$ collisions delivered by the LHC increased from 8 to 13. The small
amount of data recorded in 2015 is disregarded in the following. The peak
instantaneous luminosity achieved during the data taking in Run 2 increased
from $1.5\times 10^{34}\percms$ in 2016 to $2.0\text{--}2.1\times
10^{34}\percms$ in 2017 and 2018. The average number of simultaneous $\Pp\Pp$
interactions per bunch crossing (pileup) increased from 23 in 2016 to 32 in
2017 and 2018. However, because of two distinct beam configurations employed
during 2017, the pileup distribution exhibits a double peak structure with a
secondary peak at pileup $\approx$55\. This posed a significant challenge to
the CMS trigger system, which was tasked to maintain acceptable event rates
for storage and analysis in a pileup environment far in excess of the design
value of 20–25.

The trigger system comprises two levels [2]. The Level-1 (L1) trigger system
consists of custom hardware processors that receive data from calorimeter and
muon systems and reduce the event rate from 40MHz to about 100kHz. The
software-based high-level trigger (HLT) system, which operates on a computing
farm with up to 30 000 CPU cores (at the end of Run 2), further reduces the
rate to approximately 1kHz. The algorithms and techniques used in these
triggers, both at L1 and HLT, evolved during the data taking.

The muon reconstruction in the HLT is split into two steps: the Level-2 (L2)
reconstruction that uses information from the muon detectors, and the Level-3
(L3) reconstruction that incorporates additional information from the inner
tracking detectors. These reconstruction steps are applied in sequence,
followed by muon identification and isolation requirements, if applicable,
defining individual “triggers”. If all the requirements in a given trigger are
fulfilled, the event will be accepted by the trigger system. In this paper, we
will focus on triggers requiring the presence of either at least one (“single-
muon trigger”) or two (“double-muon trigger”) muons fulfilling certain
transverse momentum () requirements. These thresholds are chosen to control
the rate of accepted events. Dedicated triggers targeting muons from low mass
resonances or requiring the presence of at least one muon in conjunction with
other objects, such as electrons or tau leptons, which also use the same muon
reconstruction, are not discussed in this paper. At both L1 and HLT, triggers
can be prescaled, so that only a predefined fraction of events fulfilling the
trigger condition are accepted.

During Run 2, the L3 reconstruction algorithms were significantly improved
with two objectives in mind: maintaining a single-muon trigger with a
threshold low enough to retain a large fraction of $\PW\to\mu\nu$ decays, and
extending the muon reconstruction to use the additional pixel layers that were
installed during the pixel detector upgrade [3] between the 2016 and 2017 data
taking. Another consideration was to bring the trigger-level reconstruction
closer to the offline one [4] by reducing the reliance on specialized code.
With the improved algorithms, a significant increase in the purity of the muon
sample selected by the single-muon triggers was achieved without a significant
reduction in trigger efficiency.

In this paper, we summarize the reconstruction algorithms deployed in the HLT
throughout the data-taking period, focusing on the improvements made since the
start of Run 2. The selection and reconstruction efficiencies of the different
levels of the trigger systems (L1, L2, and L3), as well as the resolution for
L2 and L3 muons, are presented. For the unprescaled single- and double-muon
triggers with the lowest thresholds sustainable at the full instantaneous
luminosity during Run 2, the overall performance of the muon trigger system is
measured in terms of the efficiency, covering a range from 8 to several
hundred GeV. Additionally, the impact of the algorithmic improvements on the
purity of the selected muon sample, the event processing time, and the trigger
rate is discussed.

The description and performance of the muon trigger system used during Run 1
(years 2010–2012), for both the L1 and the HLT, are presented in Ref. [2]. For
Run 2, a detailed description of the L1 trigger system and its performance is
presented in Ref. [5]. The muon HLT performance for muons with very high is
included in Ref. [6]. This paper completes the documentation of the CMS muon
trigger system, as used during Run 2, with detailed studies of the HLT
performance for a more general selection of triggers.

This paper is organized as follows. Section 0.2 describes the CMS detector
with a focus on the subdetectors relevant for muon reconstruction. Section 0.3
describes the data used together with a brief explanation of the efficiency
measurement techniques. The hardware-based L1 trigger system and its
performance are discussed in Section 0.4. The L2 and the various L3 muon
reconstruction algorithms used in the HLT throughout the data taking are
described in Section 0.5. Section 0.6 presents the overall muon trigger system
performance combining the full L1–L3 chain, followed by a summary in Section
0.7.

##  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 pseudorapidity ($\eta$) coverage provided by the
barrel and endcap detectors. Muons are detected in gas-ionization chambers
embedded in the steel flux-return yoke outside the solenoid. A more detailed
description of the CMS detector, together with a definition of the coordinate
system used and the relevant kinematic variables, can be found in Ref. [7].

In the region $\abs{\eta}<1.74$, the HCAL cells have widths of 0.087 in both
$\eta$ and azimuth ($\phi$). In the $\eta$-$\phi$ plane, and for
$\abs{\eta}<1.48$, the HCAL cells map onto $5{\times}5$ arrays of ECAL
crystals to form calorimeter towers projecting radially outwards from close to
the nominal interaction point. For $\abs{\eta}>1.74$ , the granularity of the
towers increases progressively to a maximum of 0.174 in $\Delta\eta$ and
$\Delta\phi$.

The silicon tracker measures the trajectories of charged particles within
$\abs{\eta}<2.5$. During the data-taking period in 2016, the silicon tracker
consisted of 1440 silicon pixel and 15 148 silicon strip detector modules.
They were arranged in concentric layers, three pixel and 10 strips, around the
beam axis in the central region of the detector, and in disks, two pixel and
12 strips, perpendicular to it in the forward directions. For nonisolated
particles (i.e., with no isolation requirement applied) with $1<\pt<10\GeV$
and $\abs{\eta}<1.4$, reconstructed using the full offline event
reconstruction, the track resolutions are typically 1.5% in and 25–90
(45–150)in the transverse (longitudinal) impact parameter [8]. Before the
data-taking period in 2017, an upgraded pixel detector consisting of 1856
modules was installed, adding an additional barrel layer closer to the
interaction point and additional disks in the two forward parts of the
detector [3]. With the upgraded detector, the transverse impact parameter
resolution for nonisolated particles of $1<\pt<10\GeV$ and $\abs{\eta}<2.5$
improved to 20–75 [9].

Figure 1: R-z quadrant of the CMS detector during Run 2. The proton beams
travel along the $z$ axis and cross at the interaction point (0,0). The three
CMS muon subdetectors typically have four stations: DT in yellow, labeled MB;
CSC in green, labeled ME; and RPC in blue, labeled RB or RE.

Three types of gas-ionization chambers make up the CMS muon system: drift tube
chambers (DTs), cathode strip chambers (CSCs), and resistive-plate chambers
(RPCs). The geometrical arrangement of these three muon subdetectors in a
quadrant of the CMS detector is shown in Fig. 1. A detailed description of
these chambers, including the gas composition and operating voltage, is
reported in Ref. [10]. The DTs are segmented into drift cells; the position of
the muon is determined by measuring the drift time to an anode wire of a cell
with a shaped electric field. The CSCs operate as standard multi-wire
proportional counters but add a finely segmented cathode strip readout, which
yields a precise measurement of the position of the bending plane ($R$-$\phi$)
coordinate at which the muon crosses the gas volume. The RPCs are double-gap
chambers operated in avalanche mode and are primarily designed to provide
timing information for the muon trigger. The DT and CSC chambers are located
in the regions $\abs{\eta}<1.2$ and $0.9<\abs{\eta}<2.4$, respectively, and
are complemented by RPCs in the range $\abs{\eta}<1.9$. The chambers are
arranged to maximize the coverage and to provide some overlap where possible.
In both the barrel and endcap regions the chambers are grouped into four “muon
stations”, separated by the steel absorber of the magnet return yoke. In the
barrel region, the DT and RPC stations are arranged in five wheels along the
$z$ direction.

The thickness of the detector in radiation lengths is greater than 25 $X_{0}$
for the ECAL, and the thickness in interaction lengths varies from 7-11
$\lambda_{0}$ for HCAL depending on $\eta$. The material depth, in radiation
lengths, at the muon stations varies from 100 for the first muon station up to
280 for the outermost muon station [11].

Muon trajectories are measured in the range $\abs{\eta}<2.4$, using detection
planes comprised of the three muon subdetectors in up to four muon stations.
Using the full offline event reconstruction, the efficiency to reconstruct and
identify muons is greater than 96%. Matching muons to tracks measured in the
silicon tracker results in a relative resolution, for muons with up to 100, of
1% in the barrel and 3% in the endcaps. The resolution in the barrel is better
than 7% for muons with up to 1 [4, 6].

##  0.3 Data samples and trigger efficiencies

The results described in this paper are based on the $\Pp\Pp$ collision data
collected during the Run 2 of the LHC by the CMS experiment, corresponding to
a total integrated luminosity of 137distributed as follows: 36(2016),
41(2017), and 60(2018). Because the instantaneous luminosity of the LHC
increased during the data taking, the average pileup in the 2017 and 2018 data
samples is about 40% higher than in the 2016 sample. For performance
measurements of the full trigger, all available data for each year are used.
However, a fair comparison of the algorithmic performance of the different
steps of the reconstruction between different years is difficult because of
the continually evolving nature of the detector and the trigger algorithms and
configurations. For this reason, smaller data samples representing the best
detector performance of each year are chosen for these measurements. For 2016
and 2018, the last 5of the data of the relevant year were used since they
provide data sets with stable and well understood detector conditions. For
2017, two different data sets are used. For L1 and L2 algorithms that rely
solely on the muon detectors, the last 5collected in that year show the best
performance. The later part of the 2017 data is affected by inactive pixel
detector modules caused by a powering issue related to radiation-induced
damage in the FEAST DC-DC converter ASICs [12], which affected about 7.5% of
modules at the end of the year. Therefore, a data set of 4from early in the
data taking is used for L3 and isolation performance measurements.

The selected data samples for most of the studies shown in this paper were
recorded using a single-muon trigger; they include events with a pair of
reconstructed muons with a dimuon invariant mass consistent with the boson
resonance. Throughout this paper, efficiencies with respect to offline muons
are measured with $\PZ\rightarrow\PGm\PGm$ data events using a “tag-and-probe”
(T&P) technique [13]. Pairs of muons reconstructed by the CMS offline event
reconstruction are selected, where at least one of the muons has to pass the
strict “tag” criteria of $\pt>26\GeV,\abs{\eta}<2.4$, and tight identification
and isolation requirements. The identification requirements are based on the
number of hits in the tracker and muon systems associated with the muon, the
quality of the muon track fit and its impact parameters; the isolation
requirements are based on tracks and energy deposits in the calorimeters in
the vicinity of the muon [4]. The tag is also required to have passed an
isolated single-muon trigger requirement to ensure that the efficiency
measurement remains unbiased. The other muon is considered the “probe” muon,
and it is used to measure the efficiency of a given reconstruction step
relative to a probe selection by counting the number of probe muons that pass
the condition under study and dividing by the overall number of probes. Probe
muons are required to fulfill $\pt>24\GeV$ for most measurements. The
threshold is lowered to 10for the measurement of the trigger efficiency at low
and to $12\GeV$ for some of the L1 efficiency measurements. The probe muon
must fulfill $\abs{\eta}<2.4$ and the same tight identification and isolation
criteria as the tag muon. For L2 and L3 reconstruction, the probe muon must be
matched to an L1 muon with $\pt>22\GeV$ that passes tight L1 quality
requirements. For the isolation efficiency measurement, the probe has to
satisfy the single-muon trigger requirements without an isolation requirement.
The tag and probe muons are required to be separated by $\Delta
R=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}>0.3$, which is
sufficient to remove biases due to nearby muons. To pass the probe condition,
the probe muon is required to geometrically match the trigger object that is
being studied by requiring $\Delta R<0.1$. This criterion is relaxed to 0.5
(0.3) for the L1 (L2) muon efficiency measurement, since these objects have a
larger directional resolution. Table 0.3 summarizes the different probe
objects and the matching criteria for the probe. Finally, the tag and probe
muon pair is required to have an invariant mass in the range
$81<m_{\mu\mu}<101\GeV$. This mass window size is chosen to efficiently reject
non-boson backgrounds while retaining large enough data sets for the
measurement. After the full selection, the contribution to the data samples
from non-DY background is negligible.

Parameters of the tag-and-probe method used in this paper. $\Delta R$ is the
maximum allowed angular separation between the tag muon and the probe muon.
Efficiency Probe object $\Delta R$ trigger-probe L1 reconstruction Probe muon
0.5 L2 reconstruction Probe muon matched to L1 muon 0.3 L3 reconstruction
Probe muon matched to L1 muon 0.1 L3 isolation Probe muon matched to L3 muon
0.1 HLT (single-muon) Probe muon 0.1 HLT (double-muon) Probe muon with relaxed
requirement 0.1

##  0.4 The Level-1 trigger

During Run 1, the L1 muon trigger system included three separate hardware muon
track finders, each of which reconstructed muons using input from a single
muon subdetector: DT, CSC, or RPC [2]. The upgraded L1 trigger system in Run 2
[14] combines inputs from all geometrically available subdetectors and is
segmented into three regional track finders: the barrel muon track finder
(BMTF) covering $0<\abs{\eta}<0.83$, the overlap muon track finder (OMTF)
covering $0.83<\abs{\eta}<1.24$, and the endcap muon track finder (EMTF)
covering $1.24<\abs{\eta}<2.40$. The track finders receive hits or short track
segments called “trigger primitives” (TPs) from each station for every
subdetector in that region. The TPs carry position ($\theta$ and $\phi$)
coordinates, direction, and timing information (correlated with a collision
bunch crossing). In an improvement over Run 1, adjacent RPC hits are clustered
into TPs before being used in track building and RPC TPs are combined with
nearby DT segments in the barrel to improve the overall TP efficiency and
timing for the BMTF.

Each of the three track finders reconstructs muons on processor boards with
field-programmable gate arrays (FPGAs), following a similar sequence. Trigger
primitives aligned in $\theta$ and $\phi$ are grouped to form tracks, which
traverse the four stations in each subdetector. The angular deflection between
stations is used to assign a value, primarily using the $\phi$ coordinate,
since the magnetic field causes charged-particle tracks to curve in the
transverse plane with a radius proportional to . However, because of
significant differences between the subdetectors, and large variations in the
magnetic field strength and background muon rate, the exact track building and
assignment logic for each track finder is unique.

The Global Muon Trigger collects reconstructed muons from the three track
finders and removes geometrically overlapping tracks using information based
on and quality, sending the remainder to the L1 Global Trigger, which decides
whether to keep the event for processing by the HLT. The quality is assigned
based on the number and location of TPs on a given track [5]. Tracks passing
the tight L1 quality requirement have the best resolution and are therefore
used for single-muon L1 seeds. For multi-muon L1 seeds, the criteria are
relaxed to improve the efficiency, allowing for tracks with a lower number of
TPs to pass. This only affects the overlap and endcap regions since all L1
tracks found by the BMTF pass the tight L1 quality selection. The L1 muon
reconstruction is described in greater detail in dedicated publications [2,
5].

Some features of the upgraded system were implemented or improved
progressively throughout Run 2. The BMTF switched from DT-only segments to
combined DT+RPC TPs at the end of 2016. The OMTF developed a track building
algorithm that was less reliant on RPC TPs in 2018. In the EMTF, track timing
was improved during 2016; RPC TPs were added to the CSC-dominated track
reconstruction before 2017 collisions, along with a more accurate assignment
algorithm; and in 2018 modifications were made to reduce the impact of extra
TPs from pileup.

The efficiency of the L1 muon trigger is measured using the T&P technique. The
numerator of the efficiency includes all probe muons matched within $\Delta
R<0.5$ to an L1 trigger with $\pt>22$ (7)and passes the tight (medium) L1
quality criteria [5]. These requirements are applied to select L1 muons,
called L1 seeds, to initiate single (double) muon reconstruction at HLT. In
particular, these thresholds correspond to the lowest threshold used in an
unprescaled single-muon L1 seed, and the lowest threshold used in an
unprescaled double-muon L1 seed during Run 2, respectively.

Figure 2: Efficiency of the L1 muon trigger requiring the tight (left) and
medium (right) quality criteria as required in single-muon and double-muon
seeds, respectively, for $\PZ\to\mu\mu$ events, as a function of (upper row),
muon $\eta$ (middle row), and number of primary vertices (lower row). The
efficiency for 2016 data is shown in blue circles, for 2017 data in red
squares, and for 2018 data in black triangles. Only statistical uncertainties
are shown.

The L1 efficiency in data is shown in Fig. 2, as a function of the offline and
$\eta$ of the muon, as well as the number of primary vertices, the latter
being all vertices reconstructed in the immediate $\Pp\Pp$ interaction region,
including pileup interactions. This excludes secondary vertices from meson
decays in flight. Since very low muons are absorbed in the calorimeters and
the magnet cryostat before they reach the muon system, no measurement is
performed for offline $\pt<4\GeV$ for the medium quality. The efficiency
reaches about 93–95 (96–97)% for tight (medium) L1 quality and, above 35 (12),
is virtually independent of muon (up to 150). The performance of the L1 muon
trigger for muons with a of several hundred GeV is reduced by the high
probability for radiative energy losses in the yoke of the CMS magnet. The
impact on the trigger efficiency for muons with momenta up to 1.5was studied
in detail in Ref. [6]. The conclusion is that the efficiency has been
generally stable during the entire data-taking period; it is higher for
central muons and drops for $\abs{\eta}>1.6$, especially for tighter quality
requirements. The small variations in the efficiency as a function of $\eta$
reflect the geometric structure of the CMS muon system, in particular the
cracks in between the wheels at $\abs{\eta}\approx 0.2$. In general, for
$\abs{\eta}>1.6$ the efficiency is larger in 2017 and 2018 compared to 2016
because of the addition of RPC TPs in the EMTF. However, for 2017 the
efficiency is slightly lower in the negative endcap because of two disabled
muon CSC chambers in the first station, each covering $10^{\circ}$ in azimuth.
Both 2017 and 2018 show slightly higher performance in the endcaps, which
arises from the series of improvements to the L1 reconstruction algorithms
during the data-taking period. Because the requirements on the number of TPs
are more stringent in the case of the tight L1 quality criteria, the gain in
efficiency is larger in this case. Since the particles from pileup
interactions are mostly absorbed before they reach the muon system, the L1
trigger efficiency is roughly independent of pileup and decreases by only a
small amount as a function of the number of reconstructed vertices. This small
loss of performance as a function of the pileup is limited to the endcap
regions, where particles from pileup are concentrated.

Because of the limited time resolution of the muon detectors, L1 muons might
mistakenly be assigned to an earlier or later LHC bunch crossing (BX). This
will lead to the wrong BX being accepted by the L1 trigger system, an effect
known as “trigger prefiring” if an earlier BX is accepted. When such an event
is accepted, the CMS L1 trigger rules [2, 5] prevent the following two BXs
from generating a valid trigger accept signal, resulting in the correct event
being lost. Furthermore, while L2 muons might be reconstructed from the muon
system information now assigned to the wrong BX, the reconstruction of L3
muons using information from the tracker will fail. Thus the BX mistakenly
accepted by the L1 will be rejected by the HLT. The associated inefficiency
cannot be measured using the standard T&P technique since events containing a
prefiring L1 muon are usually not recorded at all. There exist a few rare
situations where such events are recorded [5], allowing the assessment of the
size of the effect. The overall prefire probability of 1.5% per muon at the
beginning of 2016 was reduced to 0.5% after the L1 timing measurements were
improved by the use of RPC information in the BMTF and EMTF, as mentioned
above. Since in the tag and probe measurement the tag muon is required to be
matched to a single-muon trigger, these events cannot be lost as a result of
the muon being wrongly assigned to a later BX. Trigger inefficiency due to
these prefired muons is therefore not included in the trigger efficiencies
presented above.

##  0.5 Muon reconstruction and selection in the high-level trigger

Depending on the year, 20–25% of all objects accepted by the single-muon L1
trigger do not correspond to offline reconstructed muons (as detailed in
Section 0.6.2). More sophisticated muon reconstruction and identification
algorithms are employed in the HLT to achieve the necessary precision in the
reconstructed muon candidates to reject these objects and to reduce the
trigger rate.

The reconstruction of the muon trajectory is performed in two steps: a
reconstruction within the muon system only (L2 muons) and a reconstruction in
the inner tracking detector, which is then combined with the information from
the muon spectrometer to reconstruct the full trajectory of the muon through
the detector (L3 muons). The goal of the L2 reconstruction is to refine the
initial estimate of the muon trajectory from the L1 reconstruction by using
more precise algorithms that cannot be implemented in the hardware-based L1
trigger system. This is especially important, since the L2 muons are used to
seed the track reconstruction in the inner tracker in most L3 reconstruction
algorithms. The limitations on the computing time per event in the HLT do not
allow for track reconstruction to be performed in the whole volume of the
inner tracking detector. Therefore, the L3 reconstruction algorithms have to
be limited to small regions in the detector based on the presence of L1 or L2
muons to achieve high reconstruction efficiency while keeping the use of
computing resources at acceptable levels.

The single-muon trigger is allocated the largest share of the total available
trigger bandwidth (roughly 15%), so it is crucial to ensure that only well-
measured muons are accepted. To this end, identification and isolation
criteria can be applied to the L3 muons. This helps to reject trajectories
that do not correspond to genuine muons, as well as to reject muons inside
jets or from heavy flavor decays, thus reducing the trigger rate while keeping
a high acceptance for the muons of interest.

###  0.5.1 Level-2 muon reconstruction

The reconstruction of L2 muons is identical to that used for offline
“standalone muons” [Bayatian:922757, 15, 1, 4]. It has been stable throughout
Run 2 and showed excellent performance despite the harsher pileup conditions
of Run 2. This is because the muon detectors are not significantly affected by
the harsher pileup conditions of Run 2, since the particles from pileup
collisions are absorbed in the calorimeters, the magnet cryostat, and the
steel return yoke.

The DT and CSC segments are taken as inputs and combined to produce a set of
initial track states, referred to as “L2 seeds”, which are the starting point
for the reconstruction of muon tracks. Here a “state” refers to the five-
dimensional parameterization of a trajectory on a specific detector surface,
such as one of the muon chambers. The five parameters are the ratio of charge
over momentum, the transverse and longitudinal angles with respect to the beam
direction, and the transverse and longitudinal impact parameters with respect
to the beam spot, defined as the center of the $\Pp\Pp$ interaction region.

A track seed is built using a pattern of segments across the muon stations. L2
seeds are geometrically matched to an L1 muon trigger candidate within $\Delta
R<0.3$ and only the closest seed used. For a given pattern, consisting of at
least one segment, the of the seed is estimated using a parameterization
linear in $\Delta\phi$, which is either the angle of the segment with respect
to a straight line from the interaction point to the position of the segment,
defined as the center of the CMS coordinate system, or the difference in
bending angle between two segments in different stations.

Standalone tracks are built using the Kalman filter technique [16], a
recursive algorithm that performs pattern recognition detector layer by
detector layer and, at the same time, updates the trajectory parameters, where
for the muon system this corresponds to the four stations of muon chambers.

The track-building algorithm starts from the seed state (position, direction,
). The parameters of the seed are extrapolated to the innermost compatible
muon chamber and are used to identify a measurement (track segment in the DTs
and CSCs, hit in the RPCs) consistent with the muon trajectory. The
information from the selected measurement (position, direction) is used to
update the state parameters. If multiple compatible segments or hits are found
in the same muon chamber, the measurement that minimizes the $\chi^{2}$ of the
track is selected. The updated track is then extrapolated to the next
compatible chamber and the same procedure is repeated until the outermost
chamber of the detector is reached. The track building is performed twice:
first by proceeding from the innermost layer of the muon detector toward the
outermost, and second in the opposite direction. This allows the algorithm to
remove possible biases from the initial seed.

If the track segments for one muon are not merged into a single seed, multiple
L2 seeds and subsequently duplicate tracks can be built from a single muon.
Since they typically share a fraction of hits or segments, all the track
candidates that share at least one hit are compared with each other and only
the L2 candidate of best quality is kept, on the basis of its $\chi^{2}$ and
hit multiplicity.

As a last step, the beam spot position is used to constrain the track
parameters to improve the momentum resolution of tracks. However, this
constraint is not applied in dedicated triggers targeting cosmic muons or
muons not originating from the $\Pp\Pp$ interaction region, because it would
reduce efficiency for these types of muons. These dedicated triggers rely
purely on the unconstrained L2 information and do not use the L3 track
reconstruction steps described in the upcoming sections.

The L2 reconstruction efficiency with respect to L1 muons is shown in Fig. 3
as a function of and $\eta$ of the probe muon, as well as the number of
primary vertices for data collected in 2016, 2017 and 2018. The overall
efficiency exceeds 99.5% and is independent of within uncertainties, as is
expected since the standalone muon reconstruction that is part of the offline
reconstruction, and therefore the probe object, is very close to the L2
reconstruction algorithm. Because the L1 and L2 reconstruction algorithms are
sensitive to the same detector effects, requiring the probe muon to be matched
to an L1 muon masks the impact of these effects in this measurement. However,
small residual effects remain because of the differences in the reconstruction
of trigger primitives in the L1 versus DT and CSC segments in L2. The
efficiency drops for some values of $\eta$ ($0.2<\abs{\eta}<0.3$ and
$0.9<\abs{\eta}<1.2$) are caused by the cracks in between the wheels of the
CMS muon system. In these cases, muon segments may be missing in some
stations, or are reconstructed with lower quality at the edge of the muon
chambers. Since the L1 and L2 muon reconstruction algorithms are affected by
this in different ways, the L2 efficiency is slightly reduced with respect to
L1. Furthermore a slight loss of efficiency is observed in 2017 and 2018 for
negative values of $\eta$ because of two disabled muon CSC chambers in the
first station, each covering $10^{\circ}$ in azimuth.

Since the observed relative differences between L1 and L2 are typically $<$1%,
the stability of the L2 reconstruction performance during the LHC Run 2 is
clear. Furthermore, the efficiency is independent of the number of
reconstructed vertices in the event, because particles from the additional
$\Pp\Pp$ interactions rarely reach the muon system.

Figure 3: Efficiency of the L2 reconstruction with respect to L1 muons
($\pt>22\GeV$ and “tight” quality) as a function of (upper left) and $\eta$
(upper right) of the probe muons. The dependence with the number of primary
vertices is also shown (lower middle). The efficiency for 2016 data is shown
in blue circles, for 2017 data in red squares, and for 2018 data in black
triangles. Only statistical uncertainties are shown.

###  0.5.2 Level-3 muon reconstruction

Level-3 muons are reconstructed using all available information regarding the
trajectory of the muon from both the muon spectrometer and the inner tracking
detector. The muon is reconstructed by either matching a track in the inner
tracker with an L2 muon and performing a combined track fit using the
information from both the muon system and tracking detectors, or by
identifying an inner-detector track as a muon candidate by matching it to an
L1 muon without performing a combined fit. These two types of reconstructed
muons are conceptually equivalent to the “global” and “tracker” muon
algorithms used in the offline muon reconstruction [4].

The first step in this procedure is the reconstruction of tracks in the inner
tracker. It uses the same techniques as the general CMS track reconstruction
[8]. The tracking starts by building seeds that are used for the pattern
recognition step. The trajectory is sequentially propagated to the next
detector layer and compatible hits are added using the Kalman filter technique
[16]. This propagation happens either from the center of detector towards the
muon system (“inside-out”) or starting with the outer tracker layers and
moving inwards towards the interaction point (“outside-in”). The final track
parameters are obtained from a refit that is performed once all hits
associated with the trajectory are known. Poor-quality tracks are rejected at
the end of reconstruction.

During Run 2 data taking, different L3 reconstruction algorithms were used:
during 2016, two distinct approaches were used to build an L3 muon, the
“cascade” and the “tracker muon” algorithms, described in Sections 0.5.2 and
0.5.2, respectively. During 2017 and 2018, both algorithms were replaced by a
common approach called “iterative” algorithm, described in Section 0.5.2.

#### L3 muon seeded by L2: the cascade algorithm

The cascade algorithm reconstructs muon trajectories in the inner tracker
based on L2 muons. Three different algorithms are used to create track seeds
based on the L2 information: “outside-in state”, “outside-in hit”, and
“inside-out hit”, described in more detail below. These three algorithms are
used in sequence, such that later (more precise and computationally demanding)
reconstruction algorithms are only run if the previous algorithms were not
successful in reconstructing a track for a given L2 muon, leading to the name
“cascade”. This exclusive approach reduces the required processing time. Muon
tracks reconstructed during the early steps with a transverse impact parameter
larger than 0.2 cm are rejected to allow later steps to properly reconstruct
them again and ensure that poorly reconstructed tracks are not used in the
final selection.

  * •

The outside-in state algorithm propagates the track state of the L2 muon onto
the surface of the outermost tracker layer. A “stepping helix” propagator is
used, which splits the propagation into steps of finite helix length and
includes the magnetic field, as well as energy losses and multiple scattering
in material. This propagated state, with its uncertainties enlarged by
$\eta$\- and -dependent scale factors ranging from 3 to 10, is used to
identify detector modules in a given tracker layer compatible with the state
within $\chi^{2}<40$, independent of the presence of hits. The $\chi^{2}$ is
calculated based on the position of the state on the tracker layer and the
closest edge of the module, taking into account the uncertainties of the
state. A seed is created from a state updated with the position of a
compatible module and is used to start the pattern recognition. If no
compatible module is found in a layer, the state is propagated further inwards
until a compatible module is found or all possible layers are exhausted. The
pattern recognition uses the Kalman filter, propagating layer-by-layer towards
the interaction point and adding compatible hits to the trajectory, followed
by the final track fit.

  * •

The outside-in hit algorithm propagates the track state of the L2 muon to the
outermost tracker layer. However, instead of using the propagated state
directly as the track seed, it first searches for compatible hits in that
layer, using the same criteria to identify compatible detector modules to
check for hits as in the previous case. Up to five of the most compatible hits
are then used to update the track state and create one seed per hit. Again,
the algorithm moves inwards layer-by-layer until a layer with compatible hits
is found or all layers are exhausted. If the seed positions projected onto the
beam axis fulfill $\abs{z}<25$ cm with respect to the center of the detector,
they are used to initiate the same steps of pattern recognition and track
fitting as the outside-in state algorithm.

  * •

The inside-out hit algorithm uses the L2 muon information to define a region
of interest (ROI) on the innermost pixel layer. The size of the region is
given by the minimum of either a parameterization as a function of $\eta$ and
of the L2 muon or the uncertainty in the L2 parameters propagated to the pixel
detector. However, a minimal size of $0.1{\times}0.1$ in $\eta$ and $\phi$ is
always used. Within these regions, compatible triplets (doublets) of pixel
hits on three (two) pixel layers are formed whose transverse (longitudinal)
impact parameters are required to be smaller than 0.2 (15.9) cm with respect
to the beam spot. These hit multiplets are used as the seeds to initiate the
pattern recognition from the pixel detector outwards.

The collection of tracker tracks created from the three cascade algorithms are
then matched to the L2 muons. For matching, the tracker tracks and L2 muons
are propagated onto a common surface and compared using $\eta$\- and
-dependent criteria based on geometrical distance and $\chi^{2}$
compatibility. A combined fit is performed with the Kalman filter using
information from both the selected tracker track and the matched L2 muon. The
fit with the best $\chi^{2}$ per degree of freedom is used as the final L3
muon. Approximately 90% of accepted muons are reconstructed using the outside-
in state approach, whereas about 5% each come from the outside-in hit and
inside-out hit approaches.

#### L3 muon seeded by L1: the tracker muon algorithm

The tracker muon reconstruction algorithm solely relies on the pixel and strip
tracking detectors to reconstruct the muon tracks. In this algorithm,
information from the muon detectors is only used in two places, first to
define the ROI for the track reconstruction in the tracking detector and later
to tag the reconstructed tracks as muons by matching them to segments in the
muon stations. This approach does not rely on the successful reconstruction of
an L2 muon and therefore leads to improvement in the muon trigger efficiency
in cases where the L2 muon was poorly reconstructed. When combined with the
L2-seeded approaches, this makes the overall muon trigger reconstruction more
robust.

The starting point of the inside-out track reconstruction is an L1 muon. An
ROI is generated based on the L1 parameters, extrapolated back to the
interaction point, and the track reconstruction algorithms are confined to
this region, to reduce the computing time demands. To further reduce this
timing, only L1 muons with a $\pt>15\GeV$ are used to select the region. The
number of ROIs in the tracker muon algorithm is limited to two per event, and
ROIs are created from L1 muons in descending order of . Tracks are built
following an iterative tracking approach [8], where tracks are built first
using triplets of pixel hits as seeds, which is then relaxed to using doublets
of hits in a second iteration. The collections of tracks produced in both
iterations passing a loose track quality selection based on the track quality
and impact parameters are merged into a single collection. The resulting
tracks are matched to segments in the muon system to tag them as “tracker
muons”, following the same procedure that is used for the offline
reconstruction [4].

#### Performance of the cascade and tracker muon algorithms

The L3 muon reconstruction efficiency with respect to L1 muons for both the
cascade and tracker muon algorithms is shown in Fig. 4. Since each algorithm
was used for independent triggers, the combined efficiency of the logical OR
of both algorithms is also shown. The average efficiencies are about 96% for
the tracker muon algorithm and 99% for the cascade algorithm. This difference
is caused by two effects. First, quality requirements on the muon tracks are
applied in the tracker muon algorithm, which are not present in the cascade
algorithm. Secondly, as the track reconstruction in the cascade algorithm is
seeded predominantly outside-in, it is able to reconstruct tracks with very
few hits in the pixel detector, which are missed by the tracker muon
algorithm. Combined, the two algorithms reach an efficiency of about 99.9%,
independent of $\eta$ and $\phi$ of the muons. Such high efficiencies are
confirmed also in simulation for the combination of both algorithms. Although
some loss of efficiency with increasing pileup is seen for each algorithm
individually, the combination of the algorithms is not degraded at high
pileup. The tracker muon algorithm is more strongly affected by higher pileup
than the cascade algorithm since, whereas the occupancy of the pixel detector
increases with pileup, it remains relatively low in the outer part of the
tracker where most seeds for the cascade algorithms are built. In addition,
the pixel detector in use in 2016 exhibited a loss of hit efficiency as the
occupancy of the detector increased, resulting in a loss of efficiency for the
creation of track seeds for the tracker muon algorithm. The upgraded pixel
detector installed in 2017 does not exhibit this effect.

Figure 4: Efficiency of the cascade and tracker muon L3 reconstruction
algorithms, as well as the logical OR between the two, measured in data events
as a function of (upper left), muon $\eta$ (upper right), and the number of
reconstructed primary vertices (lower middle). Only statistical uncertainties
are shown.

#### L3 muon seeded by L2 and L1: the iterative algorithm

The third muon reconstruction algorithm that was used during Run 2 is the so-
called “iterative” algorithm. With the installation of the new pixel detector
before the beginning of the 2017 data taking, a redesign of the reconstruction
techniques was required to take advantage of the additional pixel layer in the
upgraded detector [3]. The algorithm replaced the previously described cascade
and tracker muon approaches, combining the advantages of both into a single
algorithm to make a unified trigger decision. This eliminates the need to
consider two distinct triggers at analysis level. The iterative algorithm
consists of three steps: one outside-in step seeded by L2 muons, one inside-
out step seeded by L2 muons, and a second inside-out step seeded by L1 muons.
For the inside-out step seeded by an L2 muon, only muons that were not
reconstructed as an L3 muon in the outside-in step are used. For the L1-muon
seeded step, in 2017 it was run only for L1 muons that could not be matched to
an already reconstructed L3 muon. In 2018, all the L1 muons were used to
maximize the efficiency of the final algorithm.

The outside-in reconstruction step uses L2 muons to start a search for track
reconstruction seeds in the inner tracker. Using the parameters from the L2
muon, the trajectory is propagated to the outermost tracker layers using the
stepping helix propagator to find the seed, similar to what is done with the
outside-in steps of the cascade algorithm. To increase the efficiency of this
step with respect to the cascade algorithm, the iterative algorithm creates
two trajectory states on the outer tracker surface for seeding (one updated
with the interaction point as an additional constraint and one not updated).
In addition, up to five seeds are then generated by finding a silicon strip
detector hit compatible with the extrapolated state. After the pattern
recognition step, only tracks satisfying certain quality requirements [8] are
kept.

Both inside-out steps make use of the iterative tracking techniques, similar
to those used for the tracker muon algorithm. The ROIs are generated using
either L2 or L1 muons, depending on the step. Since the uncertainties in the
parameters of an L2 muon are much smaller than those for the L1 muons, the
size of the ROI is much smaller for the L2-seeded step. The first iteration is
seeded using pixel quadruplets built using the cellular automaton algorithm
[17], targeting the reconstruction of prompt tracks above 1.2. The second
iteration is seeded by pixel triplets to recover efficiency from the previous
iteration. Finally, one last iteration, seeded by pixel doublets, was added
for the data taking in 2018 to improve the resilience of the algorithm in
situations where fewer than three hits might be available for seed building.
In 2018, about 40% of muon tracks created by this algorithm on average are
reconstructed from quadruplet seeds, 50% from triplet seeds, and 10% from
doublet seeds.

Each reconstruction step produces a collection of tracker tracks that are then
combined, removing any duplicates. For the L2-seeded steps, the tracker tracks
are then matched to the L2 muon, and a combined fit of the tracker and the
muon track is performed to build an L3 muon. For the L1-seeded step, the
tracker tracks are matched to hits in the muon system to create tracker muons.
Duplicate tracks already contained in the L3 muon collections from the
previous steps are removed, and all muons are merged into one single
collection. This matching and refitting procedure is identical to that used in
offline reconstruction to produce the muon collection [4].

Figure 5 shows the reconstruction efficiency with respect to L1 muons of the
different steps of the iterative algorithm, as a function of muon and $\eta$
and of the number of reconstructed primary vertices. This measurement is
carried out on 2018 data using the best performing version of the algorithm
deployed during data taking. The stacked histograms show the increase of
tracking efficiency when the different steps of the algorithm are successively
added. The overall efficiency is almost 100%, showing how the inclusion of the
inside-out from L1 step prevents inefficiencies from relying on the L2
reconstruction and the L2-seeded tracking. Around 97% of the muons are
reconstructed by the outside-in step, and the rest of the efficiency is
recovered by the two inside-out steps. The outside-in reconstruction
efficiency is most efficient at low and with few reconstructed vertices. Since
the scale factors used to enlarge the uncertainty of the L2 muons during the
seeding stage were not optimized for high-muons, the efficiency drops to about
94% above 250. The inside-out steps recover the efficiency loss, ensuring
excellent performance at both high pileup and high .

Figure 5: Efficiency of the iterative algorithm with respect to L1 muons as a
function of (upper left), muon $\eta$ (upper right), and the number of
reconstructed primary vertices (lower middle). The efficiency contributions
from the different iterations are shown as stacked histograms. Only
statistical uncertainties are shown.

Finally, to decrease the trigger rate and increase the purity of the L3 muon
candidates, mild identification criteria were introduced to the iterative L3
algorithm at the start of 2018. At least one muon segment should match the
extrapolated tracker track, just as for offline tracker muons [4]. In
addition, the tracker tracks of the muons are required to have more than five
strip detector layers with measurements, and at least one hit in the pixel
detector. Furthermore, for muons with $\pt>8\GeV$ the track is required to be
matched to at least two muon stations, except when fewer than two matches are
expected from the detector geometry as occurs, e.g., in the cracks between the
wheels of the muon system.

#### L3 muon reconstruction and identification performance

To directly compare the performance of different algorithms, Fig. 6 shows the
efficiency of the iterative and the logical OR of the cascade and tracker muon
algorithms in data containing boson candidates, as a function of muon and
$\eta$, and the number of reconstructed primary vertices.

Figure 6: Efficiency of the iterative algorithm with respect to L1 muons
compared with the logical OR of the cascade and tracker muon reconstruction
measured in data events as a function of (upper left), muon $\eta$ (upper
right), and the number of reconstructed vertices (lower middle). Only
statistical uncertainties are shown.

For the iterative algorithm, we show the performance for 2018 data using the
most efficient configuration of the algorithm, as well as two measurements for
2017 data using the version of the algorithm used during that year. The early
2017 data provide the best performance of the upgraded pixel detector achieved
during that year, whereas the late 2017 data show the effect of inactive pixel
detector modules on the reconstruction efficiency. The 2017 version of the
iterative algorithm achieves an efficiency of up to 99% on the early 2017
data. Efficiency losses are observed at low , in the barrel-endcap overlap
region of the detector and the very forward endcaps, and with a high number of
reconstructed vertices. These trends are significantly more pronounced in the
late 2017 data where the full efficiency drops to 96% overall and as low as
88% in the overlap region. The asymmetry between negative and positive $\eta$
values in the central part of the detector highlights the random distribution
of the pixel module loss. The overlap region is especially affected since
tracks in this region pass only three layers of the pixel detector so that any
loss of modules has a particularly large effect. This motivated the
significant changes to the iterative algorithm for the 2018 data taking.

For 2018, we show both the L3 reconstruction efficiency, as presented in Fig.
5, and the full efficiency of the algorithm, including muon identification
described in the previous section. For a fair comparison, the cascade and
tracker muon algorithms have been run on 2018 data and the resulting
efficiency has been measured. On these data, both algorithms achieve
reconstruction efficiencies above 99.5%, with slightly higher efficiency in
the case of the iterative algorithm. However, after the application of the
muon identification selection, the efficiency of the iterative algorithm ends
up slightly lower than the logical OR of the cascade and tracker muon
algorithms. This small loss of efficiency is expected and acceptable
considering the benefits to the purity and trigger rates in high pileup
conditions that the muon identification selection provides, as discussed in
Section 0.6.2. In all cases, the efficiency is independent of $\eta$ and
$\phi$ of the muon, and the reconstruction is robust against pileup. A small
pileup dependence is observed for the muon identification, for which the
efficiency decreases by about 1% as the number of reconstructed vertices
increases up to 60.

Good resolution for L3 muons with respect to the offline reconstruction
ensures a sharp turn-on of the trigger efficiency at the nominal threshold.
This allows efficient rejection of low-muons that might otherwise have their
momentum overestimated and pass the trigger threshold. Moreover, a sharp
efficiency turn-on improves the offline analysis acceptance, since the turn-on
region is commonly rejected.

The resolution has been measured and compared for L2 muons and the different
L3 reconstruction algorithms used during Run 2. It is obtained from the
residual with respect to the offline muon reconstruction as

$\text{residual}=\left(q^{\text{offline}}/p_{\mathrm{T}}^{\text{offline}}-q^{\text{trigger}}/p_{\mathrm{T}}^{\text{trigger}}\right)\frac{p_{\mathrm{T}}^{\text{offline}}}{q^{\text{offline}}}.$
(1)

where $q$ is the reconstructed electric charge of the muon. This quantity is
measured in data using a T&P-like selection by selecting events with two
offline muons passing the tight identification and isolation criteria. The
invariant mass of the dimuon pairs must be in the range $81<m_{\mu\mu}<101$.
If an L3 muon track is matched to one of these offline muons within $\Delta
R<0.1$, the residual is computed. With these selection criteria, the charge
misidentification probability for L2 muons compared with offline muons rises
from 3% to 10% as increases from 30to 200. For the different L3 reconstruction
algorithms, the charge misidentification probability depends on the level of
track quality requirements applied in the algorithm, ranging from
$\mathcal{O}(10^{-3})$ for cascade to $\mathcal{O}(10^{-5})$ for the iterative
algorithm after application of muon identification requirements.

The residual distributions, integrated over $\eta$ and , are shown in Fig. 7.
Since they use only the information from the muon system, the distribution for
L2 muons is much wider than for the L3 reconstruction algorithms. The
different L3 algorithms have comparable resolution in the core of the
distributions, but differ significantly in the tails, as shown by the RMS
values. The iterative L3 and the tracker muon reconstruction have a better
resolution with fewer muons in the tails of the distribution than those for
the cascade algorithm, since the former includes a track quality selection
that is missing in the latter. The smallest tails are observed for the
iterative L3 reconstruction after applying muon identification, because this
removes a significant number of poorly reconstructed muon tracks that survived
the initial track selection at the reconstruction stage. From Fig. 7, no
significant bias in the momentum scale is visible. For L2 muons, the scale
bias is about 1% toward higher values compared with offline muons. For the
cascade algorithm it is less than 0.1% and even smaller biases are observed
for the other L3 algorithms.

Figure 7: The q/residual distributions integrated over $\eta$ and for all
different HLT muon reconstruction algorithms used in 2018. Only statistical
uncertainties are shown.

Figure 8 shows the resolution (standard deviation of the Gaussian core from a
fit of a double-sided Crystal Ball function [18]) of the residual
distributions from the L2 and L3 algorithms as a function of the and
$\abs{\eta}$ of the offline muon. The resolution for L2 muons has been reduced
by a factor of 10 for the purpose of these plots. Because the L3
reconstruction algorithms make use of the hits in the inner tracking detector,
their resolution is significantly better than that for the L2 reconstruction.
Generally, the momentum resolution worsens at high , because the tracks are
straighter with a smaller sagitta, and in the forward region, where the
$\vec{v}\times\vec{B}$ effect causes the magnetic bending to be smaller when
the track and magnetic field are nearly collinear. No significant difference
is observed between the tracker muon and iterative L3 reconstruction
algorithms, whereas the cascade algorithm has poorer resolution because of its
reliance on outside-in reconstruction, as discussed above.

Figure 8: Relative resolution obtained from the standard deviations of the
q/residual distributions as a function of (left) and $\eta$ (right) for all
different HLT muon reconstruction algorithms used in 2018. Only statistical
uncertainties are shown.

#### Muon isolation

Muons produced in the decays of vector or Higgs bosons, including the bosons
in the decay chain of top quark pairs (), as well as in many new physics
models, are mostly produced spatially isolated from other final-state
particles. Thus, the isolation of a muon from other particles in the event is
an effective procedure to suppress muons from decays in flight and meson
decays, and to select a pure sample of signal-like muons for the analysis of
these kinds of final states, allowing for reduced muon thresholds for isolated
muon triggers.

The isolation of the muon is evaluated by considering the of additional tracks
reconstructed in the inner tracker and energy deposits in the calorimeters,
computed using a clustering algorithm based on the CMS particle-flow algorithm
[19], in a cone of radius $\Delta R=0.3$ around the muon itself. The muon
track and the ionization energy it deposited in the calorimeters are excluded
from the calculation. Furthermore, the contribution from tracks not
originating at the same vertex as the muon is rejected. The estimated
contribution from pileup to the energy deposits in the calorimeter is also
subtracted. It is computed using the average energy density in the event,
$\rho$, scaled to an effective area which relates the per-event $\rho$ to the
fraction expected in the isolation cone, and subtracted from the particle-flow
cluster sums. The effective areas are computed separately for the barrel and
endcap regions, and for electromagnetic and hadronic clusters.

The isolation is evaluated separately for tracks and ECAL and HCAL clusters,
and sequential requirements are applied on the resulting momentum or energy
sums relative to the muon . Effective areas for the pileup subtraction and the
relative isolation requirements have been adjusted for each year of data-
taking, due to the different pileup conditions and minor changes in the
clustering. The relative isolation requirements range from 0.08 (0.16) to 0.14
(0.22) for electromagnetic (hadronic) clusters, and from 0.07 to 0.09 for the
track-based component. A looser version of the isolation based only on tracks,
requiring the relative track isolation to be $<0.4$ has been used in some
dimuon triggers.

The efficiency of the isolation requirement with respect to L3 muons is
measured in 2016, 2017, and 2018 data and shown in Fig. 9, as a function of
muon , $\eta$, and the number of reconstructed vertices. The overall
efficiency is about 98% on average for the three years. As a result of the
adjustment of the isolation requirements for the different years, the 2016
data show a slightly higher isolation efficiency in the endcaps, whereas the
efficiency is higher in 2017 and 2018 data in the central part of the
detector. The efficiency shows a slight drop at low in both data sets and is
constant at high . The isolation requirements were re-optimized in 2017 and
2018 to reach a more stable efficiency, as a function of pileup, as shown in
Fig. 9 (right).

Figure 9: Isolation efficiency as a function of muon (upper left)), $\eta$
(upper right), and the number of reconstructed vertices (lower middle) during
the Run 2 data taking. Only statistical uncertainties are shown.

##  0.6 Performance of selected single- and double-muon reference triggers

In this section, a selection of reference triggers is described to illustrate
the performance of the full trigger sequence, including reconstruction,
identification and, if applicable, isolation. The triggers chosen are the two
unprescaled single-muon triggers, with and without an isolation requirement,
and one unprescaled double-muon trigger. The details of these triggers,
together with a shorthand name that will be used in the following are given
here:

  * •

Isolated single-muon trigger (IsoMu24): A single-muon trigger with a threshold
of 24that requires the muon to be isolated to achieve the low-threshold. This
is the main trigger for many analyses studying standard model processes, such
as $\PW\to\PGm\Pgn$, $\PZ\to\PGm\PGm$, or semileptonic decays.

  * •

Nonisolated single-muon trigger (Mu50): A single-muon trigger with a threshold
of 50without an isolation requirement. The higher momentum threshold achieves
acceptable trigger rates without applying an isolation requirement at the HLT,
increasing the trigger efficiency slightly even for cases in which a high-muon
showered in the calorimeter. This trigger is therefore used in analyses
targetingg events with particularly high-leptons, such as searches for heavy
new particles. Other uses include searches for Lorentz-boosted objects in
which the muon may overlap with jets. To reach maximum efficiency for analyses
using dedicated high-muon identification criteria, this trigger is used in
combination with two other triggers that use the cascade and the tracker muon
algorithms with a threshold of 100that were present during the entire data-
taking period and kept as a backup solution when the iterative L3
reconstruction was introduced [6]. Therefore all performance measurements
presented for Mu50 include the logical OR with these two backup triggers.

  * •

Double-muon trigger (Mu17Mu8): A double-muon trigger where each muon is
required to pass the loose track-based isolation requirement. The threshold
applied to the muon with higher (lower) is 17 (8). The lower requirement on
the first muon compared with the single-muon triggers increases the acceptance
for many dilepton processes, such as fully leptonic decays, and many searches
for new physics. To reduce the trigger rate due to pileup events, a
requirement that the longitudinal distance between the two muons along the
beam line must be less than 0.2 cm was added during the 2016 data taking, with
negligible impact on the efficiency. Similarly, during 2017 and 2018, an extra
requirement on the invariant mass of the dimuon system to be larger than
3.8was applied to reduce the rate from low-mass dimuon resonances.

In the following, the efficiencies for the two single-muon triggers and the
low-part of the double-muon trigger are discussed in Section 0.6.1. The HLT
rate measurements and the composition of the selected muon sample are
described in Section 0.6.2. Finally, the processing time for the muon HLT
reconstruction is discussed in Section 0.6.3.

###  0.6.1 Trigger efficiency

The efficiencies of the isolated and nonisolated single-muon triggers are
shown in Fig. 10 as a function of and $\eta$ of the probe muons and the number
of reconstructed vertices. The full data sets for 2016, 2017, and 2018 are
used to illustrate the performance for the data set used in CMS analyses. To
count as passed in the efficiency calculation, the probe must be matched to an
HLT muon that satisfies the trigger under study.

For the isolated single-muon triggers, the overall efficiencies for muons with
$\pt>26\GeV$ are close to 90% in all years. The efficiency is about 1% lower
in 2017. This is attributed to both the introduction of the first version of
the iterative L3 reconstruction, which was updated several times during the
2017 data-taking period to recover the performance, and the inactive pixel
detector modules caused by the powering issue [12]. These changes offset the
improvements in the L1 efficiency described above. In 2018, a higher
efficiency was achieved, even exceeding the 2016 value because of the more
robust iterative L3 algorithm and the repairs made to the pixel detector
between the 2017 and 2018 data-taking periods. There is a strong $\eta$
dependence of the efficiency. It is mostly constant in the barrel region of
the detector with the exception of the transition region between muon wheels
at $\abs{\eta}\approx 0.2$, but the efficiency falls significantly towards
high values of $\abs{\eta}$ because of the difficulty to reconstruct L1 muons
in that region, as shown in Fig. 2. The efficiency decreases as a function of
the number of reconstructed vertices mostly because of the isolation
requirement.

For the nonisolated muon triggers, a dedicated identification for high-muons
[20] and an offline isolation requirement based only on inner tracker tracks
are used to select probe muons to reflect the offline selection typically used
together with this trigger. The overall efficiencies for muons with
$\pt>52\GeV$ are 91%, 89%, and 92% for 2016, 2017, and 2018 data,
respectively. The dependencies of the efficiency on muon , $\eta$, and the
number of reconstructed vertices are shown in Fig. 10. The observed trends are
very similar to the case of the isolated triggers, with the dependence on the
number of reconstructed vertices reduced because of the lack of isolation
requirements.

Figure 10: Single-muon trigger efficiency for IsoMu24 (left) and Mu50 (right),
as a function of muon (upper row), $\eta$ (middle row), and the number of
reconstructed vertices (lower row). Only statistical uncertainties are shown.

The single-muon triggers used during Run 2 have a threshold of 24. To probe
the performance of the trigger at low , the efficiency to trigger on the muon
with lower in a dimuon event has been measured. For this purpose, the same T&P
technique as described above is used, with the exception that the $\pt$
requirement on the probe muon is relaxed. As the tag muon is required to
satisfy the IsoMu24 trigger, the efficiency of the probe muon to satisfy the
part of the double-muon triggers that requires one muon with $\pt>8\GeV$ is
measured independently of the requirements on the higher-muon. The measured
efficiencies versus the probe muon $\pt$ for the three years are given in Fig.
11. The same dependence of the efficiency on the different years as for the
single-muon triggers is observed.

Figure 11: Trigger efficiency for the lower-muon in double-muon triggers with
respect to offline muons for 2016, 2017, and 2018. The right plot is the
zoomed version of the left plot. Only statistical uncertainties are shown.

###  0.6.2 Trigger rate

The previous sections show that the iterative algorithm is as efficient as the
trigger strategy used at the beginning of Run 2, despite the slight loss of
efficiency from the introduction of muon identification criteria in 2018. This
small loss comes with a significant reduction in rate, thus the same
thresholds could be maintained during the entire data taking despite the
higher luminosity and more challenging pileup conditions in 2018\. In the
following, HLT trigger rates for the trigger configuration that was used at
the beginning (cascade OR tracker muon in 2016) and at the end (iterative in
2018) of Run 2 will be compared.

The trigger rate is measured using dedicated data sets for trigger performance
studies that are collected without HLT requirements, and therefore contain a
representative mix of event topologies similar to those the HLT receives from
the L1 trigger during data taking. The rate is defined as the frequency with
which a trigger condition has been satisfied in a given data-taking period.
Because the rate of muons produced is given by the overall production cross
section times the instantaneous luminosity, a linear luminosity dependence of
the trigger rate is expected, as long as reconstruction or selection effects
do not introduce additional dependencies. In the following, to facilitate the
comparison between the triggers in 2016 and 2018, the rates for both years are
scaled linearly to an instantaneous luminosity of $10^{34}\percms$. Only
statistical uncertainties are considered for these measurements.

Each step of the reconstruction described in the previous sections (L1, L2,
L3) reduces the rate and thus the total number of events that are kept for
further analysis. The L1 trigger with $\pt>22\GeV$ and tight quality
requirements delivers a rate of $4506\pm 8$ ($4147\pm 10$)Hz in 2016 (2018) at
an instantaneous luminosity of $10^{34}\percms$. The difference stems from the
improvements to the L1 trigger described earlier. In 2016, L2 muons with
$\pt<10\GeV$ were rejected. In 2017 and 2018, this requirement was removed.
The L2 step introduces a rate reduction of about 50% in 2016 and 15% in 2018.
For the IsoMu24 trigger, before applying isolation, only 8% of the muons that
were originally accepted by the L1 trigger are kept after the L3 step, and the
isolation requirement reduces the L3 rate by a further factor of 3. The rate
for this trigger is about 250Hz at the highest instantaneous luminosity
achieved in Run 2. The rates for the individual steps of the trigger are
summarized in Table 0.6.2.

Rates for the individual steps of the IsoMu24 trigger. The rates are measured
in the 2016 and 2018 data sets, respectively, and then scaled to an
instantaneous luminosity of $10^{34}\percms$. Only statistical uncertainties
are considered for each measurement.  Rate (2016) [Hz] Rate (2018) [Hz] L1
rate, $\pt>22\GeV$ $4506\pm 8$ $4147\pm 10$ L2 rate, ($\pt>10\GeV$ in 2016)
$2238\pm 6$ $3559\pm 10$ L3 rate, $\pt>24\GeV$ $377\pm 2$ $363\pm 3$ Isolated
L3 rate, $\pt>24\GeV$ $125\pm 1$ $126\pm 2$

The rates for the standard isolated single-muon trigger, nonisolated single-
muon trigger, and double-muon triggers for 2016 and 2018 are shown in Table
0.6.2. The isolated triggers already select a very pure muon sample in 2016,
so no further rate reduction from the improvement of the L3 reconstruction
algorithm in 2018 is observed. However for the nonisolated triggers and the
double-muon triggers, a respective rate reduction of around 10% and 30% from
2016 to 2018 is observed. For the single-muon trigger this rate reduction is
achieved mainly by the identification requirements introduced for the 2018
data taking. For the double-muon trigger the rate has also been reduced by the
introduction of a lower bound on the invariant mass of the two muons of 3.8,
which rejects the $\JPsi$ meson peak.

Rates for the 2016 and 2018 trigger strategies. The rates are measured in the
2016 and 2018 data sets, respectively, and then scaled to an instantaneous
luminosity of $10^{34}\percms$. Only statistical uncertainties are considered
for each measurement.  Rate (2016) [Hz] Rate (2018) [Hz] IsoMu24 $124.9\pm
1.3$ $126.0\pm 1.8$ Mu50 $28.7\pm 0.6$ $24.2\pm 0.8$ Mu17Mu8 $21.2\pm 0.5$
$14.9\pm 0.6$

Figure 12 shows the trigger rates, normalized to the number of colliding
bunches to account for the different beam conditions between the years, as a
function of the mean number of interactions per bunch crossing
$n_{\mathrm{int}}$ for the isolated and nonisolated single-muon triggers as
well as the double-muon triggers.

Figure 12: Dependence of the trigger rate per colliding bunch on the mean
number of interactions per bunch crossing for the isolated (upper left) and
nonisolated (upper right) single-muon triggers as well as the double-muon
trigger (lower middle), comparing the 2016 and 2018 trigger configurations.
Quadratic (linear) fits to the 2016 (2018) data points are shown as colored
lines. Only statistical uncertainties are shown.

The mean number of interactions per bunch crossing is calculated from the
instantaneous luminosity and the inelastic $\Pp\Pp$ cross section. The data
used for this analysis was taken only in an LHC configuration with 2208
colliding bunches in 2016 and 2544 bunches in 2018, so it is a subset of the
total data set. The expected linear dependence is observed for all triggers
except the nonisolated single-muon trigger in 2016, where a faster growth
occurs at the highest number of interactions observed in that year. This
effect is also slightly visible for the isolated single-muon trigger. This
results from a slightly higher rate of nonmuon objects accepted by the trigger
in high-pileup events in 2016, which was remedied in 2018. As discussed above,
there is no significant difference between the rates observed for the isolated
single-muon trigger in 2016 and 2018. In the case of the nonisolated single-
muon trigger, the rate is similar between the years at low luminosity, but the
increase of the rate with luminosity is significantly reduced in 2018. For the
double-muon trigger there is a significant difference already at low
luminosity, originating from the introduction of the invariant mass
requirement discussed above. The dependency of the rate on the number of
interactions has been obtained from parabolic fits for 2016 and linear fits
for 2018 and the resulting parameters are summarized in Table 0.6.2.

Increase of the trigger rate as a function of the number of $\Pp\Pp$
interactions in an event ($n_{\mathrm{int}}$) in units of mHz/interaction,
parametrized with 2nd and 1st order polynomials, respectively, for 2016 and
2018.  2016 2018 IsoMu24 ($1.65\pm 0.02$) $n_{\mathrm{int}}$ \+ ($0.0047\pm
0.0007$) $n_{\mathrm{int}}^{2}$ ($1.787\pm 0.004$) $n_{\mathrm{int}}$ Mu50
($0.348\pm 0.007$) $n_{\mathrm{int}}$ \+ ($0.0019\pm 0.0002$)
$n_{\mathrm{int}}^{2}$ ($0.351\pm 0.001$) $n_{\mathrm{int}}$ Mu17Mu8 (
$0.276\pm 0.002$) $n_{\mathrm{int}}$ \+ ($0.0006\pm 0.0001$)
$n_{\mathrm{int}}^{2}$ ($0.215\pm 0.001$) $n_{\mathrm{int}}$

The limit of an average of about 1kHz on the total rate of events accepted by
the HLT, imposed by limited storage capacity and computing resources for
offline reconstruction, make the trigger bandwidth a coveted resource.
Therefore it is important to ensure that the muon sample accepted by the HLT
is as pure in genuine muons as possible; otherwise the trigger rate would be
wasted on other objects misreconstructed as muons. To illustrate the
improvements made in this regard during Run 2, the composition of the selected
sample has been measured in an unbiased sample of events accepted by the L1
trigger by geometrically matching the triggered muons to offline reconstructed
muons within $\Delta R<0.1(0.3)$ for L3 (L1 and L2) muons. The triggered muons
are then sorted into three categories:

  * •

Unmatched: the triggered muon could not be matched to an offline muon passing
identification requirements.

  * •

Nonisolated muon: the triggered muon is matched to an offline muon passing
identification but not isolation requirements.

  * •

Isolated muon: the triggered muon is matched to an offline muon passing both
identification and isolation requirements.

For this purpose we use loose identification requirements designed to accept
both isolated and nonisolated muons [4], while a tight isolation requirement
is used to ensure that only truly prompt muons are counted.

The composition of the selected sample for the four steps of the trigger
sequence, L1, L2, L3, and isolation, is shown in Fig. 13 for the cascade
algorithm measured on 2016 data and the iterative algorithm measured on 2018
data. For each step there is a minimum requirement on the muons that is given
by the values used in the trigger sequence. The thresholds are 22for the L1
muons used to seed the single-muon trigger, 10for the L2 muons used to seed L3
reconstruction (a very loose cut that was only applied in 2016), and 24for L3
muons since this is the final trigger threshold. The isolated-muon fraction is
sequentially increased with every step of the reconstruction, reaching up to
values of about 80% at the end of the trigger sequence. Comparing 2016 and
2018, one can see a significant decrease in the unmatched fraction by factors
of 2–3 because of the improved reconstruction algorithm and the application of
identification criteria at L3. A slight increase in the fraction of unmatched
L1 and L2 muons in 2018 compared to 2016 is observed. This effect is most
pronounced in the barrel-endcap overlap region; it results from the use of
lower quality DT segments, which improve the momentum assignment and, hence,
the total rate, but also increase the fraction of nonmuon candidates that fire
the trigger.

Figure 13: The composition of the selected muon sample for the 2016 (left) and
2018 (right) data.

###  0.6.3 Processing time

The high input rate from L1 severely limits the computing resources available
to make a decision to keep or reject an event at the HLT. Fast algorithms are
therefore required to keep the average time to process an event within the
budget, which is given by the available computing resources for the HLT and
increased from about 200ms to about 300ms from 2016 to 2018. The processing
time for the muon triggers has been measured using data from 2016 and 2018 on
a CMS reference machine equipped with a Intel® Xeon® E5-2650 v2 CPU running at
2.60GHz, which is slower than the latest processing nodes installed in the
HLT. Events were processed concurrently on four CPU cores in four threads. To
ensure comparability, events from runs with similar pileup conditions have
been chosen. For 2016 the number of pileup interactions ranges from 45 to 50
with a mean value of 46.3, for 2017 from 47 to 49 with a mean of 47.4, and for
2018 from 46 to 48 with a mean of 47.3. The overall processing time for the
full suite of triggers used by CMS in those data sets is 181ms/event for 2016,
180ms/event for 2017, and 296ms/event for 2018. The large increase in 2018 is
mostly driven by algorithms implemented to mitigate the inoperative modules in
the pixel detector. The muon triggers discussed here account for 4.1% of this
processing time in 2016, which increases to 5.2% in 2018.

The cumulative average processing time of every step of the HLT reconstruction
(L2 and L3) of the IsoMu24 trigger is shown in Fig. 14. The distributions are
normalized to the number of L1 muons that are used to seed the reconstruction.
No difference between the years is observed in the reconstruction time for L2
muons. The 2016 L3 reconstruction algorithms (cascade and tracker muon)
require around 29ms/muon, while the average processing time of the 2017
reconstruction is 43ms/muon and that of the 2018 reconstruction algorithm
(iterative) is 128ms/muon.

The increase in 2017, and especially 2018, is driven by several effects. In
2016, the inside-out reconstruction using multiple tracking iterations was run
once per muon candidate in the tracker muon reconstruction. In the iterative
approach, this inside-out reconstruction was initially only run for muons that
failed in the outside-in step in 2017. In 2018, however, it was run for all L1
muons, and also seeded by those L2 muons that did not result in a track in the
outside-in step. The iterative tracking in the inside-out reconstruction is
not only run more often in 2018, it is also slower in 2017 and 2018 compared
to 2016 because of the increased number of pixel detector layer combinations
in the upgraded pixel detector, resulting in a larger number of track seeds.
In 2018, it was further slowed down by the inclusion of a step that was seeded
by a pair of pixel hits. This step accounts for an increase of about 40ms/muon
in the processing time. Finally, the 2018 version of the outside-in
reconstruction creates significantly more track seeds, resulting in an
increase in the time spent on track building.

The isolation step is also slower by about 7ms, driven both by the slower
track reconstruction with the upgraded pixel detector, as well as by an
increase in the time to reconstruct isolation information in the calorimeters
because of a change in HCAL reconstruction.

Figure 14: Cumulative average processing time of each of the trigger
reconstruction steps for the IsoMu24 trigger.

The pileup dependence of the average processing time is shown in Fig. 15 for
both the IsoMu24 and Mu50 triggers. The processing times in this case are
lower than in Fig. 14 because an event sample representative of the average
composition encountered during data taking is used and events with no L1 muons
are included; these lower the average time. These results are comparable to
the processing time for the full HLT menu discussed above. The average number
of L1 muons per event in these data sets does not vary between the years by
more than a few percent. This makes these values representative of the
processing time during nominal operation of the HLT. The measurement is
performed for the combination of the cascade and tracker muon algorithms in
2016 data and for the iterative algorithm in 2018 data. Because of the
different running conditions in 2016 and 2018, the measurements span a
different pileup range. There is a moderate increase in the processing time
between Mu50 and IsoMu24 caused by the need to compute the isolation in case
of the IsoMu24. The time needed to compute the isolation does not
significantly depend on the pileup. The iterative algorithm does not only have
a higher average processing time, but also a stronger pileup dependence
compared to the cascade and tracker muon algorithms. This is due to the
greater reliance of the iterative algorithm on track seeding in the pixel
detector, especially with the doublet-seeded iterations, where the increased
hit occupancy at higher pileup increases the number of tracks seeds found
inside the ROIs.

Figure 15: Average processing time of the full trigger for the IsoMu24 (filled
symbols) and the Mu50 trigger (open symbols) as a function of pileup. The
processing time for the combination of cascade and tracker muon measured in
2016 data is shown in blue circles, while they are shown in red squares and
black triangles for the iterative algorithm measured in 2017 and 2018 data.
Only statistical uncertainties are shown.

##  0.7 Summary

To maintain the excellent performance of the CMS muon trigger system in the
much harsher running conditions of LHC Run 2, especially the high-pileup
environment in 2017 and 2018, the reconstruction algorithms and identification
criteria for muons in the high-level trigger system were continually improved
during the Run 2 data-taking period. In particular, the algorithm used to
reconstruct muon tracks in the inner tracker was improved in several steps
during the 2017 and 2018 data-taking periods. Upgrades to the hardware-based
Level-1 trigger further improved the trigger efficiency for muons. This
facilitated an increase in the purity of the selected muon sample by
introducing muon identification criteria in the high-level trigger system,
while maintaining, or even slightly improving, the overall efficiency. The
plateau efficiency for both isolated and nonisolated single muons above the
transverse momentum threshold is around 90%. Compared with previous versions
of the algorithm, a significant improvement in the momentum resolution is
achieved. For triggers without an isolation requirement, a reduction in
trigger rate of 10% for the same trigger threshold and instantaneous
luminosity is observed compared with the beginning of Run 2. Because these
improvements come at a cost of increased processing time needed to run the
algorithms, this leaves room for further optimization for the upcoming LHC Run
3 with the goal to maintain the excellent physics performance while improving
algorithmic efficiency.

###### 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 and other
centers 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, the CMS detector, and the supporting
computing infrastructure provided by the following funding agencies: BMBWF and
FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and
FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC (China);
COLCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT
(Ecuador); MoER, ERC PUT and ERDF (Estonia); Academy of Finland, MEC, and HIP
(Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT
(Greece); NKFIA (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland);
INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LAS (Lithuania);
MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI
(Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC
(Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS, RFBR, and NRC KI
(Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER (Spain); MOSTR (Sri
Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST,
STAR, and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU (Ukraine); STFC
(United Kingdom); DOE and NSF (USA). Individuals have received support from
the Marie-Curie program and the European Research Council and Horizon 2020
Grant, contract Nos. 675440, 724704, 752730, and 765710 (European Union); the
Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von
Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour
la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA-
Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-
Belgium); the F.R.S.-FNRS and FWO (Belgium) under the “Excellence of Science –
EOS” – be.h project n. 30820817; the Beijing Municipal Science & Technology
Commission, No. Z191100007219010; the Ministry of Education, Youth and Sports
(MEYS) of the Czech Republic; the Deutsche Forschungsgemeinschaft (DFG), under
Germany’s Excellence Strategy – EXC 2121 “Quantum Universe” – 390833306, and
under project number 400140256 - GRK2497; the Lendület (“Momentum”) Program
and the János Bolyai Research Scholarship of the Hungarian Academy of
Sciences, the New National Excellence Program ÚNKP, the NKFIA research grants
123842, 123959, 124845, 124850, 125105, 128713, 128786, and 129058 (Hungary);
the Council of Science and Industrial Research, India; the HOMING PLUS program
of the Foundation for Polish Science, cofinanced from European Union, Regional
Development Fund, the Mobility Plus program of the Ministry of Science and
Higher Education, the National Science Center (Poland), contracts Harmonia
2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and
2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406; the National Priorities
Research Program by Qatar National Research Fund; the Ministry of Science and
Higher Education, project no. 0723-2020-0041 (Russia); the Programa Estatal de
Fomento de la Investigación Científica y Técnica de Excelencia María de
Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa del Principado de
Asturias; the Thalis and Aristeia programs cofinanced by EU-ESF and the Greek
NSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn
University and the Chulalongkorn Academic into Its 2nd Century Project
Advancement Project (Thailand); the Kavli Foundation; the Nvidia Corporation;
the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the
Weston Havens Foundation (USA).

## References

  * [1] 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.
  * [2] CMS Collaboration, “The CMS trigger system”,  JINST 12 (2017) P01020, 10.1088/1748-0221/12/01/P01020, arXiv:1609.02366.
  * [3] CMS Collaboration, “CMS technical design report for the pixel detector upgrade”, Technical Report CERN-LHCC-2012-016. CMS-TDR-11, 2012.  https://cds.cern.ch/record/1481838.
  * [4] 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.
  * [5] CMS Collaboration, “Performance of the CMS Level-1 trigger in proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  JINST 15 (2020) P10017, 10.1088/1748-0221/15/10/P10017, arXiv:2006.10165.
  * [6] CMS Collaboration, “Performance of the reconstruction and identification of high-momentum muons in proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  JINST 15 (2020) P02027, 10.1088/1748-0221/15/02/P02027, arXiv:1912.03516.
  * [7] CMS Collaboration, “The CMS experiment at the CERN LHC”,  JINST 3 (2008) S08004, 10.1088/1748-0221/3/08/S08004.
  * [8] 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.
  * [9] CMS Collaboration, “Track impact parameter resolution in the 2017 dataset with the CMS phase-1 pixel detector”, CMS Detector Performance Note CMS-DP-2020-032, 2020.  https://cds.cern.ch/record/2723305.
  * [10] 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.
  * [11] CMS Collaboration, “CMS physics: Technical design report volume 1: Detector performance and software”, Technical Report CERN-LHCC-2006-001, CMS-TDR-8-1, 2006.  http://cds.cern.ch/record/922757.
  * [12] F. Faccio, S. Michelis, and G. Ripamonti, “Failure of FEAST2.1-based modules in the CMS pixel detector system (physics run 2017)”. https://espace.cern.ch/project-DCDC-new/_layouts/15/start.aspx#/Reports, 2018\.
  * [13] CMS Collaboration, “Measurement of the inclusive W and Z production cross sections in pp collisions at $\sqrt{s}=7$ with the CMS experiment”,  JHEP 10 (2011) 132, 10.1007/JHEP10(2011)132, arXiv:1107.4789.
  * [14] CMS Collaboration, “CMS technical design report for the Level-1 trigger upgrade”, Technical Report CERN-LHCC-2013-011, CMS-TDR-012, 2013.  https://cds.cern.ch/record/1556311.
  * [15] 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.
  * [16] 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.
  * [17] F. Pantaleo, “New track seeding techniques for the CMS experiment”.  PhD thesis, Hamburg University, 2017.  CERN-THESIS-2017-028.
  * [18] M. J. Oreglia, “A study of the reactions $\psi^{\prime}\to\gamma\gamma\psi$”.  PhD thesis, Stanford University, 1980.  SLAC Report SLAC-R-236.
  * [19] CMS Collaboration, “Particle-flow reconstruction and global event description with the CMS detector”,  JINST 12 (2017) P10003, 10.1088/1748-0221/12/10/P10003, arXiv:1706.04965.
  * [20] 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.

##  .8 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan${}^{\textrm{\textdagger}}$, A. Tumasyan Institut für
Hochenergiephysik, Wien, Austria
W. Adam, T. Bergauer, M. Dragicevic, A. Escalante Del Valle, R. Frühwirth1, M.
Jeitler1, N. Krammer, L. Lechner, D. Liko, I. Mikulec, F.M. Pitters, J.
Schieck1, R. Schöfbeck, M. Spanring, S. Templ, W. Waltenberger, C.-E. Wulz1,
M. Zarucki Institute for Nuclear Problems, Minsk, Belarus
V. Chekhovsky, A. Litomin, V. Makarenko Universiteit Antwerpen, Antwerpen,
Belgium
M.R. Darwish2, E.A. De Wolf, X. Janssen, T. Kello3, A. Lelek, H. Rejeb Sfar,
P. Van Mechelen, S. Van Putte, N. Van Remortel Vrije Universiteit Brussel,
Brussel, Belgium
F. Blekman, E.S. Bols, J. D’Hondt, J. De Clercq, S. Lowette, S. Moortgat, A.
Morton, D. Müller, A.R. Sahasransu, S. Tavernier, W. Van Doninck, P. Van
Mulders Université Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, B. Clerbaux, G. De Lentdecker, B. Dorney, L. Favart, A.
Grebenyuk, A.K. Kalsi, K. Lee, I. Makarenko, L. Moureaux, L. Pétré, A. Popov,
N. Postiau, E. Starling, L. Thomas, C. Vander Velde, P. Vanlaer, D. Vannerom,
L. Wezenbeek Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, M. Gruchala, I. Khvastunov4, G. Mestdach, M. Niedziela,
C. Roskas, K. Skovpen, M. Tytgat, W. Verbeke, B. Vermassen, M. Vit Université
Catholique de Louvain, Louvain-la-Neuve, Belgium
A. Bethani, G. Bruno, F. Bury, C. Caputo, P. David, C. Delaere, M. Delcourt,
I.S. Donertas, A. Giammanco, V. Lemaitre, K. Mondal, J. Prisciandaro, A.
Taliercio, M. Teklishyn, P. Vischia, S. Wertz, S. Wuyckens Centro Brasileiro
de Pesquisas Fisicas, Rio de Janeiro, Brazil
G.A. Alves, C. Hensel, A. Moraes Universidade do Estado do Rio de Janeiro, Rio
de Janeiro, Brazil
W.L. Aldá Júnior, E. Belchior Batista Das Chagas, H. BRANDAO MALBOUISSON, W.
Carvalho, J. Chinellato5, E. Coelho, E.M. Da Costa, G.G. Da Silveira6, D. De
Jesus Damiao, S. Fonseca De Souza, J. Martins7, D. Matos Figueiredo, C. Mora
Herrera, L. Mundim, H. Nogima, P. Rebello Teles, L.J. Sanchez Rosas, A.
Santoro, S.M. Silva Do Amaral, A. Sznajder, M. Thiel, F. Torres Da Silva De
Araujo, A. Vilela Pereira Universidade Estadual Paulista a, Universidade
Federal do ABC b, São Paulo, Brazil
C.A. Bernardesa,a, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M.
Gregoresa,b, D.S. Lemosa, P.G. Mercadantea,b, S.F. Novaesa, Sandra S. Padulaa
Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of
Sciences, Sofia, Bulgaria
A. Aleksandrov, G. Antchev, I. Atanasov, R. Hadjiiska, P. Iaydjiev, M.
Misheva, M. Rodozov, M. Shopova, G. Sultanov University of Sofia, Sofia,
Bulgaria
A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov, A. Petrov Beihang
University, Beijing, China
T. Cheng, W. Fang3, Q. Guo, M. Mittal, H. Wang, L. Yuan Department of Physics,
Tsinghua University, Beijing, China
M. Ahmad, G. Bauer, Z. Hu, Y. Wang, K. Yi8,9 Institute of High Energy Physics,
Beijing, China
E. Chapon, G.M. Chen10, H.S. Chen10, M. Chen, T. Javaid10, A. Kapoor, D.
Leggat, H. Liao, Z.-A. LIU10, R. Sharma, A. Spiezia, J. Tao, J. Thomas-
wilsker, J. Wang, H. Zhang, S. Zhang10, J. Zhao State Key Laboratory of
Nuclear Physics and Technology, Peking University, Beijing, China
A. Agapitos, Y. Ban, C. Chen, Q. Huang, A. Levin, Q. Li, M. Lu, X. Lyu, Y.
Mao, S.J. Qian, D. Wang, Q. Wang, J. Xiao Sun Yat-Sen University, Guangzhou,
China
Z. You Institute of Modern Physics and Key Laboratory of Nuclear Physics and
Ion-beam Application (MOE) - Fudan University, Shanghai, China
X. Gao3, H. Okawa Zhejiang University, Hangzhou, China
M. Xiao Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C. Florez, J. Fraga, A. Sarkar, M.A. Segura Delgado
Universidad de Antioquia, Medellin, Colombia
J. Jaramillo, J. Mejia Guisao, F. Ramirez, J.D. Ruiz Alvarez, C.A. Salazar
González, N. Vanegas Arbelaez University of Split, Faculty of Electrical
Engineering, Mechanical Engineering and Naval Architecture, Split, Croatia
D. Giljanovic, N. Godinovic, D. Lelas, I. Puljak University of Split, Faculty
of Science, Split, Croatia
Z. Antunovic, M. Kovac, T. Sculac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, D. Majumder, M. Roguljic, A. Starodumov11, T. Susa
University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, E. Erodotou, A. Ioannou, G. Kole, M. Kolosova, S.
Konstantinou, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H. Rykaczewski,
H. Saka, D. Tsiakkouri Charles University, Prague, Czech Republic
M. Finger12, M. Finger Jr.12, 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. Abdalla13, A.A. Abdelalim14,15, Y. Assran16,17 Center for High Energy
Physics (CHEP-FU), Fayoum University, El-Fayoum, Egypt
M.A. Mahmoud, Y. Mohammed National Institute of Chemical Physics and
Biophysics, Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, J. Pata, M. Raidal, C. Veelken Department of Physics, University of
Helsinki, Helsinki, Finland
P. Eerola, L. Forthomme, H. Kirschenmann, K. Osterberg, M. Voutilainen
Helsinki Institute of Physics, Helsinki, Finland
E. Brücken, F. Garcia, J. Havukainen, V. Karimäki, M.S. Kim, R. Kinnunen, T.
Lampén, K. Lassila-Perini, S. Lehti, T. Lindén, H. Siikonen, E. Tuominen, J.
Tuominiemi Lappeenranta University of Technology, Lappeenranta, Finland
P. Luukka, T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
C. Amendola, M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F.
Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
B. Lenzi, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin, A. Savoy-
Navarro18, M. Titov, G.B. Yu Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole
Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
S. Ahuja, F. Beaudette, M. Bonanomi, A. Buchot Perraguin, P. Busson, C.
Charlot, O. Davignon, B. Diab, G. Falmagne, R. Granier de Cassagnac, A.
Hakimi, I. Kucher, A. Lobanov, C. Martin Perez, M. Nguyen, C. Ochando, P.
Paganini, J. Rembser, R. Salerno, J.B. Sauvan, Y. Sirois, A. Zabi, A. Zghiche
Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France
J.-L. Agram19, J. Andrea, D. Apparu, D. Bloch, G. Bourgatte, J.-M. Brom, E.C.
Chabert, C. Collard, D. Darej, J.-C. Fontaine19, U. Goerlach, C. Grimault,
A.-C. Le Bihan, P. Van Hove Université de Lyon, Université Claude Bernard Lyon
1, CNRS-IN2P3, Institut de Physique Nucléaire de Lyon, Villeurbanne, France
E. Asilar, S. Beauceron, C. Bernet, G. Boudoul, C. Camen, A. Carle, N. Chanon,
D. Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M. Gouzevitch, B.
Ille, Sa. Jain, I.B. Laktineh, H. Lattaud, A. Lesauvage, M. Lethuillier, L.
Mirabito, K. Shchablo, L. Torterotot, G. Touquet, M. Vander Donckt, S. Viret
Georgian Technical University, Tbilisi, Georgia
I. Bagaturia20, Z. Tsamalaidze12 RWTH Aachen University, I. Physikalisches
Institut, Aachen, Germany
L. Feld, K. Klein, M. Lipinski, D. Meuser, A. Pauls, M.P. Rauch, J. Schulz, M.
Teroerde RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
D. Eliseev, M. Erdmann, P. Fackeldey, B. Fischer, S. Ghosh, T. Hebbeker, K.
Hoepfner, H. Keller, L. Mastrolorenzo, M. Merschmeyer, A. Meyer, G. Mocellin,
S. Mondal, S. Mukherjee, D. Noll, A. Novak, T. Pook, A. Pozdnyakov, Y. Rath,
H. Reithler, J. Roemer, A. Schmidt, S.C. Schuler, A. Sharma, S. Wiedenbeck, S.
Zaleski RWTH Aachen University, III. Physikalisches Institut B, Aachen,
Germany
C. Dziwok, G. Flügge, W. Haj Ahmad21, O. Hlushchenko, T. Kress, A. Nowack, C.
Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl22, T. Ziemons Deutsches
Elektronen-Synchrotron, Hamburg, Germany
H. Aarup Petersen, M. Aldaya Martin, P. Asmuss, I. Babounikau, S. Baxter, O.
Behnke, A. Bermúdez Martínez, A.A. Bin Anuar, K. Borras23, V. Botta, D.
Brunner, A. Campbell, A. Cardini, P. Connor, S. Consuegra Rodríguez, V.
Danilov, M.M. Defranchis, L. Didukh, D. Domínguez Damiani, G. Eckerlin, D.
Eckstein, L.I. Estevez Banos, E. Gallo24, A. Geiser, A. Giraldi, A. Grohsjean,
M. Guthoff, A. Harb, A. Jafari25, N.Z. Jomhari, H. Jung, A. Kasem23, M.
Kasemann, H. Kaveh, C. Kleinwort, J. Knolle, D. Krücker, W. Lange, T. Lenz, J.
Lidrych, K. Lipka, W. Lohmann26, T. Madlener, R. Mankel, I.-A. Melzer-
Pellmann, J. Metwally, A.B. Meyer, M. Meyer, J. Mnich, A. Mussgiller, V.
Myronenko, Y. Otarid, D. Pérez Adán, S.K. Pflitsch, D. Pitzl, A. Raspereza, A.
Saggio, A. Saibel, M. Savitskyi, V. Scheurer, C. Schwanenberger, A. Singh,
R.E. Sosa Ricardo, N. Tonon, O. Turkot, A. Vagnerini, M. Van De Klundert, R.
Walsh, D. Walter, Y. Wen, K. Wichmann, C. Wissing, S. Wuchterl, O. Zenaiev, R.
Zlebcik University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, K. De Leo, T. Dreyer, M. Eich, F.
Feindt, A. Fröhlich, C. Garbers, E. Garutti, P. Gunnellini, J. Haller, A.
Hinzmann, A. Karavdina, G. Kasieczka, R. Klanner, R. Kogler, V. Kutzner, J.
Lange, T. Lange, A. Malara, C.E.N. Niemeyer, A. Nigamova, K.J. Pena Rodriguez,
O. Rieger, P. Schleper, M. Schröder, J. Schwandt, D. Schwarz, J. Sonneveld, H.
Stadie, G. Steinbrück, A. Tews, B. Vormwald, I. Zoi Karlsruher Institut fuer
Technologie, Karlsruhe, Germany
J. Bechtel, T. Berger, E. Butz, R. Caspart, T. Chwalek, W. De Boer, A.
Dierlamm, A. Droll, K. El Morabit, N. Faltermann, K. Flöh, M. Giffels, J.o.
Gosewisch, A. Gottmann, F. Hartmann22, C. Heidecker, U. Husemann, I. Katkov27,
P. Keicher, R. Koppenhöfer, S. Maier, M. Metzler, S. Mitra, Th. Müller, M.
Musich, M. Neukum, G. Quast, K. Rabbertz, J. Rauser, D. Savoiu, D. Schäfer, M.
Schnepf, D. Seith, I. Shvetsov, H.J. Simonis, R. Ulrich, J. Van Der Linden,
R.F. Von Cube, M. Wassmer, M. Weber, S. Wieland, R. Wolf, S. Wozniewski, S.
Wunsch Institute of Nuclear and Particle Physics (INPP), NCSR Demokritos,
Aghia Paraskevi, Greece
G. Anagnostou, P. Asenov, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas,
G. Paspalaki, A. Stakia National and Kapodistrian University of Athens,
Athens, Greece
M. Diamantopoulou, D. Karasavvas, G. Karathanasis, P. Kontaxakis, C.K. Koraka,
A. Manousakis-katsikakis, A. Panagiotou, I. Papavergou, N. Saoulidou, S.
Sotiropoulos, K. Theofilatos, E. Tziaferi, K. Vellidis, E. Vourliotis National
Technical University of Athens, Athens, Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis, A. Zacharopoulou
University of Ioánnina, Ioánnina, Greece
K. Adamidis, I. Bestintzanos, I. Evangelou, C. Foudas, P. Gianneios, P.
Katsoulis, P. Kokkas, N. Manthos, I. Papadopoulos, J. Strologas MTA-ELTE
Lendület CMS Particle and Nuclear Physics Group, Eötvös Loránd University,
Budapest, Hungary
M. Csanad, M.M.A. Gadallah28, S. Lökös29, P. Major, K. Mandal, A. Mehta, G.
Pasztor, O. Surányi, G.I. Veres Wigner Research Centre for Physics, Budapest,
Hungary
M. Bartók30, G. Bencze, C. Hajdu, D. Horvath31, F. Sikler, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary
S. Czellar, J. Karancsi30, J. Molnar, Z. Szillasi, D. Teyssier Institute of
Physics, University of Debrecen, Debrecen, Hungary
P. Raics, Z.L. Trocsanyi32, B. Ujvari Eszterhazy Karoly University, Karoly
Robert Campus, Gyongyos, Hungary
T. Csorgo33, F. Nemes33, T. Novak Indian Institute of Science (IISc),
Bangalore, India
S. Choudhury, J.R. Komaragiri, D. Kumar, L. Panwar, P.C. Tiwari National
Institute of Science Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati34, D. Dash, C. Kar, P. Mal, T. Mishra, V.K. Muraleedharan Nair
Bindhu35, A. Nayak35, N. Sur, S.K. Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, G. Chaudhary, S. Chauhan, N. Dhingra36, R.
Gupta, A. Kaur, S. Kaur, P. Kumari, M. Meena, K. Sandeep, J.B. Singh, A.K.
Virdi University of Delhi, Delhi, India
A. Ahmed, A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, A.
Kumar, M. Naimuddin, P. Priyanka, K. Ranjan, A. Shah Saha Institute of Nuclear
Physics, HBNI, Kolkata, India
M. Bharti37, R. Bhattacharya, S. Bhattacharya, D. Bhowmik, S. Dutta, S. Ghosh,
B. Gomber38, M. Maity39, S. Nandan, P. Palit, P.K. Rout, G. Saha, B. Sahu, S.
Sarkar, M. Sharan, B. Singh37, S. Thakur37 Indian Institute of Technology
Madras, Madras, India
P.K. Behera, S.C. Behera, P. Kalbhor, A. Muhammad, R. Pradhan, P.R. Pujahari,
A. Sharma, A.K. Sikdar Bhabha Atomic Research Centre, Mumbai, India
D. Dutta, V. Jha, V. Kumar, D.K. Mishra, K. Naskar40, P.K. Netrakanti, L.M.
Pant, P. Shukla Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, S. Dugad, G.B. Mohanty, U. Sarkar Tata Institute of Fundamental
Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, R. Chudasama, M. Guchait, S.
Karmakar, S. Kumar, G. Majumder, K. Mazumdar, S. Mukherjee, D. Roy Indian
Institute of Science Education and Research (IISER), Pune, India
S. Dube, B. Kansal, S. Pandey, A. Rane, A. Rastogi, S. Sharma Department of
Physics, Isfahan University of Technology, Isfahan, Iran
H. Bakhshiansohi41, M. Zeinali42 Institute for Research in Fundamental
Sciences (IPM), Tehran, Iran
S. Chenarani43, S.M. Etesami, M. Khakzad, M. Mohammadi Najafabadi University
College Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, R. Alya,b,44, C. Arutaa,b, A. Colaleoa, D. Creanzaa,c, N. De
Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, A. Di Pilatoa,b, W.
Elmetenaweea,b, L. Fiorea, A. Gelmia,b, M. Gula, G. Iasellia,c, M. Incea,b, S.
Lezkia,b, G. Maggia,c, M. Maggia, I. Margjekaa,b, V. Mastrapasquaa,b, J.A.
Merlina, S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, A. Ranieria, G.
Selvaggia,b, L. Silvestrisa, F.M. Simonea,b, R. Vendittia, P. Verwilligena
INFN Sezione di Bologna a, Università di Bologna b, Bologna, Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia, S. Braibant-
Giacomellia,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
C. Cioccaa, M. Cuffiania,b, G.M. Dallavallea, T. Diotalevia,b, F. Fabbria, A.
Fanfania,b, E. Fontanesia,b, P. Giacomellia, L. Giommia,b, C. Grandia, L.
Guiduccia,b, F. Iemmia,b, S. Lo Meoa,45, 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,46, S. Costaa,b,46, A. Di Mattiaa, R. Potenzaa,b, A.
Tricomia,b,46, C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b,
Firenze, Italy
G. Barbaglia, A. Cassesea, R. Ceccarellia,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, F. Fioria, E. Focardia,b, G. Latinoa,b, P. Lenzia,b, M.
Lizzoa,b, M. Meschinia, S. Paolettia, R. Seiditaa,b, G. Sguazzonia, L.
Viliania INFN Laboratori Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, D. Piccolo INFN Sezione di Genova a, Università di
Genova b, Genova, Italy
M. Bozzoa,b, F. Ferroa, R. Mulargiaa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschia,b, F. Brivioa,b, F. Cetorellia,b, V. Cirioloa,b,22,
F. De Guioa,b, M.E. Dinardoa,b, P. Dinia, S. Gennaia, A. Ghezzia,b, P.
Govonia,b, L. Guzzia,b, M. Malbertia, S. Malvezzia, A. Massironia, D.
Menascea, F. Montia,b, L. Moronia, M. Paganonia,b, D. Pedrinia, S. Ragazzia,b,
T. Tabarelli de Fatisa,b, D. Valsecchia,b,22, D. Zuoloa,b INFN Sezione di
Napoli a, Università di Napoli ’Federico II’ b, Napoli, Italy, Università
della Basilicata c, Potenza, Italy, Università G. Marconi d, Roma, Italy
S. Buontempoa, N. Cavalloa,c, A. De Iorioa,b, F. Fabozzia,c, F. Fiengaa,
A.O.M. Iorioa,b, L. Listaa,b, S. Meolaa,d,22, P. Paoluccia,22, B. Rossia, C.
Sciaccaa,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, P. Bortignona, A. Bragagnoloa,b, R.
Carlina,b, P. Checchiaa, P. De Castro Manzanoa, T. Dorigoa, F. Gasparinia,b,
U. Gasparinia,b, S.Y. Hoha,b, L. Layera,47, M. Margonia,b, A.T. Meneguzzoa,b,
M. Presillaa,b, P. Ronchesea,b, R. Rossina,b, F. Simonettoa,b, G. Stronga, M.
Tosia,b, H. YARARa,b, M. Zanettia,b, P. Zottoa,b, A. Zucchettaa,b, G.
Zumerlea,b INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
C. Aime‘a,b, A. Braghieria, S. Calzaferria,b, D. Fiorinaa,b, P. Montagnaa,b,
S.P. Rattia,b, V. Rea, M. Ressegottia,b, C. Riccardia,b, P. Salvinia, I. Vaia,
P. Vituloa,b INFN Sezione di Perugia a, Università di Perugia b, Perugia,
Italy
G.M. Bileia, D. Ciangottinia,b, L. Fanòa,b, P. Laricciaa,b, G. Mantovania,b,
V. Mariania,b, M. Menichellia, F. Moscatellia, A. Piccinellia,b, A. Rossia,b,
A. Santocchiaa,b, D. Spigaa, T. Tedeschia,b INFN Sezione di Pisa a, Università
di Pisa b, Scuola Normale Superiore di Pisa c, Pisa Italy, Università di Siena
d, Siena, Italy
K. Androsova, P. Azzurria, G. Bagliesia, V. Bertacchia,c, L. Bianchinia, T.
Boccalia, E. Bossini, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, M.R. Di
Domenicoa,d, S. Donatoa, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c,
G. Mandorlia,c, A. Messineoa,b, F. Pallaa, G. Ramirez-Sancheza,c, A. Rizzia,b,
G. Rolandia,c, S. Roy Chowdhurya,c, A. Scribanoa, N. Shafieia,b, P. Spagnoloa,
R. Tenchinia, G. Tonellia,b, N. Turinia,d, A. Venturia, P.G. Verdinia INFN
Sezione di Roma a, Sapienza Università di Roma b, Rome, Italy
F. Cavallaria, M. Cipriania,b, D. Del Rea,b, E. Di Marcoa, M. Diemoza, E.
Longoa,b, P. Meridiania, G. Organtinia,b, F. Pandolfia, R. Paramattia,b, C.
Quarantaa,b, S. Rahatloua,b, C. Rovellia, F. Santanastasioa,b, L. Soffia,b, R.
Tramontanoa,b INFN Sezione di Torino a, Università di Torino b, Torino, Italy,
Università del Piemonte Orientale c, Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, A. Belloraa,b, C. Biinoa, A. Cappatia,b, N. Cartigliaa, M.
Costaa,b, R. Covarellia,b, P. De Remigisa, G. Dellacasaa, N. Demariaa, B.
Kiania,b, F. Leggera, C. Mariottia, S. Masellia, E. Migliorea,b, V. Monacoa,b,
E. Monteila,b, M. Montenoa, M.M. Obertinoa,b, G. Ortonaa, L. Pachera,b, N.
Pastronea, M. Pelliccionia, G.L. Pinna Angionia,b, M. Ruspaa,c, R.
Salvaticoa,b, F. Sivieroa,b, V. Solaa, A. Solanoa,b, D. Soldia,b, A. Staianoa,
M. Tornagoa,b, D. Trocinoa,b INFN Sezione di Trieste a, Università di Trieste
b, Trieste, Italy
S. Belfortea, V. Candelisea,b, M. Casarsaa, F. Cossuttia, A. Da Rolda,b, G.
Della Riccaa,b, F. Vazzolera,b Kyungpook National University, Daegu, Korea
S. Dogra, C. Huh, B. Kim, D.H. Kim, G.N. Kim, J. Lee, S.W. Lee, C.S. Moon,
Y.D. Oh, S.I. Pak, B.C. Radburn-Smith, S. Sekmen, Y.C. Yang Chonnam National
University, Institute for Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon Hanyang University, Seoul, Korea
B. Francois, T.J. Kim, J. Park Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, B. Hong, K. Lee, K.S. Lee, J. Lim, J. Park, S.K. Park,
J. Yoo Kyung Hee University, Department of Physics, Seoul, Republic of Korea
J. Goh, A. Gurtu Sejong University, Seoul, Korea
H.S. Kim, Y. Kim Seoul National University, Seoul, Korea
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, S. Ko, H. Kwon, H.
Lee, S. Lee, K. Nam, B.H. Oh, M. Oh, S.B. Oh, H. Seo, U.K. Yang, I. Yoon
University of Seoul, Seoul, Korea
D. Jeon, J.H. Kim, B. Ko, J.S.H. Lee, I.C. Park, Y. Roh, D. Song, I.J. Watson
Yonsei University, Department of Physics, Seoul, Korea
S. Ha, H.D. Yoo Sungkyunkwan University, Suwon, Korea
Y. Choi, C. Hwang, Y. Jeong, H. Lee, Y. Lee, I. Yu College of Engineering and
Technology, American University of the Middle East (AUM), Egaila, Kuwait
Y. Maghrbi Riga Technical University, Riga, Latvia
V. Veckalns48 Vilnius University, Vilnius, Lithuania
M. Ambrozas, A. Juodagalvis, A. Rinkevicius, G. Tamulaitis, A. Vaitkevicius
National Centre for Particle Physics, Universiti Malaya, Kuala Lumpur,
Malaysia
W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli Universidad de Sonora (UNISON),
Hermosillo, Mexico
J.F. Benitez, A. Castaneda Hernandez, J.A. Murillo Quijada, L. Valencia Palomo
Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
G. Ayala, H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz49,
R. Lopez-Fernandez, C.A. Mondragon Herrera, D.A. Perez Navarro, A. Sanchez-
Hernandez Universidad Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, M. Ramirez-Garcia, F. Vazquez Valencia
Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada University of Montenegro,
Podgorica, Montenegro
J. Mijuskovic4, N. Raicevic University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M.I. Asghar, A. Awais, M.I.M. Awan, H.R. Hoorani, W.A. Khan, M.A.
Shah, M. Shoaib, M. Waqas AGH University of Science and Technology Faculty of
Computer Science, Electronics and Telecommunications, Krakow, Poland
V. Avati, L. Grzanka, M. Malawski National Centre for Nuclear Research,
Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. Górski, M. Kazana, M.
Szleper, P. Traczyk, P. Zalewski Institute of Experimental Physics, Faculty of
Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, K. Doroba, A. Kalinowski, K. Kierzkowski, M. Konecki, J.
Krolikowski, W. Oklinski, K. Pozniak50, M. Walczak, W. Zabolotny50 Laboratório
de Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
M. Araujo, P. Bargassa, D. Bastos, A. Boletti, P. Faccioli, M. Gallinaro, J.
Hollar, N. Leonardo, T. Niknejad, J. Seixas, K. Shchelina, O. Toldaiev, J.
Varela Joint Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, D. Budkouski, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov,
A. Kamenev, V. Karjavine, A. Lanev, A. Malakhov, V. Matveev51,52, V. Palichik,
V. Perelygin, M. Savina, D. Seitova, V. Shalaev, S. Shmatov, S. Shulha, V.
Smirnov, O. Teryaev, N. Voytishin, A. Zarubin, I. Zhizhin Petersburg Nuclear
Physics Institute, Gatchina (St. Petersburg), Russia
G. Gavrilov, V. Golovtcov, Y. Ivanov, V. Kim53, E. Kuznetsova54, V. Murzin, V.
Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Volkov, A. Vorobyev
Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, G. Pivovarov, D.
Tlisov${}^{\textrm{\textdagger}}$, A. Toropin Institute for Theoretical and
Experimental Physics named by A.I. Alikhanov of NRC ‘Kurchatov Institute’,
Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, A. Nikitenko55, V. Popov, G.
Safronov, A. Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A. Zhokin Moscow
Institute of Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
O. Bychkova, M. Chadeeva56, P. Parygin, S. Polikarpov56, E. Popova 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. Dubinin57, L. Dudko, A. Ershov, A. Gribushin, V.
Klyukhin, O. Kodolova, I. Lokhtin, S. Obraztsov, S. Petrushanko, V. Savrin, A.
Snigirev Novosibirsk State University (NSU), Novosibirsk, Russia
V. Blinov58, T. Dimova58, L. Kardapoltsev58, I. Ovtin58, Y. Skovpen58
Institute for High Energy Physics of National Research Centre ‘Kurchatov
Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, V. Kachanov, A. Kalinin, D. Konstantinov, V. Petrov,
R. Ryutin, A. Sobol, S. Troshin, N. Tyurin, A. Uzunian, A. Volkov National
Research Tomsk Polytechnic University, Tomsk, Russia
A. Babaev, A. Iuzhakov, V. Okhotnikov, L. Sukhikh Tomsk State University,
Tomsk, Russia
V. Borchsh, V. Ivanchenko, E. Tcherniaev University of Belgrade: Faculty of
Physics and VINCA Institute of Nuclear Sciences, Belgrade, Serbia
P. Adzic59, M. Dordevic, P. Milenovic, J. Milosevic Centro de Investigaciones
Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M.
Barrio Luna, Cristina F. Bedoya, 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, J. León
Holgado, D. Moran, Á. Navarro Tobar, A. Pérez-Calero Yzquierdo, J. Puerta
Pelayo, I. Redondo, L. Romero, S. Sánchez Navas, M.S. Soares, L. Urda Gómez,
C. Willmott Universidad Autónoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz, R. Reyes-Almanza Universidad de Oviedo,
Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias
(ICTEA), Oviedo, Spain
B. Alvarez Gonzalez, J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras,
I. Gonzalez Caballero, E. Palencia Cortezon, C. Ramón Álvarez, J. Ripoll Sau,
V. Rodríguez Bouza, A. Trapote Instituto de Física de Cantabria (IFCA), CSIC-
Universidad de Cantabria, Santander, Spain
J.A. Brochero Cifuentes, I.J. Cabrillo, A. Calderon, B. Chazin Quero, J.
Duarte Campderros, M. Fernandez, C. Fernandez Madrazo, P.J. Fernández Manteca,
A. García Alonso, G. Gomez, C. Martinez Rivero, P. Martinez Ruiz del Arbol, F.
Matorras, J. Piedra Gomez, C. Prieels, F. Ricci-Tam, T. Rodrigo, A. Ruiz-
Jimeno, L. Scodellaro, N. Trevisani, I. Vila, J.M. Vizan Garcia University of
Colombo, Colombo, Sri Lanka
MK Jayananda, B. Kailasapathy60, D.U.J. Sonnadara, DDC Wickramarathna
University of Ruhuna, Department of Physics, Matara, Sri Lanka
W.G.D. Dharmaratna, K. Liyanage, N. Perera, N. Wickramage CERN, European
Organization for Nuclear Research, Geneva, Switzerland
T.K. Aarrestad, D. Abbaneo, E. Auffray, G. Auzinger, J. Baechler, P. Baillon,
A.H. Ball, D. Barney, J. Bendavid, N. Beni, M. Bianco, A. Bocci, E. Brondolin,
T. Camporesi, M. Capeans Garrido, G. Cerminara, S.S. Chhibra, L. Cristella, D.
d’Enterria, A. Dabrowski, N. Daci, A. David, A. De Roeck, M. Deile, R. Di
Maria, M. Dobson, M. Dünser, N. Dupont, A. Elliott-Peisert, N. Emriskova, F.
Fallavollita61, D. Fasanella, S. Fiorendi, A. Florent, G. Franzoni, J.
Fulcher, W. Funk, S. Giani, D. Gigi, K. Gill, F. Glege, L. Gouskos, M.
Haranko, J. Hegeman, Y. Iiyama, V. Innocente, T. James, P. Janot, J. Kaspar,
J. Kieseler, M. Komm, N. Kratochwil, C. Lange, S. Laurila, P. Lecoq, K. Long,
C. Lourenço, L. Malgeri, S. Mallios, M. Mannelli, F. Meijers, S. Mersi, E.
Meschi, F. Moortgat, M. Mulders, S. Orfanelli, L. Orsini, F. Pantaleo22, L.
Pape, E. Perez, M. Peruzzi, A. Petrilli, G. Petrucciani, A. Pfeiffer, M.
Pierini, M. Pitt, T. Quast, D. Rabady, A. Racz, M. Rieger, M. Rovere, H.
Sakulin, J. Salfeld-Nebgen, S. Scarfi, C. Schäfer, C. Schwick, M. Selvaggi, A.
Sharma, P. Silva, W. Snoeys, P. Sphicas62, S. Summers, V.R. Tavolaro, D.
Treille, A. Tsirou, G.P. Van Onsem, M. Verzetti, K.A. Wozniak, W.D. Zeuner
Paul Scherrer Institut, Villigen, Switzerland
L. Caminada63, A. Ebrahimi, W. Erdmann, R. Horisberger, Q. Ingram, H.C.
Kaestli, D. Kotlinski, U. Langenegger, M. Missiroli, T. Rohe ETH Zurich -
Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland
M. Backhaus, P. Berger, A. Calandri, N. Chernyavskaya, A. De Cosa, G.
Dissertori, M. Dittmar, M. Donegà, C. Dorfer, T. Gadek, T.A. Gómez Espinosa,
C. Grab, D. Hits, W. Lustermann, A.-M. Lyon, R.A. Manzoni, M.T. Meinhard, F.
Micheli, F. Nessi-Tedaldi, J. Niedziela, F. Pauss, V. Perovic, G. Perrin, S.
Pigazzini, M.G. Ratti, M. Reichmann, C. Reissel, T. Reitenspiess, B. Ristic,
D. Ruini, D.A. Sanz Becerra, M. Schönenberger, V. Stampf, J. Steggemann64, R.
Wallny, D.H. Zhu Universität Zürich, Zurich, Switzerland
C. Amsler65, C. Botta, D. Brzhechko, M.F. Canelli, A. De Wit, R. Del Burgo,
J.K. Heikkilä, M. Huwiler, A. Jofrehei, B. Kilminster, S. Leontsinis, A.
Macchiolo, P. Meiring, V.M. Mikuni, U. Molinatti, I. Neutelings, G. Rauco, A.
Reimers, P. Robmann, S. Sanchez Cruz, K. Schweiger, Y. Takahashi National
Central University, Chung-Li, Taiwan
C. Adloff66, C.M. Kuo, W. Lin, A. Roy, T. Sarkar39, S.S. Yu National Taiwan
University (NTU), Taipei, Taiwan
L. Ceard, P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.y. Li, R.-S.
Lu, E. Paganis, A. Psallidas, A. Steen, E. Yazgan, P.r. Yu Chulalongkorn
University, Faculty of Science, Department of Physics, Bangkok, Thailand
B. Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas Çukurova University,
Physics Department, Science and Art Faculty, Adana, Turkey
F. Boran, S. Damarseckin67, Z.S. Demiroglu, F. Dolek, C. Dozen68, I.
Dumanoglu69, E. Eskut, G. Gokbulut, Y. Guler, E. Gurpinar Guler70, I. Hos71,
C. Isik, E.E. Kangal72, O. Kara, A. Kayis Topaksu, U. Kiminsu, G. Onengut, K.
Ozdemir73, A. Polatoz, A.E. Simsek, B. Tali74, U.G. Tok, S. Turkcapar, I.S.
Zorbakir, C. Zorbilmez Middle East Technical University, Physics Department,
Ankara, Turkey
B. Isildak75, G. Karapinar76, K. Ocalan77, M. Yalvac78 Bogazici University,
Istanbul, Turkey
B. Akgun, I.O. Atakisi, E. Gülmez, M. Kaya79, O. Kaya80, Ö. Özçelik, S.
Tekten81, E.A. Yetkin82 Istanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak69, Y. Komurcu, S. Sen83 Istanbul University, Istanbul,
Turkey
F. Aydogmus Sen, S. Cerci74, B. Kaynak, S. Ozkorucuklu, D. Sunar Cerci74
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, A. Bundock, E. Clement, D. Cussans, H.
Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, B. Krikler, S.
Paramesvaran, T. Sakuma, S. Seif El Nasr-Storey, V.J. Smith, N. Stylianou84,
J. Taylor, A. Titterton Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev85, C. Brew, R.M. Brown, D.J.A. Cockerill, K.V. Ellis, K.
Harder, S. Harper, J. Linacre, K. Manolopoulos, D.M. Newbold, E. Olaiya, D.
Petyt, T. Reis, T. Schuh, C.H. Shepherd-Themistocleous, A. Thea, I.R. Tomalin,
T. Williams Imperial College, London, United Kingdom
R. Bainbridge, P. Bloch, S. Bonomally, J. Borg, S. Breeze, O. Buchmuller, V.
Cepaitis, G.S. Chahal86, D. Colling, P. Dauncey, G. Davies, M. Della Negra, G.
Fedi, G. Hall, M.H. Hassanshahi, G. Iles, J. Langford, L. Lyons, A.-M. Magnan,
S. Malik, A. Martelli, V. Milosevic, J. Nash87, V. Palladino, M. Pesaresi,
D.M. Raymond, A. Richards, A. Rose, E. Scott, C. Seez, A. Shtipliyski, A.
Tapper, K. Uchida, T. Virdee22, N. Wardle, S.N. Webb, D. Winterbottom, A.G.
Zecchinelli Brunel University, Uxbridge, United Kingdom
J.E. Cole, A. Khan, P. Kyberd, C.K. Mackay, I.D. Reid, L. Teodorescu, S. Zahid
Baylor University, Waco, USA
S. Abdullin, A. Brinkerhoff, B. Caraway, J. Dittmann, K. Hatakeyama, A.R.
Kanuganti, B. McMaster, N. Pastika, S. Sawant, C. Smith, C. Sutantawibul, J.
Wilson Catholic University of America, Washington, DC, USA
R. Bartek, A. Dominguez, R. Uniyal, A.M. Vargas Hernandez The University of
Alabama, Tuscaloosa, USA
A. Buccilli, O. Charaf, S.I. Cooper, D. Di Croce, S.V. Gleyzer, C. Henderson,
C.U. Perez, P. Rumerio, C. West Boston University, Boston, USA
A. Akpinar, A. Albert, D. Arcaro, C. Cosby, Z. Demiragli, D. Gastler, J.
Rohlf, K. Salyer, D. Sperka, D. Spitzbart, I. Suarez, S. Yuan, D. Zou Brown
University, Providence, USA
G. Benelli, B. Burkle, X. Coubez23, D. Cutts, Y.t. Duh, M. Hadley, U. Heintz,
J.M. Hogan88, K.H.M. Kwok, E. Laird, G. Landsberg, K.T. Lau, J. Lee, J. Luo,
M. Narain, S. Sagir89, E. Usai, W.Y. Wong, X. Yan, 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, F. Jensen, O. Kukral,
R. Lander, M. Mulhearn, D. Pellett, M. Shi, D. Taylor, M. Tripathi, Y. Yao, F.
Zhang University of California, Los Angeles, USA
M. Bachtis, R. Cousins, A. Dasgupta, A. Datta, D. Hamilton, J. Hauser, M.
Ignatenko, M.A. Iqbal, T. Lam, N. Mccoll, W.A. Nash, S. Regnard, D. Saltzberg,
C. Schnaible, B. Stone, V. Valuev University of California, Riverside,
Riverside, USA
K. Burt, Y. Chen, R. Clare, J.W. Gary, G. Hanson, G. Karapostoli, O.R. Long,
N. Manganelli, M. Olmedo Negrete, W. Si, S. Wimpenny, Y. Zhang University of
California, San Diego, La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, S. Cooperstein, N. Deelen, J. Duarte, R.
Gerosa, L. Giannini, D. Gilbert, V. Krutelyov, J. Letts, M. Masciovecchio, S.
May, S. Padhi, M. Pieri, V. Sharma, M. Tadel, A. Vartak, F. Würthwein, A.
Yagil University of California, Santa Barbara - Department of Physics, Santa
Barbara, USA
N. Amin, C. Campagnari, M. Citron, A. Dorsett, V. Dutta, J. Incandela, M.
Kilpatrick, B. Marsh, H. Mei, A. Ovcharova, H. Qu, M. Quinnan, J. Richman, U.
Sarica, D. Stuart, S. Wang California Institute of Technology, Pasadena, USA
A. Bornheim, O. Cerri, I. Dutta, J.M. Lawhorn, N. Lu, J. Mao, H.B. Newman, J.
Ngadiuba, T.Q. Nguyen, M. Spiropulu, J.R. Vlimant, C. Wang, S. Xie, Z. Zhang,
R.Y. Zhu Carnegie Mellon University, Pittsburgh, USA
J. Alison, M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, I. Vorobiev
University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, E. MacDonald, R. Patel, A. Perloff, K. Stenson, K.A.
Ulmer, S.R. Wagner Cornell University, Ithaca, USA
J. Alexander, Y. Cheng, J. Chu, D.J. Cranshaw, K. Mcdermott, J. Monroy, J.R.
Patterson, D. Quach, A. Ryd, W. Sun, S.M. Tan, Z. Tao, J. Thom, P. Wittich, M.
Zientek Fermi National Accelerator Laboratory, Batavia, USA
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,
K.F. Di Petrillo, V.D. Elvira, J. Freeman, Z. Gecse, L. Gray, D. Green, S.
Grünendahl, O. Gutsche, R.M. Harris, R. Heller, T.C. Herwig, J. Hirschauer, B.
Jayatilaka, S. Jindariani, M. Johnson, U. Joshi, P. Klabbers, T. Klijnsma, B.
Klima, M.J. Kortelainen, S. Lammel, D. Lincoln, R. Lipton, T. Liu, J. Lykken,
C. Madrid, K. Maeshima, C. Mantilla, D. Mason, P. McBride, P. Merkel, S.
Mrenna, S. Nahn, V. O’Dell, V. Papadimitriou, K. Pedro, C. Pena57, O.
Prokofyev, F. Ravera, A. Reinsvold Hall, L. Ristori, B. Schneider, E. Sexton-
Kennedy, N. Smith, A. Soha, L. Spiegel, S. Stoynev, J. Strait, L. Taylor, S.
Tkaczyk, N.V. Tran, L. Uplegger, E.W. Vaandering, H.A. Weber University of
Florida, Gainesville, USA
D. Acosta, P. Avery, D. Bourilkov, L. Cadamuro, V. Cherepanov, F. Errico, R.D.
Field, D. Guerrero, B.M. Joshi, M. Kim, J. Konigsberg, A. Korytov, K.H. Lo,
J.F. Low, A. Madorsky, K. Matchev, N. Menendez, G. Mitselmakher, D.
Rosenzweig, K. Shi, J. Sturdy, J. Wang, E. Yigitbasi, X. Zuo Florida State
University, Tallahassee, USA
T. Adams, A. Askew, D. Diaz, R. Habibullah, S. Hagopian, V. Hagopian, K.F.