MUO-16-001

$HeadURL:svn+ssh://svn.cern.ch/reps/tdr2/papers/MUO-16-001/trunk/MUO-16-001.tex$
$Id:MUO-16-001.tex4645472018-06-1413:54:53Zrakness$

MUO-16-001

# Performance of the CMS muon detector and muon reconstruction with proton-
proton collisions at $\sqrt{s}=13\TeV$

###### Abstract

The CMS muon detector system, muon reconstruction software, and high-level
trigger underwent significant changes in 2013–2014 in preparation for running
at higher LHC collision energy and instantaneous luminosity. The performance
of the modified system is studied using proton-proton collision data at
center-of-mass energy $\sqrt{s}=13\TeV$, collected at the LHC in 2015 and
2016. The measured performance parameters, including spatial resolution,
efficiency, and timing, are found to meet all design specifications and are
well reproduced by simulation. Despite the more challenging running
conditions, the modified muon system is found to perform as well as, and in
many aspects better than, previously. We dedicate this paper to the memory of
Prof. Alberto Benvenuti, whose work was fundamental for the CMS muon detector.

##  0.1 Introduction

The Compact Muon Solenoid (CMS) detector at the CERN LHC is a general purpose
device designed primarily to search for signatures of new physics in proton-
proton () and heavy ion (proton-ion and ion-ion) collisions. Since many of
these signatures include muons, CMS is constructed with subdetectors to
identify muons, trigger the CMS readout upon their detection, and measure
their momentum and charge over a broad range of kinematic parameters. In this
paper, the composite whole of muon subdetectors is called the muon detector,
and the software algorithms used to combine the data from all CMS subdetectors
to characterize the physics objects created in collisions are collectively
referred to as particle reconstruction. Previous published studies of the
performance of the CMS muon detector [1] and muon reconstruction [2] were
based on data from collisions at center-of-mass energy $\sqrt{s}=7\TeV$. These
data were collected in 2010, the first full year of LHC operations (the first
year of “Run 1”, which lasted from 2010 to 2012). To prepare for the higher
collision energy and luminosity of the subsequent running period (“Run 2”,
beginning in 2015), significant improvements were made to the muon system in
2013–2014 during the long shutdown period between Runs 1 and 2. These
improvements will be described in Section 0.2. The present paper describes the
performance of the Run 2 CMS muon system, and covers the subdetectors, the
reconstruction software, and the high-level trigger. It is based on data
collected in 2015 and 2016 from collisions at $\sqrt{s}=13\TeV$ with
instantaneous luminosities up to $8\ten{33}$. As a result of these
improvements to the muon detector and reconstruction algorithms, and in spite
of the higher instantaneous luminosity, the performance of the muon detector
and reconstruction is as good as or better than in 2010. Moreover, all
performance parameters remain well within the design specifications of the CMS
muon detector [3].

An extensive description of the performance of the muon detector and the muon
reconstruction software has been given in Ref. [1] and Ref. [2]. Therefore, in
this paper, representative performance plots from individual muon subsystems
are shown and results from the other subsystems, when pertinent, are described
in the text. A description of the different subdetectors forming the CMS muon
detector is given in Section 0.2. The muon reconstruction, identification, and
isolation algorithms are outlined in Section 0.3, followed by a short
description of the data and simulation samples used in Section 0.4. The
performance of individual muon subdetectors and that of the full system is
described in detail, particularly with regard to spatial resolution (Section
0.5), efficiency (Section 0.6), momentum scale and resolution (Section 0.7),
and timing (Section 0.8). The design and performance of the high-level trigger
is described in Section 0.9. The results are summarized in Section 0.10.

##  0.2 Muon detectors

A detailed description of the CMS detector, together with a definition of the
coordinate system and the relevant kinematic variables, can be found in Ref.
[4]. A schematic diagram of the CMS detector is shown in Fig. 1. The CMS
detector has a cylindrical geometry that is azimuthally ($\phi$) symmetric
with respect to the beamline and features a superconducting magnet, which
provides a 3.8 T solenoidal field oriented along the beamline. An inner
tracker comprising a silicon pixel detector and a silicon strip tracker is
used to measure the momentum of charged particles in the pseudorapidity range
$\abs{\eta}<2.5$. The muon system is located outside the solenoid and covers
the range $\abs{\eta}<2.4$. It is composed of gaseous detectors sandwiched
among the layers of the steel flux-return yoke that allow a traversing muon to
be detected at multiple points along the track path.

Figure 1: An R-z cross section of a quadrant of the CMS detector with the axis
parallel to the beam (z) running horizontally and the radius (R) increasing
upward. The interaction point is at the lower left corner. The locations of
the various muon stations and the steel flux-return disks (dark areas) are
shown. The drift tube stations (DTs) are labeled MB (“Muon Barrel”) and the
cathode strip chambers (CSCs) are labeled ME (“Muon Endcap”). Resistive plate
chambers (RPCs) are mounted in both the barrel and endcaps of CMS, where they
are labeled RB and RE, respectively.

Three types of gas ionization chambers were chosen to make up the CMS muon
system: drift tube chambers (DTs), cathode strip chambers (CSCs), and
resistive plate chambers (RPCs). A detailed description of these chambers,
including gas composition and operating voltage, can be found in Ref. [1]. 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 an accurate 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 $|\eta|<1.2$ and
$0.9<\abs{\eta}<2.4$, respectively, and are complemented by RPCs in the range
$\abs{\eta}<1.9$. We distinguish three regions, naturally defined by the
cylindrical geometry of CMS, referred to as the barrel ($\abs{\eta}<0.9$),
overlap ($0.9<|\abs{\eta}|<1.2$), and endcap ($1.2<\abs{\eta}<2.4$) regions.
The chambers are arranged to maximize the coverage and to provide some overlap
where possible. An event in which two muons are reconstructed, one in the
barrel and one in the endcap, is shown in Fig. 2.

Figure 2: A collision event with two reconstructed muon tracks superimposed on
a cutaway image of the CMS detector. The image has been rotated around the y
axis, which makes the inner tracker appear offset relative to its true
position in the center of the detector. The four layers of muon chambers are
interleaved with three layers of the steel flux-return yoke. The reconstructed
invariant mass of the muon pair is 2.4. One muon is reconstructed in the
barrel with a transverse momentum () of 0.7, while the second muon is
reconstructed in the endcap with of 1.0.

In the barrel, a station is a ring of chambers assembled between two layers of
the steel flux-return yoke at approximately the same value of radius R. There
are four DT and four RPC stations in the barrel, labeled MB1–MB4 and RB1–RB4,
respectively. Each DT chamber consists of three “superlayers”, each comprising
four staggered layers of parallel drift cells. The wires in each layer are
oriented so that two of the superlayers measure the muon position in the
bending plane (R-$\phi$) and one superlayer measures the position in the
longitudinal plane (R-$\theta$). However, the chambers in MB4 have only the
two R-$\phi$ superlayers. The two innermost RPC barrel stations, RB1 and RB2,
are instrumented with two layers of RPCs each, facing the innermost and
outermost sides of the DT. For stations 3 and 4 the RPCs have only one
detection layer. The RPC strips are oriented parallel to the wires of the DT
chambers that measure the coordinate in the bending plane. From the readout
point of view, every RPC is subdivided into two or three $\eta$ partitions
called “rolls” [5]. Both DT and RPC barrel stations are arranged in five
“wheels” along the z dimension, with 12 $\phi$-sectors per wheel.

In the endcap, a station is a ring of chambers assembled between two disks of
the steel flux-return yoke at approximately the same value of z. There are
four CSC and four RPC stations in each endcap, labeled ME1–ME4 and RE1–RE4,
respectively. Between Run 1 and Run 2, additional chambers were added in ME4
and RE4 to increase redundancy, improve efficiency, and reduce
misidentification rates. Each CSC chamber consists of six staggered layers,
each of which measures the muon position in two coordinates. The cathode
strips are oriented radially to measure the muon position in the bending plane
(R-$\phi$), whereas the anode wires provide a coarse measurement in R. The RPC
strips are oriented parallel to the CSC strips to measure the coordinate in
the bending plane, and each endcap chamber is divided into three $\abs{\eta}$
partitions (rolls) identified by the letters A, B, and C. In the radial
direction, stations are arranged in two or three ”rings” of endcap RPCs and
CSCs. In the inner rings of stations 2, 3, and 4, each CSC chamber subtends a
$\phi$ angle of 20; all other CSCs subtend an angle of 10.

Properties and parameters of the CMS muon subsystems during the 2016 data
collection period.  Muon subsystem DT CSC RPC $\abs{\eta}$ coverage 0.0–1.2
0.9–2.4 0.0–1.9 Number of stations 4 4 4 Number of chambers 250 540 Barrel:
480 Endcap: 576 Number of layers/chamber R-$\phi$: 8; z: 4 6 2 in RB1 and RB2
1 elsewhere Number of readout channels 172 000 Strips: 266 112 Barrel: 68 136
Anode channels: 210 816 Endcap: 55 296 Percentage of active channels 98.4%
99.0% 98.3%

Using these conventions, in this paper the performance of the DTs is specified
according to chamber type, labeled “MBn$\pm$w”, where n is the barrel station
(increasing with R), $+$ or $-$ specifies the z-direction, and w is the wheel
(increasing with $\abs{z}$, with w = 0 centered at $z=0$). The CSCs are
labeled “ME$\pm$n/m”, where $+$ or $-$ specifies the z-direction, n is the
endcap station (increasing with $\abs{z}$), and m is the ring (increasing with
R). If no sign is specified, the performance of the $+$ and $-$ stations are
combined. The inner ring of the CSC chambers in station 1 has a structure that
is different from the other rings; the primary difference is an additional
division of ME1/1 into two $\eta$ partitions called a and b [1]. An overview
of the number of chambers per chamber type, number of readout channels, and
number of active channels in 2016 is given in Table 0.2.

The CMS trigger system consists of two stages [6] and is described in more
detail in Section 0.9. A level-1 (L1) trigger based on custom-made electronics
reduces the event rate from 40MHz (LHC bunch crossing rate) to a readout rate
of 100kHz. For the muon component of the L1 trigger, CSC and DT chambers
provide “trigger primitives” constructed from hit patterns consistent with
muons that originate from the collision region, and RPC chambers provide hit
information. When a specific bunch crossing is selected by the L1 algorithms
as a potential event, readout of the precision data from the CMS detector is
initiated via the “L1-Accept” (L1A) signal, which is synchronously distributed
to all CMS subsystems. The high-level trigger (HLT), based on a farm of
microprocessors, uses the precision data to reconstruct events to further
reduce the rate of data to preserve for offline analysis to approximately
1kHz. Both L1 and HLT use information from the muon system to efficiently
identify muons over the broad energy range required for physics signatures of
interest while minimizing the trigger rate and operating within the available
latency.

The LHC is a bunched machine, in which the accelerated protons are distributed
in bunches separated by one or more time steps of 25ns. The running conditions
of the LHC have evolved continuously since the beginning of its operation, and
are expected to continue to evolve in the future [7, 8, 9]. As a
representative comparison, we compare the LHC conditions in fill 1440 (October
2010), included in the dataset analyzed in Refs. [1, 2], with the conditions
in fill 5013 (June 2016), included in the 2016 data used in this paper.
Between these two fills, the center-of-mass energy increased from
$\sqrt{s}=7\TeV$ to $\sqrt{s}=13\TeV$. The maximum instantaneous luminosity
increased by about a factor of 40, from 232to 833, as a result of the
increases in both the number of colliding bunches and the luminosity per
bunch. The number of colliding bunches increased by about a factor of 6, from
348 to 2028, facilitated by the reduction of the spacing between proton
bunches from 150ns to 25ns. The average luminosity per bunch increased by
about a factor of 6.5, from 0.630to 3.930, as a result of several changes
including increasing the number of protons per bunch, reducing the transverse
widths of the beams, and focusing the beams more tightly [8, 9]. The combined
increases in collision energy and luminosity per bunch caused the average
number of inelastic collisions per crossing (pileup) to increase by about a
factor of 8, from 3.6 to 28.

In order to prepare for these challenging LHC conditions and to exploit the
corresponding gain in luminosity, the CMS muon system was significantly
modified between Run 1 and Run 2. As mentioned previously, additional RPC and
CSC chambers, RE4 and ME4/2, were installed in the fourth station to increase
redundancy, improve efficiency, and reduce misidentification rates. The
trigger and readout electronics were improved as part of the CMS-wide trigger
upgrade [10], including optical links in the DTs and CSCs to increase
bandwidth and to ease maintenance [11, 12]. New electronics were installed in
the CSC ME1/1 chambers to read out every strip in the ME1/1a ring, covering
$2.1<\abs{\eta}<2.4$. These strips had been ganged together in Run 1,
combining every 16th strip, which led to a 3-fold ambiguity for the position
of a hit on that strip plane. The removal of the strip ganging in Run 2 leads
to reduced capacitance, in turn leading to reduced noise and a resulting
improvement in the $\phi$ resolution in ME1/1a.

##  0.3 Muon reconstruction

###  0.3.1 Hit and segment reconstruction

This section gives a brief overview of the “local” reconstruction algorithms
in the CMS muon detector. Local reconstruction uses information from only a
single muon chamber (RPC, CSC, or DT) to specify the passage of a muon through
the chamber [1].

Muons and other charged particles that traverse a muon subdetector ionize the
gas in the chambers, which eventually causes electric signals to be produced
on the wires and strips. These signals are read out by electronics and are
associated with well-defined locations, generically called “hits”, in the
detector. The precise location of each hit is reconstructed from the
electronic signals using different algorithms depending on the detector
technology.

Hit reconstruction in a DT drift cell specifies the transverse distance
between the wire and the intersection of the muon trajectory with the plane
containing the wires in the layer. The electrons produced through gas
ionization by a muon crossing the cell are collected at the anode wire. A
time-to-digital converter (TDC) registers their arrival time,
$T_{\textsc{TDC}}$. This time is then corrected by a time pedestal,
$T_{\text{ped}}$, and multiplied by the electron drift velocity, v, to
reconstruct the position of the DT hit:

$\text{position}=(T_{\textsc{TDC}}-T_{\text{ped}})\times v.$ (1)

The DT drift cell was designed to provide a uniform electric field so that the
drift velocity can be assumed to be mostly constant for tracks impinging on
the cell perpendicular to the plane of wires. The effect of deviations from
this assumption on the spatial resolution is described in Section 0.5. In
Equation 1, the time pedestal accounts for the time from the bunch crossing
until the trigger decision arrives at the chamber electronics. It includes the
time-of-flight (at the speed of light) along a straight line from the
interaction region to the center of the wire, the average signal propagation
time along the wire, the generation of trigger primitives, the processing by
the L1 trigger electronics, the distribution of the L1A signals, and the
receipt of L1A back at the readout electronics on the chamber. It also
includes a wire-by-wire component that takes into account the different signal
paths within a chamber. In another iteration, the time-of-flight and signal
propagation time are refined using the segment position from the orthogonal
superlayer available for MB1, MB2, and MB3. The calibration of
$T_{\text{ped}}$ and v is described in detail in Ref. [1]. Effectively, the
drift time is tuned to make $(T_{\textsc{TDC}}-T_{\text{ped}})=0$ for muons
that cross the chamber at the location of the wire.

Hit reconstruction in a CSC layer measures the position of the traversing muon
by combining information from the cathode strips and anode wires. The strips
are radial, each subtending an angle of about 3mrad (different chamber types
have different angular strip widths that range from 2.2 to 4.7mrad) and can
thus accurately measure the $\phi$ angle. This is the bending direction of a
muon traveling through the endcaps. In the endcaps the solenoidal field is
first parallel to the z direction but then diverges radially, so a muon is
first deflected in one azimuthal direction and then deflected in the opposite
direction, with the maximum deflection occurring in the first station. The
wires are orthogonal to the strips, except in ME1/1 where they are tilted to
compensate for the Lorentz drift of ionization electrons in the non-negligible
magnetic field in this region. They are ganged into wire groups of about
1–2width, which results in a coarser-grained measurement in the radial
direction. A CSC hit is reconstructed at the intersection points of hit strips
and wire groups. A CSC reconstructed hit also has a measured time, which is
calibrated such that hits from muons produced promptly in the triggering bunch
crossing have a time distribution centered around zero.

Hit reconstruction in an RPC chamber requires clustering of hit strips. A
charged particle passing through the RPC produces an avalanche of electrons in
the gap between two plates. This charge induces a signal on an external strip
readout plane to identify muons from collision events with a precision of a
fewns. The strips are aligned with $\eta$ with up to 2strip pitch, therefore
giving a fewspatial resolution in the $\phi$ coordinate. Since the ionization
charge from a muon can be shared by more than one strip, adjacent strips are
clustered to reconstruct one hit. An RPC hit is reconstructed as the strip
cluster centroid.

While the RPC chambers are single-layer chambers, the CSC and DT chambers are
multi-layer detectors where hits are reconstructed in each layer. From the
reconstructed hits, straight-line track “segments” are built within each CSC
or DT chamber.

Segment reconstruction in the DTs was modified prior to Run 2 [13]. The
calibration of $T_{\text{ped}}$ in Eq. 1 implicitly assumes that all muons
take the same time to reach the reconstructed hit position from the
interaction region. However, this assumption is not exactly true since hits
could come from muons originating from other bunch crossings (“out-of-time
muons”), or could be produced by heavy particles that travel at a reduced
speed. Any such shift in the muon crossing time would cause all hits produced
within a chamber to be shifted in space by the same amount. Therefore, DT
segment reconstruction was modified prior to Run 2 to include time as third
parameter, in addition to the intercept and slope of the standard two-
dimensional straight-line pattern recognition and fit algorithm (in the plane
transverse to the wire direction). The inclusion of time into segment
reconstruction allows spurious early hits, produced by delta rays, to be
removed from the segment reconstruction and thus improves the spatial
resolution (see Section 0.5). The segment time information is not needed in
the muon track reconstruction algorithm because of the negligible rate of
accidentally matching out-of-time segments. The timing data are, however, kept
with the reconstructed muon track information to be used in physics analyses
(see Section 0.8).

###  0.3.2 Muon track reconstruction

In the standard CMS reconstruction procedure for collisions [14, 2, 15],
tracks are first reconstructed independently in the inner tracker (tracker
track) and in the muon system (standalone-muon track), and then used as input
for muon track reconstruction.

Tracker tracks are built using an iterative approach, running a sequence of
tracking algorithms, each with slightly different logic. After each iteration
step, hits that have been associated with reconstructed tracks are removed
from the set of input hits to be used in the following step. This approach
maintains high performance and reduces processing time [14].

Standalone-muon tracks are built by exploiting information from muon
subdetectors to gather all CSC, DT, and RPC information along a muon
trajectory using a Kalman-filter technique [16]. Reconstruction starts from
seeds made up of groups of DT or CSC segments.

Tracker muon tracks are built “inside-out” by propagating tracker tracks to
the muon system with loose matching to DT or CSC segments. Each tracker track
with transverse momentum $\pt>0.5\GeV$ and a total momentum $p>2.5\GeV$ is
extrapolated to the muon system. If at least one muon segment matches the
extrapolated track, the tracker track qualifies as a tracker muon track. The
track-to-segment matching is performed in a local (x,y) coordinate system
defined in a plane transverse to the beam axis, where x is the better-measured
coordinate (in the R-$\phi$ plane) and y is the coordinate orthogonal to it.
The extrapolated track and the segment are matched either if the absolute
value of the difference between their positions in the x coordinate is smaller
than 3, or if the ratio of this distance to its uncertainty (pull) is smaller
than 4.

Global muon tracks are built “outside-in” by matching standalone-muon tracks
with tracker tracks. The matching is done by comparing parameters of the two
tracks propagated onto a common surface. A combined fit is performed with the
Kalman filter using information from both the tracker track and standalone-
muon track.

Owing to the high efficiency of the tracker track and muon segment
reconstruction, about 99% of the muons produced within the geometrical
acceptance of the muon system are reconstructed either as a global muon track
or as a tracker muon track, and very often as both. Global muons and tracker
muons that share the same tracker track are merged into a single candidate.

Tracker muons have high efficiency in regions of the CMS detector with less
instrumentation (for routing of detector services) and for muons with low .
The tracker muons that are not global muons typically match only to segments
in the innermost muon station, but not other stations. This increases the
probability of muon misidentification since hadron shower remnants can reach
this innermost muon station (punch-through). Global muon reconstruction, which
uses standalone-muon tracks, is designed to have high efficiency for muons
penetrating through more than one muon station, which reduces the muon
misidentification rate compared to tracker muons. By fully exploiting the
information from both the inner tracker and the muon system, the measurement
of global muons is also improved compared to tracker muons, especially for
$\pt>200\GeV$. Muons reconstructed only as standalone-muon tracks have worse
momentum resolution and a higher admixture of cosmic muons than global or
tracker muons.

Reconstructed muons are fed into the CMS particle flow (PF) algorithm [17].
The algorithm combines information from all CMS subdetectors to identify and
reconstruct all individual particles for each event, including electrons,
neutral hadrons, charged hadrons, and muons. For muons, PF applies a set of
selection criteria to candidates reconstructed with the standalone, global, or
tracker muon algorithms. The requirements are based on various quality
parameters from the muon reconstruction (described in Section 0.3.3), as well
as make use of information from other CMS subdetectors (, isolation as
described in Section 0.3.5).

Prior to Run 2, two muon-specific calculations were added to the tracker track
reconstruction to keep reconstruction and identification efficiency as high as
possible under high-pileup conditions [17]. In the first calculation, tracker
tracks identified as tracker muons are rebuilt by relaxing some quality
constraints to increase track hit efficiency. In the second, standalone-muon
tracks with $\pt>10\GeV$ that fulfill a minimal set of quality requirements
are used to seed an outside-in inner tracking reconstruction step. This
additional set of tracks is combined with those provided by the inner tracking
system and is exploited to build global and tracker muons.

###  0.3.3 Muon identification

A set of variables was studied and selection criteria were defined to allow
each analysis to tune the desired balance between efficiency and purity. Some
variables are based on muon reconstruction, such as track fit $\chi^{2}$, the
number of hits per track (either in the inner tracker or in the muon system,
or both), or the degree of matching between tracker tracks and standalone-muon
tracks (for global muons). The muon segment compatibility is computed by
propagating the tracker track to the muon system, and evaluating both the
number of matched segments in all stations and the closeness of the matching
in position and direction [15]. The algorithm returns values in a range
between 0 and 1, with 1 representing the highest degree of compatibility. A
kink-finding algorithm splits the tracker track into two separate tracks at
several places along the trajectory. For each split the algorithm makes a
comparison between the two separate tracks, with a large $\chi^{2}$ indicating
that the two tracks are incompatible with being a single track. Other
variables exploit inputs from outside the reconstructed muon track, such as
compatibility with the primary vertex (the reconstructed vertex with the
largest value of summed physics-object $\pt^{2}$ [18]). Using these variables,
the main identification types of muons used in CMS physics analyses include:

  * •

Loose muon identification (ID) aims to identify prompt muons originating at
the primary vertex, and muons from light and heavy flavor decays, as well as
maintain a low rate of the misidentification of charged hadrons as muons. A
loose muon is a muon selected by the PF algorithm that is also either a
tracker or a global muon.

  * •

Medium muon ID is optimized for prompt muons and for muons from heavy flavor
decay. A medium muon is a loose muon with a tracker track that uses hits from
more than 80% of the inner tracker layers it traverses. If the muon is only
reconstructed as a tracker muon, the muon segment compatibility must be
greater than 0.451. If the muon is reconstructed as both a tracker muon and a
global muon, the muon segment compatibility need only be greater than 0.303,
but then the global fit is required to have goodness-of-fit per degree of
freedom ($\chi^{2}/\mathrm{dof}$) less than 3, the position match between the
tracker muon and standalone-muon must have $\chi^{2}<12$, and the maximum
$\chi^{2}$ computed by the kink-finding algorithm must be less than 20. The
constraints on the segment compatibility were tuned after application of the
other constraints to target an overall efficiency of 99.5% for muons from
simulated and events.

  * •

Tight muon ID aims to suppress muons from decay in flight and from hadronic
punch-through. A tight muon is a loose muon with a tracker track that uses
hits from at least six layers of the inner tracker including at least one
pixel hit. The muon must be reconstructed as both a tracker muon and a global
muon. The tracker muon must have segment matching in at least two of the muon
stations. The global muon fit must have $\chi^{2}/\mathrm{dof}<10$ and include
at least one hit from the muon system. A tight muon must be compatible with
the primary vertex, having a transverse impact parameter $\abs{dXY}<0.2\cm$
and a longitudinal impact parameter $\abs{dz}<0.5\cm$.

  * •

Soft muon ID is optimized for low-muons for -physics and quarkonia analyses. A
soft muon is a tracker muon with a tracker track that satisfies a high purity
flag [14] and uses hits from at least six layers of the inner tracker
including at least one pixel hit. The tracker muon reconstruction must have
tight segment matching, having pulls less than 3 both in local x and in local
y. A soft muon is loosely compatible with the primary vertex, having
$\abs{dXY}<0.3\cm$ and $\abs{dz}<20\cm$.

  * •

High momentum muon ID is optimized for muons with $\pt>200\GeV$. A high
momentum muon is reconstructed as both a tracker muon and a global muon. The
requirements on the tracker track, the tracker muon, and the transverse and
longitudinal impact parameters are the same as for a tight muon, as well as
the requirement that there be at least one hit from the muon system for the
global muon. However, in contrast to the tight muon, the requirement on the
global muon fit $\chi^{2}/\mathrm{dof}$ is removed. The removal of the
$\chi^{2}$ requirement prevents inefficiencies at high when muons radiate
large electromagnetic showers as they pass through the steel flux-return yoke,
giving rise to additional hits in the muon chambers. A requirement on the
relative uncertainty, $\sigma(\pt)/\pt<30\%$, is used to ensure a proper
momentum measurement.

###  0.3.4 Determination of muon momentum

The default algorithm used by CMS to determine the muon momentum is the Tune-P
algorithm [2]. For each muon, the Tune-P algorithm selects the measurement
from one of the following refits based on goodness-of-fit information and
$\sigma(\pt)/\pt$ criteria to reduce tails in the momentum resolution
distribution due to poor quality fits.

  * •

Inner-Track fit determines the momentum using only information from the inner
tracker. While various fit methods are used to add information from the muon
detector to improve the measurement of the momentum at high , for muons with
$\pt<200\GeV$, the contribution from the muon system to the momentum
measurement is marginal. Therefore, the inner-track fit is highly favored by
Tune-P at low momentum.

  * •

Tracker-Plus-First-Muon-Station fit starts with the hits from the global muon
track and performs a refit using only information from the inner tracker and
the innermost muon station containing hits. The innermost station provides the
best information about momentum within the muon system.

  * •

Picky fit aims at properly determining the momentum for events in which
showering occurred within a chamber. This algorithm again starts with the hits
from the global muon track, but in chambers that have a large hit occupancy
(likely from a shower) the refit uses only the hits that are compatible with
the extrapolated trajectory (based on $\chi^{2}$).

  * •

Dynamic-Truncation fit accounts for cases when energy losses cause significant
bending of the muon trajectory. The algorithm propagates the tracker track to
the innermost station and performs a refit adding hits from the segment
closest to the extrapolated trajectory, if compatible. Starting from the
refit, the algorithm is repeated for each station propagating outward. If no
compatible hit is found in two consecutive muon stations, the algorithm stops.

The Tune-P algorithm was validated using cosmic ray muons, muons from
collisions, and Monte Carlo simulations generated using different misalignment
scenarios. Both the core and the tails of the momentum, curvature, and
invariant mass distributions were studied to ensure that no significant biases
in the muon momentum assignment are introduced by the algorithm.

The PF algorithm refines the information from Tune-P, exploiting information
from the full event, by selecting refits that significantly improve the
balance of missing and by using a post-processing algorithm designed to
preserve events that contain genuine missing energy [17]. The PF momentum
assignment was also validated using Monte Carlo simulation and muons from
collisions.

###  0.3.5 Muon isolation

To distinguish between prompt muons and those from weak decays within jets,
the isolation of a muon is evaluated relative to its by summing up the energy
in geometrical cones, $\Delta
R=\sqrt{\smash[b]{(\Delta\phi)^{2}+(\Delta\eta)^{2}}}$, surrounding the muon.
One strategy sums reconstructed tracks (track based isolation), while another
uses charged hadrons and neutral particles coming from PF (PF isolation).

For the computation of PF isolation [17], the of charged hadrons within the
$\Delta R$ cone originating from the primary vertex are summed together with
the energy sum of all neutral particles (hadrons and photons) in the cone. The
contribution from pileup to the neutral particles is corrected by computing
the sum of charged hadron deposits originating from pileup vertices, scaling
it by a factor of 0.5, and subtracting this from the neutral hadron and photon
sums to give the corrected energy sum from neutral particles. The factor of
0.5 is estimated from simulations to be approximately the ratio of neutral
particle to charged hadron production in inelastic proton-proton collisions.
The corrected energy sum from neutral particles is limited to be positive or
zero.

For both strategies, tight and loose working points are defined to achieve
efficiencies of 95% and 98%, respectively. They are tuned using simulated
tight muons from $\text{\cPZ}\to\MM$ decays with $\pt>20\GeV$. The values for
the tight and loose working points for PF isolation within $\Delta R<0.4$ are
0.15 and 0.25, respectively, while the values for track based isolation within
$\Delta R<0.3$ are 0.05 and 0.10. The efficiency of the working points to
reject muons in jets was tested in simulated multi-jet QCD events (events
comprised uniquely of jets produced through the strong interaction) and
simulated events containing a boson plus one or more jets (+jets).

##  0.4 Data and simulated samples

Results shown in this paper come from one of two data sets: approximately 2of
collisions collected in 2015, which will be called “2015 data”, and
approximately 4of collisions collected in 2016, which will be called “2016
data”. The data set that was used for each result in this paper was chosen
depending on the availability of the data and the analyst. In any case, the
results represent the CMS muon performance in Run 2 no matter which data set
is used, since the peak luminosity delivered by LHC in 2015 and 2016 differed
only by about a factor of three, which is small compared to the factor of 40
difference between 2010 and 2016 as described in Section 0.2. The selected
data samples consist of events with a pair of reconstructed muons with low
thresholds. Further event criteria are applied depending on the analyses
performed, and are described in detail later.

The performance results most directly applicable to physics analyses are
presented in this paper using the 2015 data. These data are compared with
simulations from several Monte Carlo event generators for signal and
background processes. The Drell-Yan
$\text{\cPZ}/\text{\PGg}^{*}\to\text{\Pl}^{+}\text{\Pl}^{-}$ signal sample is
generated at next-to-leading order (NLO) with 2.3.3 [19]. The background
samples of +jets and of pairs with one or more jets (+jets) are also produced
with the same generator. The background from single top quark production is
generated at NLO with v1.0 [20]. The 8.212 [21, 22] package is used for QCD
events enriched in muon decays, parton showering, hadronization, and
simulation of the underlying event via tune CUETP8M1 [23], using NNPDF2.3 LO
[24] as the default set of parton distribution functions. For all processes,
the detector response is simulated using a detailed description of the CMS
detector based on the package [25] and event reconstruction is performed with
the same algorithms as used for the data. The simulated samples include
pileup, and the events are weighted so that the pileup distribution matches
the 2015 data, having an average pileup of about 11.

##  0.5 Spatial resolution

The spatial resolution of a muon subdetector is quantified by the width of the
distribution of residuals between the reconstructed and expected hit
positions. The expected position is estimated from the segment fit. The
resolution is obtained from the residual width by applying standard analytical
factors calculated from the “hat matrix” that relates the residuals from a fit
to the fitted measurements, and hence the widths of the residual distributions
to the intrinsic resolution of the measurements [26]. These factors differ for
CSC, in which the reconstructed hit used for the residual is excluded from the
segment fit, and for DT where the hit is included. Both CSCs and DTs are
designed to make a precise measurement in the direction of bending of a muon
track because this directly affects the measurement of the momentum. This is
the azimuthal direction, measured in the CSCs by the strips, and in the DTs by
the $\phi$ superlayers.

The spatial resolution of the DTs is determined by computing the value of the
residual for each hit used to reconstruct each segment. Typically, eight
residual values are computed for each $\phi$ segment and four for each
$\theta$ segment. Due to the azimuthal symmetry of the DTs, a single residual
distribution is filled with all hits having the same wire orientation from all
chambers in the same wheel and station. The width of each residual
distribution is converted to position resolution using the standard
analytically computed factors described above. Figure 3 shows the spatial
resolution of DT hits sorted by station, wheel, and wire orientation. The
resolution in the $\phi$ superlayers (, in the bending plane) is better than
250in MB1, MB2 and MB3, and better than 300in MB4. In the $\theta$
superlayers, the resolution varies from about 250 to 600except in the outer
wheels of MB1.

Figure 3: Reconstructed hit resolution for DT $\phi$ superlayers (squares) and
DT $\theta$ superlayers (diamonds) measured with the 2016 data, plotted as a
function of station and wheel. The uncertainties in these values are smaller
than the marker size in the figure.

Within every station, both $\theta$ and $\phi$ superlayers show symmetric
behavior with respect to the $z=0$ plane, as expected from the detector
symmetry. In wheel 0, where tracks from the interaction region are mostly
perpendicular to all layers, the resolution is the same for $\theta$ and
$\phi$ superlayers. From wheel 0 toward the forward region, tracks from the
interaction region have increasing values of $\abs{\eta}$; this affects
$\theta$ and $\phi$ superlayers in opposite ways. In the $\theta$ superlayers
the increasing inclination angle degrades the linearity of the distance-drift
time relation, thus worsening the resolution. In contrast, in $\phi$
superlayers the inclination angle increases the track path within the tube
(along the wire direction), thus increasing the ionization charge and
improving the resolution. The resolution of the $\phi$ superlayers is worse in
MB4 because no $\theta$ measurement is available, so no corrections can be
applied to account for the muon time of flight and the signal propagation time
along the wire. The DT spatial resolution in the 2016 data is improved by
about 10% compared to the 2010 results [1] as a result of the improved track
reconstruction method in Run 2 that removes spurious early hits, produced by
delta rays, from the segment reconstruction (see Section 0.3.1).

The spatial resolution of the CSCs is studied using locally reconstructed
segments that have exactly one hit per layer. For each segment, the hit in one
layer is dropped and the segment is re-fitted with the remaining five hits.
The residual between the dropped hit and the new fit is calculated as
$R\,\Delta\phi$ in the R-$\phi$ plane, which is the precision coordinate
measured by the strips and the direction of the magnetic bending of the muon.
This procedure is repeated for each layer. These residuals are approximately
Gaussian and the residual widths are converted to position resolution by using
standard analytical factors [26]. The spatial resolution of the CSC strip
measurement depends on the relative position at which a muon crosses a strip:
it is better for a muon crossing near a strip edge than at the center because
then more of the induced charge is shared between that strip and its neighbor,
allowing a better estimate of the center of the charge distribution. To
benefit from this fact, alternate layers in a CSC are staggered by half a
strip width, except in the ME1/1 chambers where the strips are narrower and
the effect is small. Resolutions are measured separately for the central half
of a strip width ($\sigma_{\textsc{C}}$) and the quarter strip-width at each
edge ($\sigma_{\textsc{E}}$) [1]. The layer measurements are combined to give
an overall resolution $\sigma$ per CSC station by
$1/\sigma_{\text{station}}^{2}=6/\sigma_{\text{layer}}^{2}$ (ME1/1 chambers)
and
$1/\sigma_{\text{station}}^{2}=3/\sigma_{\textsc{C}}^{2}+3/\sigma_{\textsc{E}}^{2}$
(chambers other than ME1/1). Table 0.5 summarizes the mean spatial resolution
in each CSC station and ring. The design specifications for the spatial
resolutions in the CSC system were 75for ME1/1 chambers and 150for the others.
These resolutions were chosen so that the contribution of the chamber spatial
resolution to the muon momentum resolution is less than or comparable to the
contribution of multiple scattering.

CSC transverse spatial resolution per station (6 hits) measured for all
chamber types with 2016 data, compared to those measured in 2015 and 2012.
Spatial resolution ($\mu\mathrm{m}$) Station/ring Run 1 Run 2 2012 2015 2016
ME1/1a 66 48 45 ME1/1b 57 54 52 ME1/2 93 93 90 ME1/3 108 110 105 ME2/1 132 130
125 ME2/2 140 142 134 ME3/1 125 125 120 ME3/2 142 143 135 ME4/1 127 128 123
ME4/2 147 143 134

The precision of the CSC measurements is dominated by systematic effects, and
the statistical uncertainties arising from the fits to the residual
distributions are small ($<$0.2%). The precision is controlled by the size of
the induced charge distribution on the strip plane, which is affected by
geometry (the width of the strips), gas gain (high voltage, gas mix, gas
pressure), and sample selection (momenta, angle of incidence). The gas mix and
high voltage are stringently maintained constant during CSC operation, and
muon samples are selected to be as close as possible for the purposes of these
comparisons. The CSCs operate at atmospheric pressure, but a decrease of
atmospheric pressure of 1% increases the gas gain by approximately 7%, so the
values in the table have all been normalized to 965mbar, a value typical of
the annual average atmospheric pressure at CMS. In this manner we obtain
reproducible resolutions typically within 1–2, as can be seen from the values
in Tab. 0.5 for the columns for 2012 and 2015 (other than for ME1/1a). The
approximately 25% improvement in resolution in ME1/1a CSCs between 2012 and
later is because of the removal of the strip ganging that was used in the
first CMS running periods. The improved resolution is not directly related to
the spatial nature of the ganging—every 16th strip was ganged into a single
channel, rather than combining neighboring strips. Instead, the improvement is
because of the reduction of capacitance, and hence noise, with the removal of
this ganging. The spatial resolution values for 2016 are systematically better
than expected, and this was eventually traced to an incorrectly calibrated gas
flowmeter that led to a slightly increased argon fraction in the gas mix in
early 2016. Once this was corrected111Better spatial resolution is not the
only consideration in choice of gas mix for CSC operation in CMS. The gas mix
is just one of many parameters of the system design that were optimized to
provide the required spatial resolution while maintaining stable and robust
operation of the detectior and maximum longevity of the chambers in the LHC
environment. the measured values returned to those seen in earlier running
periods.

The spatial resolution of the RPCs is studied by extrapolating segments from
the closest CSC or DT to the plane of strips in the chamber under study. The
residuals are calculated transverse to the direction of the strips, which is
also the direction of the bending of muons in the magnetic field. The residual
is defined as the transverse distance between the center of the reconstructed
RPC cluster and the point of intersection of the extrapolated segment with the
plane of strips. For each station and layer, a residual distribution is filled
and fit with a Gaussian. The $\sigma$ parameter of these fits varies between
0.78–1.27in the barrel and 0.89–1.38in the endcap. These values are compatible
with the resolution expected from the widths of the strips and are consistent
with the 2010 results [1].

The spatial compatibility between tracker tracks, reconstructed with the inner
tracker, and segments, reconstructed in the muon chambers, is of primary
importance and is extensively used in the muon ID criteria presented in
Section 0.3.2. The residuals between extrapolated tracker tracks and segments
are studied using the tag-and-probe technique [2]. Oppositely charged dimuon
pairs are selected from a sample collected with a single-muon trigger. The tag
is a tight muon with tight PF isolation, which is geometrically matched with
the trigger ($\Delta R<0.1$ between the tracker track and the 4-vector
reconstructed by HLT). The probe is a tracker muon, which passes track-based
isolation and tracker track quality requirements, that is propagated to each
of the DT or CSC chambers it traverses. The segment matching in the definition
of a tracker muon is loose enough not to bias this measurement.

The transverse residual, $\Delta x$, is computed in the chamber local
reference frame for the coordinate measuring the muon position in the bending
plane ($\phi$). It corresponds to the distance between the position of the
propagated tracker track and the segment in the chamber. The RMS of the
distribution of $\Delta x$ is shown in Fig. 4 for 2015 data and simulated
$\text{\cPZ}/\text{\PGg}^{*}\to\text{\Pl}^{+}\text{\Pl}^{-}$ decays. There is
reasonable agreement between the data and simulation. The alignment precision
of the data (using the techniques described in Ref. [27] with the full 2015
data set) and of the simulation (corresponding to what would be obtained with
about 1of data) is about 100–200, and thus is not a dominant effect in these
results. Figures 4a and 4b show the RMS as a function of station for DT and
CSC chambers, respectively. The RMS increases as the muon station number
increases, which is expected because of the larger amount of material
traversed by the muons and the resultant multiple scattering. The RMS of the
residual evaluated in the first muon station is shown as a function of
momentum in Fig. 4c and Fig. 4e in the barrel and endcap regions,
respectively, while Fig. 4d shows the overlap region between the two. The RMS
decreases with momentum because of the reduction in multiple scattering. The
spatial resolution in Fig. 4 is not directly comparable with the results in
Ref. [2] because the analysis used on the 2015 data reduced the contamination
from muons that do not come from the primary interaction.

Figure 4: The RMS of transverse residuals between reconstructed segments and
propagated tracker tracks, measured in 2015 data. Results are plotted as a
function of: (upper left) MB station in the DTs; (upper right) ME station in
the CSCs; (lower left) momentum $p$ in station 1 of the barrel region
($\abs{\eta}\leq 0.9$); (lower center) momentum $p$ in station 1 of the
overlap region ($0.9\leq\abs{\eta}\leq 1.2$); (lower right) momentum $p$ in
station 1 of the endcap region ($1.2\leq\abs{\eta}\leq 2.4$). The vertical
error bars represent the statistical uncertainties of the RMS, and are smaller
than the marker size for most data points.

##  0.6 Efficiency

###  0.6.1 Hit and segment efficiency

The hit reconstruction efficiency is calculated as the ratio of the number of
reconstructed hits divided by the number of expected hits. The measurement
provided by the detecting unit under study is excluded from the computation of
the expected hit position.

The hit reconstruction efficiency of the DTs is studied using segments. To
ensure high quality segment reconstruction, segments are required to have at
least one reconstructed hit in all layers except the layer under study. For
the efficiency of a $\phi$ layer, this implies that the $\phi$ segment must
have at least seven associated hits, while for $\theta$ layers, the $\theta$
segment must have at least three associated hits. In addition to the high
quality of the segment in the view under study, there must be a segment
constructed in both $\phi$ and $\theta$ views to ensure the presence of a
genuine muon crossing the chamber. For $\phi$ superlayers, backgrounds are
reduced by requiring the segment inclination to be smaller than 45(by
construction, muons from the interaction region are mostly orthogonal to the
wire plane). The intersection of this segment with the layer under study
determines the position of the expected hit within a specific tube and
increments the denominator in the efficiency calculation. The numerator is
incremented if a hit is reconstructed in this tube. The distribution of the
hit reconstruction efficiency for each DT chamber is shown in Fig. 5a. The
average value of the DT hit reconstruction efficiency is 97.1% including the
dead cells reported in Table 0.1. The average efficiency in the 2016 data is
consistent with the 2010 average [1] within 1%.

Figure 5: Hit reconstruction efficiency measured with the 2016 data in (upper
left) DT, (upper right) RPC barrel, and (lower) RPC endcap chambers.

The hit reconstruction efficiency of the RPCs is studied with a tag-and-probe
technique. Muon pairs are selected from an event sample collected with a
single-muon trigger. The tag is a tight muon that is geometrically matched
with the trigger ($\Delta R<0.1$ between the tracker track and the 4-vector
reconstructed by HLT). The probe is a tracker muon matched to a DT or CSC
segment that is extrapolated to RPC chambers. For each RPC roll that the
extrapolated probe traverses, the denominator in the efficiency calculation is
incremented and a matching hit is sought. The numerator is incremented if the
absolute value of the difference between the hit position and the extrapolated
probe position is smaller than 10, or if the ratio of this distance to its
uncertainty (pull), including the extrapolation uncertainty, is less than 4.
Figures 5b and c show the efficiency for all RPC barrel and endcap rolls,
respectively. The average hit efficiency is 94.2% for the RPC barrel and 96.4%
for the RPC endcaps, with negligible accidental contributions from noise. The
underflow entries are from rolls with efficiency lower than 70% caused by
known hardware problems: chambers with gas leaks in the barrel, and low
voltage problems in the endcap. The rolls with zero efficiency (Tab. 0.2) are
included in the underflow and the average efficiency. Results on RPC hit
efficiency from 2010 [1] and 2016 are consistent within 1%.

Muons rarely fail to traverse an entire CSC so the CSC readout system [3]
requires hits compatible with a charged track crossing a chamber, which
suppresses readout of hits from several sources of uninteresting background.
In order to read out a cathode front-end board, which services 16 strip
channels in each of the six layers of a CSC, the basic pattern of hits
expected for a CSC trigger primitive must occur in coincidence with a level-1
trigger from CMS. A trigger primitive requires at least 4 layers in a CSC
containing strip hits, with a pattern consistent with those created by muons
originating at the collision point. This readout suppression complicates the
interpretation of straightforward measurements of CSC layer-by-layer hit
efficiencies, but since the muon track reconstruction uses segments, and not
individual hits, it is the segment efficiency that is most important to system
operation. This can be directly measured using the tag-and-probe method. The
tag is required to be a tracker muon and the probe is a tracker track that is
projected to the muon system. To reduce background and ensure that the probe
actually enters the chamber under consideration, compatible hits are also
required in a downstream CSC. In the case of station 4, an upstream segment is
required. Figure 6 shows a summary map of the measured reconstructed segment
efficiency for each CSC. The average CSC segment reconstruction efficiency is
97.4%. A few of the 540 chambers have known inefficiencies, usually caused by
one or more faulty electronics boards that cannot be repaired without major
intervention requiring the dismantling of the system. There are also
occasional temporary failures of electronics boards that last for a few hours
or days and can be recovered without major intervention. Both contribute to a
reduced segment efficiency in a localized region. The average CSC segment
efficiency in the 2016 data is within 1% of that observed in 2010 [1].

Figure 6: The efficiency (in percent) of each CSC in the CMS endcap muon
detector to provide a locally reconstructed track segment as measured from
2016 data.

###  0.6.2 Reconstruction, identification and isolation efficiency

The efficiency for muons is studied with the tag-and-probe method beginning
with tracker tracks as probes. The value of the efficiency is computed by
factorizing it into several components [2]:

$\epsilon_{\mu}=\epsilon_{\text{track}}\times\epsilon_{\text{reco+ID}}\times\epsilon_{\text{iso}}\times\epsilon_{\text{trig}}.$
(2)

Each component of $\epsilon_{\mu}$ is determined individually. The efficiency
of the tracker track reconstruction is $\epsilon_{\text{track}}$ [14]. The
reconstruction+ID efficiency, $\epsilon_{\text{reco+ID}}$, contains both the
efficiency of muon reconstruction in the muon system, including the matching
of this muon to the tracker track, and the efficiency of the ID criteria. The
efficiency of muon isolation, $\epsilon_{\text{iso}}$, is studied relative to
a probe that has passed the specified muon ID. The efficiency of the trigger,
$\epsilon_{\text{trig}}$, is described in detail in Section 0.9.2. The
application of Eq. 2 is dependent on the specific needs of each analysis. For
example, if an analysis does not require isolation, $\epsilon_{\text{iso}}$ is
removed from the equation and $\epsilon_{\text{trig}}$ is computed relative to
reconstructed muons without an isolation requirement.

As described in Ref. [2], the combinatorial background of tag-probe pairs not
coming from the resonance (where the probe is usually a charged hadron
misidentified as a muon) is subtracted by performing a simultaneous fit to the
invariant mass spectra for passing and failing probes with identical signal
shape and appropriate background shapes; the efficiency is then computed from
the normalizations of the signal shapes in the two spectra. Given the high
multiplicity of tracks in proton-proton collision events, using a tracker
track as the probe leads to a high combinatorial background in low-bins, which
can result in large uncertainties in the background subtraction method. To
mitigate this effect, the efficiency measurement is performed using only the
tag-and-probe pairs for which a single probe is associated with the tag. The
same method is also applied to simulated $\text{\cPZ}\to\MM$ events.

The $\epsilon_{\text{reco+ID}}$ for loose muons and for tight muons are shown
as a function of $\eta$ in Fig. 7, for both data and simulation. The loose ID
efficiency exceeds 99% over the entire $\eta$ range, and the data and
simulation agree to within 1%. As a function of between 20and 200(where the
efficiency is measured with reasonably small uncertainty), the loose ID
efficiency is constant with fluctuations well within 1%. The tight ID
efficiency varies between 95% and 99%, depending on $\eta$, and the data and
simulation agree to within 1–3%. The dips in efficiency close to
$\abs{\eta}=0.3$ are due to the regions with less instrumentation between the
central muon wheel and the two neighboring wheels. In Fig. 7b, the simulation
is systematically higher than the data as a result of small imperfections in
the model, which are revealed by the stringent requirements for a muon to
satisfy tight ID criteria. In the endcap, differences between the data and
simulation arise when the muon is required to be global with a combined fit
that has valid hits in the muon system, whereas in the barrel segment matching
and global reconstruction contribute to the discrepancy in a similar way.
Tracker track quality constraints contribute to a discrepancy of less than
0.5% over the full $\eta$ range.

Figure 7: Tag-and-probe efficiency for muon reconstruction and identification
in 2015 data (circles), simulation (squares), and the ratio (bottom inset) for
loose (left) and tight (right) muons with $\pt>20\GeV$. The statistical
uncertainties are smaller than the symbols used to display the measurements.

A hadron may be misidentified as a prompt muon if the hadron decays in flight,
or if hadron shower remnants penetrate through the calorimeters and reach the
muon system (punch-through), or if there is a random matching between a hadron
track in the inner tracker and a segment or standalone-muon in the muon
system. The probability of hadrons to be misidentified as muons is measured by
using data samples of pions and kaons from resonant particle decays collected
with jet triggers [2]. The probability of pions to be misidentified as loose
muons in both data and simulation is about 0.2% while for tight muons it is
about 0.1%. In the same way, 0.5% of kaons are misidentified as loose muons
and 0.3% as tight muons in both data and simulation. The uncertainty in these
measurements is at the level of 0.05% and is dominated by the limited
statistical precision. Within uncertainties, the misidentification
probabilities are independent of . These results are in good agreement with
Run 1.

The efficiency of muon isolation, $\epsilon_{\text{iso}}$, is studied relative
to a probe that passes a given muon ID criteria. For example, the tight PF
isolation efficiency relative to tight muons is shown in Fig. 8. In this case
the agreement between the data and simulation is always better than 0.5%.
Analogous to the misidentification probability study described above, the
efficiency to incorrectly label muons within jets as being isolated is
measured with simulated QCD events enriched in muon decays. In this sample,
the probability of a muon with $\pt>20\GeV$ that fulfills the tight muon ID
criteria to also satisfy tight isolation requirements is about 5% in the
barrel, and goes up to about 15% in the endcap.

Figure 8: Tag-and-probe efficiency for the tight PF isolation working point on
top of the tight ID (left) versus for muons in the acceptance of the muon
spectrometer, and (right) versus pseudorapidity for muons with $\pt>20\GeV$,
for 2015 data (circles), simulation (squares), and the ratio (bottom inset).
The statistical uncertainties are smaller than the symbols used to display the
measurements.

The systematic uncertainty in data/simulation scale factors for the
efficiencies described above is estimated by varying the tag-and-probe
conditions. The impact of the background contamination is estimated by using
different requirements on the tag muon (and isolation) and on the requirement
of a single probe being associated with the tag. The dominant uncertainty is
caused by the choice of the signal and background models used in the fits. It
is estimated by testing alternative fit functions and by varying the range and
the binning of the invariant-mass spectrum. The uncertainties are estimated to
be at the level of 1% for ID and 0.5% for isolation.

For muons with $\pt>20\GeV$, Table 0.6.2 shows the data efficiency and the
data/simulation scale factors for the muon ID and isolation working points
described in Section 0.3. For all entries, the agreement between data and
simulation is better than 1.5%. The efficiencies, systematic uncertainties,
and scale factors between data and simulation for 2015 are similar to those
found in the 2010 data. The statistical uncertainties, however, have been
reduced by a factor of 10 and become negligible in comparison with the
systematic uncertainties.

Efficiencies for several reconstruction+ID algorithms and isolation criteria
(relative to tight ID) for muons with $\pt>20\GeV$. The corresponding scale
factors are for 2015 data relative to simulation. The uncertainties in the
scale factors stem from the statistical uncertainties in the fitting
procedure. Systematic uncertainties are described in the text.  Type Label
$\abs{\eta}$ region Data eff. [%] Scale factor Muon ID Loose
$0.0<\abs{\eta}<0.9$ $99.75\pm 0.02$ $0.998\pm 0.001$ $0.9<\abs{\eta}<2.4$
$99.77\pm 0.02$ $0.9982\pm 0.0002$ Medium $0.0<\abs{\eta}<0.9$ $98.25\pm 0.02$
$0.9901\pm 0.0002$ $0.9<\abs{\eta}<2.4$ $98.55\pm 0.02$ $0.9897\pm 0.0002$
Tight $0.0<\abs{\eta}<0.9$ $96.00\pm 0.03$ $0.9869\pm 0.0004$
$0.9<\abs{\eta}<2.4$ $97.46\pm 0.04$ $0.9873\pm 0.0002$ High-
$0.0<\abs{\eta}<0.9$ $96.24\pm 0.02$ $0.9882\pm 0.0003$ $0.9<\abs{\eta}<2.4$
$98.05\pm 0.01$ $0.9891\pm 0.0002$ Isolation (relative to tight ID) Loose PF
$0.0<\abs{\eta}<0.9$ $98.60\pm 0.01$ $1.0007\pm 0.0001$ $0.9<\abs{\eta}<2.4$
$98.98\pm 0.01$ $1.0007\pm 0.0001$ Tight PF $0.0<\abs{\eta}<0.9$ $95.81\pm
0.02$ $1.0001\pm 0.0004$ $0.9<\abs{\eta}<2.4$ $96.88\pm 0.02$ $0.9995\pm
0.0003$

##  0.7 Momentum scale and resolution

Many searches for new physics are characterized by signatures involving prompt
muons with high . For muons with $\pt>200\GeV$, combining information from the
muon system with information from the inner tracker significantly improves the
momentum measurement [28]. On the other hand, for muons with lower the
momentum measurement is dominated by the performance of the inner tracker. To
assess the performance of the momentum scale and resolution, data from both
cosmic rays and collisions have been analyzed.

###  0.7.1 Low and intermediate : scale and resolution with collisions

For muons with low and intermediate , two different methods are utilized in
Run 2 to correct the muon momentum scale and to estimate the resolution. One
method derives the corrections from the mean value of the distribution of
$1/\pt^{\mu}$, $\left<1/\pt^{\mu}\right>$, for tight muons from decays, with
further tuning performed using the mean of the dimuon invariant mass spectrum,
$\left<M_{\mu\mu}\right>$ [29]. Another method determines corrections using a
Kalman filter on tight muons from and decays [30]. The magnitudes of the
momentum scale corrections are about 0.2% and 0.3% in the barrel and endcap,
respectively. After the scale is corrected, the resolution is determined
either as a function of $\eta$ (first method) or as a function of $\eta$ and
(second method), including contributions from multiple scattering, position
error, and additional smearing to make the simulation match the data. The
resolution for muons with momenta up to approximately 100is 1% in the barrel
and 3% in the endcap. For both techniques, over all $\eta$ and values, the
uncertainty in the resolution is estimated to be about 5% of its value.
Compared to the 2010 results [2], the 2015 resolution has improved, primarily
because of the improvements to the tracker alignment [27].

###  0.7.2 Momentum resolution with cosmic rays

Cosmic ray muons passing through the CMS detector are used to estimate the
momentum resolution at high by comparing the momentum measured in the upper
half of the detector with the momentum measured in the lower half [2, 15].
Events are selected with muons that cross the detector close to the
interaction point and have at least one hit in the pixel detector, so that
each leg of the cosmic ray mimics a muon from a collision. To ensure good
reconstruction, the tracker track of each muon leg is required to have at
least one pixel hit as well as five strip layers. The relative q/residual,
$R(q/\pt)$, is computed as

$R(q/\pt)=\frac{1}{\sqrt{2}}\frac{(q/\pt)_{\text{upper}}-(q/\pt)_{\text{lower}}}{(q/\pt)_{\text{lower}}},$
(3)

where q is the muon charge, and upper and lower refer to the muon tracks
reconstructed in the upper and lower halves of the CMS detector, respectively.
The quantity q/, proportional to the muon trajectory curvature, has a
symmetric, approximately Gaussian, resolution distribution. The factor of
$\sqrt{2}$ accounts for the fact that the q/measurements of the two tracks are
independent.

Figure 9 shows the RMS of $R(q/\pt)$ as a function of for cosmic rays recorded
in 2015 for fits using only the inner tracker and for fits that include the
muon system using the Tune-P algorithm. The uncertainty in the last bins is
dominated by the small number of cosmic rays collected in 2015 (66 events with
$\pt>500\GeV$). The improvement in resolution from exploiting the muon chamber
information in the momentum assigment is clearly visible. The simulation of
cosmic rays with $\pt>500\GeV$ reproduces this result within statistical
uncertainties. Compared with the 2010 results, the resolution is improved by
about 25% at high , coming as a result of the modifications to the Tune-P
algorithm in addition to the improved alignment of both the inner tracker [27]
and the muon system [1].

Figure 9: The RMS of $R(q/\pt)$ as a function of for cosmic rays recorded in
2015, using the inner tracker fit only (squares) and including the muon system
using the Tune-P algorithm (circles). The vertical error bars represent the
statistical uncertainties of the RMS.

###  0.7.3 High : momentum scale with collisions

Biases in the scale of the momentum measurement at high arising from an
inaccurate measurement of the track curvature are probed by looking for
distortions in the shape of the q/spectrum. A technique called the “endpoint
method” was developed and used extensively in Run 1, using cosmic ray data to
quantify the bias at high [2, 1]. However, since cosmic rays predominantly
cross the barrel region of the detector, they cannot be used effectively to
determine the momentum scale in the endcaps. Therefore, a generalized version
of the endpoint method has been developed to be used with collisions.

The generalized endpoint method uses prompt dimuons selected from a sample of
events collected with the single-muon trigger (see Section 0.9). Both muons
must satisfy the loose tracker relative isolation criteria and at least one of
the muons is required to have $\pt>200\GeV$. This sample is primarily composed
of muons from /∗ decays, with a minor contribution from dileptonic decays of
pairs and from diboson production.

Each muon from the event that has $\pt>200\GeV$ is used to fill a binned
distribution of $q$/. The q/data spectrum is compared to multiple samples of
simulated muons. Each sample, i, is simulated with a curvature bias,
$k^{\mathrm{i}}_{\mathrm{b}}$, injected on top of an unbiased geometry. The
bias shifts the unbiased q/spectrum by

$q/\pt\to q/\pt+k^{\mathrm{i}}_{\mathrm{b}}.$ (4)

The samples are generated with $k^{\mathrm{i}}_{\mathrm{b}}$ in steps of
0.01/between $-$1.00/and $+$1.00/. For each sample, the $\chi^{2}$ is computed
between the unweighted data distribution and the weighted simulation
distribution. The value of $\chi^{2}$ is plotted as function of
$k^{\mathrm{i}}_{\mathrm{b}}$ and fit with a second-degree polynomial. The
value of $k^{\mathrm{i}}_{\mathrm{b}}$ that gives the minimum fit $\chi^{2}$
is taken as the curvature bias in the data, $k_{\mathrm{b}}$. The statistical
uncertainty in $k_{\mathrm{b}}$ corresponds to half the range over which the
$\chi^{2}$ increases by one.

The momentum scale bias in 2015 data from the generalized endpoint method is
shown in Table 0.7.3. The bias is presented separately for the barrel and
endcaps and integrated over $\phi$. Within the statistical uncertainties, the
measurements are consistent with no bias. In both the barrel and endcaps, the
amplitude of the azimuthal dependence of $k_{\mathrm{b}}$ is less than 0.1/.
The limited statistical precision of the data precludes detailed studies of
the $\phi$ dependence and a detailed analysis of the width of
$k_{\mathrm{b}}$. An analysis using the cosmic ray endpoint method in the
barrel is consistent with Tab. 0.7.3. However, the large uncertainties in the
cosmic ray data don’t constrain the bias better than the collision data alone.
The scale bias in the 2015 data is approximately consistent with the scale
bias measured in 2010 with cosmic rays [2], within the large uncertainties in
the 2010 results.

Measurement of the momentum scale bias in 2015 data, obtained with the
generalized endpoint method using muons with $\pt>200\GeV$ from collision
data. Results are presented in three $\eta$ bins corresponding to the barrel
and endcap regions.  $\eta$ range $-2.4<\eta<-1.2$ $-1.2<\eta<1.2$
$1.2<\eta<2.4$ $\langle k_{\mathrm{b}}\rangle$ (1/) $-0.01\pm 0.06$ $-0.01\pm
0.03$ $0.01\pm 0.05$

##  0.8 Timing

The “L1 accept” signal, which is broadcast to all subdetectors, initiates the
readout of the event. Trigger synchronization is of great importance because
as simultaneous hits in multiple chambers are required for an L1 trigger, out-
of-time chambers can reduce the overall trigger efficiency. Moreover, if the
L1 muon trigger is generated early or late relative to the collision time, it
forces readout of the entire detector at the wrong bunch crossing. In this
context, the timing performance of the RPC hits and the DT and CSC trigger
primitives is discussed in Section 0.9.1.

For physics analyses, the time assigned to the muon hits once the event has
been collected and fully reconstructed is also important. This is called the
“offline time.” For a muon traveling at the speed of light, produced in a
proton-proton collision, and with the correct bunch crossing assignment, the
offline time of any muon chamber hit should be reported as $t=0$. The readout
windows of the muon subsystems are large enough to detect muons from several
bunch crossings. Any deviations from 0 may be caused by backgrounds such as
cosmic-ray muons, beam backgrounds, chamber noise, or out-of-time pileup, or
it may be an indication of new physics such as a slow moving, heavy charged
particle.

As described in Section 0.3, the timing information of DT segments is obtained
from a 3-parameter fit of segments, so that position, direction and time of a
crossing track are determined simultaneously. Single track segments were
selected to have hits in both projections (at least five in the $\phi$ view)
and to have an inclination angle below 45. The $\sigma$ parameter of a
Gaussian fit to the segment time distribution is 2.0ns, which represents an
estimate of the DT segment time resolution. An improvement of about 0.6ns is
observed with respect to the 2010 performance [1]. This improvement results
from the updated segment reconstruction algorithm that now explicitly measures
the segment time.

The time of a CSC reconstructed segment is determined by combining the times
of the cathode and anode hits used to construct the segment. The overall
precision depends mostly on the cathode timing performance. The cathode time
is determined from a template fit to the digitized cathode pulse. It is
calibrated based on dedicated studies of chamber response and a heuristic
correction measured from collision data. Figure 10 shows the distribution of
times of CSC segments associated with reconstructed muons. The RMS of the
binned segment time distribution is 3.2ns, in good agreement with the value of
about 3ns measured in 2010 [1].

Figure 10: Distribution of times from reconstructed CSC segments measured with
the 2016 data.

The timing of a standalone-muon can be determined by combining measurements
from multiple stations. In the barrel, measurements from up to four DT
stations are combined using an iterative pruning mechanism to discard outlier
hits from those associated with the track, thereby rejecting hits from delta
rays and showers within an individual chamber. The time-at-vertex distribution
for standalone-muons in the barrel is shown in Fig. 11. This distribution
comes from muons that have triggered the event readout and shows a primary
peak clearly visible at 0ns. The asymmetric tail at early values comes from
delta rays that reach the wire before the hit. The peaks periodically spaced
at 25ns both before and after the primary peak come from muons produced in LHC
collisions in bunch crossings that are out of time with regard to the trigger.
These secondary peaks come from muons that did not trigger the event readout
because of the large suppression factors in the CMS trigger system (described
in Section 0.9) but are within the readout range of the event that did cause
the trigger. A comparison with an analysis from Run 1 data [31] shows that in
2016 data the width of the primary timing peak in the muon barrel was
consistent within 0.5ns. A similar analysis using the CSC shows comparable
muon time resolution.

Figure 11: Time-at-vertex distribution for standalone-muons in the barrel,
using the times measured by DT chambers in 2016 data.

##  0.9 Trigger

For the muon component of the CMS trigger [6], CSC and DT chambers provide
“trigger primitives” constructed from hit patterns consistent with muons that
originate from the collision region, and RPC chambers provide hit information.
The custom-made electronics in the L1 trigger system utilize the chamber
information to reconstruct muon trigger candidates with a coarse measurement
of . An upgrade of the L1 trigger system was implemented between 2015 and 2016
[10]. The results presented here do not depend on the upgraded L1 components.

Events selected by the L1 trigger are passed to the HLT, which uses
information from the full CMS detector to reconstruct muons. The HLT
algorithms are simplified versions of those described in Section 0.3 in order
to reduce computing time and resources, and were updated prior to the 2015 run
to improve the performance at higher pileup. The HLT employs two different
processing schemes to reconstruct muons. The first scheme uses the L1
candidate as a seed to reconstruct muons at level-2 (L2) using information
only from the muon system, and then reconstructs level-3 (L3) muons by
combining the L2 muons with information from the inner tracker. This
combination is made using three algorithms applied sequentially from fastest
to slowest, and subsequent algorithms are attempted only if the previous one
failed to reconstruct a muon in order to minimize computation time. The first
algorithm propagates the L2 trajectory inward to the inner tracker to
reconstruct the L3 muon. The second algorithm is similar to the first, except
that it combines the L2 muon with hits in the outer layer of the inner tracker
to improve its trajectory before propagating it inward. The third algorithm is
different from the first two in that it builds tracker tracks with an inside
out approach within a region based on the position of the L2 muon. Prior to
Run 2, the L3 algorithms were improved to select hits based on $\chi^{2}$ of
the track fit rather than matching in $\Delta R$, and track quality
constraints are imposed in the first two algorithms.

The second HLT processing scheme, called “HLT tracker muon reconstruction”,
was developed prior to Run 2. This scheme employs an algorithm similar to the
tracker muon algorithm described in Section 0.3, but is optimized for
processing speed. The primary differences are that the HLT version limits the
reconstruction of tracker tracks to a region within $\Delta\phi<0.2$ and
$\Delta\eta<0.35$ of the L1 candidate, and requires $\pt>10\GeV$ for the
tracker track seeds.

After reconstruction, muon isolation is evaluated in the HLT by considering
the additional tracker tracks and calorimeter energy deposits in a cone with
radius $\Delta R=0.3$ around the muon. Each of the contributions is required
to be below a fraction of the muon : scalar sum of PF electromagnetic clusters
[17], scalar sum of PF hadronic clusters, and scalar sum of tracker tracks. To
exclude contributions that come from the muon itself, a minimum value of
$\Delta R$ is required to include the tracks or energy deposits in the sum. To
account for the effects from pileup, PF cluster sums are corrected using the
average energy density [32] in the event, $\rho$ (if the correction exceeds
the PF cluster sum, that component of the isolation is set to zero). To
determine the correction, the value of $\rho$ is scaled by its “effective
area” which estimates how much is expected in the isolation cone. Effective
areas are determined independently for electromagnetic sums and for hadronic
sums as well as separately in the barrel and in the endcaps. The average
values of the distributions of PF cluster sums are fit with a first order
polynomial as a function of the number of reconstructed primary vertices. The
same is done for $\rho$. The effective area is the ratio of the fitted slope
for the PF cluster sum divided by the fitted slope for $\rho$.

After minimal $\Delta R$ cones and effective areas are defined, a working
point is determined to simultaneously remove background effectively and to
keep signal efficiency high by tuning the thresholds below which the muon is
considered to be isolated. For example, for online isolation in the barrel in
2015, the $\rho$-corrected scalar sum of PF electromagnetic clusters within
$0.05<\Delta R<0.3$ were required to be below 11% of the muon , the
$\rho$-corrected scalar sum of PF hadronic clusters within $0.1<\Delta R<0.3$
were required to be below 21% of the muon , and the scalar sum of tracker
tracks within $0.01<\Delta R<0.3$ were required to be below 9% of the muon .

The results of the HLT reconstruction and isolation algorithms are used to
form various trigger conditions. The general-purpose muon trigger conditions
used for the 2015 data include:

  1. 1.

an isolated single-muon with a threshold of 20, which is based on a trigger
efficiency curve giving approximately 50% efficiency at 20 [6], reconstructed
with either L3 or HLT tracker muon algorithms,

  2. 2.

a nonisolated single-muon with a threshold of 45for $|\eta|<2.1$ or 50for
$\abs{\eta}<2.4$, reconstructed with L3, and

  3. 3.

two isolated muons (double-muons) that originate within a distance of $\Delta
z<0.2$of each other along the beamline, with asymmetric thresholds of 18and
7applied to the two muons.

For the double-muon triggers, the L3 algorithm is first used to reconstruct
one muon. In order to save computing time, this L3 muon must pass and quality
constraints before reconstruction of a second muon is attempted. The second
muon can be reconstructed with either the L3 or the HLT tracker muon algorithm
to maximize efficiency. Tracker track isolation criteria are then applied to
both tracks.

###  0.9.1 Trigger primitives

The absolute efficiency for creating a CSC trigger primitive is studied using
the tag-and-probe method in the same way as for CSC segments (Section 0.6).
Once again the probe is a tracker track extrapolated into the muon system. The
tag is required to have triggered the event to avoid bias from events
triggered by the probe alone. A CSC trigger primitive is expected in each
chamber traversed by the probe. To reduce background and to ensure that the
probe actually entered the chamber under consideration, a compatible segment
is required in a downstream chamber. For the outermost station 4 an upstream
chamber is required instead. A trigger primitive is required to be within 5of
the extrapolated track (corresponding to about 4–5 times the resolution, as
demonstrated in Fig. 4b) with no other track closer to it. The CSC trigger
primitive efficiency is shown in Fig. 12. The features in Fig. 12 are highly
correlated with the features in Fig. 6 because in both cases the primary
causes of significant inefficiencies were hardware failures. The average CSC
trigger primitive efficiency in 2016 data is 97%, similar to that in 2010 [1].

Figure 12: The efficiency (in percent) of each CSC to provide a trigger
primitive, measured with the 2016 data.

The efficiency for the DT local trigger electronics to reconstruct a trigger
primitive pattern is called the DTLT efficiency, and it can be studied using
segments associated with global muons. In order to ensure that the chamber
under study was not necessarily used to trigger the event, at least two other
stations are required to deliver trigger primitives. The denominator is
incremented if the segment is reconstructed in both $\theta$ and $\phi$ views,
except for MB4, which has only $\phi$ superlayers. In addition, there must be
at least four associated hits in the $\phi$ layers, the minimum number of hits
required to build a $\phi$ trigger primitive. The numerator is incremented if
a trigger primitive is delivered at the correct bunch crossing. The DTLT
efficiency is shown for each DT chamber in Fig. 13. The lower DTLT efficiency
observed in two of the chambers was due to problems with the trigger
electronics which were later repaired. The DTLT efficiency is about 1% lower
in MB4 because there are no $\theta$ superlayers to enhance the quality of the
segment. The DTLT efficiency in the 2016 data is comparable to the one
observed in the 2010 data.

Figure 13: Efficiency map for the DT local trigger ($\phi$ view) for each
chamber, measured with the 2016 data. Each map represents one station. The
z-axis color indicates the efficiency, the wheel number is on the vertical
axis, and the $\phi$ sector number is on the horizontal axis.

For RPCs, the trigger primitive efficiency is equivalent to the hit efficiency
by construction. The trigger primitive efficiency is shown in Fig. 5.

The time coincidence of trigger primitives among the many muon stations must
have a time dispersion much less than 25ns, the time separation of LHC bunch
crossings, to ensure an unambiguous identification of the correct bunch
crossing with the muon trigger. For example, the RPC chambers have been
measured to have an intrinsic time resolution of around 2ns [33] and an
overall time resolution of better than 3ns [1] after including the time
propagation along the strip, the channel-by-channel cable length differences,
and the electronics delays. Figure 14 shows the bunch crossing distribution of
RPC hits associated with global muons in the barrel. Each bin corresponds to
the 25ns bunch separation in LHC, and bin 0 is the time of the L1 trigger. In
Fig. 14, 0.5% of RPC hits are outside bin 0, whereas for both DT and CSC
trigger primitives, 2% are outside bin 0. The hits that are not in bin 0 are
caused by a combination of muons from adjacent bunch crossings or from cosmic
rays and by the finite resolution in the calibration of the electronics. The
timing of each individual system is monitored during data collection and fine
adjustments are made if necessary. In this way, the L1 trigger, which relies
on a combination of all three systems, produces less than 0.2% trigger
candidates associated with incorrect bunch crossings [6].

Figure 14: The bunch crossing distribution from reconstructed RPC hits in the
barrel (left) and in one endcap (right), using the 2016 data.

###  0.9.2 Trigger efficiencies and rates

As described in Section 0.6.2, the efficiency of the trigger is measured with
the tag-and-probe technique. In order not to bias the measurement of the
trigger efficiency, the tag is geometrically matched to the HLT trigger that
selected the event. In addition, it is also required to satisfy tight ID and
PF isolation criteria in order to reduce backgrounds. The requirements on the
probe are then tuned according to the reconstruction and isolation criteria
used in the analysis. As an example, an analysis of muons with tight ID and PF
isolation requirements might use the isolated single-muon trigger to select
events. In this case, the probe muon is required to satisfy tight ID and PF
isolation requirements as per the analysis. Using this technique, the
efficiency of the isolated single-muon trigger with HLT threshold 20is shown
in Fig. 15. The efficiency as a function of reconstructed muon (Fig. 15a)
rises sharply at the threshold. Above 22, the inefficiency of a few percent is
primarily caused by the L1 trigger and the relative isolation criteria (see
Tab. 0.9.2). Variations in efficiency as a function of $\eta$ (Fig. 15b) are
caused by geometrical features of the detector that affect the L1 trigger
efficiency. The isolation requirement is responsible for the mild efficiency
drop as a function of the number of offline reconstructed vertices (Fig. 15c).
The systematic uncertainty is estimated to be 0.5% based on methods similar to
those described in Section 0.6. The simulation is in reasonable agreement with
the data over the full momentum range and angular acceptance.

Figure 15: Isolated single-muon trigger efficiency measured with 2015 data
(squares), simulation (circles), and the ratio (bottom inset). Results are
plotted as a function of offline reconstructed muon (upper left), $\eta$
(upper right), and number of primary vertices (lower). The statistical
uncertainties in these values are smaller than the marker size in the figure.

A breakdown of the contributions to the isolated single-muon trigger
efficiency from L1, HLT track reconstruction, and online isolation is
presented in Table 0.9.2, together with the scale factors between data and
simulation. Numbers are separated into barrel and endcap regions, and are
integrated over offline reconstructed $\pt>22\GeV$ for each row, the momentum
range commonly used for CMS physics analyses with isolated single-muon
triggers. The first two rows show the efficiency of L1 candidates (threshold
16) computed with respect to tight muons passing PF isolation criteria. The
second two rows show the efficiency of HLT reconstruction (threshold 20)
computed with respect to offline muons geometrically matched to L1 candidates,
which are used as the seeds for HLT tracking. The use of two complementary
reconstruction algorithms results in an efficiency exceeding 99% for the HLT
reconstruction of isolated single-muon triggers for prompt muons passing L1
trigger requirements. The last two rows show the effect of isolation on top of
the HLT reconstruction.

Contributions to the isolated single-muon trigger efficiency in 2015 data,
integrated over $\pt>22\GeV$. The first two rows show the level-1 efficiency
(threshold 16) with respect to offline muons. The second two rows show the HLT
efficiency (threshold 20) with respect to offline muons geometrically matched
to L1 candidates. The last two rows show the online isolation efficiency with
respect to offline muons firing HLT. The uncertainties in these values are
statistical.  Step $\abs{\eta}$ region Data eff. [%] Scale factor L1 w.r.t.
offline $0.0<\abs{\eta}<0.9$ $96.86\pm 0.02$ $0.9914\pm 0.0005$
$0.9<\abs{\eta}<2.4$ $94.38\pm 0.02$ $0.9947\pm 0.0005$ HLT w.r.t L1
$0.0<\abs{\eta}<0.9$ $99.67\pm 0.02$ $0.9967\pm 0.0005$ $0.9<\abs{\eta}<2.4$
$99.46\pm 0.02$ $0.9957\pm 0.0005$ Online isolation w.r.t HLT
$0.0<\abs{\eta}<0.9$ $97.95\pm 0.02$ $0.9906\pm 0.0005$ $0.9<\abs{\eta}<2.4$
$98.28\pm 0.02$ $0.9931\pm 0.0005$

The efficiency for isolated single-muon triggers is improved with respect to
Run 1 [2, 6] as a result of a combination of the changes in HLT algorithms,
the addition of RPC and CSC chambers in station 4, and the removal of the
ganging of strips in ME1/1a. In the endcaps, the improvement in trigger
efficiency (L1+HLT+isolation) relative to the end of Run 1 [6] is about 10%
for $\abs{\eta}>1.2$ but reaches 20% for $\abs{\eta}\approx 2.4$.

A comparison of trigger rates at the same instantaneous luminosity and
threshold ($\pt>24\GeV$), and integrated over $\abs{\eta}<2.4$, shows an
increase of about 75% from Run 1 to Run 2. This increase is approximately
consistent with the increase of the inclusive production cross sections for
and bosons due to the change from $\sqrt{s}=8\TeV$ [34] to $\sqrt{s}=13\TeV$
[35] with an additional contribution from the increase in efficiency described
above.

The combination of the updated HLT algorithms and the overall increase of HLT
output rate, together with a different allocation of the bandwidth, made it
possible to reduce the thresholds on isolated single-muon triggers from 24in
2012 to 20in 2015. The nonisolated triggers operated in 2015 with thresholds
of 45($\abs{\eta}<2.1$) and 50 ($\abs{\eta}<2.4$). For inclusive double-muon
triggers, the use of track-based isolation requirements in Run 2 resulted in a
reduction of the rates of these triggers with respect to Run 1 despite the
increase in collision energy. In 2015, the thresholds for the two muons in
double-muon triggers were 18and 7, the same values as in 2012.

Figure 16 shows the invariant mass distribution of oppositely charged muon
pairs selected by the inclusive trigger on isolated double-muons. The x-axis
is logarithmic so the entries are scaled to the width of each bin. Data are
also included from specific double-muon triggers tuned to select resonances at
low invariant mass. The figure clearly demonstrates the ability of CMS to
identify muons, trigger on them, and reconstruct the muon kinematics to
unambiguously identify particles that decay into muons over a broad energy
range.

Figure 16: The dimuon invariant mass distribution reconstructed by the CMS
HLT. Data were collected in 2015 with the inclusive double-muon trigger
algorithm (gray), as well as triggers dedicated to selecting resonances at low
masses.

##  0.10 Summary

The performance of the CMS muon detector and reconstruction software has been
studied using data from proton-proton collisions at center-of-mass energy
$\sqrt{s}=13\TeV$, collected in 2015 and 2016 during LHC Run 2. These results
are compared to the previously published results collected in 2010 at
$\sqrt{s}=7\TeV$ with instantaneous luminosities about a factor of 40 lower.
Important modifications to many components of the muon system were made before
Run 2 in anticipation of the higher collision energy and the increased
luminosity. These included modifications to drift tubes, cathode strip
chambers, and resistive plate chambers, as well as improved algorithms for the
high-level trigger and offline reconstruction. Although not comprehensive, a
set of representative figures of merit for the system performance include:

  * •

reconstructed hit spatial resolution $\approx 50-300$;

  * •

reconstructed hit efficiency $\approx 94-99$%;

  * •

segment timing resolution $<3$ns;

  * •

segment efficiency $\approx 97$%;

  * •

trigger bunch crossing identification $>99$%;

  * •

trigger efficiency $>90$%;

  * •

muon timing resolution $\approx 1.4$ns;

  * •

muon reconstruction and identification efficiency $>96$%;

  * •

muon isolation efficiency $>95$%.

As a result of the improvements to the detector and the reconstruction
algorithms, and despite the higher luminosity and pileup in Run 2, the muon
performance is better than, or at least as good as, it was in 2010. Detector
performance remains within the design specifications and the muon
reconstruction results are well reproduced by Monte Carlo simulation.

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing centers and personnel of the Worldwide LHC Computing Grid for
delivering so effectively the computing infrastructure essential to our
analyses. Finally, we acknowledge the enduring support for the construction
and operation of the LHC and the CMS detector provided by the following
funding agencies: the Austrian Federal Ministry of Science, Research and
Economy and the Austrian Science Fund; the Belgian Fonds de la Recherche
Scientifique, and Fonds voor Wetenschappelijk Onderzoek; the Brazilian Funding
Agencies (CNPq, CAPES, FAPERJ, and FAPESP); the Bulgarian Ministry of
Education and Science; CERN; the Chinese Academy of Sciences, Ministry of
Science and Technology, and National Natural Science Foundation of China; the
Colombian Funding Agency (COLCIENCIAS); the Croatian Ministry of Science,
Education and Sport, and the Croatian Science Foundation; the Research
Promotion Foundation, Cyprus; the Secretariat for Higher Education, Science,
Technology and Innovation, Ecuador; the Ministry of Education and Research,
Estonian Research Council via IUT23-4 and IUT23-6 and European Regional
Development Fund, Estonia; the Academy of Finland, Finnish Ministry of
Education and Culture, and Helsinki Institute of Physics; the Institut
National de Physique Nucléaire et de Physique des Particules / CNRS, and
Commissariat à l’Énergie Atomique et aux Énergies Alternatives / CEA, France;
the Bundesministerium für Bildung und Forschung, Deutsche
Forschungsgemeinschaft, and Helmholtz-Gemeinschaft Deutscher
Forschungszentren, Germany; the General Secretariat for Research and
Technology, Greece; the National Research, Development and Innovation Fund,
Hungary; the Department of Atomic Energy and the Department of Science and
Technology, India; the Institute for Studies in Theoretical Physics and
Mathematics, Iran; the Science Foundation, Ireland; the Istituto Nazionale di
Fisica Nucleare, Italy; the Ministry of Science, ICT and Future Planning, and
National Research Foundation (NRF), Republic of Korea; the Lithuanian Academy
of Sciences; the Ministry of Education, and University of Malaya (Malaysia);
the Mexican Funding Agencies (BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-
FAI); the Ministry of Business, Innovation and Employment, New Zealand; the
Pakistan Atomic Energy Commission; the Ministry of Science and Higher
Education and the National Science Centre, Poland; the Fundação para a Ciência
e a Tecnologia, Portugal; JINR, Dubna; the Ministry of Education and Science
of the Russian Federation, the Federal Agency of Atomic Energy of the Russian
Federation, Russian Academy of Sciences and the Russian Foundation for Basic
Research; the Ministry of Education, Science and Technological Development of
Serbia; the Secretaría de Estado de Investigación, Desarrollo e Innovación,
Programa Consolider-Ingenio 2010, Plan Estatal de Investigación Científica y
Técnica y de Innovación 2013-2016, Plan de Ciencia, Tecnología e Innovación
2013-2017 del Principado de Asturias and Fondo Europeo de Desarrollo Regional,
Spain; the Swiss Funding Agencies (ETH Board, ETH Zurich, PSI, SNF, UniZH,
Canton Zurich, and SER); the Ministry of Science and Technology, Taipei; the
Thailand Center of Excellence in Physics, the Institute for the Promotion of
Teaching Science and Technology of Thailand, Special Task Force for Activating
Research and the National Science and Technology Development Agency of
Thailand; the Scientific and Technical Research Council of Turkey, and Turkish
Atomic Energy Authority; the National Academy of Sciences of Ukraine, and
State Fund for Fundamental Researches, Ukraine; the Science and Technology
Facilities Council, UK; the US Department of Energy, and the US National
Science Foundation. Individuals have received support from the Marie-Curie
program and the European Research Council and Horizon 2020 Grant, contract No.
675440 (European Union); the Leventis Foundation; the A. P. Sloan Foundation;
the Alexander von Humboldt Foundation; the Belgian Federal Science Policy
Office; the Fonds pour la Formation à la Recherche dans l’Industrie et dans
l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en
Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the
“Excellence of Science - EOS” - be.h project n. 30820817; the Ministry of
Education, Youth and Sports (MEYS) of the Czech Republic; the Lendület
(“Momentum”) Programme 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 and 125105 (Hungary); the
Council of Scientific and Industrial Research, India; the HOMING PLUS program
of the Foundation for Polish Science, cofinanced from European Union, Regional
Development Fund, the Mobility Plus program of the Ministry of Science and
Higher Education, the National Science Center (Poland), contracts Harmonia
2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and
2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406; the National Priorities
Research Program by Qatar National Research Fund; the Programa de Excelencia
María de Maeztu and the Programa Severo Ochoa del Principado de Asturias; the
Thalis and Aristeia programs cofinanced by EU-ESF and the Greek NSRF; the
Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University
and the Chulalongkorn Academic into Its 2nd Century Project Advancement
Project (Thailand); the Welch Foundation, contract C-1845; and the Weston
Havens Foundation (USA).

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##  .11 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan, A. Tumasyan Institut für Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, E. Asilar, T. Bergauer, J. Brandstetter, E. Brondolin, M.
Dragicevic, J. Erö, A. Escalante Del Valle, M. Flechl, M. Friedl, R.
Frühwirth1, V.M. Ghete, J. Grossmann, J. Hrubec, M. Jeitler1, A. König, N.
Krammer, I. Krätschmer, D. Liko, T. Madlener, I. Mikulec, E. Pree, N. Rad, H.
Rohringer, J. Schieck1, R. Schöfbeck, M. Spanring, D. Spitzbart, A. Taurok, W.
Waltenberger, J. Wittmann, C.-E. Wulz1, M. Zarucki Institute for Nuclear
Problems, Minsk, Belarus
V. Chekhovsky, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
E.A. De Wolf, D. Di Croce, X. Janssen, J. Lauwers, M. Van De Klundert, H. Van
Haevermaet, P. Van Mechelen, N. Van Remortel Vrije Universiteit Brussel,
Brussel, Belgium
S. Abu Zeid, F. Blekman, J. D’Hondt, I. De Bruyn, J. De Clercq, K. Deroover,
G. Flouris, D. Lontkovskyi, S. Lowette, I. Marchesini, S. Moortgat, L.
Moreels, Q. Python, K. Skovpen, S. Tavernier, W. Van Doninck, P. Van Mulders,
I. Van Parijs Université Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, B.
Dorney, G. Fasanella, L. Favart, R. Goldouzian, A. Grebenyuk, A.K. Kalsi, T.
Lenzi, J. Luetic, T. Maerschalk, A. Marinov, T. Seva, E. Starling, C. Vander
Velde, P. Vanlaer, D. Vannerom, R. Yonamine, F. Zenoni Ghent University,
Ghent, Belgium
T. Cornelis, D. Dobur, A. Fagot, M. Gul, I. Khvastunov2, D. Poyraz, C. Roskas,
S. Salva, D. Trocino, M. Tytgat, W. Verbeke, M. Vit, N. Zaganidis Université
Catholique de Louvain, Louvain-la-Neuve, Belgium
H. Bakhshiansohi, O. Bondu, S. Brochet, G. Bruno, C. Caputo, A. Caudron, P.
David, S. De Visscher, C. Delaere, M. Delcourt, B. Francois, A. Giammanco, M.
Komm, G. Krintiras, V. Lemaitre, A. Magitteri, A. Mertens, M. Musich, K.
Piotrzkowski, L. Quertenmont, A. Saggio, M. Vidal Marono, S. Wertz, J. Zobec
Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
W.L. Aldá Júnior, F.L. Alves, G.A. Alves, L. Brito, G. Correia Silva, C.
Hensel, A. Moraes, M.E. Pol, P. Rebello Teles Universidade do Estado do Rio de
Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, S. Fonseca De Souza, L.M.
Huertas Guativa, H. Malbouisson, M. Melo De Almeida, C. Mora Herrera, L.
Mundim, H. Nogima, L.J. Sanchez Rosas, A. Santoro, A. Sznajder, M. Thiel, E.J.
Tonelli Manganote3, F. Torres Da Silva De Araujo, A. Vilela Pereira
Universidade Estadual Paulista a, Universidade Federal do ABC b, São Paulo,
Brazil
S. Ahujaa, C.A. Bernardesa, T.R. Fernandez Perez Tomeia, E.M. Gregoresb, P.G.
Mercadanteb, S.F. Novaesa, SandraS. Padulaa, D. Romero Abadb, J.C. Ruiz
Vargasa Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy
of Sciences, Sofia, Bulgaria
A. Aleksandrov, R. Hadjiiska, P. Iaydjiev, M. Misheva, M. Rodozov, M. Shopova,
G. Sultanov University of Sofia, Sofia, Bulgaria
A. Dimitrov, L. Litov, B. Pavlov, P. Petkov Beihang University, Beijing, China
W. Fang5, X. Gao5, L. Yuan Institute of High Energy Physics, Beijing, China
M. Ahmad, J.G. Bian, G.M. Chen, H.S. Chen, M. Chen, Y. Chen, C.H. Jiang, D.
Leggat, H. Liao, Z. Liu, F. Romeo, S.M. Shaheen, A. Spiezia, J. Tao, C. Wang,
Z. Wang, E. Yazgan, H. Zhang, J. Zhao State Key Laboratory of Nuclear Physics
and Technology, Peking University, Beijing, China
Y. Ban, G. Chen, J. Li, Q. Li, S. Liu, Y. Mao, S.J. Qian, D. Wang, Z. Xu, F.
Zhang5 Tsinghua University, Beijing, China
Y. Wang Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C.A. Carrillo Montoya, L.F. Chaparro Sierra, C. Florez,
C.F. González Hernández, J.D. Ruiz Alvarez, M.A. Segura Delgado University of
Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval
Architecture, Split, Croatia
B. Courbon, N. Godinovic, D. Lelas, I. Puljak, P.M. Ribeiro Cipriano, T.
Sculac University of Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, K. Kadija, B. Mesic, A. Starodumov6, T. Susa
University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos,
P.A. Razis, H. Rykaczewski Charles University, Prague, Czech Republic
M. Finger7, M. Finger Jr.7 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. Abdalla8, Y. Assran9,10, A. Mohamed11 National Institute of Chemical
Physics and Biophysics, Tallinn, Estonia
S. Bhowmik, R.K. Dewanjee, M. Kadastik, L. Perrini, M. Raidal, C. Veelken
Department of Physics, University of Helsinki, Helsinki, Finland
P. Eerola, H. Kirschenmann, J. Pekkanen, M. Voutilainen Helsinki Institute of
Physics, Helsinki, Finland
J. Havukainen, J.K. Heikkilä, T. Järvinen, V. Karimäki, R. Kinnunen, T.
Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, P. Luukka, T.
Mäenpää, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta University of
Technology, Lappeenranta, Finland
T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F. Ferri, S.
Ganjour, S. Ghosh, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
C. Leloup, E. Locci, M. Machet, J. Malcles, G. Negro, J. Rander, A. Rosowsky,
M.Ö. Sahin, M. Titov Laboratoire Leprince-Ringuet, Ecole polytechnique,
CNRS/IN2P3, Université Paris-Saclay, Palaiseau, France
A. Abdulsalam12, C. Amendola, I. Antropov, S. Baffioni, F. Beaudette, P.
Busson, L. Cadamuro, C. Charlot, R. Granier de Cassagnac, M. Jo, I. Kucher, S.
Lisniak, A. Lobanov, J. Martin Blanco, M. Nguyen, C. Ochando, G. Ortona, P.
Paganini, P. Pigard, R. Salerno, J.B. Sauvan, Y. Sirois, A.G. Stahl Leiton, T.
Strebler, Y. Yilmaz, A. Zabi, A. Zghiche Université de Strasbourg, CNRS, IPHC
UMR 7178, Strasbourg, France
J.-L. Agram13, J. Andrea, D. Bloch, J.-M. Brom, M. Buttignol, E.C. Chabert, N.
Chanon, C. Collard, E. Conte13, X. Coubez, F. Drouhin13, J.-C. Fontaine13, D.
Gelé, U. Goerlach, M. Jansová, P. Juillot, A.-C. Le Bihan, N. Tonon, P. Van
Hove Centre de Calcul de l’Institut National de Physique Nucleaire et de
Physique des Particules, CNRS/IN2P3, Villeurbanne, France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, R. Chierici, D. Contardo, P. Depasse, H.
El Mamouni, J. Fay, L. Finco, S. Gascon, M. Gouzevitch, G. Grenier, B. Ille,
F. Lagarde, I.B. Laktineh, M. Lethuillier, L. Mirabito, A.L. Pequegnot, S.
Perries, A. Popov14, V. Sordini, M. Vander Donckt, S. Viret, S. Zhang Georgian
Technical University, Tbilisi, Georgia
I. Lomidze, T. Toriashvili15 Tbilisi State University, Tbilisi, Georgia
I. Bagaturia16, D. Lomidze RWTH Aachen University, I. Physikalisches Institut,
Aachen, Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, M. Preuten, C.
Schomakers, J. Schulz, M. Teroerde, B. Wittmer, V. Zhukov14 RWTH Aachen
University, III. Physikalisches Institut A, Aachen, Germany
A. Albert, D. Duchardt, M. Endres, M. Erdmann, S. Erdweg, T. Esch, R. Fischer,
A. Güth, T. Hebbeker, C. Heidemann, K. Hoepfner, S. Knutzen, M. Merschmeyer,
A. Meyer, P. Millet, S. Mukherjee, B. Philipps, T. Pook, M. Radziej, H.
Reithler, M. Rieger, F. Scheuch, D. Teyssier, S. Thüer, F.P. Zantis RWTH
Aachen University, III. Physikalisches Institut B, Aachen, Germany
G. Flügge, B. Kargoll, T. Kress, A. Künsken, T. Müller, A. Nehrkorn, A.
Nowack, C. Pistone, O. Pooth, A. Stahl17 Deutsches Elektronen-Synchrotron,
Hamburg, Germany
M. Aldaya Martin, T. Arndt, C. Asawatangtrakuldee, K. Beernaert, O. Behnke, U.
Behrens, A. Bermúdez Martínez, A.A. Bin Anuar, K. Borras18, V. Botta, A.
Campbell, P. Connor, C. Contreras-Campana, F. Costanza, C. Diez Pardos, G.
Eckerlin, D. Eckstein, T. Eichhorn, E. Eren, E. Gallo19, J. Garay Garcia, A.
Geiser, J.M. Grados Luyando, A. Grohsjean, P. Gunnellini, M. Guthoff, A. Harb,
J. Hauk, M. Hempel20, H. Jung, M. Kasemann, J. Keaveney, C. Kleinwort, I.
Korol, D. Krücker, W. Lange, A. Lelek, T. Lenz, K. Lipka, W. Lohmann20, R.
Mankel, I.-A. Melzer-Pellmann, A.B. Meyer, M. Missiroli, G. Mittag, J. Mnich,
A. Mussgiller, E. Ntomari, D. Pitzl, A. Raspereza, M. Savitskyi, P. Saxena, R.
Shevchenko, N. Stefaniuk, G.P. Van Onsem, R. Walsh, Y. Wen, K. Wichmann, C.
Wissing, O. Zenaiev University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, V. Blobel, M. Centis Vignali, T. Dreyer, E. Garutti, D.
Gonzalez, J. Haller, A. Hinzmann, M. Hoffmann, A. Karavdina, R. Klanner, R.
Kogler, N. Kovalchuk, S. Kurz, D. Marconi, M. Meyer, M. Niedziela, D.
Nowatschin, F. Pantaleo17, T. Peiffer, A. Perieanu, C. Scharf, P. Schleper, A.
Schmidt, S. Schumann, J. Schwandt, J. Sonneveld, H. Stadie, G. Steinbrück,
F.M. Stober, M. Stöver, H. Tholen, D. Troendle, E. Usai, A. Vanhoefer, B.
Vormwald Karlsruher Institut fuer Technology
M. Akbiyik, C. Barth, M. Baselga, S. Baur, E. Butz, R. Caspart, T. Chwalek, F.
Colombo, W. De Boer, A. Dierlamm, N. Faltermann, B. Freund, R. Friese, M.
Giffels, M.A. Harrendorf, F. Hartmann17, S.M. Heindl, U. Husemann, F.
Kassel17, S. Kudella, H. Mildner, M.U. Mozer, Th. Müller, M. Plagge, G. Quast,
K. Rabbertz, M. Schröder, I. Shvetsov, G. Sieber, H.J. Simonis, R. Ulrich, S.
Wayand, M. Weber, T. Weiler, S. Williamson, C. Wöhrmann, R. Wolf Institute of
Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas, I. Topsis-
Giotis National and Kapodistrian University of Athens, Athens, Greece
G. Karathanasis, S. Kesisoglou, A. Panagiotou, N. Saoulidou, E. Tziaferi
National Technical University of Athens, Athens, Greece
K. Kousouris University of Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, N.
Manthos, I. Papadopoulos, E. Paradas, J. Strologas, F.A. Triantis, D.
Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
M. Csanad, N. Filipovic, G. Pasztor, O. Surányi, G.I. Veres21 Wigner Research
Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath22, Á. Hunyadi, F. Sikler, V. Veszpremi, G.
Vesztergombi21 Institute of Nuclear Research ATOMKI, Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi23, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
M. Bartók21, P. Raics, Z.L. Trocsanyi, B. Ujvari Indian Institute of Science
(IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri National Institute of Science Education and
Research, HBNI, Bhubaneswar, India
S. Bahinipati24, P. Mal, K. Mandal, A. Nayak25, D.K. Sahoo24, N. Sahoo, S.K.
Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, R. Chawla, N. Dhingra, A. Kaur, M. Kaur,
S. Kaur, R. Kumar, P. Kumari, A. Mehta, J.B. Singh, G. Walia University of
Delhi, Delhi, India
A. Bhardwaj, S. Chauhan, B.C. Choudhary, R.B. Garg, S. Keshri, A. Kumar, Ashok
Kumar, S. Malhotra, M. Naimuddin, K. Ranjan, Aashaq Shah, R. Sharma Saha
Institute of Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj26, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep26, D. Bhowmik,
S. Dey, S. Dutt26, S. Dutta, S. Ghosh, N. Majumdar, A. Modak, K. Mondal, S.
Mukhopadhyay, S. Nandan, A. Purohit, P.K. Rout, A. Roy, S. Roy Chowdhury, S.
Sarkar, M. Sharan, B. Singh, S. Thakur26 Indian Institute of Technology
Madras, Madras, India
P.K. Behera Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, A.K. Mohanty17, P.K. Netrakanti,
L.M. Pant, P. Shukla, A. Topkar Tata Institute of Fundamental Research-A,
Mumbai, India
T. Aziz, S. Dugad, B. Mahakud, S. Mitra, G.B. Mohanty, N. Sur, B. Sutar Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, Sa. Jain, S.
Kumar, M. Maity27, G. Majumder, K. Mazumdar, T. Sarkar27, N. Wickramage28
Indian Institute of Science Education and Research (IISER), Pune, India
S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, S.
Sharma Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
S. Chenarani29, E. Eskandari Tadavani, S.M. Etesami29, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, S. Paktinat Mehdiabadi30, F. Rezaei
Hosseinabadi, B. Safarzadeh31, M. Zeinali University College Dublin, Dublin,
Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L. Cristellaa,b,
N. De Filippisa,c, M. De Palmaa,b, F. Erricoa,b, L. Fiorea, M. Francoa, G.
Iasellia,c, N. Lacalamitaa, S. Lezkia,b, G. Maggia,c, M. Maggia, S.
Martiradonnaa,b, G. Minielloa,b, S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G.
Pugliesea,c, R. Radognaa, A. Ranieria, G. Selvaggia,b, A. Sharmaa, L.
Silvestrisa,17, R. Vendittia, P. Verwilligena INFN Sezione di Bologna a,
Università di Bologna b, Bologna, Italy
G. Abbiendia, G. Balbi, C. Baldanzaa, C. Battilanaa,b, D. Bonacorsia,b, L.
Borgonovia,b, S. Braibant-Giacomellia,b, V.D. Cafaroa, R. Campaninia,b, P.
Capiluppia,b, A. Castroa,b, F.R. Cavalloa, S.S. Chhibraa,b, G. Codispotia,b,
M. Cuffiania,b, G.M. Dallavallea, F. Fabbria, A. Fanfania,b, D. Fasanellaa,b,
P. Giacomellia, V. Giordanoa, C. Grandia, L. Guiduccia,b, F. Iemmi, S.
Marcellinia, G. Masettia, A. Montanaria, F.L. Navarriaa,b, A. Perrottaa, A.M.
Rossia,b, T. Rovellia,b, G.P. Sirolia,b, N. Tosia, R. Travaglinia,b INFN
Sezione di Catania a, Università di Catania b, Catania, Italy
S. Albergoa,b, S. Costaa,b, A. Di Mattiaa, F. Giordanoa,b, R. Potenzaa,b, A.
Tricomia,b, C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b,
Firenze, Italy
G. Barbaglia, K. Chatterjeea,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, P. Lenzia,b, M. Meschinia, S. Paolettia, L.
Russoa,32, G. Sguazzonia, D. Stroma, L. Viliania INFN Laboratori Nazionali di
Frascati, Frascati, Italy
L. Benussi, S. Bianco, M. Caponero33, F. Fabbri, M. Ferrini, L. Passamonti, D.
Piccolo, D. Pierluigi, F. Primavera17, A. Russo, G. Saviano34 INFN Sezione di
Genova a, Università di Genova b, Genova, Italy
V. Calvellia,b, F. Ferroa, F. Raveraa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschib, L. Brianzaa,b, F. Brivioa,b, V. Cirioloa,b,17, M.E.
Dinardoa,b, S. Fiorendia,b, S. Gennaia, A. Ghezzia,b, P. Govonia,b, M.
Malbertia,b, S. Malvezzia, R.A. Manzonia,b, D. Menascea, L. Moronia, M.
Paganonia,b, K. Pauwelsa,b, D. Pedrinia, S. Pigazzinia,b,35, S. Ragazzia,b, T.
Tabarelli de Fatisa,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, S. Di Guidaa,d,17, F. Fabozzia,c, F. Fiengaa,b,
A.O.M. Iorioa,b, W.A. Khana, L. Listaa, S. Meolaa,d,17, P. Paoluccia,17, C.
Sciaccaa,b, F. Thyssena INFN Sezione di Padova a, Università di Padova b,
Padova, Italy, Università di Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, L. Barcellana, M. Bellatoa, L. Benatoa,b, M.
Benettonia, M. Biasottoa,36, D. Biselloa,b, A. Bolettia,b, A. Brancaa,b, R.
Carlina,b, P. Checchiaa, L. Cianoa, M. Dall’Ossoa,b, P. De Castro Manzanoa, T.
Dorigoa, U. Dossellia, S. Fantinela, F. Fanzagoa, F. Gasparinia,b, U.
Gasparinia,b, F. Gonellaa, A. Gozzelinoa, M. Gulminia,36, R. Isocratea, S.
Lacapraraa, M. Margonia,b, A.T. Meneguzzoa,b, G. Mocellin, F. Montecassianoa,
M. Passaseoa, M. Pegoraroa, N. Pozzobona,b, P. Ronchesea,b, R. Rossina,b, M.
Sgaravattoa, F. Simonettoa,b, A. Tiko, N. Tonioloa, E. Torassaa, S. Venturaa,
M. Zanettia,b, P. Zottoa,b, G. Zumerlea,b INFN Sezione di Pavia a, Università
di Pavia b, Pavia, Italy
A. Braghieria, A. Magnania, P. Montagnaa,b, S.P. Rattia,b, V. Rea, M.
Ressegottia,b, C. Riccardia,b, P. Salvinia, I. Vaia,b, P. Vituloa,b INFN
Sezione di Perugia a, Università di Perugia b, Perugia, Italy
L. Alunni Solestizia,b, M. Biasinia,b, G.M. Bileia, C. Cecchia,b, D.
Ciangottinia,b, L. Fanòa,b, P. Laricciaa,b, R. Leonardia,b, E. Manonia, G.
Mantovania,b, V. Mariania,b, M. Menichellia, A. Rossia,b, A. Santocchiaa,b, D.
Spigaa INFN Sezione di Pisa a, Università di Pisa b, Scuola Normale Superiore
di Pisa c, Pisa, Italy
K. Androsova, P. Azzurria,17, G. Bagliesia, L. Bianchinia, T. Boccalia, L.
Borrello, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, L.
Gianninia,c, A. Giassia, M.T. Grippoa,32, F. Ligabuea,c, T. Lomtadzea, E.
Mancaa,c, G. Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, A. Savoy-
Navarroa,37, P. Spagnoloa, R. Tenchinia, G. Tonellia,b, A. Venturia, P.G.
Verdinia INFN Sezione di Roma a, Sapienza Università di Roma b, Rome, Italy
L. Baronea,b, F. Cavallaria, M. Cipriania,b, N. Dacia, D. Del Rea,b, E. Di
Marcoa,b, M. Diemoza, S. Gellia,b, E. Longoa,b, F. Margarolia,b, B.
Marzocchia,b, P. Meridiania, G. Organtinia,b, R. Paramattia,b, F. Preiatoa,b,
S. Rahatloua,b, C. Rovellia, F. Santanastasioa,b INFN Sezione di Torino a,
Università di Torino b, Torino, Italy, Università del Piemonte Orientale c,
Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, C. Biinoa, N. Cartigliaa, F. Cennaa,b, M. Costaa,b, G. Cottoa,b,
R. Covarellia,b, D. Dattolaa, P. De Remigisa, G. Dellacasaa, N. Demariaa, B.
Kiania,b, C. Mariottia, S. Masellia, G. Mazzaa, E. Migliorea,b, V. Monacoa,b,
E. Monteila,b, M. Montenoa, M.M. Obertinoa,b, L. Pachera,b, N. Pastronea, M.
Pelliccionia, G.L. Pinna Angionia,b, F. Rotondoa, M. Ruspaa,c, R. Sacchia,b,
K. Shchelinaa,b, V. Solaa, A. Solanoa,b, A. Staianoa, P. Traczyka,b INFN
Sezione di Trieste a, Università di Trieste b, Trieste, Italy
S. Belfortea, M. Casarsaa, F. Cossuttia, G. Della Riccaa,b, A. Zanettia
Kyungpook National University
D.H. Kim, G.N. Kim, M.S. Kim, J. Lee, S. Lee, S.W. Lee, C.S. Moon, Y.D. Oh, S.
Sekmen, D.C. Son, Y.C. Yang Chonnam National University, Institute for
Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon, G. Oh Hanyang University, Seoul, Korea
J.A. Brochero Cifuentes, J. Goh, T.J. Kim Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, Y. Jo, Y. Kim, K. Lee, K.S.
Lee, S. Lee, J. Lim, S.K. Park, Y. Roh Seoul National University, Seoul, Korea
J. Almond, J. Kim, J.S. Kim, H. Lee, K. Lee, K. Nam, S.B. Oh, B.C. Radburn-
Smith, S.h. Seo, U.K. Yang, H.D. Yoo, G.B. Yu University of Seoul, Seoul,
Korea
H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park Sungkyunkwan University, Suwon, Korea
Y. Choi, C. Hwang, J. Lee, I. Yu Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, J. Vaitkus National Centre for Particle Physics,
Universiti Malaya, Kuala Lumpur, Malaysia
I. Ahmed, Z.A. Ibrahim, M.A.B. Md Ali38, F. Mohamad Idris39, W.A.T. Wan
Abdullah, M.N. Yusli, Z. Zolkapli Centro de Investigacion y de Estudios
Avanzados del IPN, Mexico City, Mexico
M.C. Duran-Osuna, H. Castilla-Valdez, E. De La Cruz-Burelo, G. Ramirez-
Sanchez, I. Heredia-De La Cruz40, R.I. Rabadan-Trejo, R. Lopez-Fernandez, J.
Mejia Guisao, R Reyes-Almanza, A. Sanchez-Hernandez Universidad
Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, F. Vazquez Valencia Benemerita
Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M. Ahmad, M.I. Asghar, Q. Hassan, H.R. Hoorani, M.A. Shah, M.
Shoaib, M. Waqas National Centre for Nuclear Research, Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. Górski, M. Kazana, K.
Nawrocki, M. Szleper, P. Zalewski Institute of Experimental Physics, Faculty
of Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk41, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Misiura, M. Olszewski, A. Pyskir, M. Walczak Laboratório de
Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
P. Bargassa, C. Beirão Da Cruz E Silva, A. Di Francesco, P. Faccioli, B.
Galinhas, M. Gallinaro, J. Hollar, N. Leonardo, L. Lloret Iglesias, M.V.
Nemallapudi, J. Seixas, G. Strong, O. Toldaiev, D. Vadruccio, J. Varela Joint
Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, P. Bunin, Y. Ershov, A. Evdokimov, M. Gavrilenko, A. Golunov, I.
Golutvin, I. Gorbunov, A. Kamenev, V. Karjavine, A. Kurenkov, A. Lanev, A.
Makankin, A. Malakhov, V. Matveev42,43, P. Moisenz, V. Palichik, V. Perelygin,
S. Shmatov, S. Shulha, N. Skatchkov, V. Smirnov, S. Vasil’ev, N. Voytishin, A.
Zarubin Petersburg Nuclear Physics Institute, Gatchina (St. Petersburg),
Russia
Y. Ivanov, V. Kim44, E. Kuznetsova45, P. Levchenko, V. Murzin, V. Oreshkin, I.
Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Vavilov, A. Vorobyev Institute
for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, D. Tlisov, A. Toropin Institute for Theoretical
and Experimental Physics, Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, V. Popov, I. Pozdnyakov, G.
Safronov, A. Spiridonov, A. Stepennov, V. Stolin, M. Toms, E. Vlasov, A.
Zhokin Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev, A. Bylinkin43 National Research Nuclear University ’Moscow
Engineering Physics Institute’ (MEPhI), Moscow, Russia
S. Polikarpov P.N. Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin43, I. Dremin43, M. Kirakosyan43, S.V. Rusakov, A.
Terkulov Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
A. Baskakov, A. Belyaev, G. Bogdanova, E. Boos, L. Dudko, A. Ershov, A.
Gribushin, V. Klyukhin, O. Kodolova, I. Lokhtin, I. Miagkov, S. Obraztsov, S.
Petrushanko, V. Savrin, V. Volkov Novosibirsk State University (NSU),
Novosibirsk, Russia
V. Blinov46, D. Shtol46, Y. Skovpen46 State Research Center of Russian
Federation, Institute for High Energy Physics of NRC “Kurchatov Institute”,
Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, D. Elumakhov, A. Godizov, V. Kachanov,
A. Kalinin, D. Konstantinov, P. Mandrik, V. Petrov, R. Ryutin, A. Sobol, S.
Troshin, N. Tyurin, A. Uzunian, A. Volkov University of Belgrade, Faculty of
Physics and Vinca Institute of Nuclear Sciences, Belgrade, Serbia
P. Adzic47, P. Cirkovic, D. Devetak, M. Dordevic, J. Milosevic Centro de
Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid,
Spain