MUO-21-001

MUO-21-001

# Performance of CMS muon reconstruction from proton-proton to heavy ion
collisions

###### Abstract

The performance of muon tracking, identification, triggering, momentum
resolution, and momentum scale has been studied with the CMS detector at the
LHC using data collected at $\sqrtsNN=5.02\TeV$ in proton-proton () and lead-
lead ($\mathrm{PbPb}$) collisions in 2017 and 2018, respectively, and at
$\sqrtsNN=8.16\TeV$ in proton-lead ($\Pp{}\mathrm{Pb}$) collisions in 2016.
Muon efficiencies, momentum resolutions, and momentum scales are compared by
focusing on how the muon reconstruction performance varies from relatively
small occupancy collisions to the larger occupancies of $\Pp{}\mathrm{Pb}$
collisions and, finally, to the highest track multiplicity $\mathrm{PbPb}$
collisions. We find the efficiencies of muon tracking, identification, and
triggering to be above 90% throughout most of the track multiplicity range.
The momentum resolution and scale are unaffected by the detector occupancy.
The excellent muon reconstruction of the CMS detector enables precision
studies across all available collision systems.

##  0.1 Introduction

An estimate of the capability of a detector to reconstruct and identify
particles of interest is essential to ensure the reliability of the data and
the precision of measurements. A crucial measure of the performance of both
the detector and its reconstruction algorithms is the efficiency, which is a
required correction for any measurement of cross sections or particle yields.
Efficiency is defined as a fraction of a given species of particles within the
detector acceptance that are successfully reconstructed with a given set of
selection criteria. The efficiency could be estimated using Monte Carlo (MC)
simulations, where the particles present in the event are known a priori.
However, estimates from MC simulations assume an accurate description of a
detector and its interaction processes, which may have characteristics that
are challenging to quantify. Rather than relying solely on detector
simulations, data-driven approaches for estimating efficiencies have been
developed. We use the data-driven tag-and-probe method that has been
successfully used in various analyses in proton-proton (), proton-lead
($\Pp{}\mathrm{Pb}$), and lead-lead ($\mathrm{PbPb}$) collisions performed by
the CMS detector at the CERN Large Hadron Collider (LHC) [1, 2, 3].
Performance studies of the CMS detector have been carried out since the
beginning of LHC operations in collisions [4, 5], and we now extend these
studies to $\Pp{}\mathrm{Pb}$ and $\mathrm{PbPb}$ collisions.

We use heavy ion collisions to gain insight into the collective thermodynamic
properties of cold and hot QCD matter, including the color-deconfined quark-
gluon plasma (QGP) [6]. Resonances that decay into muons (, , , ) provide
information about the QGP, because its presence has measurable effects on
particle yields and distributions. Muon measurements are more challenging in
the high-occupancy environments of heavy ion collisions. One quantifiable
indicator of occupancy is the charged particle multiplicity of the event,
which in $\mathrm{PbPb}$ collisions is highly correlated with the collision
impact parameter (distance between the centers of the two nuclei in the plane
perpendicular to the beam line). For the collisions with near-zero impact
parameter (central events), the multiplicities can be hundreds of times larger
than those observed in a single collision [7]. These central events result in
extremely high multiplicity per unit area, especially in the innermost CMS
subdetectors used for tracking. Consequently, any degradation in tracking
performance is likely to affect the muon measurements, despite the fact that
the occupancies in the outermost CMS muon subdetectors remain low.

In this paper we report on the performance of muon reconstruction,
identification (ID), triggering, dimuon-mass resolution, and dimuon-mass
scale. The reference physical processes are the decays of and resonances,
whose decay muons cover a wide kinematic range, thus providing a good data
sample for measuring the muon performance in the CMS detector. The
efficiencies are estimated using a tag-and-probe method, which is described in
detail in Section 0.5. The efficiencies are reported in Sections 0.6–0.8; the
mass resolution and scale are presented in Section 0.9. We summarize the paper
in Section 0.10.

##  0.2 CMS detector

A detailed description of the CMS detector is reported in Ref. [8]. A
schematic view of the detector is shown in Fig. 1. Muon reconstruction is
performed using the all-silicon inner tracker at the center of the detector
immersed in a 3.8T solenoidal magnetic field, and with up to four stations of
gas-ionization muon detectors installed outside the solenoid and sandwiched
between the layers of the steel flux-return yoke. The inner tracker is
composed of a pixel detector and a silicon strip tracker, and measures
charged-particle trajectories in the pseudorapidity range $\abs{\eta}<2.5$.
The muon system covers the pseudorapidity region $\abs{\eta}<2.4$ and performs
three main tasks: triggering on muons, identifying muons, and improving the
momentum measurement and charge determination of high transverse momentum ()
muons. Drift tube (DT) detectors and cathode strip chambers (CSC) detect muons
in the $\eta$ regions of $\abs{\eta}<1.2$ and $0.9<\abs{\eta}<2.4$,
respectively, and are complemented by a system of resistive-plate chambers
(RPC) covering the range of $\abs{\eta}<1.9$. The coverage of the DT and CSC
detectors defines three regions, referred to as barrel ($\abs{\eta}<0.9$),
overlap ($0.9<\abs{\eta}<1.2$), and endcap ($1.2<\abs{\eta}<2.4$) regions. The
electromagnetic calorimeter (ECAL) and the hadron calorimeter (HCAL), divided
into barrel and endcap sections, are also shown in Fig. 1. The hadron forward
(HF) calorimeters extend the absolute pseudorapidity range of the HCAL to 5.0,
and are commonly used in trigger algorithms and estimates of the collision
impact parameter.

Events of interest are acquired using a two-tiered trigger system [9]. The
first level (L1), composed of custom hardware processors, uses information
from the calorimeters and muon detectors to select events at a rate of around
100kHz for and $\Pp{}\mathrm{Pb}$ collisions (30kHz for $\mathrm{PbPb}$
collisions) within a fixed latency of 4 [10]. The second level, known as the
high-level trigger (HLT), consists of a farm of processors running a version
of the full event reconstruction software optimized for fast processing. The
HLT algorithms, which are seeded by the L1 triggers, further reduce the rate
of data collection to around 1kHz for collisions before data storage. The
corresponding HLT rate is up to 20kHz for $\Pp{}\mathrm{Pb}$ collisions and
7kHz for $\mathrm{PbPb}$ collisions.

Figure 1: Longitudinal layout of one quadrant of the CMS detector. The drawing
shows four DT stations in the muon barrel (MB1–MB4, yellow), four CSC stations
in the muon endcap (ME1–ME4, green) and the RPC stations (RB1–RB4 and RE1–RE4,
blue).

##  0.3 Data and Monte Carlo samples

The data used in this paper were taken during the dedicated heavy ion run
periods of Run 2 between 2016 and 2018. The events were recorded from
$\Pp{}\mathrm{Pb}$ collisions in 2016 at a nucleon-nucleon center-of-mass
energy of $\sqrtsNN=8.16\TeV$, from collisions in 2017 at $\sqrt{s}=5.02\TeV$,
and from $\mathrm{PbPb}$ collisions in 2018 at $\sqrtsNN=5.02\TeV$. In
addition, a smaller $\Pp{}\mathrm{Pb}$ data sample at $\sqrtsNN=5.02\TeV$ was
collected in 2016, which allowed us to quantify the difference between 5.02and
8.16collisions. All studied events are from hadronic collisions.

Results from the data are compared with those obtained from MC samples, in
which the simulation includes the full CMS detector response using the package
[11]. The software used to generate the nucleon-nucleon interactions that
produce the particle of interest depends on the process and collision system:
for mesons we use 8 (v2.3.0) [12]; for bosons in $\mathrm{PbPb}$,
$\Pp{}\mathrm{Pb}$, and collisions we use (v2.4.2) [13], v2 [14, 15, 16, 17],
and 8, respectively. Differences in the details of the simulation of the hard
process have a negligible impact on the studies presented in this paper. In
the case of the $\Pp{}\mathrm{Pb}$ and $\mathrm{PbPb}$ MC samples, the hard-
scattering nucleon-nucleon events were embedded into an underlying heavy ion
collision that was simulated with either epos lhc (v3400) [18]
($\Pp{}\mathrm{Pb}$) or v1.9 [19] ($\mathrm{PbPb}$). After the detailed
simulation of the CMS detector response with , MC events were processed
through the trigger emulation and reconstructed by the same algorithms that
were used for the collision data sample for a given year and collision system.

###  0.3.1 Possible effects of detector and alignment changes

The reconstruction performance is influenced not only by varying the collision
system from to $\mathrm{PbPb}$, but also by the changes in the detector during
the years when the data were collected. The most significant addition
affecting the data was the pixel-detector upgrade after the $\Pp{}\mathrm{Pb}$
data-taking period at the end of 2016. As part of the upgrade, an extra layer
was added to the pixel detector in the midrapidity region, and an additional
disk was added to both sides of the tracker [20].

The alignment conditions in the pixel and silicon-strip tracker detector
differ across the data sets as well. The $\Pp{}\mathrm{Pb}$ and
$\mathrm{PbPb}$ samples considered here were reconstructed using the alignment
configurations that were derived during data taking. In contrast, the sample
takes advantage of the improved end-of-year reconstruction, in which the
alignment constants are rederived using the data collected during the entire
year. Two examples of measurements that could be sensitive to the alignment
conditions are those of the mass scale and of the mass resolution (Section
0.9). The details of the alignment procedure and the effects of the alignment
conditions on the boson mass reconstructed via muons were studied in Ref.
[21]. Although the boson decays are used in the alignment procedure, its mass
is not fixed to a specific value. In particular, the alignment parameters
obtained for a given system are identical irrespective of the multiplicity of
the collision. Hence, the boson remains a useful probe into differences in
resolution and scale. It was verified in one of the studies discussed in Ref.
[21] that the difference in the reconstructed boson mass between the alignment
conditions derived during data taking and those derived at the end of the year
was ${\lesssim}0.1\%$. Based on the small size of the effect, we conclude that
the alignment differences do not significantly affect the comparisons among
the , $\Pp{}\mathrm{Pb}$, and $\mathrm{PbPb}$ data sets.

##  0.4 Muons in the heavy ion environment

Figure 2: Left: Distribution of $N_{\mathrm{tracks}}$ in collisions at
$\sqrt{s}=5.02\TeV$ (black); $\Pp{}\mathrm{Pb}$ collisions at
$\sqrtsNN=5.02\TeV$ (magenta) and at 8.16(blue); and $\mathrm{PbPb}$
collisions at $\sqrtsNN=5.02\TeV$ (red). The distribution of
$N_{\mathrm{tracks}}$ in the 0–20% most central $\mathrm{PbPb}$ collisions is
shown in green. The values are not corrected for efficiency. Right:
Translation of the centrality in $\mathrm{PbPb}$ collisions at
$\sqrtsNN=5.02\TeV$ into an equivalent pileup in collisions at
$\sqrt{s}=14\TeV$.

The main difference among the collision systems that affects the detector
performance is the number of charged particles produced per collision. The
variable $N_{\mathrm{tracks}}$, which is obtained by counting tracks in the
pseudorapidity range $\abs{\eta}<2.4$, characterizes the total charged
particle multiplicity. The distributions of $N_{\mathrm{tracks}}$ in ,
$\Pp{}\mathrm{Pb}$, and $\mathrm{PbPb}$ collision events are shown in the left
panel of Fig. 2. In $\mathrm{PbPb}$ collisions, events with a large number of
tracks correspond to the most central collisions (large nucleus-nucleus
overlap, small impact parameter), whereas a small number of tracks indicates a
peripheral collision (small nucleus-nucleus overlap, large impact parameter).
Centrality is a variable used to characterize the amount of overlap between
colliding nuclei. The centrality bins are given in percentile ranges of the
total inelastic hadronic cross section, calculated from the energy deposited
in the HF calorimeters, such that 0% centrality corresponds to the most
central collisions and 100% corresponds to the most peripheral (see Ref. [22],
Fig. 3, where the centrality definition used in CMS is discussed). To
illustrate the highest multiplicities achievable in $\mathrm{PbPb}$
collisions, the distribution of $N_{\mathrm{tracks}}$ in the 0–20% most
central $\mathrm{PbPb}$ collisions is shown in green. Similarly, larger
multiplicities than those of collisions are seen in the $\Pp{}\mathrm{Pb}$
system. We also compare the $N_{\mathrm{tracks}}$ distributions for
$\Pp{}\mathrm{Pb}$ collisions at $\sqrtsNN=5.02\TeV$ and at 8.16. We see that
an increase of 63% in $\sqrtsNN$ results, on average, in the creation of 24%
more tracks.

Average number of reconstructed tracks, $\langle N_{\mathrm{tracks}}\rangle$,
in our data sets. The values are not corrected for pileup nor efficiency. Two
columns are shown for $\mathrm{PbPb}$ collisions. The left column shows the
values that are obtained from a minimum-bias data set, and correspond to
distributions shown in the left panel of Fig. 2. The right column shows the
values in muon-triggered data, and correspond to the location of points shown
in the performance plots of this paper. The values for $\Pp{}\mathrm{Pb}$ are
for illustrative purposes only; $\Pp{}\mathrm{Pb}$ results are binned directly
in $N_{\mathrm{tracks}}$. Details of the centrality conversion to $\langle
N_{\mathrm{tracks}}\rangle$ are provided in Appendix .11.  $\langle
N_{\mathrm{tracks}}\rangle$ System Centrality range minimum-bias muon-
triggered $\mathrm{PbPb}$, $\sqrtsNN=5.02\TeV$ 0–10% 2345 2397 10–20% 1721
1782 20–30% 1224 1271 30–40% 819 865 40–50% 503 536 50–60% 279 299 60–100% 33
112 $\Pp{}\mathrm{Pb}$, $\sqrtsNN=5.02\TeV$ 39 $\Pp{}\mathrm{Pb}$,
$\sqrtsNN=8.16\TeV$ 48 , $\sqrt{s}=5.02\TeV$ 19

The collisions involve only two participating nucleons. However, pileup (mean
number of collisions per bunch crossing) is significant in the environment due
to the high instantaneous luminosities, and this affects the shape and extent
of the $N_{\mathrm{tracks}}$ distribution. Even with pileup, the collisions
analyzed in this paper generate far fewer tracks than $\mathrm{PbPb}$ and
$\Pp{}\mathrm{Pb}$ collisions. The average number of reconstructed tracks in a
given event, $\langle N_{\mathrm{tracks}}\rangle$, in $\mathrm{PbPb}$
collisions at $\sqrtsNN=5.02\TeV$ corresponding to each centrality range is
tabulated in Table 0.4, and the procedure for obtaining the $\langle
N_{\mathrm{tracks}}\rangle$ is described in Appendix .11. The pileup for each
collision system is shown in Table 0.4, which also shows other important
characteristics of the collisions discussed in this paper. The pileup in
$\mathrm{PbPb}$ collisions is negligible, but must be taken into account in
collisions. The multiplicities quoted in Table 0.4 include pileup present in
the event. As seen in the table, the 0–10% most central $\mathrm{PbPb}$
collisions have an average number of tracks $\langle
N_{\mathrm{tracks}}\rangle_{0\text{--}10\%}=2345$. Taking the pileup into
account (3 collisions per bunch crossing, as noted in Table 0.4), a single
collision at $\sqrt{s}=5.02\TeV$ has an average multiplicity of $\langle
N_{\mathrm{tracks}}\rangle\approx 19/3\approx 6$. This makes the multiplicity
in each 0–10% central $\mathrm{PbPb}$ event roughly equivalent to
$2345/6\approx 400$ collisions occurring simultaneously. The equivalent
pileup, $\langle N_{\mathrm{tracks}}\rangle_{\mathrm{PbPb}}/\langle
N_{\mathrm{tracks}}\rangle_{\Pp{}\Pp{}}$, for a given $\mathrm{PbPb}$
collision centrality is plotted in the right panel of Fig. 2 as a red curve.
The calculation uses a simulation of collisions at the highest planned energy
for the LHC, 14, instead of 5.02. The increase in multiplicity due to this
greater energy results in a smaller equivalent pileup for the most central
$\mathrm{PbPb}$ events of ${\approx}300$. The expected pileup for collisions
in the high-luminosity phase is 200, shown as a black dashed line. Therefore,
the central $\mathrm{PbPb}$ collisions correspond to charged particle
multiplicities that exceed those expected even in the high-luminosity phase of
the LHC.

Characteristics of selected collisions collected during LHC Run 2. More
information on the corresponding luminosity measurements can be found in Refs.
[23, 24, 25, 26].  System $\Pp{}\mathrm{Pb}$ $\mathrm{PbPb}$ Year 2016 2017
2018 $\sqrtsNN$ () 8.16 5.02 5.02 Bunch spacing (ns) 100–200 25 75–150 Average
pileup 0.25 3 0.0004 Peak instantaneous luminosity () $8\ten{29}$ $1\ten{34}$
$6\ten{27}$ Integrated luminosity () $1.73\ten{2}$ $3.04\ten{5}$ 1.70 Peak
interaction rate (kHz) 23 44 50

A typical dimuon event in the heavy ion environment is displayed in Fig. 3.
The inner part of the detector reconstructs the large number of tracks
produced in the collision, shown as blue lines in the figure. The
electromagnetic and hadronic energy captured by the calorimeters is shown as
the semitransparent towers surrounding the inner tracks (the endcap
calorimeters are hidden for clarity). Most of the event activity is contained
within the inner detectors. Two muons are displayed as two long red tracks
reaching the outermost part of the detector, where the muon chambers are
located. The high occupancy in the inner detector might impact the performance
of the pixel detector and silicon strip tracker and reduce the muon tracking
efficiency.

Figure 3: Dimuon event from a $\mathrm{PbPb}$ collision in the CMS detector.
Particles reconstructed in the inner tracker are colored blue; two muons from
a boson candidate are colored red. See text for details.

Most tracks in $\mathrm{PbPb}$ collisions originate from a single primary
vertex, in contrast to high-luminosity collisions, where the tracks originate
from vertices with different positions along the beam line. The separation
between vertices in collisions is used to mitigate the effect of pileup on the
reconstruction. The single-vertex origin, together with higher multiplicity,
are factors that were addressed in the reconstruction of $\mathrm{PbPb}$
events.

###  0.4.1 Muon triggers in the heavy ion environment

An important component of all muon measurements involves an efficient
selection of collision events that contain muons. The real-time (online) muon
reconstruction algorithms in heavy ion collisions are generally identical to
those used in collisions. This is true for both the L1 trigger and the HLT.
There are three different types of muons defined in the CMS trigger system,
commonly referred to as L1, L2, and L3 muons. An L1 muon is identified by the
hardware-based L1 trigger system using solely the muon detectors. The L2 and
L3 muons are defined within the HLT: an L2 muon uses only information from the
muon detectors, while an L3 muon additionally matches the track from the muon
detectors to a track from the inner tracker. In each subsequent level, the
momentum resolution and purity (defined in Section 0.7.2) are improved, as
studied in Ref. [27].

The heavy ion runs are characterized by a much smaller instantaneous
luminosity and interaction rate when compared with 13 runs. The lower rate
allows the use of looser trigger requirements in $\mathrm{PbPb}$: lower
thresholds are used for muons, as well as lower quality requirements (, no
isolation criterion). Relaxing the requirements benefits the physics program
that is based on heavy ion muon data, focusing on open heavy flavor,
quarkonium, and electroweak boson production; particularly by increasing the
quarkonium acceptance in the low-part of the spectrum.

Two main triggers used in analyses involving muons are studied in this paper:
a high-single-muon trigger (designed for the measurements of electroweak
bosons and top quarks), and a low-double-muon trigger (for quarkonia and
mesons). Each of these triggers is described in more detail in Sections 0.7.2
(high-single-muon) and 0.8.2 (low-double-muon). To quantify the performance of
these triggers, we collected an independent sample of muons using monitoring
triggers. The monitoring triggers were single-muon HLT triggers with varying
thresholds. The single-muon triggers were used because any muon that satisfies
the trigger can be used as a tag, and any other muon in the event can be used
as a probe, since it has no trigger requirement imposed on it. Thus, the
monitoring triggers allowed us to collect a muon-rich sample that is minimally
biased for the purpose of the tag-and-probe analyses.

##  0.5 Extraction of efficiencies

We express the total muon efficiency, $\epsilon_{\mu}$, as the product of
three contributions: muon reconstruction efficiency
($\epsilon_{\text{reco\textbar track}}$), identification efficiency
($\epsilon_{\text{ID\textbar reco}}$), and triggering efficiency
($\epsilon_{\text{trig\textbar ID}}$), such that:

$\epsilon_{\mu}=\epsilon_{\text{reco\textbar
track}}\,\epsilon_{\text{ID\textbar reco}}\,\epsilon_{\text{trig\textbar ID}}$
(1)

Each efficiency is calculated as the fraction of muons that pass the specific
selection in Eq. (1). The numerator in one step (, muon passing the
identification requirements) is used as a denominator in the next step (,
trigger), ensuring the factorization of the total efficiency. Therefore, the
particular efficiencies are relative rather than absolute, showing the
efficiency with respect to the previous step. In the case of the first step,
$\epsilon_{\text{reco\textbar track}}$, the denominator consists of tracks
from the silicon tracker with minimal quality requirements, such that very few
genuine muons are removed from the denominator. In most studies, the
efficiency related to finding the tracks in the inner tracker,
$\epsilon_{\text{track}}$, is investigated separately, and for purposes of
this paper not included in Eq. (1). The variable $\epsilon_{\text{reco\textbar
track}}$ quantifies the reconstruction efficiency in the outer muon detectors
and the efficiency of matching tracks from the silicon tracker to tracks from
the muon detectors.

The muon efficiencies are calculated using the data-driven tag-and-probe
method (an example of its use in CMS is presented in Ref. [4]). First, we
select events where a resonance, such as the meson or the boson, was likely to
have decayed into a muon pair, producing a distinct peak in the dimuon
invariant mass distribution. In these events, the ‘tag’ muon is a muon that
satisfies strict requirements, such that its probability of being a genuine
muon is as high as possible. Once we have ‘tagged’ a muon with a high level of
confidence, we pair it with a second muon picked with much more relaxed
requirements, in order to ‘probe’ its properties. The requirements placed on
the probe muon define the denominator of the efficiency and are thus chosen in
accordance with the factorization in Eq. (1). With one tag muon and one probe
muon, we construct tag-probe pairs and obtain an invariant mass distribution.
The left panel of Fig. 4 shows an example of this invariant mass distribution,
in which we have reconstructed the boson mass peak to study the probe muons in
the kinematic region of $15<\pt<200\GeV$. Next, we apply to the probes the
selection criteria that we want to study. The subset of muons that pass these
selection criteria are labeled ‘passing probes’, which then define the
numerator in the efficiency. Those muons that fail the selection in question
are dubbed ‘failing probes’. We construct invariant mass distributions for
each, seen in the middle panel of Fig. 4 for passing probes, and in the right
panel for failing probes.

Figure 4: Example of a fit to the data in $\mathrm{PbPb}$ collisions. The
three panels show invariant mass distributions of the tag-and-probe pairs in
central 0–10% collisions fitted with the sum of signal and background
components. Left panel: total spectrum. Middle panel: spectrum for pairs where
the probe passed the muon identification selection. Right panel: spectrum for
muon pairs where the probe failed the selection. The vertical scale of the
failing probes is enhanced by a factor of 5 compared with the other panels for
visibility. The efficiency is retrieved directly from the simultaneous fit and
in this particular case is $\epsilon=0.968\pm 0.002$.

We perform a simultaneous fit to passing and failing distributions to measure
the efficiency. The simultaneous fit includes both a signal and a background
component for each invariant mass distribution. Only contributions from the
signal peaks are considered in the efficiency calculation. The signal yields
from the fit are denoted as $N_{\text{total}}$, $N_{\text{pass}}$, and
$N_{\text{fail}}$ for total, passing, and failing probes, respectively. Since
the sum of passing and failing probes equals the total of all probe muons
contained in the signal peak, the corresponding yields are related as follows:

$\displaystyle N_{\text{pass}}$ $\displaystyle=\epsilon\,N_{\text{total}}$ (2)
$\displaystyle N_{\text{fail}}$ $\displaystyle=(1-\epsilon)\,N_{\text{total}}$
(3)

We can obtain the efficiency, $\epsilon$, by appropriately including it as a
parameter in the simultaneous fit. The statistical uncertainty of the
efficiency is calculated directly in the fitting procedure, and takes into
account the uncertainties in the parameters that describe both the signal and
the background. The systematic uncertainty is obtained by choosing alternative
functions to describe the signal and background shapes and by varying the
fitting range. The systematic uncertainty is ${\approx}1\text{--}2\%$ for each
of the three efficiencies discussed in Eq. (1). All results presented in the
paper, including those of mass resolution and scale, show only statistical
uncertainties. For the MC simulations, the sizes of the statistical
uncertainties are generally smaller than the symbols.

Events with muon pairs from decays of boson and resonances are used to measure
the efficiencies. Because of the different mass of these particles, the decay
muons have different spectra, allowing for the study of efficiencies in
different kinematic regimes. In particular, muons from the boson peak are used
in the range 15–200, whereas the muons from the peak are used for studies in
the low-range 0.8–25. The efficiency corrections obtained from the tag-and-
probe method are then applied to each muon that enters a CMS analysis, under
the assumption that the muon detection efficiency is independent of the type
of parent particle that produces the decay muon. This assumption holds when
the two decay muons are sufficiently separated in $(\eta,\phi)$, where $\phi$
is the azimuthal angle in radians, such that the detection of the one muon is
independent of the detection of the other. With these considerations, the tag-
and-probe method is used in Section 0.6 for studying the muon reconstruction
efficiency, and in Sections 0.7 and 0.8 for identification and trigger
efficiencies using the and resonances, respectively.

##  0.6 Muon reconstruction

The algorithms for offline muon reconstruction for 13 data in Run 2 are
documented in Ref. [28]. The same algorithms are used for the reconstruction
of $\Pp{}\mathrm{Pb}$ data, as well as of reference data for heavy ions. The
algorithms used in 2018 $\mathrm{PbPb}$ data taking were based on the
algorithms with only minor customizations related primarily to the track
selection. The customized algorithms worked in the $\mathrm{PbPb}$
environment, despite the large increase in detector occupancy, because the
addition of a fourth layer to the pixel detector, as mentioned in Section
0.3.1. The fourth layer allowed the use of higher quality four-hit seeds for
the first tracking step, which significantly reduced the rate of spurious
combinations of hits and thus the processing time needed per event.

The CMS muon reconstruction starts by creating ‘tracker tracks’, which are
tracks reconstructed using only information from the inner tracker; and
‘standalone muons’, which are reconstructed only from the muon system. Two
approaches are then used [29, 4]:

  * •

Global muon reconstruction (outside-in): Reconstruction starts with a
standalone muon, which is then used to look for possible track matches in the
inner tracker. A ‘global muon’ is then obtained from fitting the combined hits
of the two track components.

  * •

Tracker muon reconstruction (inside-out): A complementary approach starts with
all tracker tracks with $\pt>0.5\GeV$ ($\pt>0.8\GeV$ for $\mathrm{PbPb}$
collisions) and momentum $p>2.5\GeV$. These are extrapolated to the muon
system, and if at least one matching muon segment is found, the track is
qualified as a ‘tracker muon’. The principal advantage of this approach is
that it reconstructs lower-momentum muons more efficiently than the outside-in
method. The drawback is that the algorithm starts with a much higher number of
tracks, as the occupancy in the tracker is much larger than in the muon
detectors.

Almost all of the muons (${\approx}99\%$) in the detector’s kinematic range
are successfully reconstructed as global or tracker muons [4].

Figure 5: Muon reconstruction efficiency (defined as the probability that a
given muon that produced a tracker track will be reconstructed as both a
global and a PF muon) plotted as a function of the number of tracks in ,
$\Pp{}\mathrm{Pb}$, and $\mathrm{PbPb}$ collisions. Open symbols are the MC
results corresponding to each data set. Lower panel shows the ratio between
data and MC simulation. Only statistical uncertainties are shown.

Reconstructed muons are used as input into the CMS particle-flow (PF)
algorithm [30], which combines information from all CMS subdetectors to
identify and reconstruct all individual particles for each event, including
electrons, photons, neutral hadrons, charged hadrons, and muons. For muons,
the PF algorithm applies a set of selection criteria to candidates
reconstructed with the standalone, global, or tracker muon algorithms [28].

Figure 5 shows the reconstruction efficiency $\epsilon_{\text{reco\textbar
track}}$ for muons with $\pt>15\GeV$ in , $\Pp{}\mathrm{Pb}$, and
$\mathrm{PbPb}$ collisions as a function of $N_{\mathrm{tracks}}$, obtained
from fits to the boson resonance. In this case, probes are defined as any
track reconstructed in the inner tracking system with a set of basic quality
requirements (at least one hit in the pixel detector and a signal in at least
six tracker layers). The passing probe satisfies both global-muon and PF-muon
requirements. The reconstruction has high efficiency (${\gtrsim}97\%$) across
the range of occupancies, although it exhibits a minor deterioration towards
the most central $\mathrm{PbPb}$ collisions, both in collision data (filled
points) and MC simulations (open points). We note that the track multiplicity
increases by about 2 orders of magnitude from to central $\mathrm{PbPb}$
collisions, while the efficiency drops only 2 percentage points. To obtain the
tracking efficiency in the inner tracker specifically, which can be added to
Eq. (1) to account for the tracking efficiency factor,
$\epsilon_{\text{track}}$, we can use a standalone muon as a probe and a
corresponding inner track as a passing probe. From this complementary approach
(not shown) we find that the inner tracking efficiency is high
(${\gtrsim}97\%$) even in the large multiplicity environment. The results
indicate that the CMS muon reconstruction is robust against large increases in
the inner detector occupancy.

##  0.7 Studies in the boson kinematic region

The dimuon decay of the resonance is used to study the efficiencies for muons
in the kinematic range $15<\pt<200\GeV$. The range is bounded by the
kinematics of the boson decay muons and by the number of such decays in the
studied data set. The efficiencies obtained from the boson peak region have
been used for several high-momentum analyses in $\Pp{}\mathrm{Pb}$ and
$\mathrm{PbPb}$ collisions, such as the studies of boson [31, 32], boson [33],
and production via $\cPqt\to\PWp\cPqb\to\Pgmp\cPgn\,\cPqb$ [34]. Table 0.7
summarizes the settings that are commonly used in CMS analyses with muons in
the boson kinematic region, and in the kinematic region (Section 0.8).
Following Eq. (1), muon identification efficiency in the boson kinematic range
starts with the set of muons that satisfy both the global and the PF muon
requirement ($\epsilon_{\text{ID\textbar reco}}$ denominator). This is done
because the set constitutes the passing probes in the previous step
($\epsilon_{\text{reco\textbar track}}$ numerator). Section 0.7.1 explains the
muon ID and Section 0.7.2 discusses the trigger.

Overview of muon identification and triggers used in the boson kinematic
region (, , , and analyses) and in the kinematic region (, and analyses). A
description of muon identification and triggers is given in the text.  Region
Collision system Muon ID Trigger /$\Pp{}\mathrm{Pb}$/$\mathrm{PbPb}$ Tight
Single muon; $\pt>12\GeV$; decision at HLT Hybrid-soft Double muon; no
requirement; decision at L1 $\Pp{}\mathrm{Pb}$ Soft Double muon; no
requirement; decision at L1 $\mathrm{PbPb}$ Hybrid-soft Double muon; , mass,
and $\Delta R$ requirements; decision at HLT

###  0.7.1 Muon identification efficiency

The majority of the CMS heavy ion results that are using muons in the
15–200range apply the ‘tight-muon’ selection, which is identical to that used
in analyses [4]. The selection is as follows:

  * •

Global muon and PF muon: The muon is reconstructed as both a global and a PF
muon, as outlined in Section 0.6.

  * •

Fit $\chi^{2}/\mathrm{dof}<10$ and number of muon hits ${>}0$: The global muon
fit must be of good quality to reduce the number of muons from nonprompt
hadron decays and the number of hadrons that traverse the inner detectors and
pass through the magnet, reaching the muon chambers (hadronic punch-through).
The selection places a limit on the fit $\chi^{2}/\mathrm{dof}$ (where dof
stands for degrees of freedom) and requires at least one muon detector hit
included in the fit.

  * •

Number of matched muon stations ${\geq}2$: At least two muon stations must be
matched to the muon to further reduce hadronic punch-through.

  * •

Number of pixel hits ${>}0$ and number of strip layers ${>}5$: To further
improve the muon track quality and provide a good measurement, minimal numbers
of pixel hits and strip layers with hits are required.

  * •

$D_{\mathrm{xy}}<0.2\unit{cm}$ and $D_{\mathrm{z}}<0.5\unit{cm}$: An upper
limit on the distance of closest approach between the primary vertex and the
muon track in the transverse plane, $D_{\mathrm{xy}}$, is used. This
requirement selects muons from prompt decays and reduces contributions from
cosmic rays and from nonprompt decays of hadrons. The corresponding limit in
the longitudinal direction $D_{\mathrm{z}}$ is larger, due to coarser
resolution of the tracker in the z coordinate.

Figure 6: Tight ID efficiency for global and PF muons as a function of the
number of tracks in , $\Pp{}\mathrm{Pb}$, and $\mathrm{PbPb}$ collisions. Open
symbols are the MC results corresponding to each data set. Lower panel shows
the ratio between data and MC simulation. Only statistical uncertainties are
shown.

Figure 7: Tight ID efficiency as a function of $\eta$ (left) and (right) in ,
$\Pp{}\mathrm{Pb}$, and $\mathrm{PbPb}$ collisions. The probe is both a global
muon and a PF muon. Open symbols are the MC results corresponding to each data
set. Lower panels show the ratio between data and MC simulation. Only
statistical uncertainties are shown.

Figure 6 displays the $N_{\mathrm{tracks}}$ dependence of the tight muon ID
efficiency. The efficiencies in , $\Pp{}\mathrm{Pb}$, and $\mathrm{PbPb}$
collisions are in good agreement with each other. The values are consistent
with a constant in the full range of $N_{\mathrm{tracks}}$ reached in and
$\Pp{}\mathrm{Pb}$ collisions, but exhibit a minor drop for the central
$\mathrm{PbPb}$ collisions. Even with track multiplicity over $2000$, the
efficiency only falls to about 96%. Figure 7 shows the kinematic dependence.
The efficiency in $\mathrm{PbPb}$ collisions is comparable with that from
lower multiplicity collisions in most of the studied kinematic range. However,
the $\mathrm{PbPb}$ efficiency is 1–2% lower in the region of $15<\pt\lesssim
40\GeV$ and in the barrel-endcap overlap region ($0.9<\abs{\eta}<1.2$). This
loss of efficiency is caused primarily by the tight ID requirement of at least
two matched muon stations. This requirement is met by fewer muons when the
occupancy is high, which is the reason for the slight efficiency decrease in
central collisions seen in Fig. 6. The two-matched-stations requirement is
also the primary cause for the small difference between the data and MC
results in both Figs. 6 and 7. Even though the MC description of the tight
muon identification slightly overestimates its efficiency, it captures all the
trends visible in data.

###  0.7.2 Muon trigger efficiency and purity

The tag-and-probe method is used to measure the efficiency of both trigger
levels (L1+HLT) combined. In all the collision systems, the HLT studied here
is a single-muon trigger with a requirement of $\pt>12\GeV$ at the HLT. The L1
seed was tailored to the specific conditions of each data-taking run. In
$\mathrm{PbPb}$ collisions, the seed has a threshold of $3\GeV$; in and
$\Pp{}\mathrm{Pb}$ collisions only L1 muons with $\pt>7\GeV$ were considered
by the HLT.

Figure 8: Trigger efficiency of tight muons as a function of the number of
tracks. The trigger requires a single muon with above $12\GeV$. The efficiency
is calculated for muons with $\pt>15\GeV$ to avoid threshold effects. Open
symbols are the MC results corresponding to each data set. Lower panel shows
the ratio between data and MC simulation. Only statistical uncertainties are
shown.

Figure 9: Trigger efficiency of tight muons as a function of $\eta$ (left) and
(right) in , $\Pp{}\mathrm{Pb}$, and $\mathrm{PbPb}$ collisions. Lower panels
show the ratio between data and MC simulation (MC points are omitted in the
upper panels for clarity). Only statistical uncertainties are shown.

Figure 8 shows the trigger efficiency as a function of $N_{\mathrm{tracks}}$.
The probes for the trigger efficiency (denominator) are the tight muons; the
passing probes (numerator) are those tight muons that also pass the trigger
requirement, following the factorization scheme of Eq. (1). The quantitative
comparison among all three data sets is affected by the upgrades to the
detector between each corresponding run. In addition, the 2018 $\mathrm{PbPb}$
data set is affected by the optimizations of the HLT that were done between
2017 and 2018 [27]. The combination of the detector upgrades and trigger
optimizations increased the overall efficiency in $\mathrm{PbPb}$ data when
compared with the and $\Pp{}\mathrm{Pb}$ data taken in previous years, as seen
by the efficiency shift from $\Pp{}\mathrm{Pb}$ to peripheral $\mathrm{PbPb}$
in the figure. With these changes taken into consideration, the efficiency is
effectively flat in the $N_{\mathrm{tracks}}$ range covered by and
$\Pp{}\mathrm{Pb}$ collisions, but begins to decline in more central
$\mathrm{PbPb}$ events after the number of tracks in the detector surpasses
several hundred. This drop is more pronounced for the trigger efficiency than
was observed for muon ID (Fig. 6), suggesting that the former is more
sensitive to detector occupancy than the latter. The MC simulations (open
symbols) describe the general trends in data fairly well, however the trigger
simulation for $\mathrm{PbPb}$ collisions overestimates the efficiency for
central events. Figure 9 shows the efficiency dependence on the muon
kinematics. We observe similar trends to those for the ID efficiency: the
trigger in $\mathrm{PbPb}$ collisions is less efficient in the intermediate
pseudorapidity region ($1.0<\abs{\eta}<1.6$). While the efficiency stays
mostly flat as a function of in and $\Pp{}\mathrm{Pb}$ events, it decreases
towards low in $\mathrm{PbPb}$ events. In both and $\Pp{}\mathrm{Pb}$
collisions the trigger efficiency reaches a plateau at ${\approx}95\%$ for
muons with $\pt>20\GeV$. The $\mathrm{PbPb}$ muons at 20have a trigger
efficiency of about 89%; the efficiency rises until $\pt\approx 40\GeV$, at
which point it reaches a plateau. From the data to MC simulation ratio, we can
infer that the simulation captures the trends fairly well, but overestimates
the efficiency in $\mathrm{PbPb}$ events for muons with $\pt\lesssim 70\GeV$.
The $\eta$-dependence of the data/simulation ratio for $\mathrm{PbPb}$
collisions is reasonably flat; the overall shift in the results of the
simulation is caused by the overestimation in the high-$N_{\mathrm{tracks}}$
low-region that was previously noted.

Figure 10: Purity for the L1, L2, and L3 trigger steps, compared among ,
$\Pp{}\mathrm{Pb}$, and $\mathrm{PbPb}$ collisions. The online muon must have
$\pt>15\GeV$ and the offline muon matched to it must pass the tight ID
selection and have $\abs{\eta}<2.4$. Details of the purity definition are
given in the text. Statistical uncertainties are smaller than the symbol
sizes.

The trigger purity, which quantifies the capability to avoid triggering on
particles that are not genuine prompt muons, is an important component for the
determination of the trigger rate. We explore the purity of the three online
muon definitions described in Section 0.4.1—L1, L2, and L3—to illustrate
improvements in purity at each subsequent trigger stage. We define the purity
as a ratio of the numbers of muons in the two samples. The sample in the
denominator is the set of online muons passing the relevant trigger step and
having an online $\pt>15\GeV$ requirement in order to focus on muons from the
boson decay. The numerator is meant to be genuine muons, which in our case are
the subset of online muons consisting of those matched geometrically within
$\Delta R\equiv\sqrt{\smash[b]{(\Delta\phi)^{2}+(\Delta\eta)^{2}}}<0.3$ (0.1
for L3) to an offline muon that must satisfy the tight ID requirement. Offline
muons must have $\abs{\eta}<2.4$ without an additional requirement. For our
study, muons were collected with dimuon triggers that utilize L1/L2/L3 trigger
selections. Although the exact values of the purity are dependent on the
details of the trigger that was used to collect the muon sample, the study
provides useful qualitative comparisons among the collision systems and
trigger steps. The results of the purity study are shown in Fig. 10. The
purity values in and $\Pp{}\mathrm{Pb}$ collisions are similar, whereas the
purity in $\mathrm{PbPb}$ collisions is lower. The figure also illustrates how
the purity improves in each successive and more complex trigger step, thus
reducing the rate. Although the rate is reduced, the HLT maintains a very high
efficiency, as seen earlier in Fig. 8. Backgrounds to the purity include
primarily muons from long-lived hadron decays. For the L3 trigger, the
background also includes muons where a track from the muon chambers is matched
to one inner track online, but offline it is matched to a different track.
This occurs more often in high multiplicity $\mathrm{PbPb}$ collisions.

##  0.8 Studies in the J/$\boldsymbol{\psi}$ kinematic region

The resonance is used to derive the efficiencies for low-muons. The acceptance
at low-is limited by the material in front of the muon stations [35] and by
the strength of the CMS magnetic field. Figure 11 details the acceptance
regions commonly used for the various collision systems. The green curves,
with higher-thresholds, pertain to analyses that use the most frequently
employed muon trigger in heavy ion collisions. The red curves, with lower
thresholds in , are used for muon selection by those analyses that use only
muon identification and do not use a dedicated muon trigger (, they use a jet
trigger or a minimum-bias trigger to collect their data samples). The
existence of the two distinct acceptance regions in each collision system
arises from the better low-efficiency of muon identification compared with
that of the muon trigger. The lower portion of Table 0.7 summarizes muon ID
selections and triggers commonly used in the kinematic region in the various
collision systems. The muon ID selections are explored in Section 0.8.1 and
the triggers are discussed in Section 0.8.2.

Figure 11: Regions of the CMS detector commonly used in the heavy ion muon
analyses for $\Pp{}\mathrm{Pb}$ (left) and $\mathrm{PbPb}$ (right) collisions.
For each panel, the combined reconstruction and identification efficiency for
simulated muons is plotted as a function of generated muon $\abs{\eta}$ and .
The lower-threshold curves (red) are for muon identification, and are used
only by those analyses that do not use a dedicated muon trigger. The higher-
threshold curves (green) are used by most analyses (those using the muon
trigger information).

###  0.8.1 Muon identification efficiency

The ‘soft muon’ ID [4, 28] denotes a set of selection criteria that is
commonly used within CMS for low-muons. The selection has been further
optimized for the heavy ion environment and the resulting selection is called
‘hybrid-soft muon’. The hybrid-soft muon ID is used in $\mathrm{PbPb}$
analyses, as well as for reference data, and consists of the following
requirements:

  * •

Tracker muon and global muon: The muon is reconstructed as both a tracker and
a global muon (defined in Section 0.6) to reduce the misidentification rate.

  * •

Number of pixel layers ${>}0$ and number of strip layers with hits ${>}5$: To
further improve muon-track quality, we impose a minimum on the numbers of
pixel layers and strip layers associated with the track.

  * •

$D_{\mathrm{xy}}<0.3\unit{cm}$ and $D_{\mathrm{z}}<20\unit{cm}$: An upper
limit on the distance of closest approach between the event vertex and the
muon track in the transverse plane, $D_{\mathrm{xy}}$, selects muons from
prompt decays, and reduces contributions from cosmic rays and from nonprompt
hadron decays. The variable $D_{\mathrm{z}}$ is the corresponding longitudinal
component of the distance of closest approach, and the requirement on
$D_{\mathrm{z}}$ is very loose.

The soft muon ID differs from the hybrid-soft ID by having a high-purity
requirement [36] applied to the tracker tracks, instead of requiring the muon
to be a global muon. The soft muon ID is used in $\Pp{}\mathrm{Pb}$ and other
collisions in general, as it allows reaching lower-than does hybrid-soft ID.
The soft muon ID is not ideal for $\mathrm{PbPb}$ collision data, since the
high-purity requirement has a low efficiency in the central collisions.

Figure 12 shows the pseudorapidity and dependence of the efficiency of the
hybrid-soft selection. The kinematic reach at low is better in the endcap
region (as seen in Fig. 11). The efficiency is comparable between the and
$\mathrm{PbPb}$ collisions, except for the region of $\abs{\eta}>2$ and
$\pt<5\GeV$, where the occupancy in the tracker is the highest. We found that
the drop occurs primarily in central $\mathrm{PbPb}$ events (not shown), in
which the high number of tracks in the tracker leads to a decrease in matching
efficiency with the standalone muons. This trend is well-described by the MC
simulations (open points), indicating that they successfully model this
behavior.

Figure 12: Hybrid-soft ID efficiency for global muons as a function of $\eta$
(left) and (right) in and $\mathrm{PbPb}$ collisions. The muons are restricted
in acceptance as shown by the red line in the right panel of Fig. 11. Lower
panels show the ratio between data and MC simulation. Only statistical
uncertainties are shown.

###  0.8.2 Muon triggers

The muon trigger used for quarkonia (, and mesons) studies in
$\Pp{}\mathrm{Pb}$ and for reference data sets is as loose as possible: a
double-muon trigger at L1. It simply required two tracks in the muon system,
with only very loose quality requirements and no explicit threshold. The
trigger decision is taken at L1 with no further filtering at the HLT stage. In
$\mathrm{PbPb}$ collisions, the rate of this double-muon trigger at L1 was
above the acceptable limit, so that additional quality requirements had to be
placed at the HLT level to reduce the rate. One of the two muons was required
to be an L2 muon; the other was required to be an L3 muon. This was done as a
compromise between reducing the rate and keeping the efficiency as high as
possible for low-quarkonia. Two main HLT paths were used: one for charmonia
and one for bottomonia. The charmonia path required the invariant mass of the
dimuon to be within 1 and 5, and required a $\Delta R<3.5$ between the two
muons to suppress uncorrelated muons. The bottomonia path required a minimum
mass of 7to cover the bottomonia mass region around 9.4–10.5, and minimum
requirements of 2.0for the L2 muon and 2.5for the L3 muon. Both triggers
required that the muons in the pair have opposite charge. To complement the
two main HLT trigger paths, centrality-gated triggers were used to select
peripheral events, which have the smallest occupancy in $\mathrm{PbPb}$
collisions. Therefore, the data-stream size was able to be kept sufficiently
low to satisfy bandwidth and storage limitations. The differences among the
triggers used in , $\Pp{}\mathrm{Pb}$, and $\mathrm{PbPb}$ collisions preclude
meaningful comparisons of the trigger efficiency.

##  0.9 Mass resolution and scale

The masses of the boson and the meson are well known from other results [37].
Measuring these particles via their dimuon decay can provide additional
information about the muon reconstruction. The dimuon mass resolution provides
a suitable window into the precision of the momentum measurement; the mean
value of the mass peak is sensitive to the momentum scale of the detector. The
momentum calibration procedure for our data sets is described in Ref. [21].
The mass resolution is obtained from the width of the fitted dimuon resonance.
We apply the same selection as we did for the trigger-efficiency evaluation,
described in Sections 0.7 and 0.8 for each corresponding resonance. To study
the absolute scale of the momentum calibration, we plot the reconstructed mass
of the resonance obtained from the fit, $m_{\textrm{fit}}$, scaled by the
world-average mass obtained by the Particle Data Group (PDG),
$m_{\textrm{PDG}}$ [37]. Since measurements of particle masses using heavy ion
data are rare, a small bias in the momentum scale has had little effect on the
success of the overall program. In contrast, heavy-ion measurements are more
sensitive to momentum resolution. For example, a degraded resolution would
impact the signal-to-background ratio for resonances.

Figure 13: Mass resolution at the boson peak (left) and the peak (right) as a
function of the number of tracks from MC simulations (open points) and
collision data (filled points) in , $\Pp{}\mathrm{Pb}$, and $\mathrm{PbPb}$
collisions. The mass resolution is scaled by the world-average mass
$m_{\textrm{PDG}}$. Lower panels show the ratio between data and MC
simulation. Only statistical uncertainties are shown.

For the boson, the signal peak is described by a Breit–Wigner (BW) function
convolved with a Crystal Ball (CB) function. The CB function consists of a
Gaussian peak with a power-law tail grafted onto the lower-valued side, such
that the function and its derivative are continuous [38]. In this case, the
reconstructed mass, $m_{\textrm{fit}}$, is a common parameter between the CB
and the BW functions that is both the mean of the Gaussian core of the CB
function and the location parameter of the BW function. The width of the BW
function is fixed to the natural width of the boson ($\Gamma_{Z}\approx
2.5\GeV$). The Gaussian width of the CB function, $\sigma_{\textrm{CB}}$, is a
free parameter in the fit and summarizes our information about the muon
resolution in the detector. We plot the parameter $\sigma_{\textrm{CB}}$
divided by the boson PDG mass, $m_{\textrm{PDG}}$.

Figure 14: Mass scale at the boson peak (left) and the peak (right) as a
function of the number of tracks from MC simulations (open points) and
collision data (filled points) in , $\Pp{}\mathrm{Pb}$, and $\mathrm{PbPb}$
collisions, calculated as the measured mass $m_{\textrm{fit}}$ divided by the
world-average mass $m_{\textrm{PDG}}$. Lower panels show the ratio between
data and MC simulation. Only statistical uncertainties are shown.

For the meson, the BW function is not used because the natural width is
negligible. Instead, the signal is described by a sum of a CB and a Gaussian
function. In this case, the reconstructed mass, $m_{\textrm{fit}}$, is a mean
common to the Gaussian core of the CB and to the additional Gaussian function.
The two Gaussian width parameters of the signal function are kept distinct to
account for the varying resolution in the barrel and endcap regions of the
detector. To estimate the mass resolution of the peak, we use the average of
these two Gaussian widths, weighted by the amount of signal contained in each
of the two functions. The extracted width is then divided by the corresponding
$m_{\textrm{PDG}}$.

Figure 15: Mass resolution at the boson peak (left) and peak (right) as a
function of $\abs{y^{\mu\mu}}$ from MC simulations (open points) and collision
data (filled points) in , $\Pp{}\mathrm{Pb}$, and $\mathrm{PbPb}$ collisions.
The mass resolution is scaled by the world-average mass $m_{\textrm{PDG}}$.
Lower panels show the ratio between data and MC simulation. Only statistical
uncertainties are shown.

Figure 16: Mass scale at the boson peak (left) and the peak (right) as a
function of $\abs{y^{\mu\mu}}$ from MC simulations (open points) and real data
(filled points) in , $\Pp{}\mathrm{Pb}$, and $\mathrm{PbPb}$ collisions,
calculated as the measured mass $m_{\textrm{fit}}$ divided by the PDG mass
$m_{\textrm{PDG}}$. Lower panels show the ratio between data and MC
simulation. Only statistical uncertainties are shown.

The estimated mass resolution is plotted in Fig. 13 as a function of
$N_{\mathrm{tracks}}$ for the boson (left) and the meson (right). The
resolution in both cases is consistent with a constant value, which indicates
that the resolution is not affected by the level of occupancy in the tracker.
The estimated mass scale is plotted in Fig. 14 as a function of
$N_{\mathrm{tracks}}$ for the boson (left) and the meson (right). The
reconstructed mass is lower than the true mass for both particles, but this
scaling is consistent across the different collision systems and for all
values of $N_{\mathrm{tracks}}$, indicating that the momentum scale is
accurate to approximately 0.3% and is unaffected by the level of occupancy in
the tracker or the data-taking year. A change in the mass scale of this order
does not generally affect heavy ion measurements, but there are cases (, Ref.
[39]) in which such a change can be indicative of interesting physics.

The mass resolution and mass scale versus dimuon rapidity, $y^{\mu\mu}$, are
shown in Figs. 15 and 16, respectively. The resolution varies significantly
with rapidity due to the varying angle between the magnetic field $\vec{B}$
and the particle velocity $\vec{v}$. At forward rapidity, where $\vec{v}$ and
$\vec{B}$ are almost collinear, a smaller Lorentz force reduces the magnetic
bending. The resulting smaller sagitta leads to a greater uncertainty in the
momentum measurement. Since the resolution is not constant, for signal
description we use the two distinct Gaussian parameters mentioned earlier. The
mass scale decreases slightly with rapidity at the peak in data but not in MC
simulations. At the boson peak, the corresponding decrease is more prominent
and is present in both data and MC simulations. In most rapidity bins, the
mass scale values tend to be slightly reduced (${\approx}0.2\%$) in data when
compared with the MC results. The results are consistent among the ,
$\Pp{}\mathrm{Pb}$, and $\mathrm{PbPb}$ collision systems and display the same
trends.

Figure 17: Mass resolution (left) and mass scale (right) versus the muon
pseudorapidity $\abs{\eta^{\mu}}$ at the peak in collisions. The values are
scaled by the world-average mass $m^{\JPsi}_{\textrm{PDG}}$. The filled points
are collision data and the open points are MC simulations. Our results (black
diamonds) are compared with measurements from a previous analysis done at
$\sqrt{s}=7\TeV$ (blue crosses) [40]. Only statistical uncertainties are
shown. See details in text.

Previous estimates of mass resolution and mass scale were carried out on a
data set at a center-of-mass energy $\sqrt{s}=7\TeV$ in 2010 [40]. A
comparison of these results with our data is shown in Fig. 17 as a function of
muon pseudorapidity $\abs{\eta^{\mu}}$. In order to plot the mass resolution
and scale versus pseudorapidity of a single muon, each value of the dimuon
mass is entered twice in the distribution used to extract these results: once
with the pseudorapidity of the first muon, and then with the pseudorapidity of
the second muon. The results of Ref. [40] were averaged over the forward and
backward pseudorapidity bins. The comparison shows that the muon momentum
scale and resolution have been remarkably stable throughout the lifetime of
the experiment, indicating that the differences in center-of-mass energy,
collision system, accelerator/detector conditions, and kinematic coverage have
been successfully addressed by the CMS calibration and reconstruction
frameworks.

##  0.10 Summary

We have presented efficiencies of muon reconstruction, identification, and
triggering, as well as measurements of the dimuon mass scale and resolution of
the CMS detector. The efficiencies were estimated using the data-driven tag-
and-probe technique. Mass scale and resolution were derived through fits to
the invariant mass spectra of and boson resonances. We have extended previous
studies of the muon performance in collisions to the heavy ion environment
using $\mathrm{PbPb}$ data at $\sqrtsNN=5.02\TeV$ and $\Pp{}\mathrm{Pb}$ data
at $\sqrtsNN=8.16\TeV$. All the results were also measured in data at
$\sqrt{s}=5.02\TeV$ for comparison across all collision systems as a function
of the charged particle multiplicity, $N_{\mathrm{tracks}}$.

The efficiencies are high (typically above 90%) in all cases, even at
extremely high occupancy. In the low-region, the muon-identification
efficiency is comparable between and $\mathrm{PbPb}$ collisions, except in the
region of highest occupancy at very low (${<}5\GeV$) and forward
pseudorapidity ($\abs{\eta}>2$). This drop in efficiency is expected because
the high number of tracks in the inner parts of the detector complicates the
matching of tracks between the muon chambers and the tracker. In the high-
region, we observe a slight drop (1–2%) in the muon-identification efficiency
in the most central $\mathrm{PbPb}$ events at multiplicities that are
unattainable in $\Pp{}\mathrm{Pb}$ or events. A slight decrease
(${\approx}3\%$) in the reconstruction efficiency at high occupancies is also
observed. Additionally, the trigger efficiency decreases in the most central
$\mathrm{PbPb}$ events. A relative reduction of ${\approx}8\%$ in the trigger
efficiency occurs between the lowest and highest $N_{\mathrm{tracks}}$ bins in
$\mathrm{PbPb}$ collisions. This reduction is more pronounced than the
corresponding decrease in the muon identification and reconstruction
efficiencies, suggesting that the CMS single-muon trigger is more sensitive to
detector occupancy. In most cases, the efficiencies calculated using Monte
Carlo simulations capture the trends seen in data, indicating that the main
features are contained in the detector simulation. In a few instances, the MC
efficiencies overestimate those obtained from collision data (by up to 4
percentage points), highlighting the need for independent techniques of
efficiency estimation based directly on collision data.

The excellent muon performance of the CMS detector has made possible a robust
muon and dimuon program in the heavy ion environment, leading to many muon-
based measurements, where some examples include heavy quarkonia, heavy-flavor
mesons [41, 3, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51]; and electroweak bosons
[33, 52, 31, 53, 32].

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing centers and personnel of the Worldwide LHC Computing Grid and other
centers for delivering so effectively the computing infrastructure essential
to our analyses. Finally, we acknowledge the enduring support for the
construction and operation of the LHC, the CMS detector, and the supporting
computing infrastructure provided by the following funding agencies: SC
(Armenia), BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES,
FAPERJ, FAPERGS, and FAPESP (Brazil); MES and BNSF (Bulgaria); CERN; CAS,
MoST, and NSFC (China); MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF
(Cyprus); SENESCYT (Ecuador); ERC PRG, RVTT3 and MoER TK202 (Estonia); Academy
of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); SRNSF
(Georgia); BMBF, DFG, and HGF (Germany); GSRI (Greece); NKFIH (Hungary); DAE
and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF
(Republic of Korea); MES (Latvia); LMTLT (Lithuania); MOE and UM (Malaysia);
BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro);
MBIE (New Zealand); PAEC (Pakistan); MES and NSC (Poland); FCT (Portugal);
MESTD (Serbia); MCIN/AEI and PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding
Agencies (Switzerland); MST (Taipei); MHESI and NSTDA (Thailand); TUBITAK and
TENMAK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA).
Individuals have received support from the Marie-Curie program and the
European Research Council and Horizon 2020 Grant, contract Nos. 675440,
724704, 752730, 758316, 765710, 824093, 101115353, 101002207, and COST Action
CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan
Foundation; the Alexander von Humboldt Foundation; the Science Committee,
project no. 22rl-037 (Armenia); the Belgian Federal Science Policy Office; the
Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture
(FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie
(IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the “Excellence of
Science – EOS” – be.h project n. 30820817; the Beijing Municipal Science &
Technology Commission, No. Z191100007219010 and Fundamental Research Funds for
the Central Universities (China); the Ministry of Education, Youth and Sports
(MEYS) of the Czech Republic; the Shota Rustaveli National Science Foundation,
grant FR-22-985 (Georgia); the Deutsche Forschungsgemeinschaft (DFG), under
Germany’s Excellence Strategy – EXC 2121 “Quantum Universe” – 390833306, and
under project number 400140256 - GRK2497; the Hellenic Foundation for Research
and Innovation (HFRI), Project Number 2288 (Greece); the Hungarian Academy of
Sciences, the New National Excellence Program - ÚNKP, the NKFIH research
grants K 131991, K 133046, K 138136, K 143460, K 143477, K 146913, K 146914, K
147048, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64 (Hungary); the Council
of Science and Industrial Research, India; ICSC – National Research Center for
High Performance Computing, Big Data and Quantum Computing and FAIR – Future
Artificial Intelligence Research, funded by the EU NexGeneration program
(Italy); the Latvian Council of Science; the Ministry of Education and
Science, project no. 2022/WK/14, and the National Science Center, contracts
Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552 (Poland); the Fundação para a
Ciência e a Tecnologia, grant CEECIND/01334/2018 (Portugal); the National
Priorities Research Program by Qatar National Research Fund;
MCIN/AEI/10.13039/501100011033, ERDF “a way of making Europe”, and the
Programa Estatal de Fomento de la Investigación Científica y Técnica de
Excelencia María de Maeztu, grant MDM-2017-0765 and Programa Severo Ochoa del
Principado de Asturias (Spain); the Chulalongkorn Academic into Its 2nd
Century Project Advancement Project, and the National Science, Research and
Innovation Fund via the Program Management Unit for Human Resources &
Institutional Development, Research and Innovation, grant B37G660013
(Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro
Corporation; the Welch Foundation, contract C-1845; and the Weston Havens
Foundation (USA).

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##  .11 Converting centrality to number of tracks

It is common in analyses of $\mathrm{PbPb}$ collisions to study observables in
bins of centrality, whereas in and $\Pp{}\mathrm{Pb}$ collisions it is common
to do so in bins of $N_{\mathrm{tracks}}$. In order to compare the existing
efficiency results in all three collision systems as a function of a common
event activity variable, we converted the $\mathrm{PbPb}$ centrality bins to
corresponding average values of $N_{\mathrm{tracks}}$, which we label $\langle
N_{\mathrm{tracks}}\rangle$ in tables and figures. The $N_{\mathrm{tracks}}$
variable was chosen because it is suitable for studying occupancy-related
effects.

The conversion was done by plotting the distribution of $N_{\mathrm{tracks}}$
in the $\mathrm{PbPb}$ data set for each centrality bin and taking the mean of
this distribution as the value of $N_{\mathrm{tracks}}$ for that efficiency
datum point. Examples of the conversion procedure are shown in Fig. .11.1. The
plot on the left was generated from a minimum-bias data set, which required
that events must have a coincidence signal in both the positive-$z$ and
negative-$z$ side of the HF. The plot on the right was from a muon-triggered
data set, which required that events must have at least two muons passing
various selection criteria common to dimuon analyses. Requiring two muons
biases the average $N_{\mathrm{tracks}}$ toward higher values. The triggered
data are used to translate centrality bins to $N_{\mathrm{tracks}}$ because
they describe most accurately the occupancy conditions of the detector for our
data samples.

11.1.Figure: Distributions of $N_{\mathrm{tracks}}$ in the $\mathrm{PbPb}$
collisions for various ranges of centrality in a minimum-bias (left) and a
muon-triggered (right) data set.

##  .12 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia A. Hayrapetyan, A.
Tumasyan10009-0000-0684-6742

Institut für Hochenergiephysik, Vienna, Austria W. Adam0000-0001-9099-4341,
J.W. Andrejkovic, T. Bergauer0000-0002-5786-0293, S.
Chatterjee0000-0003-2660-0349, K. Damanakis0000-0001-5389-2872, M.
Dragicevic0000-0003-1967-6783, P.S. Hussain0000-0002-4825-5278, M.
Jeitler20000-0002-5141-9560, N. Krammer0000-0002-0548-0985, A.
Li0000-0002-4547-116X, D. Liko0000-0002-3380-473X, I.
Mikulec0000-0003-0385-2746, J. Schieck20000-0002-1058-8093, R.
Schöfbeck0000-0002-2332-8784, D. Schwarz0000-0002-3821-7331, M.
Sonawane0000-0003-0510-7010, S. Templ0000-0003-3137-5692, W.
Waltenberger0000-0002-6215-7228, C.-E. Wulz20000-0001-9226-5812

Universiteit Antwerpen, Antwerpen, Belgium M.R. Darwish30000-0003-2894-2377,
T. Janssen0000-0002-3998-4081, P. Van Mechelen0000-0002-8731-9051

Vrije Universiteit Brussel, Brussel, Belgium E.S. Bols0000-0002-8564-8732, J.
D’Hondt0000-0002-9598-6241, S. Dansana0000-0002-7752-7471, A. De
Moor0000-0001-5964-1935, M. Delcourt0000-0001-8206-1787, H. El
Faham0000-0001-8894-2390, S. Lowette0000-0003-3984-9987, I.
Makarenko0000-0002-8553-4508, D. Müller0000-0002-1752-4527, A.R.
Sahasransu0000-0003-1505-1743, S. Tavernier0000-0002-6792-9522, M.
Tytgat40000-0002-3990-2074, S. Van Putte0000-0003-1559-3606, D.
Vannerom0000-0002-2747-5095

Université Libre de Bruxelles, Bruxelles, Belgium B.
Clerbaux0000-0001-8547-8211, G. De Lentdecker0000-0001-5124-7693, L.
Favart0000-0003-1645-7454, D. Hohov0000-0002-4760-1597, J.
Jaramillo0000-0003-3885-6608, A. Khalilzadeh, K. Lee0000-0003-0808-4184, M.
Mahdavikhorrami0000-0002-8265-3595, A. Malara0000-0001-8645-9282, S.
Paredes0000-0001-8487-9603, L. Pétré0009-0000-7979-5771, N. Postiau, L.
Thomas0000-0002-2756-3853, M. Vanden Bemden0009-0000-7725-7945, C. Vander
Velde0000-0003-3392-7294, P. Vanlaer0000-0002-7931-4496

Ghent University, Ghent, Belgium M. De Coen0000-0002-5854-7442, D.
Dobur0000-0003-0012-4866, Y. Hong0000-0003-4752-2458, J.
Knolle0000-0002-4781-5704, L. Lambrecht0000-0001-9108-1560, G. Mestdach, C.
Rendón, A. Samalan, K. Skovpen0000-0002-1160-0621, N. Van Den
Bossche0000-0003-2973-4991, J. van der Linden0000-0002-7174-781X, L.
Wezenbeek0000-0001-6952-891X

Université Catholique de Louvain, Louvain-la-Neuve, Belgium A.
Benecke0000-0003-0252-3609, G. Bruno0000-0001-8857-8197, C.
Caputo0000-0001-7522-4808, C. Delaere0000-0001-8707-6021, I.S.
Donertas0000-0001-7485-412X, A. Giammanco0000-0001-9640-8294, K.
Jaffel0000-0001-7419-4248, Sa. Jain0000-0001-5078-3689, V. Lemaitre, J.
Lidrych0000-0003-1439-0196, P. Mastrapasqua0000-0002-2043-2367, K.
Mondal0000-0001-5967-1245, T.T. Tran0000-0003-3060-350X, S.
Wertz0000-0002-8645-3670

Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil G.A.
Alves0000-0002-8369-1446, E. Coelho0000-0001-6114-9907, C.
Hensel0000-0001-8874-7624, T. Menezes De Oliveira0009-0009-4729-8354, A.
Moraes0000-0002-5157-5686, P. Rebello Teles0000-0001-9029-8506, M. Soeiro

Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil W.L. Aldá
Júnior0000-0001-5855-9817, M. Alves Gallo Pereira0000-0003-4296-7028, M.
Barroso Ferreira Filho0000-0003-3904-0571, H. Brandao
Malbouisson0000-0002-1326-318X, W. Carvalho0000-0003-0738-6615, J.
Chinellato5, E.M. Da Costa0000-0002-5016-6434, G.G. Da
Silveira60000-0003-3514-7056, D. De Jesus Damiao0000-0002-3769-1680, S.
Fonseca De Souza0000-0001-7830-0837, R. Gomes De Souza, J.
Martins70000-0002-2120-2782, C. Mora Herrera0000-0003-3915-3170, K. Mota
Amarilo0000-0003-1707-3348, L. Mundim0000-0001-9964-7805, H.
Nogima0000-0001-7705-1066, A. Santoro0000-0002-0568-665X, A.
Sznajder0000-0001-6998-1108, M. Thiel0000-0001-7139-7963, A. Vilela
Pereira0000-0003-3177-4626

Universidade Estadual Paulista, Universidade Federal do ABC, São Paulo, Brazil
C.A. Bernardes60000-0001-5790-9563, L. Calligaris0000-0002-9951-9448, T.R.
Fernandez Perez Tomei0000-0002-1809-5226, E.M. Gregores0000-0003-0205-1672,
P.G. Mercadante0000-0001-8333-4302, S.F. Novaes0000-0003-0471-8549, B.
Orzari0000-0003-4232-4743, Sandra S. Padula0000-0003-3071-0559

Institute for Nuclear Research and Nuclear Energy, Bulgarian Academy of
Sciences, Sofia, Bulgaria A. Aleksandrov0000-0001-6934-2541, G.
Antchev0000-0003-3210-5037, R. Hadjiiska0000-0003-1824-1737, P.
Iaydjiev0000-0001-6330-0607, M. Misheva0000-0003-4854-5301, M.
Shopova0000-0001-6664-2493, G. Sultanov0000-0002-8030-3866

University of Sofia, Sofia, Bulgaria A. Dimitrov0000-0003-2899-701X, L.
Litov0000-0002-8511-6883, B. Pavlov0000-0003-3635-0646, P.
Petkov0000-0002-0420-9480, A. Petrov0009-0003-8899-1514, E.
Shumka0000-0002-0104-2574

Instituto De Alta Investigación, Universidad de Tarapacá, Casilla 7 D, Arica,
Chile S. Keshri0000-0003-3280-2350, S. Thakur0000-0002-1647-0360

Beihang University, Beijing, China T. Cheng0000-0003-2954-9315, Q. Guo, T.
Javaid0009-0007-2757-4054, L. Yuan0000-0002-6719-5397

Department of Physics, Tsinghua University, Beijing, China Z.
Hu0000-0001-8209-4343, J. Liu, K. Yi8,90000-0002-2459-1824

Institute of High Energy Physics, Beijing, China E. Chapon0000-0001-6968-9828,
G.M. Chen100000-0002-2629-5420, H.S. Chen100000-0001-8672-8227, M.
Chen100000-0003-0489-9669, F. Iemmi0000-0001-5911-4051, C.H. Jiang, A.
Kapoor110000-0002-1844-1504, H. Liao0000-0002-0124-6999, Z.-A.
Liu120000-0002-2896-1386, R. Sharma130000-0003-1181-1426, J.N. Song12, J.
Tao0000-0003-2006-3490, C. Wang10, J. Wang0000-0002-3103-1083, Z. Wang10, H.
Zhang0000-0001-8843-5209

State Key Laboratory of Nuclear Physics and Technology, Peking University,
Beijing, China A. Agapitos0000-0002-8953-1232, Y. Ban0000-0002-1912-0374, A.
Levin0000-0001-9565-4186, C. Li0000-0002-6339-8154, Q. Li0000-0002-8290-0517,
Y. Mao, S.J. Qian0000-0002-0630-481X, X. Sun0000-0003-4409-4574, D.
Wang0000-0002-9013-1199, H. Yang, L. Zhang0000-0001-7947-9007, C.
Zhou0000-0001-5904-7258

Sun Yat-Sen University, Guangzhou, China Z. You0000-0001-8324-3291

University of Science and Technology of China, Hefei, China N.
Lu0000-0002-2631-6770

Nanjing Normal University, Nanjing, China G. Bauer14

Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam
Application (MOE) - Fudan University, Shanghai, China X.
Gao150000-0001-7205-2318, D. Leggat, H. Okawa0000-0002-2548-6567, Y.
Zhang0000-0002-4554-2554

Zhejiang University, Hangzhou, Zhejiang, China Z. Lin0000-0003-1812-3474, C.
Lu0000-0002-7421-0313, M. Xiao0000-0001-9628-9336

Universidad de Los Andes, Bogota, Colombia C. Avila0000-0002-5610-2693, D.A.
Barbosa Trujillo, A. Cabrera0000-0002-0486-6296, C. Florez0000-0002-3222-0249,
J. Fraga0000-0002-5137-8543, J.A. Reyes Vega

Universidad de Antioquia, Medellin, Colombia J. Mejia
Guisao0000-0002-1153-816X, F. Ramirez0000-0002-7178-0484, M.
Rodriguez0000-0002-9480-213X, J.D. Ruiz Alvarez0000-0002-3306-0363

University of Split, Faculty of Electrical Engineering, Mechanical Engineering
and Naval Architecture, Split, Croatia D. Giljanovic0009-0005-6792-6881, N.
Godinovic0000-0002-4674-9450, D. Lelas0000-0002-8269-5760, A.
Sculac0000-0001-7938-7559

University of Split, Faculty of Science, Split, Croatia M.
Kovac0000-0002-2391-4599, T. Sculac0000-0002-9578-4105

Institute Rudjer Boskovic, Zagreb, Croatia P. Bargassa0000-0001-8612-3332, V.
Brigljevic0000-0001-5847-0062, B.K. Chitroda0000-0002-0220-8441, D.
Ferencek0000-0001-9116-1202, S. Mishra0000-0002-3510-4833, A.
Starodumov160000-0001-9570-9255, T. Susa0000-0001-7430-2552

University of Cyprus, Nicosia, Cyprus A. Attikis0000-0002-4443-3794, K.
Christoforou0000-0003-2205-1100, S. Konstantinou0000-0003-0408-7636, J.
Mousa0000-0002-2978-2718, C. Nicolaou, F. Ptochos0000-0002-3432-3452, P.A.
Razis0000-0002-4855-0162, H. Rykaczewski, H. Saka0000-0001-7616-2573, A.
Stepennov0000-0001-7747-6582

Charles University, Prague, Czech Republic M. Finger0000-0002-7828-9970, M.
Finger Jr.0000-0003-3155-2484, A. Kveton0000-0001-8197-1914

Escuela Politecnica Nacional, Quito, Ecuador E. Ayala0000-0002-0363-9198

Universidad San Francisco de Quito, Quito, Ecuador E. Carrera
Jarrin0000-0002-0857-8507

Academy of Scientific Research and Technology of the Arab Republic of Egypt,
Egyptian Network of High Energy Physics, Cairo, Egypt S. Elgammal17, A.
Ellithi Kamel18

Center for High Energy Physics (CHEP-FU), Fayoum University, El-Fayoum, Egypt
M. Abdullah Al-Mashad0000-0002-7322-3374, M.A. Mahmoud0000-0001-8692-5458

National Institute of Chemical Physics and Biophysics, Tallinn, Estonia R.K.
Dewanjee190000-0001-6645-6244, K. Ehataht0000-0002-2387-4777, M. Kadastik, T.
Lange0000-0001-6242-7331, S. Nandan0000-0002-9380-8919, C.
Nielsen0000-0002-3532-8132, J. Pata0000-0002-5191-5759, M.
Raidal0000-0001-7040-9491, L. Tani0000-0002-6552-7255, C.
Veelken0000-0002-3364-916X

Department of Physics, University of Helsinki, Helsinki, Finland H.
Kirschenmann0000-0001-7369-2536, K. Osterberg0000-0003-4807-0414, M.
Voutilainen0000-0002-5200-6477

Helsinki Institute of Physics, Helsinki, Finland S.
Bharthuar0000-0001-5871-9622, E. Brücken0000-0001-6066-8756, F.
Garcia0000-0002-4023-7964, J. Havukainen0000-0003-2898-6900, K.T.S.
Kallonen0000-0001-9769-7163, R. Kinnunen, T. Lampén0000-0002-8398-4249, K.
Lassila-Perini0000-0002-5502-1795, S. Lehti0000-0003-1370-5598, T.
Lindén0009-0002-4847-8882, M. Lotti, L. Martikainen0000-0003-1609-3515, M.
Myllymäki0000-0003-0510-3810, M.m. Rantanen0000-0002-6764-0016, H.
Siikonen0000-0003-2039-5874, E. Tuominen0000-0002-7073-7767, J.
Tuominiemi0000-0003-0386-8633

Lappeenranta-Lahti University of Technology, Lappeenranta, Finland P.
Luukka0000-0003-2340-4641, H. Petrow0000-0002-1133-5485, T.
Tuuva${}^{\textrm{\textdagger}}$

IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France M.
Besancon0000-0003-3278-3671, F. Couderc0000-0003-2040-4099, M.
Dejardin0009-0008-2784-615X, D. Denegri, J.L. Faure, F.
Ferri0000-0002-9860-101X, S. Ganjour0000-0003-3090-9744, P.
Gras0000-0002-3932-5967, G. Hamel de Monchenault0000-0002-3872-3592, V.
Lohezic0009-0008-7976-851X, J. Malcles0000-0002-5388-5565, J. Rander, A.
Rosowsky0000-0001-7803-6650, M.Ö. Sahin0000-0001-6402-4050, A. Savoy-
Navarro200000-0002-9481-5168, P. Simkina0000-0002-9813-372X, M.
Titov0000-0002-1119-6614, M. Tornago0000-0001-6768-1056

Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut
Polytechnique de Paris, Palaiseau, France C. Baldenegro
Barrera0000-0002-6033-8885, F. Beaudette0000-0002-1194-8556, A. Buchot
Perraguin0000-0002-8597-647X, P. Busson0000-0001-6027-4511, A.
Cappati0000-0003-4386-0564, C. Charlot0000-0002-4087-8155, F.
Damas0000-0001-6793-4359, O. Davignon0000-0001-8710-992X, A. De
Wit0000-0002-5291-1661, G. Falmagne0000-0002-6762-3937, B.A. Fontana Santos
Alves0000-0001-9752-0624, S. Ghosh0009-0006-5692-5688, A.
Gilbert0000-0001-7560-5790, R. Granier de Cassagnac0000-0002-1275-7292, A.
Hakimi0009-0008-2093-8131, B. Harikrishnan0000-0003-0174-4020, L.
Kalipoliti0000-0002-5705-5059, G. Liu0000-0001-7002-0937, J.
Motta0000-0003-0985-913X, M. Nguyen0000-0001-7305-7102, C.
Ochando0000-0002-3836-1173, L. Portales0000-0002-9860-9185, R.
Salerno0000-0003-3735-2707, J.B. Sauvan0000-0001-5187-3571, Y.
Sirois0000-0001-5381-4807, A. Tarabini0000-0001-7098-5317, E.
Vernazza0000-0003-4957-2782, A. Zabi0000-0002-7214-0673, A.
Zghiche0000-0002-1178-1450

Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France J.-L.
Agram210000-0001-7476-0158, J. Andrea0000-0002-8298-7560, D.
Apparu0009-0004-1837-0496, D. Bloch0000-0002-4535-5273, J.-M.
Brom0000-0003-0249-3622, E.C. Chabert0000-0003-2797-7690, C.
Collard0000-0002-5230-8387, S. Falke0000-0002-0264-1632, U.
Goerlach0000-0001-8955-1666, C. Grimault, R. Haeberle0009-0007-5007-6723,
A.-C. Le Bihan0000-0002-8545-0187, M. Meena0000-0003-4536-3967, G.
Saha0000-0002-6125-1941, M.A. Sessini0000-0003-2097-7065, P. Van
Hove0000-0002-2431-3381

Institut de Physique des 2 Infinis de Lyon (IP2I ), Villeurbanne, France S.
Beauceron0000-0002-8036-9267, B. Blancon0000-0001-9022-1509, G.
Boudoul0009-0002-9897-8439, N. Chanon0000-0002-2939-5646, J.
Choi0000-0002-6024-0992, D. Contardo0000-0001-6768-7466, P.
Depasse0000-0001-7556-2743, C. Dozen220000-0002-4301-634X, H. El Mamouni, J.
Fay0000-0001-5790-1780, S. Gascon0000-0002-7204-1624, M.
Gouzevitch0000-0002-5524-880X, C. Greenberg, G. Grenier0000-0002-1976-5877, B.
Ille0000-0002-8679-3878, I.B. Laktineh, M. Lethuillier0000-0001-6185-2045, L.
Mirabito, S. Perries, A. Purohit0000-0003-0881-612X, M. Vander
Donckt0000-0002-9253-8611, P. Verdier0000-0003-3090-2948, J.
Xiao0000-0002-7860-3958

Georgian Technical University, Tbilisi, Georgia I.
Bagaturia230000-0001-8646-4372, I. Lomidze0009-0002-3901-2765, Z.
Tsamalaidze160000-0001-5377-3558

RWTH Aachen University, I. Physikalisches Institut, Aachen, Germany V.
Botta0000-0003-1661-9513, L. Feld0000-0001-9813-8646, K.
Klein0000-0002-1546-7880, M. Lipinski0000-0002-6839-0063, D.
Meuser0000-0002-2722-7526, A. Pauls0000-0002-8117-5376, N.
Röwert0000-0002-4745-5470, M. Teroerde0000-0002-5892-1377

RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany S.
Diekmann0009-0004-8867-0881, A. Dodonova0000-0002-5115-8487, N.
Eich0000-0001-9494-4317, D. Eliseev0000-0001-5844-8156, F.
Engelke0000-0002-9288-8144, J. Erdmann0000-0002-8073-2740, M.
Erdmann0000-0002-1653-1303, P. Fackeldey0000-0003-4932-7162, B.
Fischer0000-0002-3900-3482, T. Hebbeker0000-0002-9736-266X, K.
Hoepfner0000-0002-2008-8148, F. Ivone0000-0002-2388-5548, A.
Jung0000-0002-2511-1490, M.y. Lee0000-0002-4430-1695, L. Mastrolorenzo, F.
Mausolf0000-0003-2479-8419, M. Merschmeyer0000-0003-2081-7141, A.
Meyer0000-0001-9598-6623, S. Mukherjee0000-0001-6341-9982, D.
Noll0000-0002-0176-2360, A. Novak0000-0002-0389-5896, F. Nowotny, A.
Pozdnyakov0000-0003-3478-9081, Y. Rath, W. Redjeb0000-0001-9794-8292, F. Rehm,
H. Reithler0000-0003-4409-702X, U. Sarkar0000-0002-9892-4601, V.
Sarkisovi0000-0001-9430-5419, A. Schmidt0000-0003-2711-8984, A.
Sharma0000-0002-5295-1460, J.L. Spah0000-0002-5215-3258, A.
Stein0000-0003-0713-811X, F. Torres Da Silva De Araujo240000-0002-4785-3057,
L. Vigilante, S. Wiedenbeck0000-0002-4692-9304, S. Zaleski

RWTH Aachen University, III. Physikalisches Institut B, Aachen, Germany C.
Dziwok0000-0001-9806-0244, G. Flügge0000-0003-3681-9272, W. Haj
Ahmad250000-0003-1491-0446, T. Kress0000-0002-2702-8201, A.
Nowack0000-0002-3522-5926, O. Pooth0000-0001-6445-6160, A.
Stahl0000-0002-8369-7506, T. Ziemons0000-0003-1697-2130, A.
Zotz0000-0002-1320-1712

Deutsches Elektronen-Synchrotron, Hamburg, Germany H. Aarup
Petersen0009-0005-6482-7466, M. Aldaya Martin0000-0003-1533-0945, J.
Alimena0000-0001-6030-3191, S. Amoroso, Y. An0000-0003-1299-1879, S.
Baxter0009-0008-4191-6716, M. Bayatmakou0009-0002-9905-0667, H. Becerril
Gonzalez0000-0001-5387-712X, O. Behnke0000-0002-4238-0991, A.
Belvedere0000-0002-2802-8203, S. Bhattacharya0000-0002-3197-0048, F.
Blekman260000-0002-7366-7098, K. Borras270000-0003-1111-249X, D.
Brunner0000-0001-9518-0435, A. Campbell0000-0003-4439-5748, A.
Cardini0000-0003-1803-0999, C. Cheng, F. Colombina0009-0008-7130-100X, S.
Consuegra Rodríguez0000-0002-1383-1837, G. Correia Silva0000-0001-6232-3591,
M. De Silva0000-0002-5804-6226, G. Eckerlin, D. Eckstein0000-0002-7366-6562,
L.I. Estevez Banos0000-0001-6195-3102, O. Filatov0000-0001-9850-6170, E.
Gallo260000-0001-7200-5175, A. Geiser0000-0003-0355-102X, A.
Giraldi0000-0003-4423-2631, G. Greau, V. Guglielmi0000-0003-3240-7393, M.
Guthoff0000-0002-3974-589X, A. Hinzmann0000-0002-2633-4696, A.
Jafari280000-0001-7327-1870, L. Jeppe0000-0002-1029-0318, N.Z.
Jomhari0000-0001-9127-7408, B. Kaech0000-0002-1194-2306, M.
Kasemann0000-0002-0429-2448, H. Kaveh0000-0002-3273-5859, C.
Kleinwort0000-0002-9017-9504, R. Kogler0000-0002-5336-4399, M.
Komm0000-0002-7669-4294, D. Krücker0000-0003-1610-8844, W. Lange, D. Leyva
Pernia0009-0009-8755-3698, K. Lipka290000-0002-8427-3748, W.
Lohmann300000-0002-8705-0857, R. Mankel0000-0003-2375-1563, I.-A. Melzer-
Pellmann0000-0001-7707-919X, M. Mendizabal Morentin0000-0002-6506-5177, J.
Metwally, A.B. Meyer0000-0001-8532-2356, G. Milella0000-0002-2047-951X, A.
Mussgiller0000-0002-8331-8166, L.P. Nair0000-0002-2351-9265, A.
Nürnberg0000-0002-7876-3134, Y. Otarid, J. Park0000-0002-4683-6669, D. Pérez
Adán0000-0003-3416-0726, E. Ranken0000-0001-7472-5029, A.
Raspereza0000-0003-2167-498X, B. Ribeiro Lopes0000-0003-0823-447X, J.
Rübenach, A. Saggio0000-0002-7385-3317, M. Scham31,270000-0001-9494-2151, S.
Schnake270000-0003-3409-6584, P. Schütze0000-0003-4802-6990, C.
Schwanenberger260000-0001-6699-6662, D. Selivanova0000-0002-7031-9434, M.
Shchedrolosiev0000-0003-3510-2093, R.E. Sosa Ricardo0000-0002-2240-6699, D.
Stafford, F. Vazzoler0000-0001-8111-9318, A. Ventura
Barroso0000-0003-3233-6636, R. Walsh0000-0002-3872-4114, Q.
Wang0000-0003-1014-8677, Y. Wen0000-0002-8724-9604, K. Wichmann, L.
Wiens270000-0002-4423-4461, C. Wissing0000-0002-5090-8004, Y.
Yang0009-0009-3430-0558, A. Zimermmane Castro Santos0000-0001-9302-3102

University of Hamburg, Hamburg, Germany A. Albrecht0000-0001-6004-6180, S.
Albrecht0000-0002-5960-6803, M. Antonello0000-0001-9094-482X, S.
Bein0000-0001-9387-7407, L. Benato0000-0001-5135-7489, M.
Bonanomi0000-0003-3629-6264, P. Connor0000-0003-2500-1061, M. Eich, K. El
Morabit0000-0001-5886-220X, Y. Fischer0000-0002-3184-1457, A. Fröhlich, C.
Garbers0000-0001-5094-2256, E. Garutti0000-0003-0634-5539, A.
Grohsjean0000-0003-0748-8494, M. Hajheidari, J. Haller0000-0001-9347-7657,
H.R. Jabusch0000-0003-2444-1014, G. Kasieczka0000-0003-3457-2755, P. Keicher,
R. Klanner0000-0002-7004-9227, W. Korcari0000-0001-8017-5502, T.
Kramer0000-0002-7004-0214, V. Kutzner0000-0003-1985-3807, F.
Labe0000-0002-1870-9443, J. Lange0000-0001-7513-6330, A.
Lobanov0000-0002-5376-0877, C. Matthies0000-0001-7379-4540, A.
Mehta0000-0002-0433-4484, L. Moureaux0000-0002-2310-9266, M. Mrowietz, A.
Nigamova0000-0002-8522-8500, Y. Nissan, A. Paasch0000-0002-2208-5178, K.J.
Pena Rodriguez0000-0002-2877-9744, T. Quadfasel0000-0003-2360-351X, B.
Raciti0009-0005-5995-6685, M. Rieger0000-0003-0797-2606, D.
Savoiu0000-0001-6794-7475, J. Schindler0009-0006-6551-0660, P.
Schleper0000-0001-5628-6827, M. Schröder0000-0001-8058-9828, J.
Schwandt0000-0002-0052-597X, M. Sommerhalder0000-0001-5746-7371, H.
Stadie0000-0002-0513-8119, G. Steinbrück0000-0002-8355-2761, A. Tews, M.
Wolf0000-0003-3002-2430

Karlsruher Institut fuer Technologie, Karlsruhe, Germany S.
Brommer0000-0001-8988-2035, M. Burkart, E. Butz0000-0002-2403-5801, T.
Chwalek0000-0002-8009-3723, A. Dierlamm0000-0001-7804-9902, A. Droll, N.
Faltermann0000-0001-6506-3107, M. Giffels0000-0003-0193-3032, A.
Gottmann0000-0001-6696-349X, F. Hartmann320000-0001-8989-8387, R.
Hofsaess0009-0008-4575-5729, M. Horzela0000-0002-3190-7962, U.
Husemann0000-0002-6198-8388, J. Kieseler0000-0003-1644-7678, M.
Klute0000-0002-0869-5631, R. Koppenhöfer0000-0002-6256-5715, J.M.
Lawhorn0000-0002-8597-9259, M. Link, A. Lintuluoto0000-0002-0726-1452, S.
Maier0000-0001-9828-9778, S. Mitra0000-0002-3060-2278, M.
Mormile0000-0003-0456-7250, Th. Müller0000-0003-4337-0098, M. Neukum, M.
Oh0000-0003-2618-9203, M. Presilla0000-0003-2808-7315, G.
Quast0000-0002-4021-4260, K. Rabbertz0000-0001-7040-9846, B.
Regnery0000-0003-1539-923X, N. Shadskiy0000-0001-9894-2095, I.
Shvetsov0000-0002-7069-9019, H.J. Simonis0000-0002-7467-2980, M.
Toms330000-0002-7703-3973, N. Trevisani0000-0002-5223-9342, R.
Ulrich0000-0002-2535-402X, R.F. Von Cube0000-0002-6237-5209, M.
Wassmer0000-0002-0408-2811, S. Wieland0000-0003-3887-5358, F. Wittig, R.
Wolf0000-0001-9456-383X, S. Wunsch, X. Zuo0000-0002-0029-493X

Institute of Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia
Paraskevi, Greece G. Anagnostou, G. Daskalakis0000-0001-6070-7698, A.
Kyriakis, A. Papadopoulos32, A. Stakia0000-0001-6277-7171

National and Kapodistrian University of Athens, Athens, Greece P.
Kontaxakis0000-0002-4860-5979, G. Melachroinos, A. Panagiotou, I.
Papavergou0000-0002-7992-2686, I. Paraskevas0000-0002-2375-5401, N.
Saoulidou0000-0001-6958-4196, K. Theofilatos0000-0001-8448-883X, E.
Tziaferi0000-0003-4958-0408, K. Vellidis0000-0001-5680-8357, I.
Zisopoulos0000-0001-5212-4353

National Technical University of Athens, Athens, Greece G.
Bakas0000-0003-0287-1937, T. Chatzistavrou, G. Karapostoli0000-0002-4280-2541,
K. Kousouris0000-0002-6360-0869, I. Papakrivopoulos0000-0002-8440-0487, E.
Siamarkou, G. Tsipolitis, A. Zacharopoulou

University of Ioánnina, Ioánnina, Greece K. Adamidis, I. Bestintzanos, I.
Evangelou0000-0002-5903-5481, C. Foudas, P. Gianneios0009-0003-7233-0738, C.
Kamtsikis, P. Katsoulis, P. Kokkas0009-0009-3752-6253, P.G. Kosmoglou
Kioseoglou0000-0002-7440-4396, N. Manthos0000-0003-3247-8909, I.
Papadopoulos0000-0002-9937-3063, J. Strologas0000-0002-2225-7160

HUN-REN Wigner Research Centre for Physics, Budapest, Hungary M.
Bartók340000-0002-4440-2701, C. Hajdu0000-0002-7193-800X, D.
Horvath35,360000-0003-0091-477X, F. Sikler0000-0001-9608-3901, V.
Veszpremi0000-0001-9783-0315

MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös Loránd
University, Budapest, Hungary M. Csanád0000-0002-3154-6925, K.
Farkas0000-0003-1740-6974, M.M.A. Gadallah370000-0002-8305-6661, Á.
Kadlecsik0000-0001-5559-0106, P. Major0000-0002-5476-0414, K.
Mandal0000-0002-3966-7182, G. Pásztor0000-0003-0707-9762, A.J.
Rádl380000-0001-8810-0388, G.I. Veres0000-0002-5440-4356

Faculty of Informatics, University of Debrecen, Debrecen, Hungary P. Raics, B.
Ujvari0000-0003-0498-4265, G. Zilizi0000-0002-0480-0000

Institute of Nuclear Research ATOMKI, Debrecen, Hungary G. Bencze, S. Czellar,
J. Karancsi340000-0003-0802-7665, J. Molnar, Z. Szillasi

Karoly Robert Campus, MATE Institute of Technology, Gyongyos, Hungary T.
Csorgo380000-0002-9110-9663, F. Nemes380000-0002-1451-6484, T.
Novak0000-0001-6253-4356

Panjab University, Chandigarh, India J. Babbar0000-0002-4080-4156, S.
Bansal0000-0003-1992-0336, S.B. Beri, V. Bhatnagar0000-0002-8392-9610, G.
Chaudhary0000-0003-0168-3336, S. Chauhan0000-0001-6974-4129, N.
Dhingra390000-0002-7200-6204, A. Kaur0000-0002-1640-9180, A.
Kaur0000-0003-3609-4777, H. Kaur0000-0002-8659-7092, M.
Kaur0000-0002-3440-2767, S. Kumar0000-0001-9212-9108, K.
Sandeep0000-0002-3220-3668, T. Sheokand, J.B. Singh0000-0001-9029-2462, A.
Singla0000-0003-2550-139X

University of Delhi, Delhi, India A. Ahmed0000-0002-4500-8853, A.
Bhardwaj0000-0002-7544-3258, A. Chhetri0000-0001-7495-1923, B.C.
Choudhary0000-0001-5029-1887, A. Kumar0000-0003-3407-4094, A.
Kumar0000-0002-5180-6595, M. Naimuddin0000-0003-4542-386X, K.
Ranjan0000-0002-5540-3750, S. Saumya0000-0001-7842-9518

Saha Institute of Nuclear Physics, HBNI, Kolkata, India S.
Baradia0000-0001-9860-7262, S. Barman400000-0001-8891-1674, S.
Bhattacharya0000-0002-8110-4957, S. Dutta0000-0001-9650-8121, S. Dutta, P.
Palit0000-0002-1948-029X, S. Sarkar

Indian Institute of Technology Madras, Madras, India M.M.
Ameen0000-0002-1909-9843, P.K. Behera0000-0002-1527-2266, S.C.
Behera0000-0002-0798-2727, S. Chatterjee0000-0003-0185-9872, P.
Jana0000-0001-5310-5170, P. Kalbhor0000-0002-5892-3743, J.R.
Komaragiri410000-0002-9344-6655, D. Kumar410000-0002-6636-5331, L.
Panwar410000-0003-2461-4907, P.R. Pujahari0000-0002-0994-7212, N.R.
Saha0000-0002-7954-7898, A. Sharma0000-0002-0688-923X, A.K.
Sikdar0000-0002-5437-5217, S. Verma0000-0003-1163-6955

Tata Institute of Fundamental Research-A, Mumbai, India S. Dugad, M.
Kumar0000-0003-0312-057X, G.B. Mohanty0000-0001-6850-7666, P. Suryadevara

Tata Institute of Fundamental Research-B, Mumbai, India A.
Bala0000-0003-2565-1718, S. Banerjee0000-0002-7953-4683, R.M. Chatterjee, M.
Guchait0009-0004-0928-7922, Sh. Jain0000-0003-1770-5309, S.
Karmakar0000-0001-9715-5663, S. Kumar0000-0002-2405-915X, G.
Majumder0000-0002-3815-5222, K. Mazumdar0000-0003-3136-1653, S.
Parolia0000-0002-9566-2490, A. Thachayath0000-0001-6545-0350

National Institute of Science Education and Research, An OCC of Homi Bhabha
National Institute, Bhubaneswar, Odisha, India S.
Bahinipati420000-0002-3744-5332, A.K. Das, C. Kar0000-0002-6407-6974, D.
Maity430000-0002-1989-6703, P. Mal0000-0002-0870-8420, T.
Mishra0000-0002-2121-3932, V.K. Muraleedharan Nair
Bindhu430000-0003-4671-815X, K. Naskar430000-0003-0638-4378, A.
Nayak430000-0002-7716-4981, P. Sadangi, P. Saha0000-0002-7013-8094, S.K.
Swain0000-0001-6871-3937, S. Varghese430009-0000-1318-8266, D.
Vats430009-0007-8224-4664

Indian Institute of Science Education and Research (IISER), Pune, India S.
Acharya440009-0001-2997-7523, A. Alpana0000-0003-3294-2345, S.
Dube0000-0002-5145-3777, B. Gomber440000-0002-4446-0258, B.
Kansal0000-0002-6604-1011, A. Laha0000-0001-9440-7028, B.
Sahu440000-0002-8073-5140, S. Sharma0000-0001-6886-0726

Isfahan University of Technology, Isfahan, Iran H.
Bakhshiansohi450000-0001-5741-3357, E. Khazaie460000-0001-9810-7743, M.
Zeinali470000-0001-8367-6257

Institute for Research in Fundamental Sciences (IPM), Tehran, Iran S.
Chenarani480000-0002-1425-076X, S.M. Etesami0000-0001-6501-4137, M.
Khakzad0000-0002-2212-5715, M. Mohammadi Najafabadi0000-0001-6131-5987

University College Dublin, Dublin, Ireland M. Grunewald0000-0002-5754-0388

INFN Sezione di Baria, Università di Barib, Politecnico di Baric, Bari, Italy
M. Abbresciaa,b0000-0001-8727-7544, R. Alya,c,490000-0001-6808-1335, A.
Colaleoa,b0000-0002-0711-6319, D. Creanzaa,c0000-0001-6153-3044, B.
D’Anzia,b0000-0002-9361-3142, N. De Filippisa,c0000-0002-0625-6811, M. De
Palmaa,b0000-0001-8240-1913, A. Di Florioa,c0000-0003-3719-8041, W.
Elmetenaweea,b,490000-0001-7069-0252, L. Fiorea0000-0002-9470-1320, G.
Iasellia,c0000-0003-2546-5341, M. Loukaa,b, G. Maggia,c0000-0001-5391-7689, M.
Maggia0000-0002-8431-3922, I. Margjekaa,b0000-0002-3198-3025, V.
Mastrapasquaa,b0000-0002-9082-5924, S. Mya,b0000-0002-9938-2680, S.
Nuzzoa,b0000-0003-1089-6317, A. Pellecchiaa,b0000-0003-3279-6114, A.
Pompilia,b0000-0003-1291-4005, G. Pugliesea,c0000-0001-5460-2638, R.
Radognaa0000-0002-1094-5038, G. Ramirez-Sancheza,c0000-0001-7804-5514, D.
Ramosa0000-0002-7165-1017, A. Ranieria0000-0001-7912-4062, L.
Silvestrisa0000-0002-8985-4891, F.M. Simonea,b0000-0002-1924-983X, Ü.
Sözbilira0000-0001-6833-3758, A. Stamerraa0000-0003-1434-1968, R.
Vendittia0000-0001-6925-8649, P. Verwilligena0000-0002-9285-8631, A.
Zazaa,b0000-0002-0969-7284

INFN Sezione di Bolognaa, Università di Bolognab, Bologna, Italy G.
Abbiendia0000-0003-4499-7562, C. Battilanaa,b0000-0002-3753-3068, D.
Bonacorsia,b0000-0002-0835-9574, L. Borgonovia0000-0001-8679-4443, P.
Capiluppia,b0000-0003-4485-1897, A. Castroa,b0000-0003-2527-0456, F.R.
Cavalloa0000-0002-0326-7515, M. Cuffiania,b0000-0003-2510-5039, G.M.
Dallavallea0000-0002-8614-0420, T. Diotalevia,b0000-0003-0780-8785, F.
Fabbria0000-0002-8446-9660, A. Fanfania,b0000-0003-2256-4117, D.
Fasanellaa,b0000-0002-2926-2691, P. Giacomellia0000-0002-6368-7220, L.
Giommia,b0000-0003-3539-4313, C. Grandia0000-0001-5998-3070, L.
Guiduccia,b0000-0002-6013-8293, S. Lo Meoa,500000-0003-3249-9208, L.
Lunertia,b0000-0002-8932-0283, S. Marcellinia0000-0002-1233-8100, G.
Masettia0000-0002-6377-800X, F.L. Navarriaa,b0000-0001-7961-4889, A.
Perrottaa0000-0002-7996-7139, F. Primaveraa,b0000-0001-6253-8656, A.M.
Rossia,b0000-0002-5973-1305, T. Rovellia,b0000-0002-9746-4842, G.P.
Sirolia,b0000-0002-3528-4125

INFN Sezione di Cataniaa, Università di Cataniab, Catania, Italy S.
Costaa,b,510000-0001-9919-0569, A. Di Mattiaa0000-0002-9964-015X, R.
Potenzaa,b, A. Tricomia,b,510000-0002-5071-5501, C. Tuvea,b0000-0003-0739-3153

INFN Sezione di Firenzea, Università di Firenzeb, Firenze, Italy P.
Assiourasa0000-0002-5152-9006, G. Barbaglia0000-0002-1738-8676, G.
Bardellia,b0000-0002-4662-3305, B. Camaiania,b0000-0002-6396-622X, A.
Cassesea0000-0003-3010-4516, R. Ceccarellia0000-0003-3232-9380, V.
Ciullia,b0000-0003-1947-3396, C. Civininia0000-0002-4952-3799, R.
D’Alessandroa,b0000-0001-7997-0306, E. Focardia,b0000-0002-3763-5267, T.
Kelloa, G. Latinoa,b0000-0002-4098-3502, P. Lenzia,b0000-0002-6927-8807, M.
Lizzoa0000-0001-7297-2624, M. Meschinia0000-0002-9161-3990, S.
Paolettia0000-0003-3592-9509, A. Papanastassioua,b, G.
Sguazzonia0000-0002-0791-3350, L. Viliania0000-0002-1909-6343

INFN Laboratori Nazionali di Frascati, Frascati, Italy L.
Benussi0000-0002-2363-8889, S. Bianco0000-0002-8300-4124, S.
Meola520000-0002-8233-7277, D. Piccolo0000-0001-5404-543X

INFN Sezione di Genovaa, Università di Genovab, Genova, Italy P.
Chatagnona0000-0002-4705-9582, F. Ferroa0000-0002-7663-0805, E.
Robuttia0000-0001-9038-4500, S. Tosia,b0000-0002-7275-9193

INFN Sezione di Milano-Bicoccaa, Università di Milano-Bicoccab, Milano, Italy
A. Benagliaa0000-0003-1124-8450, G. Boldrinia,b0000-0001-5490-605X, F.
Brivioa0000-0001-9523-6451, F. Cetorellia0000-0002-3061-1553, F. De
Guioa,b0000-0001-5927-8865, M.E. Dinardoa,b0000-0002-8575-7250, P.
Dinia0000-0001-7375-4899, S. Gennaia0000-0001-5269-8517, R.
Gerosaa,b0000-0001-8359-3734, A. Ghezzia,b0000-0002-8184-7953, P.
Govonia,b0000-0002-0227-1301, L. Guzzia0000-0002-3086-8260, M.T.
Lucchinia,b0000-0002-7497-7450, M. Malbertia0000-0001-6794-8419, S.
Malvezzia0000-0002-0218-4910, A. Massironia0000-0002-0782-0883, D.
Menascea0000-0002-9918-1686, L. Moronia0000-0002-8387-762X, M.
Paganonia,b0000-0003-2461-275X, D. Pedrinia0000-0003-2414-4175, B.S.
Pinolinia, S. Ragazzia,b0000-0001-8219-2074, T. Tabarelli de
Fatisa,b0000-0001-6262-4685, D. Zuoloa0000-0003-3072-1020

INFN Sezione di Napolia, Università di Napoli ’Federico II’b, Napoli, Italy;
Università della Basilicatac, Potenza, Italy; Scuola Superiore Meridionale
(SSM)d, Napoli, Italy S. Buontempoa0000-0001-9526-556X, A.
Cagnottaa,b0000-0002-8801-9894, F. Carnevalia,b, N.
Cavalloa,c0000-0003-1327-9058, A. De Iorioa,b0000-0002-9258-1345, F.
Fabozzia,c0000-0001-9821-4151, A.O.M. Iorioa,b0000-0002-3798-1135, L.
Listaa,b,530000-0001-6471-5492, P. Paoluccia,320000-0002-8773-4781, B.
Rossia0000-0002-0807-8772, C. Sciaccaa,b0000-0002-8412-4072

INFN Sezione di Padovaa, Università di Padovab, Padova, Italy; Università di
Trentoc, Trento, Italy R. Ardinoa0000-0001-8348-2962, P.
Azzia0000-0002-3129-828X, N. Bacchettaa,540000-0002-2205-5737, D.
Biselloa,b0000-0002-2359-8477, P. Bortignona0000-0002-5360-1454, A.
Bragagnoloa,b0000-0003-3474-2099, T. Dorigoa0000-0002-1659-8727, F.
Gasparinia,b0000-0002-1315-563X, U. Gasparinia,b0000-0002-7253-2669, E.
Lusiania0000-0001-8791-7978, M. Margonia,b0000-0003-1797-4330, G.
Marona,550000-0003-3970-6986, A.T. Meneguzzoa,b0000-0002-5861-8140, M.
Michelottoa0000-0001-6644-987X, M. Migliorinia,b0000-0002-5441-7755, J.
Pazzinia,b0000-0002-1118-6205, P. Ronchesea,b0000-0001-7002-2051, R.
Rossina,b0000-0003-3466-7500, F. Simonettoa,b0000-0002-8279-2464, G.
Stronga0000-0002-4640-6108, M. Tosia,b0000-0003-4050-1769, A.
Triossia,b0000-0001-5140-9154, S. Venturaa0000-0002-8938-2193, H. Yarara,b, M.
Zanettia,b0000-0003-4281-4582, P. Zottoa,b0000-0003-3953-5996, A.
Zucchettaa,b0000-0003-0380-1172, G. Zumerlea,b0000-0003-3075-2679

INFN Sezione di Paviaa, Università di Paviab, Pavia, Italy S. Abu
Zeida,560000-0002-0820-0483, C. Aimèa,b0000-0003-0449-4717, A.
Braghieria0000-0002-9606-5604, S. Calzaferria0000-0002-1162-2505, D.
Fiorinaa0000-0002-7104-257X, P. Montagnaa,b0000-0001-9647-9420, V.
Rea0000-0003-0697-3420, C. Riccardia,b0000-0003-0165-3962, P.
Salvinia0000-0001-9207-7256, I. Vaia,b0000-0003-0037-5032, P.
Vituloa,b0000-0001-9247-7778

INFN Sezione di Perugiaa, Università di Perugiab, Perugia, Italy S.
Ajmala,b0000-0002-2726-2858, P. Asenova,570000-0003-2379-9903, G.M.
Bileia0000-0002-4159-9123, D. Ciangottinia,b0000-0002-0843-4108, L.
Fanòa,b0000-0002-9007-629X, M. Magherinia,b0000-0003-4108-3925, G.
Mantovania,b, V. Mariania,b0000-0001-7108-8116, M.
Menichellia0000-0002-9004-735X, F. Moscatellia,570000-0002-7676-3106, A.
Rossia,b0000-0002-2031-2955, A. Santocchiaa,b0000-0002-9770-2249, D.
Spigaa0000-0002-2991-6384, T. Tedeschia,b0000-0002-7125-2905

INFN Sezione di Pisaa, Università di Pisab, Scuola Normale Superiore di Pisac,
Pisa, Italy; Università di Sienad, Siena, Italy P.
Azzurria0000-0002-1717-5654, G. Bagliesia0000-0003-4298-1620, R.
Bhattacharyaa0000-0002-7575-8639, L. Bianchinia,b0000-0002-6598-6865, T.
Boccalia0000-0002-9930-9299, E. Bossinia0000-0002-2303-2588, D.
Bruschinia,c0000-0001-7248-2967, R. Castaldia0000-0003-0146-845X, M.A.
Cioccia,b0000-0003-0002-5462, M. Cipriania,b0000-0002-0151-4439, V.
D’Amantea,d0000-0002-7342-2592, R. Dell’Orsoa0000-0003-1414-9343, S.
Donatoa0000-0001-7646-4977, A. Giassia0000-0001-9428-2296, F.
Ligabuea,c0000-0002-1549-7107, D. Matos Figueiredoa0000-0003-2514-6930, A.
Messineoa,b0000-0001-7551-5613, M. Musicha,b0000-0001-7938-5684, F.
Pallaa0000-0002-6361-438X, A. Rizzia,b0000-0002-4543-2718, G.
Rolandia,c0000-0002-0635-274X, S. Roy Chowdhurya0000-0001-5742-5593, T.
Sarkara0000-0003-0582-4167, A. Scribanoa0000-0002-4338-6332, P.
Spagnoloa0000-0001-7962-5203, R. Tenchinia0000-0003-2574-4383, G.
Tonellia,b0000-0003-2606-9156, N. Turinia,d0000-0002-9395-5230, A.
Venturia0000-0002-0249-4142, P.G. Verdinia0000-0002-0042-9507

INFN Sezione di Romaa, Sapienza Università di Romab, Roma, Italy P.
Barriaa0000-0002-3924-7380, M. Campanaa,b0000-0001-5425-723X, F.
Cavallaria0000-0002-1061-3877, L. Cunqueiro Mendeza,b0000-0001-6764-5370, D.
Del Rea,b0000-0003-0870-5796, E. Di Marcoa0000-0002-5920-2438, M.
Diemoza0000-0002-3810-8530, F. Erricoa,b0000-0001-8199-370X, E.
Longoa,b0000-0001-6238-6787, P. Meridiania0000-0002-8480-2259, J.
Mijuskovica,b0009-0009-1589-9980, G. Organtinia,b0000-0002-3229-0781, F.
Pandolfia0000-0001-8713-3874, R. Paramattia,b0000-0002-0080-9550, C.
Quarantaa,b0000-0002-0042-6891, S. Rahatloua,b0000-0001-9794-3360, C.
Rovellia0000-0003-2173-7530, F. Santanastasioa,b0000-0003-2505-8359, L.
Soffia0000-0003-2532-9876

INFN Sezione di Torinoa, Università di Torinob, Torino, Italy; Università del
Piemonte Orientalec, Novara, Italy N. Amapanea,b0000-0001-9449-2509, R.
Arcidiaconoa,c0000-0001-5904-142X, S. Argiroa,b0000-0003-2150-3750, M.
Arneodoa,c0000-0002-7790-7132, N. Bartosika0000-0002-7196-2237, R.
Bellana,b0000-0002-2539-2376, A. Belloraa,b0000-0002-2753-5473, C.
Biinoa0000-0002-1397-7246, N. Cartigliaa0000-0002-0548-9189, M.
Costaa,b0000-0003-0156-0790, R. Covarellia,b0000-0003-1216-5235, N.
Demariaa0000-0003-0743-9465, L. Fincoa0000-0002-2630-5465, M.
Grippoa,b0000-0003-0770-269X, B. Kiania,b0000-0002-1202-7652, F.
Leggera0000-0003-1400-0709, F. Luongoa,b0000-0003-2743-4119, C.
Mariottia0000-0002-6864-3294, S. Masellia0000-0001-9871-7859, A.
Meccaa,b0000-0003-2209-2527, E. Migliorea,b0000-0002-2271-5192, M.
Montenoa0000-0002-3521-6333, R. Mulargiaa0000-0003-2437-013X, M.M.
Obertinoa,b0000-0002-8781-8192, G. Ortonaa0000-0001-8411-2971, L.
Pachera,b0000-0003-1288-4838, N. Pastronea0000-0001-7291-1979, M.
Pelliccionia0000-0003-4728-6678, M. Ruspaa,c0000-0002-7655-3475, F.
Sivieroa,b0000-0002-4427-4076, V. Solaa,b0000-0001-6288-951X, A.
Solanoa,b0000-0002-2971-8214, A. Staianoa0000-0003-1803-624X, C.
Tarriconea,b0000-0001-6233-0513, D. Trocinoa0000-0002-2830-5872, G.
Umoreta,b0000-0002-6674-7874, E. Vlasova,b0000-0002-8628-2090

INFN Sezione di Triestea, Università di Triesteb, Trieste, Italy S.
Belfortea0000-0001-8443-4460, V. Candelisea,b0000-0002-3641-5983, M.
Casarsaa0000-0002-1353-8964, F. Cossuttia0000-0001-5672-214X, K. De
Leoa,b0000-0002-8908-409X, G. Della Riccaa,b0000-0003-2831-6982

Kyungpook National University, Daegu, Korea S. Dogra0000-0002-0812-0758, J.
Hong0000-0002-9463-4922, C. Huh0000-0002-8513-2824, B. Kim0000-0002-9539-6815,
D.H. Kim0000-0002-9023-6847, J. Kim, H. Lee, S.W. Lee0000-0002-1028-3468, C.S.
Moon0000-0001-8229-7829, Y.D. Oh0000-0002-7219-9931, M.S.
Ryu0000-0002-1855-180X, S. Sekmen0000-0003-1726-5681, Y.C.
Yang0000-0003-1009-4621

Department of Mathematics and Physics - GWNU, Gangneung, Korea M.S.
Kim0000-0003-0392-8691

Chonnam National University, Institute for Universe and Elementary Particles,
Kwangju, Korea G. Bak0000-0002-0095-8185, P. Gwak0009-0009-7347-1480, H.
Kim0000-0001-8019-9387, D.H. Moon0000-0002-5628-9187

Hanyang University, Seoul, Korea E. Asilar0000-0001-5680-599X, D.
Kim0000-0002-8336-9182, T.J. Kim0000-0001-8336-2434, J.A. Merlin

Korea University, Seoul, Korea S. Choi0000-0001-6225-9876, S. Han, B.
Hong0000-0002-2259-9929, K. Lee, K.S. Lee0000-0002-3680-7039, S.
Lee0000-0001-9257-9643, J. Park, S.K. Park, J. Yoo0000-0003-0463-3043

Kyung Hee University, Department of Physics, Seoul, Korea J.
Goh0000-0002-1129-2083, S. Yang0000-0001-6905-6553

Sejong University, Seoul, Korea H. S. Kim0000-0002-6543-9191, Y. Kim, S. Lee

Seoul National University, Seoul, Korea J. Almond, J.H. Bhyun, J.
Choi0000-0002-2483-5104, W. Jun0009-0001-5122-4552, J. Kim0000-0001-9876-6642,
J.S. Kim, S. Ko0000-0003-4377-9969, H. Kwon0009-0002-5165-5018, H.
Lee0000-0002-1138-3700, J. Lee0000-0001-6753-3731, J. Lee0000-0002-5351-7201,
B.H. Oh0000-0002-9539-7789, S.B. Oh0000-0003-0710-4956, H.
Seo0000-0002-3932-0605, U.K. Yang, I. Yoon0000-0002-3491-8026

University of Seoul, Seoul, Korea W. Jang0000-0002-1571-9072, D.Y. Kang, Y.
Kang0000-0001-6079-3434, S. Kim0000-0002-8015-7379, B. Ko, J.S.H.
Lee0000-0002-2153-1519, Y. Lee0000-0001-5572-5947, I.C.
Park0000-0003-4510-6776, Y. Roh, I.J. Watson0000-0003-2141-3413

Yonsei University, Department of Physics, Seoul, Korea S.
Ha0000-0003-2538-1551, H.D. Yoo0000-0002-3892-3500

Sungkyunkwan University, Suwon, Korea M. Choi0000-0002-4811-626X, M.R.
Kim0000-0002-2289-2527, H. Lee, Y. Lee0000-0001-6954-9964, I.
Yu0000-0003-1567-5548

College of Engineering and Technology, American University of the Middle East
(AUM), Dasman, Kuwait T. Beyrouthy, Y. Maghrbi0000-0002-4960-7458

Riga Technical University, Riga, Latvia K. Dreimanis0000-0003-0972-5641, A.
Gaile0000-0003-1350-3523, G. Pikurs, A. Potrebko0000-0002-3776-8270, M.
Seidel0000-0003-3550-6151, V. Veckalns580000-0003-3676-9711

University of Latvia (LU), Riga, Latvia N.R. Strautnieks0000-0003-4540-9048

Vilnius University, Vilnius, Lithuania M. Ambrozas0000-0003-2449-0158, A.
Juodagalvis0000-0002-1501-3328, A. Rinkevicius0000-0002-7510-255X, G.
Tamulaitis0000-0002-2913-9634

National Centre for Particle Physics, Universiti Malaya, Kuala Lumpur,
Malaysia N. Bin Norjoharuddeen0000-0002-8818-7476, I.
Yusuff590000-0003-2786-0732, Z. Zolkapli

Universidad de Sonora (UNISON), Hermosillo, Mexico J.F.
Benitez0000-0002-2633-6712, A. Castaneda Hernandez0000-0003-4766-1546, H.A.
Encinas Acosta, L.G. Gallegos Maríñez, M. León Coello0000-0002-3761-911X, J.A.
Murillo Quijada0000-0003-4933-2092, A. Sehrawat0000-0002-6816-7814, L.
Valencia Palomo0000-0002-8736-440X

Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
G. Ayala0000-0002-8294-8692, H. Castilla-Valdez0009-0005-9590-9958, E. De La
Cruz-Burelo0000-0002-7469-6974, I. Heredia-De La Cruz600000-0002-8133-6467, R.
Lopez-Fernandez0000-0002-2389-4831, C.A. Mondragon Herrera, A. Sánchez
Hernández0000-0001-9548-0358

Universidad Iberoamericana, Mexico City, Mexico C. Oropeza
Barrera0000-0001-9724-0016, M. Ramírez García0000-0002-4564-3822

Benemerita Universidad Autonoma de Puebla, Puebla, Mexico I.
Bautista0000-0001-5873-3088, I. Pedraza0000-0002-2669-4659, H.A. Salazar
Ibarguen0000-0003-4556-7302, C. Uribe Estrada0000-0002-2425-7340

University of Montenegro, Podgorica, Montenegro I. Bubanja0009-0005-4364-277X,
N. Raicevic0000-0002-2386-2290

University of Canterbury, Christchurch, New Zealand P.H.
Butler0000-0001-9878-2140

National Centre for Physics, Quaid-I-Azam University, Islamabad, Pakistan A.
Ahmad0000-0002-4770-1897, M.I. Asghar, A. Awais0000-0003-3563-257X, M.I.M.
Awan, H.R. Hoorani0000-0002-0088-5043, W.A. Khan0000-0003-0488-0941

AGH University of Krakow, Faculty of Computer Science, Electronics and
Telecommunications, Krakow, Poland V. Avati, L. Grzanka0000-0002-3599-854X, M.
Malawski0000-0001-6005-0243

National Centre for Nuclear Research, Swierk, Poland H.
Bialkowska0000-0002-5956-6258, M. Bluj0000-0003-1229-1442, B.
Boimska0000-0002-4200-1541, M. Górski0000-0003-2146-187X, M.
Kazana0000-0002-7821-3036, M. Szleper0000-0002-1697-004X, P.
Zalewski0000-0003-4429-2888

Institute of Experimental Physics, Faculty of Physics, University of Warsaw,
Warsaw, Poland K. Bunkowski0000-0001-6371-9336, K. Doroba0000-0002-7818-2364,
A. Kalinowski0000-0002-1280-5493, M. Konecki0000-0001-9482-4841, J.
Krolikowski0000-0002-3055-0236, A. Muhammad0000-0002-7535-7149

Warsaw University of Technology, Warsaw, Poland K. Pozniak0000-0001-5426-1423,
W. Zabolotny0000-0002-6833-4846

Laboratório de Instrumentação e Física Experimental de Partículas, Lisboa,
Portugal M. Araujo0000-0002-8152-3756, D. Bastos0000-0002-7032-2481, C. Beirão
Da Cruz E Silva0000-0002-1231-3819, A. Boletti0000-0003-3288-7737, M.
Bozzo0000-0002-1715-0457, T. Camporesi0000-0001-5066-1876, G. Da
Molin0000-0003-2163-5569, P. Faccioli0000-0003-1849-6692, M.
Gallinaro0000-0003-1261-2277, J. Hollar0000-0002-8664-0134, N.
Leonardo0000-0002-9746-4594, T. Niknejad0000-0003-3276-9482, A.
Petrilli0000-0003-0887-1882, M. Pisano0000-0002-0264-7217, J.
Seixas0000-0002-7531-0842, J. Varela0000-0003-2613-3146, J.W. Wulff

Faculty of Physics, University of Belgrade, Belgrade, Serbia P.
Adzic0000-0002-5862-7397, P. Milenovic0000-0001-7132-3550

VINCA Institute of Nuclear Sciences, University of Belgrade, Belgrade, Serbia
M. Dordevic0000-0002-8407-3236, J. Milosevic0000-0001-8486-4604, V. Rekovic

Centro de Investigaciones Energéticas Medioambientales y Tecnológicas
(CIEMAT), Madrid, Spain M. Aguilar-Benitez, J. Alcaraz
Maestre0000-0003-0914-7474, Cristina F. Bedoya0000-0001-8057-9152, M.
Cepeda0000-0002-6076-4083, M. Cerrada0000-0003-0112-1691, N.
Colino0000-0002-3656-0259, B. De La Cruz0000-0001-9057-5614, A. Delgado
Peris0000-0002-8511-7958, A. Escalante Del Valle0000-0002-9702-6359, D.
Fernández Del Val0000-0003-2346-1590, J.P. Fernández Ramos0000-0002-0122-313X,
J. Flix0000-0003-2688-8047, M.C. Fouz0000-0003-2950-976X, O. Gonzalez
Lopez0000-0002-4532-6464, S. Goy Lopez0000-0001-6508-5090, J.M.
Hernandez0000-0001-6436-7547, M.I. Josa0000-0002-4985-6964, D.
Moran0000-0002-1941-9333, C. M. Morcillo Perez0000-0001-9634-848X, Á. Navarro
Tobar0000-0003-3606-1780, C. Perez Dengra0000-0003-2821-4249, A. Pérez-Calero
Yzquierdo0000-0003-3036-7965, J. Puerta Pelayo0000-0001-7390-1457, I.
Redondo0000-0003-3737-4121, D.D. Redondo Ferrero0000-0002-3463-0559, L.
Romero, S. Sánchez Navas0000-0001-6129-9059, L. Urda Gómez0000-0002-7865-5010,
J. Vazquez Escobar0000-0002-7533-2283, C. Willmott

Universidad Autónoma de Madrid, Madrid, Spain J.F. de
Trocóniz0000-0002-0798-9806

Universidad de Oviedo, Instituto Universitario de Ciencias y Tecnologías
Espaciales de Asturias (ICTEA), Oviedo, Spain B. Alvarez
Gonzalez0000-0001-7767-4810, J. Cuevas0000-0001-5080-0821, J. Fernandez
Menendez0000-0002-5213-3708, S. Folgueras0000-0001-7191-1125, I. Gonzalez
Caballero0000-0002-8087-3199, J.R. González Fernández0000-0002-4825-8188, E.
Palencia Cortezon0000-0001-8264-0287, C. Ramón Álvarez0000-0003-1175-0002, V.
Rodríguez Bouza0000-0002-7225-7310, A. Soto Rodríguez0000-0002-2993-8663, A.
Trapote0000-0002-4030-2551, C. Vico Villalba0000-0002-1905-1874, P.
Vischia0000-0002-7088-8557

Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria,
Santander, Spain S. Bhowmik0000-0003-1260-973X, S. Blanco
Fernández0000-0001-7301-0670, J.A. Brochero Cifuentes0000-0003-2093-7856, I.J.
Cabrillo0000-0002-0367-4022, A. Calderon0000-0002-7205-2040, J. Duarte
Campderros0000-0003-0687-5214, M. Fernandez0000-0002-4824-1087, G.
Gomez0000-0002-1077-6553, C. Lasaosa García0000-0003-2726-7111, C. Martinez
Rivero0000-0002-3224-956X, P. Martinez Ruiz del Arbol0000-0002-7737-5121, F.
Matorras0000-0003-4295-5668, P. Matorras Cuevas0000-0001-7481-7273, E.
Navarrete Ramos0000-0002-5180-4020, J. Piedra Gomez0000-0002-9157-1700, L.
Scodellaro0000-0002-4974-8330, I. Vila0000-0002-6797-7209, J.M. Vizan
Garcia0000-0002-6823-8854

University of Colombo, Colombo, Sri Lanka M.K. Jayananda0000-0002-7577-310X,
B. Kailasapathy610000-0003-2424-1303, D.U.J. Sonnadara0000-0001-7862-2537,
D.D.C. Wickramarathna0000-0002-6941-8478

University of Ruhuna, Department of Physics, Matara, Sri Lanka W.G.D.
Dharmaratna620000-0002-6366-837X, K. Liyanage0000-0002-3792-7665, N.
Perera0000-0002-4747-9106, N. Wickramage0000-0001-7760-3537

CERN, European Organization for Nuclear Research, Geneva, Switzerland D.
Abbaneo0000-0001-9416-1742, C. Amendola0000-0002-4359-836X, E.
Auffray0000-0001-8540-1097, G. Auzinger0000-0001-7077-8262, J. Baechler, D.
Barney0000-0002-4927-4921, A. Bermúdez Martínez0000-0001-8822-4727, M.
Bianco0000-0002-8336-3282, B. Bilin0000-0003-1439-7128, A.A. Bin
Anuar0000-0002-2988-9830, A. Bocci0000-0002-6515-5666, E.
Brondolin0000-0001-5420-586X, C. Caillol0000-0002-5642-3040, G.
Cerminara0000-0002-2897-5753, N. Chernyavskaya0000-0002-2264-2229, D.
d’Enterria0000-0002-5754-4303, A. Dabrowski0000-0003-2570-9676, A.
David0000-0001-5854-7699, A. De Roeck0000-0002-9228-5271, M.M.
Defranchis0000-0001-9573-3714, M. Deile0000-0001-5085-7270, M.
Dobson0009-0007-5021-3230, F. Fallavollita63, L. Forthomme0000-0002-3302-336X,
G. Franzoni0000-0001-9179-4253, W. Funk0000-0003-0422-6739, S. Giani, D. Gigi,
K. Gill0009-0001-9331-5145, F. Glege0000-0002-4526-2149, L.
Gouskos0000-0002-9547-7471, M. Haranko0000-0002-9376-9235, J.
Hegeman0000-0002-2938-2263, B. Huber, V. Innocente0000-0003-3209-2088, T.
James0000-0002-3727-0202, P. Janot0000-0001-7339-4272, S.
Laurila0000-0001-7507-8636, P. Lecoq0000-0002-3198-0115, E.
Leutgeb0000-0003-4838-3306, C. Lourenço0000-0003-0885-6711, B.
Maier0000-0001-5270-7540, L. Malgeri0000-0002-0113-7389, M.
Mannelli0000-0003-3748-8946, A.C. Marini0000-0003-2351-0487, M. Matthewman, F.
Meijers0000-0002-6530-3657, S. Mersi0000-0003-2155-6692, E.
Meschi0000-0003-4502-6151, V. Milosevic0000-0002-1173-0696, F.
Monti0000-0001-5846-3655, F. Moortgat0000-0001-7199-0046, M.
Mulders0000-0001-7432-6634, I. Neutelings0009-0002-6473-1403, S. Orfanelli, F.
Pantaleo0000-0003-3266-4357, G. Petrucciani0000-0003-0889-4726, A.
Pfeiffer0000-0001-5328-448X, M. Pierini0000-0003-1939-4268, D.
Piparo0009-0006-6958-3111, H. Qu0000-0002-0250-8655, D.
Rabady0000-0001-9239-0605, G. Reales Gutiérrez, M. Rovere0000-0001-8048-1622,
H. Sakulin0000-0003-2181-7258, S. Scarfi0009-0006-8689-3576, C. Schwick, M.
Selvaggi0000-0002-5144-9655, A. Sharma0000-0002-9860-1650, K.
Shchelina0000-0003-3742-0693, P. Silva0000-0002-5725-041X, P.
Sphicas640000-0002-5456-5977, A.G. Stahl Leiton0000-0002-5397-252X, A.
Steen0009-0006-4366-3463, S. Summers0000-0003-4244-2061, D.
Treille0009-0005-5952-9843, P. Tropea0000-0003-1899-2266, A. Tsirou, D.
Walter0000-0001-8584-9705, J. Wanczyk650000-0002-8562-1863, J. Wang, S.
Wuchterl0000-0001-9955-9258, P. Zehetner0009-0002-0555-4697, P.
Zejdl0000-0001-9554-7815, W.D. Zeuner

Paul Scherrer Institut, Villigen, Switzerland T.
Bevilacqua660000-0001-9791-2353, L. Caminada660000-0001-5677-6033, A.
Ebrahimi0000-0003-4472-867X, W. Erdmann0000-0001-9964-249X, R.
Horisberger0000-0002-5594-1321, Q. Ingram0000-0002-9576-055X, H.C.
Kaestli0000-0003-1979-7331, D. Kotlinski0000-0001-5333-4918, C.
Lange0000-0002-3632-3157, M. Missiroli660000-0002-1780-1344, L.
Noehte660000-0001-6125-7203, T. Rohe0009-0005-6188-7754

ETH Zurich - Institute for Particle Physics and Astrophysics (IPA), Zurich,
Switzerland T.K. Aarrestad0000-0002-7671-243X, K.
Androsov650000-0003-2694-6542, M. Backhaus0000-0002-5888-2304, A.
Calandri0000-0001-7774-0099, C. Cazzaniga0000-0003-0001-7657, K.
Datta0000-0002-6674-0015, A. De Cosa0000-0003-2533-2856, G.
Dissertori0000-0002-4549-2569, M. Dittmar, M. Donegà0000-0001-9830-0412, F.
Eble0009-0002-0638-3447, M. Galli0000-0002-9408-4756, K.
Gedia0009-0006-0914-7684, F. Glessgen0000-0001-5309-1960, C.
Grab0000-0002-6182-3380, D. Hits0000-0002-3135-6427, W.
Lustermann0000-0003-4970-2217, A.-M. Lyon0009-0004-1393-6577, R.A.
Manzoni0000-0002-7584-5038, M. Marchegiani0000-0002-0389-8640, L.
Marchese0000-0001-6627-8716, C. Martin Perez0000-0003-1581-6152, A.
Mascellani650000-0001-6362-5356, F. Nessi-Tedaldi0000-0002-4721-7966, F.
Pauss0000-0002-3752-4639, V. Perovic0009-0002-8559-0531, S.
Pigazzini0000-0002-8046-4344, M. Reichmann0000-0002-6220-5496, C.
Reissel0000-0001-7080-1119, T. Reitenspiess0000-0002-2249-0835, B.
Ristic0000-0002-8610-1130, F. Riti0000-0002-1466-9077, D. Ruini, D.A. Sanz
Becerra0000-0002-6610-4019, R. Seidita0000-0002-3533-6191, J.
Steggemann650000-0003-4420-5510, D. Valsecchi0000-0001-8587-8266, R.
Wallny0000-0001-8038-1613

Universität Zürich, Zurich, Switzerland C. Amsler670000-0002-7695-501X, P.
Bärtschi0000-0002-8842-6027, C. Botta0000-0002-8072-795X, D. Brzhechko, M.F.
Canelli0000-0001-6361-2117, K. Cormier0000-0001-7873-3579, R. Del Burgo, J.K.
Heikkilä0000-0002-0538-1469, M. Huwiler0000-0002-9806-5907, W.
Jin0009-0009-8976-7702, A. Jofrehei0000-0002-8992-5426, B.
Kilminster0000-0002-6657-0407, S. Leontsinis0000-0002-7561-6091, S.P.
Liechti0000-0002-1192-1628, A. Macchiolo0000-0003-0199-6957, P.
Meiring0009-0001-9480-4039, V.M. Mikuni0000-0002-1579-2421, U.
Molinatti0000-0002-9235-3406, A. Reimers0000-0002-9438-2059, P. Robmann, S.
Sanchez Cruz0000-0002-9991-195X, K. Schweiger0000-0002-5846-3919, M.
Senger0000-0002-1992-5711, Y. Takahashi0000-0001-5184-2265, R.
Tramontano0000-0001-5979-5299

National Central University, Chung-Li, Taiwan C. Adloff68, D. Bhowmik, C.M.
Kuo, W. Lin, P.K. Rout0000-0001-8149-6180, P.C. Tiwari410000-0002-3667-3843,
S.S. Yu0000-0002-6011-8516

National Taiwan University (NTU), Taipei, Taiwan L. Ceard, Y.
Chao0000-0002-5976-318X, K.F. Chen0000-0003-1304-3782, P.s. Chen, Z.g. Chen,
W.-S. Hou0000-0002-4260-5118, T.h. Hsu, Y.w. Kao, R. Khurana, G.
Kole0000-0002-3285-1497, Y.y. Li0000-0003-3598-556X, R.-S.
Lu0000-0001-6828-1695, E. Paganis0000-0002-1950-8993, X.f.
Su0009-0009-0207-4904, J. Thomas-Wilsker0000-0003-1293-4153, L.s. Tsai, H.y.
Wu, E. Yazgan0000-0001-5732-7950

High Energy Physics Research Unit, Department of Physics, Faculty of Science,
Chulalongkorn University, Bangkok, Thailand C.