BPH-20-001

BPH-20-001

# Measurement of the $CP$-violating phase $\phi_{\mathrm{s}}$ in the
$\PBzs\to\PJGy\,\PGf(1020)\to\PGmp\PGmm\,\PKp\PKm$ channel in proton-proton
collisions at $\sqrt{s}=13\TeV$

###### Abstract

The $CP$-violating weak phase $\phi_{\mathrm{s}}$ and the decay width
difference $\Delta\Gamma_{\mathrm{s}}$ between the light and heavy mass
eigenstates are measured with the CMS detector at the LHC in a sample of
$48\,500$ reconstructed $\PBzs\to\PJGy\,\PGf(1020)\to\PGmp\PGmm\,\PKp\PKm$
events. The measurement is based on a data sample corresponding to an
integrated luminosity of $96.4\fbinv$, collected in proton-proton collisions
at $\sqrt{s}=13\TeV$ in 2017–2018. To extract the values of
$\phi_{\mathrm{s}}$ and $\Delta\Gamma_{\mathrm{s}}$, a time-dependent and
flavor-tagged angular analysis of the $\PGmp\PGmm\PKp\PKm$ final state is
performed. The analysis employs a dedicated tagging trigger and a novel
opposite-side muon flavor tagger based on machine learning techniques. The
measurement yields $\phi_{\mathrm{s}}=-11\pm 50\stat\pm 10\syst\unit{mrad}$
and $\Delta\Gamma_{\mathrm{s}}=0.114\pm 0.014\stat\pm
0.007\syst{\unit{ps}^{-1}}$, in agreement with the standard model predictions.
When combined with the previous CMS measurement at $\sqrt{s}=8\TeV$, the
following values are obtained: $\phi_{\mathrm{s}}=-21\pm 44\stat\pm
10\syst\unit{mrad}$, $\Delta\Gamma_{\mathrm{s}}=0.1032\pm 0.0095\stat\pm
0.0048\syst{\unit{ps}^{-1}}$, a significant improvement over the 8result.

##  0.1 Introduction

Precision tests of the standard model (SM) of particle physics have become
increasingly important, since no direct evidence for new physics has been
found so far at the CERN LHC. Decays of mesons present important opportunities
to probe the consistency of the SM. In this Letter, a new measurement of the
$CP$-violating weak phase $\phi_{\mathrm{s}}$ and the decay width difference
$\Delta\Gamma_{\mathrm{s}}$ between the light
($\PB^{\mathrm{L}}_{\mathrm{s}}$) and heavy ($\PB^{\mathrm{H}}_{\mathrm{s}}$)
meson mass eigenstates is presented. Charge-conjugate states are implied
throughout, unless stated otherwise.

The weak phase $\phi_{\mathrm{s}}$ arises from the interference between direct
meson decays to a $CP$ eigenstate of $\PQc\PAQc\PQs\PAQs$ and decays through
mixing to the same final state. In the SM, $\phi_{\mathrm{s}}$ is related to
the elements of the Cabibbo–Kobayashi–Maskawa matrix via
$\phi_{\mathrm{s}}\simeq-2\beta_{\mathrm{s}}=-2\arg(-V_{\PQt\PQs}V_{\PQt\PQb}^{*}/V_{\PQc\PQs}V_{\PQc\PQb}^{*})$,
neglecting penguin diagram contributions, where $\beta_{\mathrm{s}}$ is one of
the angles of the unitary triangles. The current best determination of
$-2\beta_{\mathrm{s}}$ comes from a global fit to experimental data on hadron
and kaon decays. Assuming no physics beyond the SM (BSM) in the mixing and
decays, a $-2\beta_{\mathrm{s}}$ value of
$-36.96\,^{+0.72}_{-0.84}\unit{mrad}$ is determined [1]. New physics can
modify this phase via the contribution of BSM particles to mixing [2, 3].
Since the numerical value of $\phi_{\mathrm{s}}$ in the SM is known very
precisely, even a small deviation from this value would constitute evidence of
BSM physics. The decay width difference between the
$\PB^{\mathrm{L}}_{\mathrm{s}}$ and $\PB^{\mathrm{H}}_{\mathrm{s}}$
eigenstates, on the other hand, is predicted less precisely at
$\Delta\Gamma_{\mathrm{s}}=0.091\pm 0.013{\unit{ps}^{-1}}$ [4]. Its
measurement provides an important test for theoretical predictions and can be
used to further constrain new-physics effects [4].

The weak phase $\phi_{\mathrm{s}}$ was first measured by the Fermilab Tevatron
experiments [5, 6, 7, 8, 9], and then at the LHC by the ATLAS, CMS, and LHCb
experiments [10, 11, 12, 13, 14, 15, 16, 17, 18, 19], using
$\PBzs\to\PJGy\,\PGf(1020)$ (referred to as $\PBzs\to\PJGy\,\PGf$ in what
follows), $\PBzs\to\JPsi\,\PfDzP{980}$, and $\PBzs\to\JPsi\,\PSh^{+}\PSh^{-}$
decays, where $\PSh$ stands for a kaon or pion. Measurements of
$\phi_{\mathrm{s}}$ in decays to $\PGyP{2S}\Pgf$ and $\PDps\PDms$ were
performed by the LHCb Collaboration [20, 21].

In this Letter, CMS results on the $\PBzs\to\PJGy\,\PGf$ decay to the
$\PGmp\PGmm\PKp\PKm$ final state are presented, and possible additional
contributions to this final state from the $\PBzs\to\PJGy\,\PfDzP{980}$ and
nonresonant $\PBzs\to\PJGy\,\PKp\PKm$ decays are taken into account by
including a term for an additional $S$-wave amplitude in the decay model.
Compared to our previous measurement [14] at $\sqrt{s}=8\TeV$, we benefit from
the increase in the center-of-mass energy from 8 to 13that nearly doubles the
production cross section and a novel opposite-side (OS) muon flavor tagger.
The new tagger employs machine learning techniques and achieves better
discrimination power than previous methods. We also make use of a specialized
trigger that requires an additional (third) muon, which can be used for flavor
tagging, improving the tagging efficiency at the cost of a reduced number of
signal events. As a result, the new measurement, while based on a similar
number of candidates as the earlier one [14], allows us to double the
precision in the determination of $\phi_{\mathrm{s}}$, as well as measure some
of the parameters that were constrained to their world-average values in our
previous work [14]. At the same time, the precision on parameters that do not
benefit from the tagging information, such as $\Delta\Gamma_{\mathrm{s}}$, is
comparable to that in the previous measurement.

Final states that are mixtures of $CP$ eigenstates require an angular analysis
to separate the $CP$-odd and $CP$-even components. A time-dependent angular
analysis can be performed by measuring the decay angles of the final-state
particles and the proper decay length of the reconstructed candidate, which is
equal to the proper decay time $t$ multiplied by the speed of light, and
referred to as $ct$ in what follows.

In this measurement, we use the transversity basis [22] defined by the three
decay angles
$\Theta=(\theta_{\mathrm{T}},\psi_{\mathrm{T}},\varphi_{\mathrm{T}})$, as
illustrated in Fig. 1. The angles $\theta_{\mathrm{T}}$ and
$\varphi_{\mathrm{T}}$ are, respectively, the polar and azimuthal angles of
the in the rest frame of the meson, where the $x$ axis is defined by the
direction of the meson momentum and the $x$-$y$ plane is defined by the plane
of the $\PGf\to\PKp\PKm$ decay. The helicity angle $\psi_{\mathrm{T}}$ is the
angle of the meson momentum in the meson rest frame with respect to the
negative meson momentum direction.

Figure 1: Definition of the three angles $\theta_{\mathrm{T}}$,
$\psi_{\mathrm{T}}$, and $\varphi_{\mathrm{T}}$ describing the topology of the
$\PBzs\to\PJGy\,\PGf\to\PGmp\PGmm\,\PKp\PKm$ decay.

The differential decay rate of $\PBzs\to\PJGy\,\PGf\to\PGmp\PGmm\,\PKp\PKm$ is
described by a function $\mathcal{F}(\Theta,ct,\alpha)$, as in Ref. [23]:

$\frac{\rd^{4}\Gamma\\!\left(\PBzs\right)}{\rd\Theta\,\rd\\!\left(ct\right)}=\mathcal{F}(\Theta,ct,\alpha)\propto\sum^{10}_{i=1}O_{i}(ct,\alpha)\,g_{i}(\Theta),$
(1)

where $O_{i}$ are time-dependent functions, $g_{i}$ are angular functions, and
$\alpha$ is a set of physics parameters.

The functions $O_{i}(ct,\alpha)$ are:

$O_{i}(ct,\alpha)=N_{i}\re^{-\Gamma_{\mathrm{s}}t}\left[a_{i}\cosh\left(\frac{\Delta\Gamma_{\mathrm{s}}t}{2}\right)+b_{i}\sinh\left(\frac{\Delta\Gamma_{\mathrm{s}}t}{2}\right)+c_{i}\cos(\Delta
m_{\mathrm{s}}t)+d_{i}\sin(\Delta m_{\mathrm{s}}t)\right],$ (2)

where $\Delta m_{\mathrm{s}}$ ($\Delta\Gamma_{\mathrm{s}}$) is the absolute
mass (decay width) difference between the $\PB^{\mathrm{L}}_{\mathrm{s}}$ and
$\PB^{\mathrm{H}}_{\mathrm{s}}$ mass eigenstates, and $\Gamma_{\mathrm{s}}$ is
the average decay width, defined as the arithmetic average of the
$\PB^{\mathrm{L}}_{\mathrm{s}}$ and $\PB^{\mathrm{H}}_{\mathrm{s}}$ decay
widths. The functions $g_{i}(\Theta)$ and the parameters $N_{i}$, $a_{i}$,
$b_{i}$, $c_{i}$, and $d_{i}$ are defined in Table 0.1.

Angular and time-dependent terms of the signal model.  $i$
$g_{i}(\theta_{\mathrm{T}},\psi_{\mathrm{T}},\varphi_{\mathrm{T}})$ $N_{i}$
$a_{i}$ $b_{i}$ $c_{i}$ $d_{i}$ 1
$2\cos^{2}{\psi_{\mathrm{T}}}(1-\sin^{2}{\theta_{\mathrm{T}}}\cos^{2}{\varphi_{\mathrm{T}}})$
$\abs{A_{0}(0)}^{2}$ 1 $D$ $C$ $-S$ 2
$\sin^{2}{\psi_{\mathrm{T}}}(1-\sin^{2}{\theta_{\mathrm{T}}}\sin^{2}{\varphi_{\mathrm{T}}})$
$\abs{A_{\parallel}(0)}^{2}$ 1 $D$ $C$ $-S$ 3
$\sin^{2}{\psi_{\mathrm{T}}}\sin^{2}{\theta_{\mathrm{T}}}$
$\abs{A_{\perp}(0)}^{2}$ 1 $-D$ $C$ $S$ 4 $-\sin^{2}{\psi_{\mathrm{T}}}\sin
2\theta_{\mathrm{T}}\sin\varphi_{\mathrm{T}}$
$\abs{A_{\parallel}(0)}\abs{A_{\perp}(0)}$
$C\sin(\delta_{\perp}-\delta_{\parallel})$
$S\cos(\delta_{\perp}-\delta_{\parallel})$
$\sin(\delta_{\perp}-\delta_{\parallel})$
$D\cos(\delta_{\perp}-\delta_{\parallel})$ 5 $\frac{1}{\sqrt{2}}\sin
2\psi_{\mathrm{T}}\sin^{2}{\theta_{\mathrm{T}}}\sin 2\varphi_{\mathrm{T}}$
$\abs{A_{0}(0)}\abs{A_{\parallel}(0)}$ $\cos(\delta_{\parallel}-\delta_{0})$
$D\cos(\delta_{\parallel}-\delta_{0})$ $C\cos(\delta_{\parallel}-\delta_{0})$
$-S\cos(\delta_{\parallel}-\delta_{0})$ 6 $\frac{1}{\sqrt{2}}\sin
2\psi_{\mathrm{T}}\sin 2\theta_{\mathrm{T}}\cos\varphi_{\mathrm{T}}$
$\abs{A_{0}(0)}\abs{A_{\perp}(0)}$ $C\sin(\delta_{\perp}-\delta_{0})$
$S\cos(\delta_{\perp}-\delta_{0})$ $\sin(\delta_{\perp}-\delta_{0})$
$D\cos(\delta_{\perp}-\delta_{0})$ 7
$\frac{2}{3}(1-\sin^{2}{\theta_{\mathrm{T}}}\cos^{2}{\varphi_{\mathrm{T}}})$
$\abs{A_{\mathrm{S}}(0)}^{2}$ 1 $-D$ $C$ $S$ 8
$\frac{1}{3}\sqrt{6}\sin\psi_{\mathrm{T}}\sin^{2}{\theta_{\mathrm{T}}}\sin
2\varphi_{\mathrm{T}}$ $\abs{A_{\mathrm{S}}(0)}\abs{A_{\parallel}(0)}$
$C\cos(\delta_{\parallel}-\delta_{\mathrm{S}})$
$S\sin(\delta_{\parallel}-\delta_{\mathrm{S}})$
$\cos(\delta_{\parallel}-\delta_{\mathrm{S}})$
$D\sin(\delta_{\parallel}-\delta_{\mathrm{S}})$ 9
$\frac{1}{3}\sqrt{6}\sin\psi_{\mathrm{T}}\sin
2\theta_{\mathrm{T}}\cos\varphi_{\mathrm{T}}$
$\abs{A_{\mathrm{S}}(0)}\abs{A_{\perp}(0)}$
$\sin(\delta_{\perp}-\delta_{\mathrm{S}})$
$-D\sin(\delta_{\perp}-\delta_{\mathrm{S}})$
$C\sin(\delta_{\perp}-\delta_{\mathrm{S}})$
$S\sin(\delta_{\perp}-\delta_{\mathrm{S}})$ 10
$\frac{4}{3}\sqrt{3}\cos\psi_{\mathrm{T}}(1-\sin^{2}{\theta_{\mathrm{T}}}\cos^{2}{\varphi_{\mathrm{T}}})$
$\abs{A_{\mathrm{S}}(0)}\abs{A_{0}(0)}$
$C\cos(\delta_{0}-\delta_{\mathrm{S}})$
$S\sin(\delta_{0}-\delta_{\mathrm{S}})$ $\cos(\delta_{0}-\delta_{\mathrm{S}})$
$D\sin(\delta_{0}-\delta_{\mathrm{S}})$

The coefficients $C$, $S$, and $D$ contain the information about $CP$
violation, and are defined as:

$\displaystyle C$
$\displaystyle=\frac{1-\abs{\lambda}^{2}}{1+\abs{\lambda}^{2}},$
$\displaystyle S$
$\displaystyle=-\frac{2\abs{\lambda}\sin\phi_{\mathrm{s}}}{1+\abs{\lambda}^{2}},$
$\displaystyle D$
$\displaystyle=-\frac{2\abs{\lambda}\cos\phi_{\mathrm{s}}}{1+\abs{\lambda}^{2}},$

using the same sign convention as that in the LHCb measurement [16]. The
amount of $CP$ violation in the -system is given by the complex parameter
$\lambda$, defined as $\lambda=(q/p)(\overline{A}_{f}/A_{f})$, where $A_{f}$
($\overline{A}_{f})$ is the decay amplitude of the () meson to the final state
$f$, and the parameters $p$ and $q$ relate the mass and flavor eigenstates
through $\PB^{\mathrm{H}}_{\mathrm{s}}=p|\PBzs\rangle-q|\PABzs\rangle$ and
$\PB^{\mathrm{L}}_{\mathrm{s}}=p|\PBzs\rangle+q|\PABzs\rangle$ [24]. The
parameters $\abs{A_{\perp}}^{2}$, $\abs{A_{0}}^{2}$, and
$\abs{A_{\parallel}}^{2}$ are the magnitudes of the perpendicular,
longitudinal, and parallel transversity amplitudes of the
$\PBzs\to\PJGy\,\PGf$ decay, respectively; $\abs{A_{\mathrm{S}}}^{2}$ is the
magnitude of the $S$-wave amplitude from $\PBzs\to\PJGy\,\PfDzP{980}$ and
nonresonant $\PBzs\to\PJGy\,\PKp\PKm$ decays, and the parameters
$\delta_{\perp}$, $\delta_{0}$, $\delta_{\parallel}$, and
$\delta_{\mathrm{S}}$ are the respective strong phases.

Equation (1) represents the model for the meson decay, while the model for the
meson decay is obtained by changing the sign of the $c_{i}$ and $d_{i}$ terms
in Eq. (2).

##  0.2 The CMS detector

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

The silicon tracker measures charged particles within the range
$\abs{\eta}<2.5$. During the LHC running period when the data used in this
Letter were recorded, the silicon tracker consisted of 1856 silicon pixel and
15 148 silicon strip detector modules.

Muons are measured in the range $\abs{\eta}<2.4$, with detection planes made
using three technologies: drift tubes, cathode strip chambers, and resistive
plate chambers. The efficiency to reconstruct and identify muons is greater
than 96%. Matching muons to tracks measured in the silicon tracker results in
a relative transverse momentum () resolution, for muons with up to 100, of 1%
in the barrel and 3% in the endcaps [25].

Events of interest are selected using a two-tiered trigger system [26]. 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
$100\unit{kHz}$ within a fixed time interval of less than $4\mus$. 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, and reduces the event rate to around $1\unit{kHz}$ before data
storage.

A more detailed description of the CMS detector, together with a definition of
the coordinate system used and the relevant kinematic variables, can be found
in Ref. [27].

##  0.3 Event selection and simulated samples

The analysis is performed using data collected in proton-proton ($\Pp\Pp$)
collisions at $\sqrt{s}=13\TeV$ during 2017–2018, corresponding to an
integrated luminosity of $96.4\fbinv$. A trigger optimized for the detection
of hadrons decaying to mesons, along with an additional muon potentially
usable for flavor tagging, is used to collect the data sample for the
analysis. At L1, the trigger requires three muons, with the minimum
requirement on the highest (leading, $\PGm_{1}$) and second-highest
(subleading, $\PGm_{2}$) muons of $\pt>5$ and 3, respectively, and the dimuon
invariant mass $m_{\PGm_{1}\PGm_{2}}<9\GeV$. There is no requirement on the
third muon at L1. At the HLT, the three muons are required to be within the
CMS geometrical acceptance $\abs{\eta}<2.5$; two of these muons must be
oppositely charged, each have $\pt>3.5\GeV$, form a candidate with an
invariant mass in the range 2.95–3.25, and have a probability to originate
from a common vertex larger than 0.5%. The third muon is required to have
$\pt>2\GeV$ and can be used to infer the flavor of the meson at production (,
its particle/antiparticle state), exploiting semileptonic $\PQb\to\PGmm+\PX$
decays, as discussed further in Section 0.4.

Additional selection criteria are applied to events passing the HLT
requirements. The numerical values of the selection cuts have been optimized
with the help of the tmva package [28, 29], using a genetic algorithm, to
maximize the signal purity. First, meson candidates are constructed using
pairs of opposite-sign muons with $\pt>3.5\GeV$ and $\abs{\eta}<2.4$, and
compatible with originating from a common vertex, obtained from a Kalman fit
[30]. Candidates are accepted only if their invariant mass is within $150\MeV$
of the world-average meson mass [31]. Next, pairs of opposite-sign tracks
satisfying the high-purity requirement [32] with $\pt>1.2\GeV$ and
$\abs{\eta}<2.5$, not associated with the muons that form the candidate, are
used to form candidates. The candidates are selected if the track pair has an
invariant mass, assuming the kaon mass for both particles, within $10\MeV$ of
the world-average meson mass [31]. Finally, the and candidates are combined to
form candidates: a common vertex (“vertex”) is obtained from a fit with the
four tracks, two for muons and two for kaons. The invariant mass of the
candidate is obtained from a kinematic fit, where the invariant mass of the
two muons is constrained to the world-average meson mass [31]. The mass of the
candidate is not constrained since its natural width exceeds the mass
resolution.

Due to the high instantaneous luminosity of proton-proton collisions at the
LHC, several primary vertices (PVs) are reconstructed in each event. The
vertex that minimizes the angle between the candidate momentum vector and the
line connecting this vertex with the decay vertex is chosen as the production
vertex and is used to determine the characteristics of the candidate, such as
proper decay length. We used simulations to study if the PV selection
procedure introduces any bias in the measurement. It was found that in about
97% of the events, the selected PV is also the closest one to the point of
origin of the meson. The impact of choosing a different vertex in the
remaining cases on the final results is found to be negligible with respect to
the total systematic uncertainties discussed in Section 0.6. The proper decay
length is measured as $ct=cm^{\text{PDG}}_{\PBzs}L_{\text{xy}}/\pt$, where
$m^{\text{PDG}}_{\PBzs}$ is the world-average mass [31] and $L_{\text{xy}}$ is
the reconstructed transverse decay length, which is defined as the distance in
the transverse plane from the production vertex to the vertex. Additional
selection criteria are applied to candidates, requiring $\pt>11\GeV$, the
four-track vertex fit $\chi^{2}$ probability $>2\%$, an invariant mass in the
5.24–5.49range, and a proper decay length $ct>70\mum$, with an uncertainty
$\sigma_{ct}<50\mum$. The proper decay length uncertainty is obtained by
propagating the uncertainties in the decay distance and the of the candidate
to $ct$. In about 2% of the events more than one candidate is selected. In
these cases, the candidate with the highest vertex fit probability is chosen.
The impact of this choice on the measurement has been evaluated by redoing the
analysis using the candidate with the lowest vertex fit probability. No
sizable bias has been observed with respect to the total systematic
uncertainties discussed in Section 0.6. A total of $65\,500$
$\PBzs\to\PJGy\,\PGf$ candidates are selected.

Simulated event samples are used to measure the selection efficiency and the
flavor tagging performance. These samples are produced using the 8.230 Monte
Carlo (MC) event generator [33] with the underlying event tune CP5 [34] and
the parton distribution function set NNPDF3.1 [35]. The hadron decays are
modeled with the 1.6.0 package [36]. Final-state photon radiation is accounted
for in the simulation with 215.5 [37, 38]. The response of the CMS detector is
simulated using the package [39]. The effect of multiple collisions in the
same or neighboring bunch crossings (pileup) is accounted for by overlaying
simulated minimum bias events on the hard-scattering process. Simulated
samples are then reconstructed using the same software as for collision data.

The simulation is validated via comparison with background-subtracted data in
a number of control distributions. The candidate invariant mass distribution
after the signal selection is shown in Fig. 2, whereas the proper decay length
and its uncertainty distributions are shown in Fig. 3.

Figure 2:  The invariant mass distribution of the
$\PBzs\to\PJGy\,\PGf\to\PGmp\PGmm\,\PKp\PKm$ candidates in data. The vertical
bars on the points represent the statistical uncertainties. The solid line
represents a projection of the fit to data (as discussed in Section 0.5, solid
markers), the dashed line corresponds to the signal, the dotted line to the
combinatorial background, and the long-dashed line to the peaking background
from $\PBz\to\PJGy\,\PKstP{892}^{0}\to\PGmp\PGmm\,\PKp\PGpm$, as obtained from
the fit. The distribution of the differences between the data and the fit,
divided by the combined uncertainty in the data and the best fit function for
each bin (pulls) is displayed in the lower panel.

Figure 3: The $ct$ distribution (left) and its uncertainty (right) for the
$\PBzs\to\PJGy\,\PGf\to\PGmp\PGmm\,\PKp\PKm$ candidates in data. The notations
are as in Fig. 2.

##  0.4 Flavor tagging

The flavor of the candidate at production is determined with an OS flavor
tagging algorithm. The OS approach is based on the fact that quarks are
predominantly produced in $\bbbar$ pairs, and therefore one can infer the
initial meson flavor by determining the flavor of the other (“OS”) quark in
the event.

In this analysis, the flavor of the OS hadron is deduced by exploiting the
semileptonic $\PQb\to\PGmm+\PX$ decay, where the muon sign $\xi$ is used as
the tagging variable ($\xi=-1$ for ). This technique works on a probabilistic
basis. If no OS muon is found, the event is considered as untagged ($\xi=0$).
The tagging efficiency $\varepsilon_{\text{tag}}$ is defined as the fraction
of candidate events that are tagged. When a muon is found, the tag is defined
to be correct (“right tag”) if the flavor predicted using the muon sign and
the actual meson flavor at production coincide. The correlation between the
muon sign and the signal meson flavor is diluted by wrong tags (mistags)
originating from cascade $\PQb\to\PQc\to\PGmp+\PX$ decays, oscillation of the
OS or meson, and muons originating from other sources, such as meson and
charged pion and kaon decays. The mistag fraction $\omega_{\text{tag}}$ is
defined as the ratio between the number of wrongly tagged events and the total
number of tagged events. It is used to compute the dilution
${\mathcal{D}}\equiv 1-2\omega_{\text{tag}}$, which is a measure of the
performance degradation due to mistagged events. The tagging power
$P_{\text{tag}}\equiv\varepsilon_{\text{tag}}{\mathcal{D}}^{2}$ is the
effective tagging efficiency, which takes into account the dilution and is
used as a figure of merit in maximizing the algorithm performance.

To maximize the sensitivity of this measurement, we have developed a novel OS
muon tagger taking advantage of machine learning techniques. The use of deep
neural networks (DNNs) in the new tagger leads to lowering of the mistag
probability $\omega_{\text{tag}}$ and reducing of the related systematic
uncertainties. The use of a dedicated trigger, which requires an OS muon,
dramatically increases the fraction of tagged candidates compared to our
earlier measurement [14]. Taken together, these two improvements increase the
muon tagging performance by ${\approx}$20% compared to that in Ref. [14].

For each event, we search for a candidate OS muon consistent with originating
from the same production vertex as the signal meson. This tagging muon is
required to have $\pt>2\GeV$, $\abs{\eta}<2.4$, the longitudinal impact
parameter with respect to the production vertex
$\mathrm{IP}_{z}<1.0\unit{cm}$, and the distance from the candidate momenta in
the $(\eta,\phi)$ plane $\Delta R_{\eta,\phi}>0.4$. Tracks that belong to the
reconstructed $\PBzs\to\PJGy\,\PGf\to\PGmp\PGmm\,\PKp\PKm$ decay are
explicitly excluded from consideration. In order to reduce the contamination
from light-flavor hadrons misreconstructed as tagging muons, a discriminator
based on a DNN was developed using the Keras library [40] within the tmva
toolkit. This discriminator, called the “DNN against light hadrons” in the
following, uses 25 input features related to the muon kinematics and
reconstruction quality, and is trained with $3.5\times 10^{6}$ simulated muon
candidates of which $2.5\times 10^{5}$ are misreconstructed hadrons. The
following DNN hyperparameters are optimized through a grid scan to maximize
the discrimination power: number of layers, number of neurons for each layer,
and the dropout probability. No signs of overtraining are observed at the
chosen hyperparameters configuration when comparing the output distributions
from the testing and training samples. Tagging muons are required to pass a
working point of the DNN output that has an efficiency of ${\approx}$98% for
genuine muons and ${\approx}$33% for misreconstructed light-flavor hadrons,
when evaluated using muon candidates reconstructed with the CMS particle-flow
(PF) algorithm [41]. In ${\approx}$3% of the events where more than one
tagging muon candidate passes all the above selections, only the highest one
is kept.

Another DNN is used to further discriminate the right- and wrong-tag muons, as
well as to predict the mistag probability on a per-event basis. This DNN,
referred to as the muon tagger DNN, has been developed using the Keras library
within the tmva toolkit, based on simulated
$\PBzs\to\PJGy\,\PGf\to\PGmp\PGmm\,\PKp\PKm$ events, and calibrated with self-
tagging $\PBpm\to\PJGy\,\PKpm$ MC and data samples, as described below.

The input features of the muon tagger DNN are of two kinds: muon variables and
cone variables. The muon variables are the muon , $\eta$, transverse and
longitudinal impact parameters with respect to the production vertex, along
with their uncertainties, the distance $\Delta R_{\eta,\phi}$ to the signal
candidate, and the discriminant of the DNN against light hadrons. The cone
variables are related to the activity in a cone of radius $\Delta
R_{\eta,\,\phi}=0.4$ around the muon momentum direction and include the
relative PF isolation [41], the scalar sum of all additional tracks within the
cone, the sum of their charges weighted by the track , the muon relative
momentum and $\Delta R_{\eta,\,\phi}$ with respect to the vector sum of the
momenta of all additional tracks within the cone, and the ratio of the energy
of the muon to the total energy of all additional tracks within the cone
(assuming the pion mass for each track). The muon tagger DNN is trained on
$2.8\times 10^{5}$ simulated $\PBzs\to\PJGy\,\PGf$ events, of which about
$85\,000$ have a wrong tag. Its structure is optimized similarly to that for
the DNN against light hadrons. The optimal DNN has three dense layers of 200
neurons, each with a rectified linear unit activation function. A dropout
layer with a dropout probability of 40% is placed after each dense layer. The
cross-entropy loss function and the Adam optimizer [42] are used. The DNN is
constructed in such a way that its output score $d$ is equal to the
probability of tagging the event correctly. Therefore, the per-event mistag
probability is simply $\omega_{\text{evt}}=1-d$.

The output $d$ of the tagger is calibrated using a self-tagging data sample of
$\PBpm\to\PJGy\,\PKpm\to\PGmp\PGmm\,\PKpm$ events, where the charge of the
kaon corresponds to the charge and flavor of the meson. The same trigger and
candidate reconstruction requirements as for the signal sample are applied. A
charged particle with $\pt>1.6\GeV$, assumed to be a kaon, is combined in a
kinematic fit with the dimuon pair to form the candidate. The calibration is
performed separately for the 2017 and 2018 data samples, by comparing the
measured mistag fraction ($\omega_{\text{meas}}$) with the
$\omega_{\text{evt}}$ predicted by the muon tagger DNN. The events are divided
into 100 bins in $\omega_{\text{evt}}$ and the right- and wrong-tag events are
separately counted in each bin to extract the corresponding
$\omega_{\text{meas}}$ value. The signal in each bin is discriminated from the
background via a binned likelihood fit to the $\PJGy\PKpm$ invariant mass
distribution in the 5.10–5.65range.

The calibration results for the 2017 and 2018 data are shown in Fig. 4. The
data points are fitted with a linear function $a+b\omega_{\text{evt}}$. The
calibration parameters returned by the fit for the 2017 (2018) data samples
are $a=-0.0010\pm 0.0040$, $b=1.012\pm 0.013$ ($a=0.0031\pm 0.0031$,
$b=1.011\pm 0.010$), statistically compatible with a unit slope and zero
offset.

Figure 4:  Results of the calibration of the per-event mistag probability
$\omega_{\text{evt}}$ based on $\PBpm\to\PJGy\,\PKpm\to\PGmp\PGmm\,\PKpm$
decays from the 2017 (left) and 2018 (right) data samples. The vertical bars
represent the statistical uncertainties. The solid line shows a linear fit to
data (solid markers). The pull distributions between the data and the fit
function in each bin are shown in the lower panels.

The calibration of the DNN output is also verified with a procedure similar to
that described above using an independent sample of simulated
$\PBzs\to\PJGy\,\PGf$ and $\PBpm\to\PJGy\,\PKpm$ events. The reconstructed and
mesons are matched to the generated ones in order to find their true flavor at
production. In general, the measured mistag probability is predicted very
accurately by $\omega_{\text{evt}}$ over the entire measured range for all the
examined samples and processes, with more than 90% of the tagged events
falling in the $\omega_{\text{evt}}=$ 0.1–0.5 range in all cases. Residual
differences are well approximated by linear functions with slopes close to
unity and offsets consistent with zero. The $\chi^{2}$ per degree of freedom
values for all fits are below 2. We conclude that the value of
$\omega_{\text{evt}}$ returned by the tagging DNN is a good approximation of
the true mistag probability in data, with minor residual differences taken
into account with calibration functions.

The calibrated flavor tagger performance, evaluated using
$\PBpm\to\PJGy\,\PKpm$ events in data, is shown in Table 0.4. A tagging
efficiency of ${\approx}$50% and a tagging power of ${\approx}$10% are
achieved in both the 2017 and 2018 data samples. The efficiency is much higher
than the semileptonic hadron branching fraction due to the requirement of an
additional OS muon at the HLT, as described in Section 0.3.

Possible differences in the mistag probability calibration between the and
samples, as well as the statistical uncertainties in the calibration
parameters and possible variations from linearity of the calibration function,
are considered as systematic uncertainties and described in Section 0.6.

Calibrated opposite-side muon tagger performance evaluated using
$\PBpm\to\PJGy\,\PKpm$ events in the 2017 and 2018 data samples. The
uncertainties shown are statistical only.  Data sample
$\varepsilon_{\text{tag}}$ (%) $\omega_{\text{tag}}$ (%) $P_{\text{tag}}$ (%)
2017 $45.7\pm 0.1$ $27.1\pm 0.1$ $\hphantom{1}9.6\pm 0.1$ 2018 $50.9\pm 0.1$
$27.3\pm 0.1$ $10.5\pm 0.1$

##  0.5 Maximum-likelihood fit

An unbinned multidimensional extended maximum-likelihood fit is performed on
the combined data samples using 8 observables as input: the candidate
invariant mass $m_{\PBzs}$, the three decay angles $\Theta$ of the
reconstructed candidate, the flavor tag decision $\xi$, the mistag fraction
$\omega_{\text{evt}}$, the proper decay length of the candidate $ct$, and its
uncertainty $\sigma_{ct}$.

From the multidimensional fit, the physics parameters of interest
$\phi_{\mathrm{s}}$, $\Delta\Gamma_{\mathrm{s}}$, $\Gamma_{\mathrm{s}}$,
$\Delta m_{\mathrm{s}}$, $\abs{\lambda}$, the squares of amplitudes
$\abs{A_{\perp}}^{2}$, $\abs{A_{0}}^{2}$, $\abs{A_{\mathrm{S}}}^{2}$, and the
strong phases $\delta_{\parallel}$, $\delta_{\perp}$, and
$\delta_{\mathrm{S}\perp}$ are determined, where $\delta_{\mathrm{S}\perp}$ is
defined as the difference $\delta_{\mathrm{S}}-\delta_{\perp}$. The
$\PBzs\to\PJGy\,\PGf$ amplitudes are normalized to unity by constraining
$\abs{A_{\parallel}}^{2}$ to $1-\abs{A_{\perp}}^{2}-\abs{A_{0}}^{2}$. The fit
model is validated with simulated pseudo-experiments and with simulated
samples with different input parameter sets.

The likelihood function is composed of the probability density functions
(pdfs) describing the signal and background components. The likelihood fit
algorithm is implemented using the RooFit package [28, 43]. The signal and
background pdfs are formed as the product of functions that model the
invariant mass distribution and the time-dependent decay rates of the
reconstructed candidates. In addition, the signal pdf includes the efficiency
functions. The event pdf $P$ is defined as:

$P=\frac{N_{\text{sig}}}{N_{\text{tot}}}\,P_{\text{sig}}+\frac{N_{\text{bkg}}}{N_{\text{tot}}}\,P_{\text{bkg}},$
(3)

where

$P_{\text{sig}}=\varepsilon(ct)\,\varepsilon(\Theta)\,[\widetilde{\mathcal{F}}(\Theta,ct,\alpha)\otimes
G(ct,\sigma_{ct})]\,P_{\text{sig}}(m_{\PBzs})\,P_{\text{sig}}(\sigma_{ct})\,P_{\text{sig}}(\xi)$
(4)

and

$P_{\text{bkg}}=P_{\text{bkg}}(\cos{\theta_{T}},\phi_{T})\,P_{\text{bkg}}(\cos{\psi_{\mathrm{T}}})\,P_{\text{bkg}}(ct)\,P_{\text{bkg}}(m_{\PBzs})\,P_{\text{bkg}}(\sigma_{ct})P_{\text{bkg}}(\xi).$
(5)

The corresponding negative log likelihood is:

$-\ln{\mathcal{L}}=-\sum^{N_{\text{evt}}}_{i=0}\ln{P_{i}}+N_{\text{tot}}-N_{\text{evt}}\ln{N_{\text{tot}}}.$
(6)

Here, $P_{\text{sig}}$ and $P_{\text{bkg}}$ are the pdfs that describe the
$\PBzs\to\PJGy\,\PGf\to\PGmp\PGmm\,\PKp\PKm$ signal and background
contributions, respectively. The yields of signal and background events are
$N_{\text{sig}}$ and $N_{\text{bkg}}$, respectively, $N_{\text{tot}}$ is their
sum, and $N_{\text{evt}}=65\,500$ is the number of candidates selected in
data. The pdf $\widetilde{\mathcal{F}}(\Theta,ct,\alpha)$ is the differential
decay rate function $\mathcal{F}(\Theta,ct,\alpha)$ defined in Eq. (1),
modified to include the flavor information $\xi$ and the dilution term
$(1-2\omega_{\text{evt}})$, which are applied as multiplicative factors to
each of the $c_{i}$ and $d_{i}$ terms in Eq. (2). In the
$\widetilde{\mathcal{F}}$ expression, the value of $\delta_{0}$ is set to
zero, following a general convention [6, 8], and the value of
$\Delta\Gamma_{\mathrm{s}}$ is constrained to be positive, based on the LHCb
measurement [44]. All the parameters of the pdfs are allowed to float in the
final fit, unless explicitly stated otherwise.

The functions $\varepsilon(ct)$ and $\varepsilon(\Theta)$ model the dependence
of the signal reconstruction efficiency on the proper decay length and the
three angles of the transversity basis, respectively. The proper decay length
efficiency is parameterized with a fourth-order Chebyshev polynomial
multiplied by an exponential function with a negative slope, while the angular
efficiency is parameterized with spherical harmonics and Legendre polynomials
up to order six. Both parameterizations are obtained from fits to the
respective efficiency histograms in $\PBzs\to\PJGy\,\PGf$ simulated events,
and are fixed in the fit to data.

The term $G(ct,\sigma_{ct})$ is a Gaussian resolution function, which makes
use of the per-event decay length uncertainty $\sigma_{ct}$, scaled by a
correction factor $\kappa$ introduced to account for the residual effects when
the decay length uncertainty is used to model the $ct$ resolution. The value
of $\kappa$ is estimated using simulated samples and is equal to
${\approx}$1.2 for both the 2017 and 2018 data samples.

The signal mass pdf $P_{\text{sig}}(m_{\PBzs})$ is a Johnson’s
$S_{\mathrm{U}}$ distribution [45], while the decay length uncertainty pdf
$P_{\text{sig}}(\sigma_{ct})$ is described by the sum of two Gamma
distributions. These pdfs best model each individual variable in one-
dimensional fits to simulated samples.

The background pdf contains two terms to model both the combinatorial
background and the peaking background, dominated by
$\PBz\to\PJGy\,\PKstP{892}^{0}\to\PGmp\PGmm\,\PKp\PGpm$, where the pion is
assumed to be a kaon candidate. The background from
$\PGLzb\to\PJGy\,\Pp\PKm\to\PGmp\PGmm\,\Pp\PKm$, where the proton is assumed
to be a kaon candidate, is estimated using simulated events to have a
negligible effect on the fit results compared to the systematic uncertainties
discussed in Section 0.6. The background invariant mass pdf
$P_{\text{bkg}}(m_{\PBzs}$) is described by an exponential function for the
combinatorial background and a Johnson’s $S_{\mathrm{U}}$ distribution for the
peaking background. The background decay length pdf $P_{\text{bkg}}(ct)$ is
described by the sum of two exponential distributions for the combinatorial
background, while a single exponential distribution is used for the peaking
background. The angular parts of the background pdfs
$P_{\text{bkg}}(\cos\theta_{\mathrm{T}},\varphi_{\mathrm{T}})$ and
$P_{\text{bkg}}(\cos\psi_{\mathrm{T}})$ are described analytically by a series
of Legendre polynomials for $\cos\theta_{\mathrm{T}}$ and
$\cos\psi_{\mathrm{T}}$, and sinusoidal functions for $\varphi_{\mathrm{T}}$.
For the $\cos\theta_{\mathrm{T}}$ and $\varphi_{\mathrm{T}}$ variables, a two-
dimensional pdf is used to take into account a possible correlation between
the two. The background decay length uncertainty pdf
$P_{\text{bkg}}(\sigma_{ct})$ is described by a sum of two Gamma distributions
for the combinatorial background, while the peaking background is fixed to
that for the signal.

The tag pdfs are defined as $P(\xi)=1-\varepsilon_{\text{tag}}$ for the
untagged events ($\xi=0$) and $P(\xi)=\varepsilon_{\text{tag}}(1\pm
A_{\text{tag}})/2$ for the tagged ones ($\xi=\pm 1$), where
$\varepsilon_{\text{tag}}$ is the tagging efficiency and $A_{\text{tag}}$ is
the tagging asymmetry, defined as the difference between the numbers of
positively and negatively tagged events ($\xi=\pm 1$) divided by the total
number. The measured tagging asymmetry is found to be compatible with zero.

The correlation between the different fit components has been studied in both
data and simulations, and found to be negligible.

The peaking background part of $P_{\text{bkg}}$ is determined using simulated
samples, while the initial combinatorial background part is found from a fit
to the invariant mass sidebands 5.24–5.28and 5.45–5.49in data, and then left
free to float in the final fit, starting from this initial pdf. The signal and
background components of the decay length uncertainty pdf are fixed to the
ones obtained from a two-dimensional fit together with the invariant mass pdf.
The 2017 and 2018 data samples are fitted simultaneously. The joint likelihood
function of the simultaneous fit shares the decay rate model, the invariant
mass pdfs, the peaking background model, and the lifetime and angular
components of the combinatorial background model between the two samples. The
number of signal and background events are measured separately in each data
sample, as is the tagging efficiency. The efficiency functions,
$P_{\mathrm{}}(\sigma_{ct})$ pdfs, tag pdfs, and $\kappa$ factors are also
specific to each data sample.

##  0.6 Systematic uncertainties and results

The results of the fit with their statistical and systematic uncertainties are
given in Table 0.6, whereas the statistical correlations between the measured
parameters are reported in Appendix .9. Statistical uncertainties are obtained
from the increase in $-\log{\mathcal{L}}$ by 0.5, whereas systematic
uncertainties are described below and summarized in Table 0.6. The measured
number of $\PBzs\to\PJGy\,\PGf\to\PGmp\PGmm\,\PKp\PKm$ signal events from the
fit is $48\,500\pm 250$. The distributions of the input observables and the
corresponding fit projections are shown in Figs. 2, 3, and 5.

Results of the fit to data. Statistical uncertainties are obtained from the
increase in $-\log{\mathcal{L}}$ by 0.5, whereas systematic uncertainties are
described below and summarized in Table 0.6.  Parameter Fit value Stat. uncer.
Syst. uncer. $\phi_{\mathrm{s}}$ [mrad] $-11$ $\pm\,50$ $\pm\,10$
$\Delta\Gamma_{\mathrm{s}}$ [ps-1] $0$ $.114$ $\pm\,0.014$ $\pm\,0.007$
$\Delta m_{\mathrm{s}}~{}$[$\hslash\unit{ps}^{-1}$] $17$ $.51$
${}^{+\,0.10}_{-\,0.09}$ $\pm\,0.03$ $\abs{\lambda}$ $0$ $.972$ $\pm\,0.026$
$\pm\,0.008$ $\Gamma_{\mathrm{s}}$ [ps-1] $0$ $.6531$ $\pm\,0.0042$
$\pm\,0.0026$ $\abs{A_{0}}^{2}$ $0$ $.5350$ $\pm\,0.0047$ $\pm\,0.0049$
$\abs{A_{\perp}}^{2}$ $0$ $.2337$ $\pm\,0.0063$ $\pm\,0.0045$
$\abs{A_{\mathrm{S}}}^{2}$ $0$ $.022$ ${}^{+\,0.008}_{-\,0.007}$ $\pm\,0.016$
$\delta_{\parallel}$ [rad] $3$ $.18$ $\pm\,0.12$ $\pm\,0.03$ $\delta_{\perp}$
[rad] $2$ $.77$ $\pm\,0.16$ $\pm\,0.05$ $\delta_{\mathrm{S}\perp}$ [rad] $0$
$.221$ ${}^{+\,0.083}_{-\,0.070}$ $\pm\,0.048$

Figure 5: The angular distributions $\cos\theta_{\mathrm{T}}$ (left),
$\cos\psi_{\mathrm{T}}$ (middle), and $\varphi_{\mathrm{T}}$ (right) for the
candidates and the projections from the fit. The notations are as in Fig. 2.

Several sources of systematic uncertainties in the physics parameters are
studied by testing the various assumptions made in the fit model and those
associated with the fitting procedure.

Summary of the systematic uncertainties. The dashes () mean that the
corresponding uncertainty is not applicable. The total systematic uncertainty
is obtained as the quadratic sum of the individual contributions.
$\phi_{\mathrm{s}}$ $\Delta\Gamma_{\mathrm{s}}$ $\Delta m_{\mathrm{s}}$
$\abs{\lambda}$ $\Gamma_{\mathrm{s}}$ $\abs{A_{0}}^{2}$ $\abs{A_{\perp}}^{2}$
$\abs{A_{\mathrm{S}}}^{2}$ $\delta_{\parallel}$ $\delta_{\perp}$
$\delta_{\mathrm{S}\perp}$ [mrad] [ps-1] [$\hslash\unit{ps}^{-1}$] [ps-1]
[rad] [rad] [rad] Statistical uncertainty $50$ $0.014$ $0.10$ $0.026$ $0.0042$
$0.0047$ $0.0063$ $0.0077$ $0.12$ $0.16$ $0.083$ Model bias $7.9$ $0.0019$
$0.0035$ $0.0005$ $0.0002$ $0.0012$ $0.001$ $0.020$ $0.016$ $0.006$ Model
assumptions $0.0046$ $0.0003$ $0.0013$ $0.001$ $0.017$ $0.019$ $0.011$ Angular
efficiency $3.8$ $0.0006$ $0.007$ $0.0057$ $0.0002$ $0.0008$ $0.0010$ $0.002$
$0.006$ $0.015$ $0.015$ Proper decay length efficiency $0.3$ $0.0062$ $0.001$
$0.0002$ $0.0022$ $0.0014$ $0.0023$ $0.001$ $0.001$ $0.002$ $0.002$ Proper
decay length resolution $3.5$ $0.0009$ $0.021$ $0.0015$ $0.0006$ $0.0007$
$0.0009$ $0.007$ $0.006$ $0.025$ $0.022$ Data/simulation difference $0.6$
$0.0008$ $0.004$ $0.0003$ $0.0003$ $0.0044$ $0.0029$ $0.007$ $0.007$ $0.007$
$0.028$ Flavor tagging $0.5$ $<$$10^{-4}$ $0.006$ $0.0002$ $<$$10^{-4}$
$0.0003$ $<$$10^{-4}$ $<$$10^{-3}$ $0.001$ $0.007$ $0.001$ Sig./bkg.
$\omega_{\text{evt}}$ difference $3.0$ $0.0005$ $0.0008$ $0.006$ Peaking
background $0.3$ $0.0008$ $0.011$ $<$$10^{-4}$ $0.0002$ $0.0005$ $0.0002$
$0.003$ $0.005$ $0.007$ $0.011$ $S$-$P$ wave interference $0.0010$ $0.019$
$0.0005$ $0.0005$ $0.013$ $0.019$ $0.019$ $P(\sigma_{ct})$ uncertainty
$<$$10^{-1}$ $0.0019$ $0.028$ $0.0004$ $0.0008$ $0.0006$ $0.0008$ $0.001$
$0.001$ $0.002$ $0.005$ Total systematic uncertainty $10.0$ $0.0070$ $0.032$
$0.0083$ $0.0026$ $0.0049$ $0.0045$ $0.016$ $0.028$ $0.045$ $0.048$

Model bias: Possible biases in the fitting procedure are evaluated by
generating 1000 pseudo-experiments, each statistically equivalent to the data
samples, from the fitted model in data (referred to as “nominal-model pseudo-
experiments” in what follows). Each of them is fitted with the nominal model,
and the pull distributions (, the difference divided by the combined
uncertainty) between the parameters obtained from the fit and their input
values are produced. Each pull distribution is fitted with a Gaussian
function, and the estimated central value is taken as the corresponding
systematic uncertainty, if different from zero by more than its error. To
avoid double-counting this uncertainty, whenever pseudo-experiments are used
to evaluate other systematic uncertainties, the model bias is always
subtracted. In these cases, the corresponding pull distributions are compared
to those obtained with the nominal-model pseudo-experiments. If the mean of
the pull distribution differs from the mean of the nominal-model distribution
by more than their combined RMS, the difference is taken as the corresponding
systematic uncertainty.

Model assumptions: The assumptions made in defining the likelihood functions
are tested by generating pseudo-experiments with different hypotheses and
fitting the samples with the nominal model. The following assumptions are
tested: signal and background invariant mass models, background proper decay
length model, and background angular model. Pull distributions with respect to
the input values are used to evaluate the systematic uncertainty, as described
in the “model bias” paragraph.

Angular efficiency: The systematic uncertainty related to the limited MC event
count used to estimate the angular efficiency function is evaluated by
regenerating the efficiency histograms 1000 times using the reference one,
with the fit repeated after reestimating the efficiency. The root mean square
(RMS) of the obtained physics parameter distributions is taken as the
systematic uncertainty.

Proper decay length efficiency: The proper decay length efficiency is first
validated by fitting the proper decay length distribution in the control
$\PBpm\to\PJGy\,\PKpm$ channel, using several different data-taking periods.
Each fit is performed applying the efficiency function evaluated using
simulated $\PBpm\to\PJGy\,\PKpm$ samples with the same procedure used for the
$\PBzs\to\PJGy\,\PGf$ analysis. We consider eight different data-taking
periods, each with the number of candidates comparable with the number of
signal candidates in the sample used in the analysis. We also consider the
2017 and 2018 data-taking periods as two additional large control data sets.
The results are in good agreement with the world-average meson lifetime [31],
with differences no larger than 1.5 standard deviations, showing no bias or
instabilities during the data taking. Having verified that the efficiency
parameterization does not introduce any noticeable bias, we evaluate the
related systematic uncertainty by varying the parameters of the proper decay
length efficiency function within their statistical uncertainties. The RMS of
the distribution of each extracted physics parameter of interest with respect
to the nominal fit value is taken as the corresponding systematic uncertainty.
We assign a systematic uncertainty to the efficiency model by repeating the
fit using the efficiency histogram instead of a smooth efficiency function,
and taking the difference from the nominal result as the uncertainty.

Proper decay length resolution: A systematic uncertainty is assigned to the
proper decay length resolution by varying the $\kappa$ correction factor by
${\pm}10\%$, as estimated from a data-to-simulation comparison, repeating the
fit, and taking the largest difference from the nominal result as the
uncertainty. We also evaluate a systematic uncertainty related to the
assumption that $\kappa$ is independent of the proper decay length, by
parametrizing $\kappa$ as a function of $ct$ using simulated samples. A
systematic uncertainty is assigned with the same methodology used to evaluate
the “model assumption” systematic uncertainties, using the $\kappa(ct)$
parametrization as an alternative hypothesis.

Data/simulation difference: The efficiency parametrization is found to be very
sensitive to the muon and kaon , and meson rapidity distributions, hence a
systematic uncertainty is assigned to cover the differences in each of these
variables, between data and simulation. The effect is evaluated by reweighting
the simulated distributions in each variable to agree with the data. The same
weights are applied to the simulated samples used to estimate the
efficiencies, which are then recomputed. The fit is repeated in each case and
the sum in quadrature of the differences from the nominal result is taken as
the systematic uncertainty.

Flavor tagging: The uncertainties associated with the flavor tagging are
propagated by varying the parameters of the mistag probability calibration
curves within their statistical uncertainties. For each variation, new
calibration curves are produced and the data are refitted. The RMS of each
fitted parameter distribution is then taken as the corresponding systematic
uncertainty. We also evaluate the effect of the assumption that the signal and
calibration channels have the same mistag calibration. The difference between
the and calibrations is evaluated using simulated samples and is taken as the
systematic uncertainty. The effect of the calibration function shape is
evaluated by repeating the fit using a third-order polynomial and taking the
difference with respect to the nominal result as the systematic uncertainty.
The combined contribution of the three sources to the total systematic
uncertainty is negligible.

Different $\omega_{\text{evt}}$ distribution in signal and background: A
systematic uncertainty is assigned to the possible differences in the mistag
probabilities between signal and background. The separate signal and
background $\omega_{\text{evt}}$ distributions in data are first measured by
using the candidate invariant mass signal and sidebands regions. These
distributions are separately modeled using the Kernel Density Estimation
method [46, 47] and added to the fitting model. One thousand pseudo-
experiments are generated and pull distributions with respect to the input
values are used to evaluate the systematic uncertainty, as described in the
“model bias” paragraph.

Peaking background: The systematic uncertainty related to the fixed yield of
the peaking background component is evaluated by repeating the fit using a
different yield obtained from a $\PBz\to\PJGy\,\PKstP{892}^{0}$ control sample
in data. The difference with respect to the nominal result is taken as the
systematic uncertainty. A systematic uncertainty is also assigned to the
proper decay length modeling of the peaking background by forcing the lifetime
to match the world-average value [31], repeating the fit, and taking the
difference from the nominal result as the systematic uncertainty.

$S$-$P$ wave interference: The fit model does not take into account the
difference in the invariant mass dependence between the $P$-wave from the
$\PBzs\to\PJGy\,\PGf$ decay and the $S$-wave, which modifies their
interference by a factor $k_{\mathrm{SP}}$. The corresponding systematic
uncertainty is estimated using pseudo-experiments. The $k_{\text{SP}}$ factor
is computed by integrating the $P$\- and $S$-wave interference term in the
candidate mass range, assuming that the $P$-wave amplitude is described by a
relativistic Breit–Wigner distribution and the $S$-wave amplitude by a
constant, and found to be $k_{\mathrm{SP}}=0.54$. Different $S$-wave
lineshapes are found to lead to very similar values of $k_{\mathrm{SP}}$, with
a variation no larger than ${\approx}$2%. One thousand pseudo-experiments are
generated applying $k_{\mathrm{SP}}=0.54$ to the $i=8,\,9,\,10$ terms in Table
0.1 related to the $S$\- and $P$-wave interference. Pull distributions with
respect to the input values are used to evaluate the systematic uncertainty,
as described in the “model bias” paragraph. The parameters
$\abs{A_{\mathrm{S}}}^{2}$ and $\Delta m_{\mathrm{s}}$ are the only ones whose
total uncertainty is affected significantly by this approximation.

$P(\sigma_{ct})$ uncertainty: In the fit to data the proper decay length
uncertainty pdf is fixed to the one obtained from a pre-fit, as described in
Section 0.5. A systematic uncertainty is assigned by sampling this
distribution 1000 times, using the parameter uncertainties obtained from the
pre-fit. Each time the fit to data is repeated and the standard deviation of
the obtained physics parameter distributions is taken as the systematic
uncertainty.

A summary of the systematic uncertainties is given in Table 0.6. After adding
the systematic uncertainties in quadrature, we measure the following values of
the $CP$-violating phase and the width difference between the two mass
eigenstates:

$\displaystyle\phi_{\mathrm{s}}=-11\pm 50\stat\pm 10\syst\unit{mrad},$
$\displaystyle\Delta\Gamma_{\mathrm{s}}=0.114\pm 0.014\stat\pm
0.007\syst{\unit{ps}^{-1}}.$

The $\abs{\lambda}$ parameter is measured to be $\abs{\lambda}=0.972\pm
0.026\stat\pm 0.008\syst$, consistent with no direct $CP$ violation
($\abs{\lambda}=1$). The average of the heavy and light mass eigenstate decay
widths is determined to be $\Gamma_{\mathrm{s}}=0.6531\pm 0.0042\stat\pm
0.0026\syst{\unit{ps}^{-1}}$, consistent with the world-average value
$\Gamma_{\mathrm{s}}=0.6624\pm 0.0018{\unit{ps}^{-1}}$ [31]. The mass
difference between the heavy and light meson mass eigenstates is measured to
be $\Delta m_{\mathrm{s}}=17.51\,^{+\,0.10}_{-\,0.09}\stat\pm
0.03\syst\,\hslash\unit{ps}^{-1}$, consistent with the theoretical prediction
$\Delta m_{\mathrm{s}}=18.77\pm 0.86\,\hslash\unit{ps}^{-1}$ [4], and in
slight tension with the world-average value $\Delta m_{\mathrm{s}}=17.757\pm
0.021\,\hslash\unit{ps}^{-1}$ [31]. The uncertainties in all these measured
parameters are dominated by the statistical component. This analysis
represents the first measurement by CMS of the mass difference $\Delta
m_{\mathrm{s}}$ between the heavy and light mass eigenstates and of the direct
$CP$ observable $\abs{\lambda}$.

##  0.7 Combination with 8 results

The results presented in this Letter are in agreement with the earlier CMS
result at a center-of-mass energy of 8 [14]. As explained in Section 0.1, both
measurements are performed with a similar number of events, with the one at
$\sqrt{s}=13\TeV$ having a higher tagging efficiency. This leads to an
improvement in the uncertainty in quantities that require tagging, such as
$\phi_{\mathrm{s}}$, while but the uncertainties in those that do not use
tagging, such as $\Delta\Gamma_{\mathrm{s}}$, depend on the raw number of
events and are not improved relative to the $8\TeV$ result. The two sets of
results are combined using the BLUE method [48, 49] as implemented in the root
package [50, 51, 52] using the following physics parameters:
$\phi_{\mathrm{s}}$, $\Delta\Gamma_{\mathrm{s}}$, $\Gamma_{\mathrm{s}}$,
$\abs{A_{0}}^{2}$, $\abs{A_{\perp}}^{2}$, $\abs{A_{\mathrm{S}}}^{2}$,
$\delta_{\parallel}$, $\delta_{\perp}$, and $\delta_{\mathrm{S}\perp}$. The
statistical correlations between the parameters obtained in each measurement
are taken into account as well as the correlations of the systematic
uncertainties discussed in Section 0.6. Different sources of systematic
uncertainties are assumed to be uncorrelated. The systematic uncertainty
correlation between the parameters of the $8\TeV$ result is assumed to be
zero. This assumption has been found to not impact the results in a noticeable
way. Since the muon tagging, the efficiency evaluation, and part of the fit
model are different in the two measurements, the respective systematic
uncertainties are treated as uncorrelated between the two sets of results. The
combined results for the $CP$-violating phase and lifetime difference between
the two mass eigenstates are:

$\displaystyle\phi_{\mathrm{s}}=-21\pm 44\stat\pm 10\syst\unit{mrad},$
$\displaystyle\Delta\Gamma_{\mathrm{s}}=0.1032\pm 0.0095\stat\pm
0.0048\syst{\unit{ps}^{-1}},$

with a correlation between the two parameters of $+0.02$. The full combination
results and the correlations between the various extracted parameters are
reported in Appendix .9.

The two-dimensional $\phi_{\mathrm{s}}$ vs. $\Delta\Gamma_{\mathrm{s}}$
likelihood contours at 68% confidence level () for the individual and combined
results, as well as the SM prediction, are shown in Fig. 6. The contours for
the individual results are obtained with likelihood scans, which are used to
obtain the combined contour. The contours only account for the statistical
uncertainty and the correlation between the two scanned variables, while the
results from the combination obtained using the BLUE method take into account
the statistical and systematic correlations of a wider range of variables. The
results are in agreement with each other and with the SM predictions.

Figure 6: The two-dimensional likelihood contours at 68% in the
$\phi_{\mathrm{s}}$-$\Delta\Gamma_{\mathrm{s}}$ plane, for the CMS 8(dashed
line), 13(dotted line), and combined (solid line) results. The contours for
the individual results are obtained with likelihood scans, which are used to
obtain the combined contour. In all contours only statistical uncertainties
are taken into account. The SM prediction is shown with the diamond marker [1,
4].

##  0.8 Summary

The $CP$-violating phase $\phi_{\mathrm{s}}$ and the decay width difference
$\Delta\Gamma_{\mathrm{s}}$ between the light and heavy meson mass eigenstates
are measured using a total of $48\,500$
$\PBzs\to\PJGy\,\PGf(1020)\to\PGmp\PGmm\,\PKp\PKm$ signal events, collected by
the CMS experiment at the LHC in proton-proton collisions at
$\sqrt{s}=13\TeV$, corresponding to an integrated luminosity of $96.4\fbinv$.
Events are selected using a trigger that requires an additional muon, which
can be exploited to infer the flavor of the meson at the time of production. A
novel opposite-side muon tagger based on deep neural networks has been
developed to maximize the sensitivity of the present analysis. A high tagging
power of ${\approx}$10% is achieved, aided by the requirement of an additional
muon in the signal sample imposed at the trigger level.

The $CP$-violating phase is measured to be $\phi_{\mathrm{s}}=-11\pm
50\stat\pm 10\syst\unit{mrad}$, consistent both with the SM prediction
$\phi_{\mathrm{s}}=-36.96\,^{+0.72}_{-0.84}\unit{mrad}$ [1] and with the
absence of $CP$ violation in the mixing-decay interference. The decay width
difference between the mass eigenstates is measured to be
$\Delta\Gamma_{\mathrm{s}}=0.114\pm 0.014\stat\pm 0.007\syst{\unit{ps}^{-1}}$,
consistent with the theoretical prediction $\Delta\Gamma_{\mathrm{s}}=0.091\pm
0.013{\unit{ps}^{-1}}$ [4]. In addition, the $CP$-violating parameter
$\abs{\lambda}$ and the average lifetime of the heavy and light mass
eigenstates, as well as their mass difference, have been measured. The
uncertainties in all these measurements are dominated by the statistical
components.

The results presented in this Letter are further combined with those obtained
by CMS at $\sqrt{s}=8\TeV$ [14], yielding $\phi_{\mathrm{s}}=-21\pm 44\stat\pm
10\syst\unit{mrad}$ and $\Delta\Gamma_{\mathrm{s}}=0.1032\pm 0.0095\stat\pm
0.0048\syst{\unit{ps}^{-1}}$. These results are significantly more precise
than those from the previous CMS measurement at 8, and can be used to further
constrain possible new-physics effects in meson decay and mixing.

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing centers and personnel of the Worldwide LHC Computing Grid for
delivering so effectively the computing infrastructure essential to our
analyses. Finally, we acknowledge the enduring support for the construction
and operation of the LHC and the CMS detector provided by the following
funding agencies: BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq,
CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST,
and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RIF
(Cyprus); SENESCYT (Ecuador); MoER, ERC IUT, PUT and ERDF (Estonia); Academy
of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG,
and HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM
(Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES
(Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT,
LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC
(Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom,
RAS, RFBR, and NRC KI (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER
(Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST
(Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK
(Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA).
Individuals have received support from the Marie-Curie program and the
European Research Council and Horizon 2020 Grant, contract Nos. 675440,
752730, and 765710 (European Union); the Leventis Foundation; the A.P. Sloan
Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science
Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et
dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door
Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium)
under the “Excellence of Science – EOS” – be.h project n. 30820817; the
Beijing Municipal Science & Technology Commission, No. Z191100007219010; the
Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the
Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy –
EXC 2121 “Quantum Universe” – 390833306; the Lendület (“Momentum”) Program and
the János Bolyai Research Scholarship of the Hungarian Academy of Sciences,
the New National Excellence Program ÚNKP, the NKFIA research grants 123842,
123959, 124845, 124850, 125105, 128713, 128786, and 129058 (Hungary); the
Council of Science and Industrial Research, India; the HOMING PLUS program of
the Foundation for Polish Science, cofinanced from European Union, Regional
Development Fund, the Mobility Plus program of the Ministry of Science and
Higher Education, the National Science Center (Poland), contracts Harmonia
2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and
2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406; the National Priorities
Research Program by Qatar National Research Fund; the Ministry of Science and
Higher Education, project no. 02.a03.21.0005 (Russia); the Programa Estatal de
Fomento de la Investigación Científica y Técnica de Excelencia María de
Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa del Principado de
Asturias; the Thalis and Aristeia programs cofinanced by EU-ESF and the Greek
NSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn
University and the Chulalongkorn Academic into Its 2nd Century Project
Advancement Project (Thailand); the Kavli Foundation; the Nvidia Corporation;
the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the
Weston Havens Foundation (USA).

## References

  * [1] The CKMfitter Group Collaboration, “Predictions of selected flavour observables within the Standard Model”,  Phys. Rev. D 84 (2011) 033005, 10.1103/PhysRevD.84.033005, arXiv:1106.4041. Updated with Summer 2019 results: http://ckmfitter.in2p3.fr/www/results/plots_summer19/num/ckmEval_results_summer19.pdf.
  * [2] C.-W. Chiang et al., “New physics in $\PBzs\to\PJGy\PGf$: a general analysis”,  JHEP 04 (2010) 031, 10.1007/JHEP04(2010)031, arXiv:0910.2929.
  * [3] M. Artuso, G. Borissov, and A. Lenz, “CP violation in the $\PBzs$ system”,  Rev. Mod. Phys. 88 (2016), no. 4, 045002, 10.1103/RevModPhys.88.045002, arXiv:1511.09466. [Addendum: Rev.Mod.Phys. 91, 049901 (2019)].
  * [4] A. Lenz and G. Tetlalmatzi-Xolocotzi, “Model-independent bounds on new physics effects in non-leptonic tree-level decays of B-mesons”,  JHEP 07 (2020) 177, 10.1007/JHEP07(2020)177, arXiv:1912.07621.
  * [5] D0 Collaboration, “Measurement of mixing parameters from the flavor-tagged decay $\PBzs\to\PJGy\PGf$”,  Phys. Rev. Lett. 101 (2008) 241801, 10.1103/PhysRevLett.101.241801, arXiv:0802.2255.
  * [6] D0 Collaboration, “Measurement of the CP-violating phase $\phi_{\mathrm{s}}^{\PJGy\PGf}$ using the flavor-tagged decay $\PBzs\to\PJGy\PGf$ in 8of p$\overline{\textrm{p}}$ collisions”,  Phys. Rev. D 85 (2012) 032006, 10.1103/PhysRevD.85.032006, arXiv:1109.3166.
  * [7] CDF Collaboration, “First flavor-tagged determination of bounds on mixing-induced CP violation in $\PBzs\to\PJGy\PGf$ decays”,  Phys. Rev. Lett. 100 (2008) 161802, 10.1103/PhysRevLett.100.161802, arXiv:0712.2397.
  * [8] CDF Collaboration, “Measurement of the CP-violating phase $\beta_{\mathrm{s}}^{\PJGy\PGf}$ in $\PBzs\to\PJGy\PGf$ decays with the CDF II detector”,  Phys. Rev. D 85 (2012) 072002, 10.1103/PhysRevD.85.072002, arXiv:1112.1726.
  * [9] CDF Collaboration, “Measurement of the bottom-strange meson mixing phase in the full CDF data set”,  Phys. Rev. Lett. 109 (2012) 171802, 10.1103/PhysRevLett.109.171802, arXiv:1208.2967.
  * [10] ATLAS Collaboration, “Time-dependent angular analysis of the decay $\PBzs\to\PJGy\PGf$ and extraction of $\Delta\Gamma_{\mathrm{s}}$ and the CP-violating weak phase $\phi_{\mathrm{s}}$ by ATLAS”,  JHEP 12 (2012) 072, 10.1007/JHEP12(2012)072, arXiv:1208.0572.
  * [11] ATLAS Collaboration, “Flavor tagged time-dependent angular analysis of the $\PBzs\to\PJGy\PGf$ decay and extraction of $\Delta\Gamma_{\mathrm{s}}$ and the weak phase $\phi_{\mathrm{s}}$ in ATLAS”,  Phys. Rev. D 90 (2014) 052007, 10.1103/PhysRevD.90.052007, arXiv:1407.1796.
  * [12] ATLAS Collaboration, “Measurement of the CP-violating phase $\phi_{\mathrm{s}}$ and the meson decay width difference with $\PBzs\to\PJGy\PGf$ decays in ATLAS”,  JHEP 12 (2016) 072, 10.1007/JHEP08(2016)147, arXiv:1601.03297.
  * [13] ATLAS Collaboration, “Measurement of the $CP$-violating phase $\phi_{s}$ in $\PBzs\to\PJGy\PGf$ decays in ATLAS at 13 TeV”, (2020). arXiv:2001.07115. Submitted to EPJC.
  * [14] CMS Collaboration, “Measurement of the CP-violating weak phase $\phi_{\mathrm{s}}$ and the decay width difference $\Delta\Gamma_{\mathrm{s}}$ using the $\PBzs\to\PJGy\,\PGf$ decay channel in $\Pp\Pp$ collisions at $\sqrt{s}=8$ TeV”,  Phys. Lett. B 757 (2016) 97, 10.1016/j.physletb.2016.03.046, arXiv:1507.07527.
  * [15] LHCb Collaboration, “Measurement of the CP-violating phase $\phi_{\mathrm{s}}$ in $\PABzs\to\PJGy\pi^{+}\pi^{-}$ decays”,  Phys. Lett. B 713 (2012) 378, 10.1016/j.physletb.2012.06.032, arXiv:1204.5675.
  * [16] LHCb Collaboration, “Measurement of CP violation and the $\PBzs$ meson decay width difference with $\PBzs\to\PJGy\mathrm{K}^{+}\mathrm{K}^{-}$ and $\PBzs\to\PJGy\pi^{+}\pi^{-}$ decays”,  Phys. Rev. D 87 (2013) 112010, 10.1103/PhysRevD.87.112010, arXiv:1304.2600.
  * [17] LHCb Collaboration, “Measurement of the CP-violating phase $\phi_{\mathrm{s}}$ in $\PABzs\to\PJGy\pi^{+}\pi^{-}$ decays”,  Phys. Lett. B 736 (2014) 186, 10.1016/j.physletb.2014.06.079, arXiv:1405.4140.
  * [18] LHCb Collaboration, “Precision measurement of CP violation in $\PBzs\to\PJGy\mathrm{K}^{+}\mathrm{K}^{-}$ decays”,  Phys. Rev. Lett. 114 (2015) 041801, 10.1103/PhysRevLett.114.041801, arXiv:1411.3104.
  * [19] LHCb Collaboration, “Updated measurement of time-dependent $CP$-violating observables in $B^{0}_{s}\to J/\psi K^{+}K^{-}$ decays”,  Eur. Phys. J. C 79 (2019), no. 8, 706, 10.1140/epjc/s10052-019-7159-8, arXiv:1906.08356. [Erratum: 10.1140/epjc/s10052-020-7875-0].
  * [20] LHCb Collaboration, “First study of the CP-violating phase and decay-width difference in $\PBzs\to\Pgy\PGf$”,  Phys. Lett. B 762 (2016) 253, 10.1016/j.physletb.2016.09.028, arXiv:1608.04855.
  * [21] LHCb Collaboration, “Measurement of the CP-violating phase $\phi_{\mathrm{s}}$ in $\PABzs\to\PsDp\PsDm$ decays”,  Phys. Rev. Lett. 113 (2014) 211801, 10.1103/PhysRevLett.113.211801, arXiv:1409.4619.
  * [22] A. S. Dighe, I. Dunietz, and R. Fleischer, “Extracting CKM phases and $\PBzs$–$\PABzs$ mixing parameters from angular distributions of non-leptonic B decays”,  Eur. Phys. J. C 6 (1999) 647, 10.1007/s100529800954, arXiv:hep-ph/9804253.
  * [23] A. S. Dighe, I. Dunietz, H. J. Lipkin, and J. L. Rosner, “Angular distributions and lifetime differences in $\PBzs\to\PJGy\PGf$ decays”,  Phys. Lett. B 369 (1996) 144, 10.1016/0370-2693(95)01523-X, arXiv:hep-ph/9511363.
  * [24] G. C. Branco, L. Lavoura, and J. P. Silva, “CP Violation”, volume 103 of  International Series of Monographs on Physics.  Clarendon Press, Oxford, UK, 1999.  ISBN 0198503997.
  * [25] CMS Collaboration, “Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at $\sqrt{s}=13$ TeV”,  JINST 13 (2018) P06015, 10.1088/1748-0221/13/06/P06015, arXiv:1804.04528.
  * [26] CMS Collaboration, “The CMS trigger system”,  JINST 12 (2017) P01020, 10.1088/1748-0221/12/01/P01020, arXiv:1609.02366.
  * [27] CMS Collaboration, “The CMS Experiment at the CERN LHC”,  JINST 3 (2008) S08004, 10.1088/1748-0221/3/08/S08004.
  * [28] I. Antcheva et al., “ROOT — A C++ framework for petabyte data storage, statistical analysis and visualization”,  Comput. Phys. Commun. 180 (2009) 2499, 10.1016/j.cpc.2009.08.005, arXiv:1508.07749.
  * [29] H. Voss, A. Höcker, J. Stelzer, and F. Tegenfeldt, “TMVA, the toolkit for multivariate data analysis with ROOT”, in  XIth International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT), p. 40.  2007\.  arXiv:physics/0703039.  [PoS(ACAT)040]. 10.22323/1.050.0040.
  * [30] R. Frühwirth, “Application of Kalman filtering to track and vertex fitting”,  Nucl. Instrum. Meth. A 262 (1987) 444, 10.1016/0168-9002(87)90887-4.
  * [31] Particle Data Group, M. Tanabashi et al., “Review of particle physics”,  Phys. Rev. D 98 (2018) 030001, 10.1103/PhysRevD.98.030001.
  * [32] CMS Collaboration, “Description and performance of track and primary-vertex reconstruction with the CMS tracker”,  JINST 9 (2014) P10009, 10.1088/1748-0221/9/10/P10009, arXiv:1405.6569.
  * [33] T. Sjöstrand et al., “An introduction to PYTHIA 8.2”,  Comput. Phys. Commun. 191 (2015) 159, 10.1016/j.cpc.2015.01.024, arXiv:1410.3012.
  * [34] CMS Collaboration, “Extraction and validation of a new set of CMS PYTHIA8 tunes from underlying-event measurements”,  Eur. Phys. J. C 80 (2020) 4, 10.1140/epjc/s10052-019-7499-4, arXiv:1903.12179.
  * [35] NNPDF Collaboration, “Parton distributions from high-precision collider data”,  Eur. Phys. J. C 77 (2017) 663, 10.1140/epjc/s10052-017-5199-5, arXiv:1706.00428.
  * [36] D. J. Lange, “The EvtGen particle decay simulation package”,  Nucl. Instrum. Meth. A 462 (2001) 152, 10.1016/S0168-9002(01)00089-4.
  * [37] E. Barberio, B. van Eijk, and Z. Wa̧s, “ — a universal Monte Carlo for QED radiative corrections in decays”,  Comput. Phys. Commun. 66 (1991) 115, 10.1016/0010-4655(91)90012-A.
  * [38] E. Barberio and Z. Wa̧s, “ — a universal Monte Carlo for QED radiative corrections: version 2.0”,  Comput. Phys. Commun. 79 (1994) 291, 10.1016/0010-4655(94)90074-4.
  * [39] GEANT4 Collaboration, “ — a simulation toolkit”,  Nucl. Instrum. Meth. A 506 (2003) 250, 10.1016/S0168-9002(03)01368-8.
  * [40] F. Chollet et al., “Keras”. https://keras.io, 2015.
  * [41] CMS Collaboration, “Particle-flow reconstruction and global event description with the CMS detector”,  JINST 12 (2017) P10003, 10.1088/1748-0221/12/10/P10003, arXiv:1706.04965.
  * [42] D. Kingma and J. Ba, “Adam: A method for stochastic optimization”, (12, 2014). arXiv:1412.6980.
  * [43] W. Verkerke and D. Kirkby, “The roofit toolkit for data modeling”, 2003.
  * [44] LHCb Collaboration, “Determination of the sign of the decay width difference in the $B_{s}$ system”,  Phys. Rev. Lett. 108 (2012) 241801, 10.1103/PhysRevLett.108.241801, arXiv:1202.4717.
  * [45] N. L. Johnson, “Systems of frequency curves generated by methods of translation”,  Biometrika 36 (1949) 149, 10.1093/biomet/36.1-2.149.
  * [46] M. Rosenblatt, “Remarks on some nonparametric estimates of a density function”,  Ann. Math. Statist. 27 (1956) 832, 10.1214/aoms/1177728190.
  * [47] E. Parzen, “On estimation of a probability density function and mode”,  Ann. Math. Statist. 33 (1962) 1065, 10.1214/aoms/1177704472.
  * [48] L. Lyons, D. Gibaut, and P. Clifford, “How to combine correlated estimates of a single physical quantity”,  Nucl. Instrum. Meth. A 270 (1988) 110, 10.1016/0168-9002(88)90018-6.
  * [49] A. Valassi, “Combining correlated measurements of several different physical quantities”,  Nucl. Instrum. Meth. A 500 (2003) 391, 10.1016/S0168-9002(03)00329-2.
  * [50] R. Brun and F. Rademakers, “ROOT: An object oriented data analysis framework”,  Nucl. Instrum. Meth. A 389 (1997) 81, 10.1016/S0168-9002(97)00048-X.
  * [51] R. Nisius, “On the combination of correlated estimates of a physics observable”,  Eur. Phys. J. C 74 (2014), no. 8, 3004, 10.1140/epjc/s10052-014-3004-2, arXiv:1402.4016.
  * [52] R. Nisius, “BLUE: combining correlated estimates of physics observables within ROOT using the Best Linear Unbiased Estimate method”,  SoftwareX 11 (1, 2020) 100468, 10.1016/j.softx.2020.100468, arXiv:2001.10310.

##  .9 Supplemental material: correlation matrices and additional results

Statistical correlation matrix between the physics parameters as obtained from
the ML fit to the 13data.  $\phi_{\mathrm{s}}$ $\Delta\Gamma_{\mathrm{s}}$
$\Delta m_{\mathrm{s}}$ $\abs{\lambda}$ $\Gamma_{\mathrm{s}}$
$\abs{A_{0}}^{2}$ $\abs{A_{\perp}}^{2}$ $\abs{A_{\mathrm{S}}}^{2}$
$\delta_{\parallel}$ $\delta_{\perp}$ $\delta_{\mathrm{S}\perp}$
$\phi_{\mathrm{s}}$ $+1.00$ $-0.02$ $-0.19$ $+0.22$ $\hphantom{+}0.00$ $-0.01$
$+0.01$ $-0.01$ $-0.02$ $-0.09$ $+0.03$ $\Delta\Gamma_{\mathrm{s}}$ $+1.00$
$-0.02$ $\hphantom{+}0.00$ $-0.48$ $+0.63$ $-0.71$ $\hphantom{+}0.00$ $+0.01$
$-0.01$ $-0.04$ $\Delta m_{\mathrm{s}}$ $+1.00$ $-0.14$ $+0.03$ $-0.01$
$+0.02$ $+0.03$ $+0.01$ $+0.68$ $-0.05$ $\abs{\lambda}$ $+1.00$ $-0.02$
$\hphantom{+}0.00$ $-0.01$ $-0.03$ $-0.06$ $-0.18$ $+0.05$
$\Gamma_{\mathrm{s}}$ $+1.00$ $-0.31$ $+0.42$ $+0.15$ $-0.02$ $+0.02$ $-0.05$
$\abs{A_{0}}^{2}$ $+1.00$ $-0.61$ $+0.15$ $-0.01$ $-0.01$ $-0.09$
$\abs{A_{\perp}}^{2}$ $+1.00$ $-0.11$ $-0.06$ $\hphantom{+}0.00$ $+0.06$
$\abs{A_{\mathrm{S}}}^{2}$ $+1.00$ $-0.07$ $+0.02$ $-0.44$
$\delta_{\parallel}$ $+1.00$ $+0.27$ $-0.02$ $\delta_{\perp}$ $+1.00$ $-0.10$
$\delta_{\mathrm{S}\perp}$ $+1.00$

Physics parameters values as obtained from the combination between the CMS
8and 13results.  Parameter Value Stat. uncer. Syst. uncer. $\phi_{\mathrm{s}}$
[mrad] $-21$ $\pm\,44$ $\pm\,10$ $\Delta\Gamma_{\mathrm{s}}$ [ps-1] $0$
$.1032$ $\pm\,0.0095$ $\pm\,0.0048$ $\Gamma_{\mathrm{s}}$ [ps-1] $0$ $.6590$
$\pm\,0.0032$ $\pm\,0.0023$ $\abs{A_{0}}^{2}$ $0$ $.5289$ $\pm\,0.0038$
$\pm\,0.0041$ $\abs{A_{\perp}}^{2}$ $0$ $.2393$ $\pm\,0.0050$ $\pm\,0.0037$
$\abs{A_{\mathrm{S}}}^{2}$ $0$ $.016$ $\pm\,0.006$ $\pm\,0.013$
$\delta_{\parallel}$ [rad] $3$ $.19$ $\pm\,0.12$ $\pm\,0.04$ $\delta_{\perp}$
[rad] $2$ $.78$ $\pm\,0.15$ $\pm\,0.06$ $\delta_{\mathrm{S}\perp}$ [rad] $0$
$.238$ $\pm\,0.078$ $\pm\,0.046$

Correlations between the physics parameters as obtained from the combination
between the CMS 8and 13results. Correlations are both statistical and
systematic.  $\phi_{\mathrm{s}}$ $\Delta\Gamma_{\mathrm{s}}$
$\Gamma_{\mathrm{s}}$ $\abs{A_{0}}^{2}$ $\abs{A_{\perp}}^{2}$
$\abs{A_{\mathrm{S}}}^{2}$ $\delta_{\parallel}$ $\delta_{\perp}$
$\delta_{\mathrm{S}\perp}$ $\phi_{\mathrm{s}}$ $+1.00$ $+0.02$ $-0.03$ $+0.01$
$-0.01$ $+0.01$ $-0.01$ $-0.08$ $+0.03$ $\Delta\Gamma_{\mathrm{s}}$ $+1.00$
$-0.45$ $+0.43$ $-0.57$ $+0.01$ $+0.01$ $\hphantom{+}0.00$ $-0.01$
$\Gamma_{\mathrm{s}}$ $+1.00$ $-0.17$ $+0.30$ $+0.06$ $-0.03$
$\hphantom{+}0.00$ $-0.08$ $\abs{A_{0}}^{2}$ $+1.00$ $-0.56$ $+0.25$ $-0.03$
$+0.01$ $-0.18$ $\abs{A_{\perp}}^{2}$ $+1.00$ $-0.08$ $-0.03$ $+0.01$ $+0.14$
$\abs{A_{\mathrm{S}}}^{2}$ $+1.00$ $-0.02$ $+0.02$ $-0.20$
$\delta_{\parallel}$ $+1.00$ $+0.26$ $\hphantom{+}0.00$ $\delta_{\perp}$
$+1.00$ $-0.05$ $\delta_{\mathrm{S}\perp}$ $+1.00$

##  .10 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan${}^{\textrm{\textdagger}}$, A. Tumasyan Institut für
Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, T. Bergauer, M. Dragicevic, J. Erö, A. Escalante Del
Valle, R. Frühwirth1, M. Jeitler1, N. Krammer, L. Lechner, D. Liko, T.
Madlener, I. Mikulec, F.M. Pitters, N. Rad, J. Schieck1, R. Schöfbeck, M.
Spanring, S. Templ, W. Waltenberger, C.-E. Wulz1, M. Zarucki Institute for
Nuclear Problems, Minsk, Belarus
V. Chekhovsky, A. Litomin, V. Makarenko, J. Suarez Gonzalez Universiteit
Antwerpen, Antwerpen, Belgium
M.R. Darwish2, E.A. De Wolf, D. Di Croce, X. Janssen, T. Kello3, A. Lelek, M.
Pieters, H. Rejeb Sfar, H. Van Haevermaet, P. Van Mechelen, S. Van Putte, N.
Van Remortel Vrije Universiteit Brussel, Brussel, Belgium
F. Blekman, E.S. Bols, S.S. Chhibra, J. D’Hondt, J. De Clercq, D. Lontkovskyi,
S. Lowette, I. Marchesini, S. Moortgat, A. Morton, Q. Python, S. Tavernier, W.
Van Doninck, P. Van Mulders Université Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, B. Clerbaux, G. De Lentdecker, H. Delannoy, B. Dorney, L.
Favart, A. Grebenyuk, A.K. Kalsi, I. Makarenko, L. Moureaux, L. Pétré, A.
Popov, N. Postiau, E. Starling, L. Thomas, C. Vander Velde, P. Vanlaer, D.
Vannerom, L. Wezenbeek Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, M. Gruchala, I. Khvastunov4, M. Niedziela, C. Roskas,
K. Skovpen, M. Tytgat, W. Verbeke, B. Vermassen, M. Vit Université Catholique
de Louvain, Louvain-la-Neuve, Belgium
G. Bruno, F. Bury, C. Caputo, P. David, C. Delaere, M. Delcourt, I.S.
Donertas, A. Giammanco, V. Lemaitre, K. Mondal, J. Prisciandaro, A. Taliercio,
M. Teklishyn, P. Vischia, S. Wuyckens, J. Zobec Centro Brasileiro de Pesquisas
Fisicas, Rio de Janeiro, Brazil
G.A. Alves, G. Correia Silva, C. Hensel, A. Moraes Universidade do Estado do
Rio de Janeiro, Rio de Janeiro, Brazil
W.L. Aldá Júnior, E. Belchior Batista Das Chagas, H. BRANDAO MALBOUISSON, W.
Carvalho, J. Chinellato5, E. Coelho, E.M. Da Costa, G.G. Da Silveira6, D. De
Jesus Damiao, S. Fonseca De Souza, J. Martins7, D. Matos Figueiredo, M. Medina
Jaime8, M. Melo De Almeida, C. Mora Herrera, L. Mundim, H. Nogima, P. Rebello
Teles, L.J. Sanchez Rosas, A. Santoro, S.M. Silva Do Amaral, A. Sznajder, M.
Thiel, E.J. Tonelli Manganote5, F. Torres Da Silva De Araujo, A. Vilela
Pereira Universidade Estadual Paulista a, Universidade Federal do ABC b, São
Paulo, Brazil
C.A. Bernardesa, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M. Gregoresb,
D.S. Lemosa, P.G. Mercadanteb, S.F. Novaesa, Sandra S. Padulaa Institute for
Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia,
Bulgaria
A. Aleksandrov, G. Antchev, I. Atanasov, R. Hadjiiska, P. Iaydjiev, M.
Misheva, M. Rodozov, M. Shopova, G. Sultanov University of Sofia, Sofia,
Bulgaria
M. Bonchev, A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov, A. Petrov
Beihang University, Beijing, China
W. Fang3, Q. Guo, H. Wang, L. Yuan Department of Physics, Tsinghua University,
Beijing, China
M. Ahmad, Z. Hu, Y. Wang Institute of High Energy Physics, Beijing, China
E. Chapon, G.M. Chen9, H.S. Chen9, M. Chen, D. Leggat, H. Liao, Z. Liu, R.
Sharma, A. Spiezia, J. Tao, J. Thomas-wilsker, J. Wang, H. Zhang, S. Zhang9,
J. Zhao State Key Laboratory of Nuclear Physics and Technology, Peking
University, Beijing, China
A. Agapitos, Y. Ban, C. Chen, A. Levin, J. Li, Q. Li, M. Lu, X. Lyu, Y. Mao,
S.J. Qian, D. Wang, Q. Wang, J. Xiao Sun Yat-Sen University, Guangzhou, China
Z. You Institute of Modern Physics and Key Laboratory of Nuclear Physics and
Ion-beam Application (MOE) - Fudan University, Shanghai, China
X. Gao3 Zhejiang University, Hangzhou, China
M. Xiao Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C. Florez, J. Fraga, A. Sarkar, M.A. Segura Delgado
Universidad de Antioquia, Medellin, Colombia
J. Jaramillo, J. Mejia Guisao, F. Ramirez, J.D. Ruiz Alvarez, C.A. Salazar
González, N. Vanegas Arbelaez University of Split, Faculty of Electrical
Engineering, Mechanical Engineering and Naval Architecture, Split, Croatia
D. Giljanovic, N. Godinovic, D. Lelas, I. Puljak, T. Sculac University of
Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, D. Majumder, B. Mesic, M. Roguljic, A.
Starodumov10, T. Susa University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, E. Erodotou, A. Ioannou, G. Kole, M. Kolosova, S.
Konstantinou, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A.
Razis, H. Rykaczewski, H. Saka, D. Tsiakkouri Charles University, Prague,
Czech Republic
M. Finger11, M. Finger Jr.11, A. Kveton, J. Tomsa Escuela Politecnica
Nacional, Quito, Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
A.A. Abdelalim12,13, S. Abu Zeid14, S. Khalil13 Center for High Energy Physics
(CHEP-FU), Fayoum University, El-Fayoum, Egypt
A. Lotfy, M.A. Mahmoud National Institute of Chemical Physics and Biophysics,
Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, M. Raidal, C. Veelken Department of Physics, University of Helsinki,
Helsinki, Finland
P. Eerola, L. Forthomme, H. Kirschenmann, K. Osterberg, M. Voutilainen
Helsinki Institute of Physics, Helsinki, Finland
E. Brücken, F. Garcia, J. Havukainen, V. Karimäki, M.S. Kim, R. Kinnunen, T.
Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, H. Siikonen, E.
Tuominen, J. Tuominiemi Lappeenranta University of Technology, Lappeenranta,
Finland
P. Luukka, T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F. Ferri, S.
Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry, B. Lenzi,
E. Locci, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin, A. Savoy-Navarro15,
M. Titov, G.B. Yu Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole
Polytechnique, Institut Polytechnique de Paris, Paris, France
S. Ahuja, C. Amendola, F. Beaudette, M. Bonanomi, P. Busson, C. Charlot, O.
Davignon, B. Diab, G. Falmagne, R. Granier de Cassagnac, I. Kucher, A.
Lobanov, C. Martin Perez, M. Nguyen, C. Ochando, P. Paganini, J. Rembser, R.
Salerno, J.B. Sauvan, Y. Sirois, A. Zabi, A. Zghiche Université de Strasbourg,
CNRS, IPHC UMR 7178, Strasbourg, France
J.-L. Agram16, J. Andrea, D. Bloch, G. Bourgatte, J.-M. Brom, E.C. Chabert, C.
Collard, J.-C. Fontaine16, D. Gelé, U. Goerlach, C. Grimault, A.-C. Le Bihan,
P. Van Hove Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
E. Asilar, S. Beauceron, C. Bernet, G. Boudoul, C. Camen, A. Carle, N. Chanon,
D. Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M. Gouzevitch, B.
Ille, Sa. Jain, I.B. Laktineh, H. Lattaud, A. Lesauvage, M. Lethuillier, L.
Mirabito, L. Torterotot, G. Touquet, M. Vander Donckt, S. Viret Georgian
Technical University, Tbilisi, Georgia
T. Toriashvili17, Z. Tsamalaidze11 RWTH Aachen University, I. Physikalisches
Institut, Aachen, Germany
L. Feld, K. Klein, M. Lipinski, D. Meuser, A. Pauls, M. Preuten, M.P. Rauch,
J. Schulz, M. Teroerde RWTH Aachen University, III. Physikalisches Institut A,
Aachen, Germany
D. Eliseev, M. Erdmann, P. Fackeldey, B. Fischer, S. Ghosh, T. Hebbeker, K.
Hoepfner, H. Keller, L. Mastrolorenzo, M. Merschmeyer, A. Meyer, P. Millet, G.
Mocellin, S. Mondal, S. Mukherjee, D. Noll, A. Novak, T. Pook, A. Pozdnyakov,
T. Quast, M. Radziej, Y. Rath, H. Reithler, J. Roemer, A. Schmidt, S.C.
Schuler, A. Sharma, S. Wiedenbeck, S. Zaleski RWTH Aachen University, III.
Physikalisches Institut B, Aachen, Germany
C. Dziwok, G. Flügge, W. Haj Ahmad18, O. Hlushchenko, T. Kress, A. Nowack, C.
Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl19, T. Ziemons Deutsches
Elektronen-Synchrotron, Hamburg, Germany
H. Aarup Petersen, M. Aldaya Martin, P. Asmuss, I. Babounikau, S. Baxter, O.
Behnke, A. Bermúdez Martínez, A.A. Bin Anuar, K. Borras20, V. Botta, D.
Brunner, A. Campbell, A. Cardini, P. Connor, S. Consuegra Rodríguez, V.
Danilov, A. De Wit, M.M. Defranchis, L. Didukh, D. Domínguez Damiani, G.
Eckerlin, D. Eckstein, T. Eichhorn, A. Elwood, L.I. Estevez Banos, E. Gallo21,
A. Geiser, A. Giraldi, A. Grohsjean, M. Guthoff, A. Harb, A. Jafari22, N.Z.
Jomhari, H. Jung, A. Kasem20, M. Kasemann, H. Kaveh, J. Keaveney, C.
Kleinwort, J. Knolle, D. Krücker, W. Lange, T. Lenz, J. Lidrych, K. Lipka, W.
Lohmann23, R. Mankel, I.-A. Melzer-Pellmann, J. Metwally, A.B. Meyer, M.
Meyer, M. Missiroli, J. Mnich, A. Mussgiller, V. Myronenko, Y. Otarid, D.
Pérez Adán, S.K. Pflitsch, D. Pitzl, A. Raspereza, A. Saggio, A. Saibel, M.
Savitskyi, V. Scheurer, P. Schütze, C. Schwanenberger, R. Shevchenko, A.
Singh, R.E. Sosa Ricardo, H. Tholen, N. Tonon, O. Turkot, A. Vagnerini, M. Van
De Klundert, R. Walsh, D. Walter, Y. Wen, K. Wichmann, C. Wissing, S.
Wuchterl, O. Zenaiev, R. Zlebcik University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, K. De Leo, T. Dreyer, A.
Ebrahimi, F. Feindt, A. Fröhlich, C. Garbers, E. Garutti, D. Gonzalez, P.
Gunnellini, J. Haller, A. Hinzmann, A. Karavdina, G. Kasieczka, R. Klanner, R.
Kogler, S. Kurz, V. Kutzner, J. Lange, T. Lange, A. Malara, J. Multhaup,
C.E.N. Niemeyer, A. Nigamova, K.J. Pena Rodriguez, O. Rieger, P. Schleper, S.
Schumann, J. Schwandt, D. Schwarz, J. Sonneveld, H. Stadie, G. Steinbrück, B.
Vormwald, I. Zoi Karlsruher Institut fuer Technologie, Karlsruhe, Germany
M. Baselga, S. Baur, J. Bechtel, T. Berger, E. Butz, R. Caspart, T. Chwalek,
W. De Boer, A. Dierlamm, A. Droll, K. El Morabit, N. Faltermann, K. Flöh, M.
Giffels, A. Gottmann, F. Hartmann19, C. Heidecker, U. Husemann, M.A. Iqbal, I.
Katkov24, P. Keicher, R. Koppenhöfer, S. Kudella, S. Maier, M. Metzler, S.
Mitra, M.U. Mozer, D. Müller, Th. Müller, M. Musich, G. Quast, K. Rabbertz, J.
Rauser, D. Savoiu, D. Schäfer, M. Schnepf, M. Schröder, D. Seith, I. Shvetsov,
H.J. Simonis, R. Ulrich, M. Wassmer, M. Weber, C. Wöhrmann, R. Wolf, S.
Wozniewski Institute of Nuclear and Particle Physics (INPP), NCSR Demokritos,
Aghia Paraskevi, Greece
G. Anagnostou, P. Asenov, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas,
G. Paspalaki, A. Stakia National and Kapodistrian University of Athens,
Athens, Greece
M. Diamantopoulou, D. Karasavvas, G. Karathanasis, P. Kontaxakis, C.K. Koraka,
A. Manousakis-katsikakis, A. Panagiotou, I. Papavergou, N. Saoulidou, K.
Theofilatos, K. Vellidis, E. Vourliotis National Technical University of
Athens, Athens, Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis, A. Zacharopoulou
University of Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, K.
Manitara, N. Manthos, I. Papadopoulos, J. Strologas MTA-ELTE Lendület CMS
Particle and Nuclear Physics Group, Eötvös Loránd University, Budapest,
Hungary
M. Bartók25, R. Chudasama, M. Csanad, M.M.A. Gadallah26, S. Lökös27, P. Major,
K. Mandal, A. Mehta, G. Pasztor, O. Surányi, G.I. Veres Wigner Research Centre
for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath28, F. Sikler, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary
S. Czellar, J. Karancsi25, J. Molnar, Z. Szillasi, D. Teyssier Institute of
Physics, University of Debrecen, Debrecen, Hungary
P. Raics, Z.L. Trocsanyi, B. Ujvari Eszterhazy Karoly University, Karoly
Robert Campus, Gyongyos, Hungary
T. Csorgo, F. Nemes, T. Novak Indian Institute of Science (IISc), Bangalore,
India
S. Choudhury, J.R. Komaragiri, D. Kumar, L. Panwar, P.C. Tiwari National
Institute of Science Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati29, D. Dash, C. Kar, P. Mal, T. Mishra, V.K. Muraleedharan Nair
Bindhu, A. Nayak30, D.K. Sahoo29, N. Sur, S.K. Swain Panjab University,
Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, N. Dhingra31, R. Gupta, A.
Kaur, A. Kaur, S. Kaur, P. Kumari, M. Lohan, M. Meena, K. Sandeep, S. Sharma,
J.B. Singh, A.K. Virdi University of Delhi, Delhi, India
A. Ahmed, A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, A.
Kumar, M. Naimuddin, P. Priyanka, K. Ranjan, A. Shah Saha Institute of Nuclear
Physics, HBNI, Kolkata, India
M. Bharti32, R. Bhattacharya, S. Bhattacharya, D. Bhowmik, S. Dutta, S. Ghosh,
B. Gomber33, M. Maity34, S. Nandan, P. Palit, A. Purohit, P.K. Rout, G. Saha,
S. Sarkar, M. Sharan, B. Singh32, S. Thakur32 Indian Institute of Technology
Madras, Madras, India
P.K. Behera, S.C. Behera, P. Kalbhor, A. Muhammad, R. Pradhan, P.R. Pujahari,
A. Sharma, A.K. Sikdar Bhabha Atomic Research Centre, Mumbai, India
D. Dutta, V. Jha, V. Kumar, D.K. Mishra, K. Naskar35, P.K. Netrakanti, L.M.
Pant, P. Shukla Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, R. Kumar Verma, U. Sarkar Tata Institute of
Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, S. Karmakar,
S. Kumar, G. Majumder, K. Mazumdar, S. Mukherjee, D. Roy, N. Sahoo Indian
Institute of Science Education and Research (IISER), Pune, India
S. Dube, B. Kansal, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, A. Rastogi, S.
Sharma Department of Physics, Isfahan University of Technology, Isfahan, Iran
H. Bakhshiansohi36 Institute for Research in Fundamental Sciences (IPM),
Tehran, Iran
S. Chenarani37, S.M. Etesami, M. Khakzad, M. Mohammadi Najafabadi, M. Naseri
University College Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, R. Alya,b,38, C. Arutaa,b, A. Colaleoa, D. Creanzaa,c, N. De
Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, A. Di Pilatoa,b, W.
Elmetenaweea,b, L. Fiorea, A. Gelmia,b, M. Gula, G. Iasellia,c, M. Incea,b, S.
Lezkia,b, G. Maggia,c, M. Maggia, I. Margjekaa,b, J.A. Merlina, S. Mya,b, S.
Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, G. Selvaggia,b, L. Silvestrisa, F.M.
Simonea,b, R. Vendittia, P. Verwilligena INFN Sezione di Bologna a, Università
di Bologna b, Bologna, Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia,b, S. Braibant-
Giacomellia,b, L. Brigliadoria,b, R. Campaninia,b, P. Capiluppia,b, A.
Castroa,b, F.R. Cavalloa, C. Cioccaa, M. Cuffiania,b, G.M. Dallavallea, T.
Diotalevia,b, F. Fabbria, A. Fanfania,b, E. Fontanesia,b, P. Giacomellia, C.
Grandia, L. Guiduccia,b, F. Iemmia,b, S. Lo Meoa,39, S. Marcellinia, G.
Masettia, F.L. Navarriaa,b, A. Perrottaa, F. Primaveraa,b, T. Rovellia,b, G.P.
Sirolia,b, N. Tosia INFN Sezione di Catania a, Università di Catania b,
Catania, Italy
S. Albergoa,b,40, S. Costaa,b, A. Di Mattiaa, R. Potenzaa,b, A. Tricomia,b,40,
C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b, Firenze, Italy
G. Barbaglia, A. Cassesea, R. Ceccarellia,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, F. Fioria, E. Focardia,b, G. Latinoa,b, P. Lenzia,b, M.
Lizzoa,b, M. Meschinia, S. Paolettia, R. Seiditaa,b, G. Sguazzonia, L.
Viliania INFN Laboratori Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, D. Piccolo INFN Sezione di Genova a, Università di
Genova b, Genova, Italy
M. Bozzoa,b, F. Ferroa, R. Mulargiaa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschia,b, F. Brivioa,b, F. Cetorellia,b, V. Cirioloa,b,19,
F. De Guioa,b, M.E. Dinardoa,b, P. Dinia, S. Gennaia, A. Ghezzia,b, P.
Govonia,b, L. Guzzia,b, M. Malbertia, S. Malvezzia, D. Menascea, F. Montia,b,
L. Moronia, M. Paganonia,b, D. Pedrinia, S. Ragazzia,b, T. Tabarelli de
Fatisa,b, D. Valsecchia,b,19, D. Zuoloa,b INFN Sezione di Napoli a, Università
di Napoli ’Federico II’ b, Napoli, Italy, Università della Basilicata c,
Potenza, Italy, Università G. Marconi d, Roma, Italy
S. Buontempoa, N. Cavalloa,c, A. De Iorioa,b, F. Fabozzia,c, F. Fiengaa,
A.O.M. Iorioa,b, L. Layera,b, L. Listaa,b, S. Meolaa,d,19, P. Paoluccia,19, B.
Rossia, C. Sciaccaa,b, E. Voevodinaa,b INFN Sezione di Padova a, Università di
Padova b, Padova, Italy, Università di Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, D. Biselloa,b, A. Bolettia,b, A. Bragagnoloa,b, R.
Carlina,b, P. Checchiaa, P. De Castro Manzanoa, T. Dorigoa, F. Gasparinia,b,
U. Gasparinia,b, S.Y. Hoha,b, E. Lusiani, M. Margonia,b, A.T. Meneguzzoa,b, M.
Presillab, P. Ronchesea,b, R. Rossina,b, F. Simonettoa,b, G. Strong, A. Tikoa,
M. Tosia,b, H. YARARa,b, M. Zanettia,b, A. Zucchettaa,b INFN Sezione di Pavia
a, Università di Pavia b, Pavia, Italy
A. Braghieria, S. Calzaferria,b, D. Fiorinaa,b, P. Montagnaa,b, S.P. Rattia,b,
V. Rea, M. Ressegottia,b, C. Riccardia,b, P. Salvinia, I. Vaia, P. Vituloa,b
INFN Sezione di Perugia a, Università di Perugia b, Perugia, Italy
M. Biasinia,b, G.M. Bileia, D. Ciangottinia,b, L. Fanòa,b, P. Laricciaa,b, G.
Mantovania,b, V. Mariania,b, M. Menichellia, F. Moscatellia, A. Rossia,b, A.
Santocchiaa,b, D. Spigaa, T. Tedeschia,b INFN Sezione di Pisa a, Università di
Pisa b, Scuola Normale Superiore di Pisa c, Pisa, Italy
K. Androsova, P. Azzurria, G. Bagliesia, V. Bertacchia,c, L. Bianchinia, T.
Boccalia, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, M.R. Di Domenicoa,b, S.
Donatoa, L. Gianninia,c, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c,
G. Mandorlia,c, A. Messineoa,b, F. Pallaa, G. Ramirez-Sancheza,c, A. Rizzia,b,
G. Rolandia,c, S. Roy Chowdhurya,c, A. Scribanoa, N. Shafieia,b, P. Spagnoloa,
R. Tenchinia, G. Tonellia,b, N. Turinia, A. Venturia, P.G. Verdinia INFN
Sezione di Roma a, Sapienza Università di Roma b, Rome, Italy
F. Cavallaria, M. Cipriania,b, D. Del Rea,b, E. Di Marcoa, M. Diemoza, E.
Longoa,b, P. Meridiania, G. Organtinia,b, F. Pandolfia, R. Paramattia,b, C.
Quarantaa,b, S. Rahatloua,b, C. Rovellia, F. Santanastasioa,b, L. Soffia,b, R.
Tramontanoa,b INFN Sezione di Torino a, Università di Torino b, Torino, Italy,
Università del Piemonte Orientale c, Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, A. Belloraa,b, C. Biinoa, A. Cappatia,b, N. Cartigliaa, S.
Comettia, M. Costaa,b, R. Covarellia,b, N. Demariaa, B. Kiania,b, F. Leggera,
C. Mariottia, S. Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M.
Montenoa, M.M. Obertinoa,b, G. Ortonaa, L. Pachera,b, N. Pastronea, M.
Pelliccionia, G.L. Pinna Angionia,b, M. Ruspaa,c, R. Salvaticoa,b, F.
Sivieroa,b, V. Solaa, A. Solanoa,b, D. Soldia,b, A. Staianoa, D. Trocinoa,b
INFN Sezione di Trieste a, Università di Trieste b, Trieste, Italy
S. Belfortea, V. Candelisea,b, M. Casarsaa, F. Cossuttia, A. Da Rolda,b, G.
Della Riccaa,b, F. Vazzolera,b Kyungpook National University, Daegu, Korea
S. Dogra, C. Huh, B. Kim, D.H. Kim, G.N. Kim, J. Lee, S.W. Lee, C.S. Moon,
Y.D. Oh, S.I. Pak, S. Sekmen, Y.C. Yang Chonnam National University, Institute
for Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon Hanyang University, Seoul, Korea
B. Francois, T.J. Kim, J. Park Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, S. Ha, B. Hong, K. Lee, K.S. Lee, J. Lim, J. Park,
S.K. Park, J. Yoo Kyung Hee University, Department of Physics, Seoul, Republic
of Korea
J. Goh, A. Gurtu Sejong University, Seoul, Korea
H.S. Kim, Y. Kim Seoul National University, Seoul, Korea
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, S. Ko, H. Kwon, H.
Lee, K. Lee, S. Lee, K. Nam, B.H. Oh, M. Oh, S.B. Oh, B.C. Radburn-Smith, H.
Seo, U.K. Yang, I. Yoon University of Seoul, Seoul, Korea
D. Jeon, J.H. Kim, B. Ko, J.S.H. Lee, I.C. Park, Y. Roh, D. Song, I.J. Watson
Yonsei University, Department of Physics, Seoul, Korea
H.D. Yoo Sungkyunkwan University, Suwon, Korea
Y. Choi, C. Hwang, Y. Jeong, H. Lee, J. Lee, Y. Lee, I. Yu Riga Technical
University, Riga, Latvia
V. Veckalns41 Vilnius University, Vilnius, Lithuania
A. Juodagalvis, A. Rinkevicius, G. Tamulaitis National Centre for Particle
Physics, Universiti Malaya, Kuala Lumpur, Malaysia
W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli Universidad de Sonora (UNISON),
Hermosillo, Mexico
J.F. Benitez, A. Castaneda Hernandez, J.A. Murillo Quijada, L. Valencia Palomo
Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz42, R. Lopez-
Fernandez, A. Sanchez-Hernandez Universidad Iberoamericana, Mexico City,
Mexico
S. Carrillo Moreno, C. Oropeza Barrera, M. Ramirez-Garcia, F. Vazquez Valencia
Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Montenegro, Podgorica, Montenegro
J. Mijuskovic4, N. Raicevic University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M.I. Asghar, M.I.M. Awan, Q. Hassan, H.R. Hoorani, W.A. Khan, M.A.
Shah, M. Shoaib, M. Waqas AGH University of Science and Technology Faculty of
Computer Science, Electronics and Telecommunications, Krakow, Poland
V. Avati, L. Grzanka, M. Malawski National Centre for Nuclear Research,
Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. Górski, M. Kazana, M.
Szleper, P. Traczyk, P. Zalewski Institute of Experimental Physics, Faculty of
Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk43, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Olszewski, M. Walczak Laboratório de Instrumentação e Física
Experimental de Partículas, Lisboa, Portugal
M. Araujo, P. Bargassa, D. Bastos, A. Di Francesco, P. Faccioli, B. Galinhas,
M. Gallinaro, J. Hollar, N. Leonardo, T. Niknejad, J. Seixas, K. Shchelina, O.
Toldaiev, J. Varela Joint Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov, A. Kamenev,
V. Karjavine, A. Lanev, A. Malakhov, V. Matveev44,45, P. Moisenz, V. Palichik,
V. Perelygin, M. Savina, D. Seitova, V. Shalaev, S. Shmatov, S. Shulha, V.
Smirnov, O. Teryaev, N. Voytishin, A. Zarubin, I. Zhizhin Petersburg Nuclear
Physics Institute, Gatchina (St. Petersburg), Russia
G. Gavrilov, V. Golovtcov, Y. Ivanov, V. Kim46, E. Kuznetsova47, V. Murzin, V.
Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Volkov, A. Vorobyev
Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, G. Pivovarov, D. Tlisov, A. Toropin Institute for
Theoretical and Experimental Physics named by A.I. Alikhanov of NRC ‘Kurchatov
Institute’, Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, A. Nikitenko48, V. Popov, I.
Pozdnyakov, G. Safronov, A. Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A.
Zhokin Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
O. Bychkova, R. Chistov49, M. Danilov49, D. Philippov, S. Polikarpov49 P.N.
Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin, M. Kirakosyan, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia
A. Belyaev, E. Boos, M. Dubinin50, L. Dudko, A. Ershov, A. Gribushin, V.
Klyukhin, O. Kodolova, I. Lokhtin, S. Obraztsov, S. Petrushanko, V. Savrin, A.
Snigirev Novosibirsk State University (NSU), Novosibirsk, Russia
V. Blinov51, T. Dimova51, L. Kardapoltsev51, I. Ovtin51, Y. Skovpen51
Institute for High Energy Physics of National Research Centre ‘Kurchatov
Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, V. Kachanov, A. Kalinin, D. Konstantinov, V. Petrov,
R. Ryutin, A. Sobol, S. Troshin, N. Tyurin, A. Uzunian, A. Volkov National
Research Tomsk Polytechnic University, Tomsk, Russia
A. Babaev, A. Iuzhakov, V. Okhotnikov, L. Sukhikh Tomsk State University,
Tomsk, Russia
V. Borchsh, V. Ivanchenko, E. Tcherniaev University of Belgrade: Faculty of
Physics and VINCA Institute of Nuclear Sciences, Belgrade, Serbia
P. Adzic52, P. Cirkovic, M. Dordevic, P. Milenovic, J. Milosevic Centro de
Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid,
Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M.
Barrio Luna, Cristina F. Bedoya, J.A. Brochero Cifuentes, C.A. Carrillo
Montoya, M. Cepeda, M. Cerrada, N. Colino, B. De La Cruz, A. Delgado Peris,
J.P. Fernández Ramos, J. Flix, M.C. Fouz, O. Gonzalez Lopez, S. Goy Lopez,
J.M. Hernandez, M.I. Josa, D. Moran, Á. Navarro Tobar, A. Pérez-Calero
Yzquierdo, J. Puerta Pelayo, I. Redondo, L. Romero, S. Sánchez Navas, M.S.
Soares, A. Triossi, C. Willmott Universidad Autónoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz, R. Reyes-Almanza Universidad de Oviedo,
Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias
(ICTEA), Oviedo, Spain
B. Alvarez Gonzalez, J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras,
I. Gonzalez Caballero, E. Palencia Cortezon, C. Ramón Álvarez, V. Rodríguez
Bouza, S. Sanchez Cruz Instituto de Física de Cantabria (IFCA), CSIC-
Universidad de Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, J. Duarte Campderros, M.
Fernandez, P.J. Fernández Manteca, A. García Alonso, G. Gomez, C. Martinez
Rivero, P. Martinez Ruiz del Arbol, F. Matorras, J. Piedra Gomez, C. Prieels,
F. Ricci-Tam, T. Rodrigo, A. Ruiz-Jimeno, L. Russo53, L. Scodellaro, I. Vila,
J.M. Vizan Garcia University of Colombo, Colombo, Sri Lanka
MK Jayananda, B. Kailasapathy54, D.U.J. Sonnadara, DDC Wickramarathna
University of Ruhuna, Department of Physics, Matara, Sri Lanka
W.G.D. Dharmaratna, K. Liyanage, N. Perera, N. Wickramage CERN, European
Organization for Nuclear Research, Geneva, Switzerland
T.K. Aarrestad, D. Abbaneo, B. Akgun, E. Auffray, G. Auzinger, J. Baechler, P.
Baillon, A.H. Ball, D. Barney, J. Bendavid, N. Beni, M. Bianco, A. Bocci, P.
Bortignon, E. Bossini, E. Brondolin, T. Camporesi, G. Cerminara, L. Cristella,
D. d’Enterria, A. Dabrowski, N. Daci, V. Daponte, A. David, A. De Roeck, M.
Deile, R. Di Maria, M. Dobson, M. Dünser, N. Dupont, A. Elliott-Peisert, N.
Emriskova, F. Fallavollita55, D. Fasanella, S. Fiorendi, G. Franzoni, J.
Fulcher, W. Funk, S. Giani, D. Gigi, K. Gill, F. Glege, L. Gouskos, M.
Guilbaud, D. Gulhan, M. Haranko, J. Hegeman, Y. Iiyama, V. Innocente, T.
James, P. Janot, J. Kaspar, J. Kieseler, M. Komm, N. Kratochwil, C. Lange, P.
Lecoq, K. Long, C. Lourenço, L. Malgeri, M. Mannelli, A. Massironi, F.
Meijers, S. Mersi, E. Meschi, F. Moortgat, M. Mulders, J. Ngadiuba, J.
Niedziela, S. Orfanelli, L. Orsini, F. Pantaleo19, L. Pape, E. Perez, M.
Peruzzi, A. Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, D. Rabady, A.
Racz, M. Rieger, M. Rovere, H. Sakulin, J. Salfeld-Nebgen, S. Scarfi, C.
Schäfer, C. Schwick, M. Selvaggi, A. Sharma, P. Silva, W. Snoeys, P.
Sphicas56, J. Steggemann, S. Summers, V.R. Tavolaro, D. Treille, A. Tsirou,
G.P. Van Onsem, A. Vartak, M. Verzetti, K.A. Wozniak, W.D. Zeuner Paul
Scherrer Institut, Villigen, Switzerland
L. Caminada57, W. Erdmann, R. Horisberger, Q. Ingram, H.C. Kaestli, D.
Kotlinski, U. Langenegger, T. Rohe ETH Zurich - Institute for Particle Physics
and Astrophysics (IPA), Zurich, Switzerland
M. Backhaus, P. Berger, A. Calandri, N. Chernyavskaya, G. Dissertori, M.
Dittmar, M. Donegà, C. Dorfer, T. Gadek, T.A. Gómez Espinosa, C. Grab, D.
Hits, W. Lustermann, A.-M. Lyon, R.A. Manzoni, M.T. Meinhard, F. Micheli, P.
Musella, F. Nessi-Tedaldi, F. Pauss, V. Perovic, G. Perrin, L. Perrozzi, S.
Pigazzini, M.G. Ratti, M. Reichmann, C. Reissel, T. Reitenspiess, B. Ristic,
D. Ruini, D.A. Sanz Becerra, M. Schönenberger, L. Shchutska, V. Stampf, M.L.
Vesterbacka Olsson, R. Wallny, D.H. Zhu Universität Zürich, Zurich,
Switzerland
C. Amsler58, C. Botta, D. Brzhechko, M.F. Canelli, A. De Cosa, R. Del Burgo,
J.K. Heikkilä, M. Huwiler, A. Jofrehei, B. Kilminster, S. Leontsinis, A.
Macchiolo, P. Meiring, V.M. Mikuni, U. Molinatti, I. Neutelings, G. Rauco, A.
Reimers, P. Robmann, K. Schweiger, Y. Takahashi, S. Wertz National Central
University, Chung-Li, Taiwan
C. Adloff59, C.M. Kuo, W. Lin, A. Roy, T. Sarkar34, S.S. Yu National Taiwan
University (NTU), Taipei, Taiwan
L. Ceard, P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.y. Li, R.-S.
Lu, E. Paganis, A. Psallidas, A. Steen, E. Yazgan Chulalongkorn University,
Faculty of Science, Department of Physics, Bangkok, Thailand
B. Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas Çukurova University,
Physics Department, Science and Art Faculty, Adana, Turkey
F. Boran, S. Damarseckin60, Z.S. Demiroglu, F. Dolek, C. Dozen61, I.
Dumanoglu62, E. Eskut, G. Gokbulut, Y. Guler, E. Gurpinar Guler63, I. Hos64,
C. Isik, E.E. Kangal65, O. Kara, A. Kayis Topaksu, U. Kiminsu, G. Onengut, K.
Ozdemir66, A. Polatoz, A.E. Simsek, B. Tali67, U.G. Tok, S. Turkcapar, I.S.
Zorbakir, C. Zorbilmez Middle East Technical University, Physics Department,
Ankara, Turkey
B. Isildak68, G. Karapinar69, K. Ocalan70, M. Yalvac71 Bogazici University,
Istanbul, Turkey
I.O. Atakisi, E. Gülmez, M. Kaya72, O. Kaya73, Ö. Özçelik, S. Tekten74, E.A.
Yetkin75 Istanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak62, Y. Komurcu, S. Sen76 Istanbul University, Istanbul,
Turkey
F. Aydogmus Sen, S. Cerci67, B. Kaynak, S. Ozkorucuklu, D. Sunar Cerci67
Institute for Scintillation Materials of National Academy of Science of
Ukraine, Kharkov, Ukraine
B. Grynyov National Scientific Center, Kharkov Institute of Physics and
Technology, Kharkov, Ukraine
L. Levchuk University of Bristol, Bristol, United Kingdom
E. Bhal, S. Bologna, J.J. Brooke, D. Burns77, E. Clement, D. Cussans, H.
Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, B. Krikler, S.
Paramesvaran, T. Sakuma, S. Seif El Nasr-Storey, V.J. Smith, J. Taylor, A.
Titterton Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev78, C. Brew, R.M. Brown, D.J.A. Cockerill, K.V. Ellis, K.
Harder, S. Harper, J. Linacre, K. Manolopoulos, D.M. Newbold, E. Olaiya, D.
Petyt, T. Reis, T. Schuh, C.H. Shepherd-Themistocleous, A. Thea, I.R. Tomalin,
T. Williams Imperial College, London, United Kingdom
R. Bainbridge, P. Bloch, S. Bonomally, J. Borg, S. Breeze, O. Buchmuller, A.
Bundock, V. Cepaitis, G.S. Chahal79, D. Colling, P. Dauncey, G. Davies, M.
Della Negra, P. Everaerts, G. Fedi, G. Hall, G. Iles, J. Langford, L. Lyons,
A.-M. Magnan, S. Malik, A. Martelli, V. Milosevic, J. Nash80, V. Palladino, M.
Pesaresi, D.M. Raymond, A. Richards, A. Rose, E. Scott, C. Seez, A.
Shtipliyski, M. Stoye, A. Tapper, K. Uchida, T. Virdee19, N. Wardle, S.N.
Webb, D. Winterbottom, A.G. Zecchinelli, S.C. Zenz Brunel University,
Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, C.K. Mackay, I.D. Reid, L.
Teodorescu, S. Zahid Baylor University, Waco, USA
A. Brinkerhoff, K. Call, B. Caraway, J. Dittmann, K. Hatakeyama, A.R.
Kanuganti, C. Madrid, B. McMaster, N. Pastika, S. Sawant, C. Smith Catholic
University of America, Washington, DC, USA
R. Bartek, A. Dominguez, R. Uniyal, A.M. Vargas Hernandez The University of
Alabama, Tuscaloosa, USA
A. Buccilli, O. Charaf, S.I. Cooper, S.V. Gleyzer, C. Henderson, P. Rumerio,
C. West Boston University, Boston, USA
A. Akpinar, A. Albert, D. Arcaro, C. Cosby, Z. Demiragli, D. Gastler, C.
Richardson, J. Rohlf, K. Salyer, D. Sperka, D. Spitzbart, I. Suarez, S. Yuan,
D. Zou Brown University, Providence, USA
G. Benelli, B. Burkle, X. Coubez20, D. Cutts, Y.t. Duh, M. Hadley, U. Heintz,
J.M. Hogan81, K.H.M. Kwok, E. Laird, G. Landsberg, K.T. Lau, J. Lee, M.
Narain, S. Sagir82, R. Syarif, E. Usai, W.Y. Wong, D. Yu, W. Zhang University
of California, Davis, Davis, USA
R. Band, C. Brainerd, R. Breedon, M. Calderon De La Barca Sanchez, M. Chertok,
J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk, F. Jensen, W.
Ko${}^{\textrm{\textdagger}}$, O. Kukral, R. Lander, M. Mulhearn, D. Pellett,
J. Pilot, M. Shi, D. Taylor, K. Tos, M. Tripathi, Y. Yao, F. Zhang University
of California, Los Angeles, USA
M. Bachtis, C. Bravo, R. Cousins, A. Dasgupta, A. Florent, D. Hamilton, J.
Hauser, M. Ignatenko, T. Lam, N. Mccoll, W.A. Nash, S. Regnard, D. Saltzberg,
C. Schnaible, B. Stone, V. Valuev University of California, Riverside,
Riverside, USA
K. Burt, Y. Chen, R. Clare, J.W. Gary, S.M.A. Ghiasi Shirazi, G. Hanson, G.
Karapostoli, O.R. Long, N. Manganelli, M. Olmedo Negrete, M.I. Paneva, W. Si,
S. Wimpenny, Y. Zhang University of California, San Diego, La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, S. Cooperstein, N. Deelen, M. Derdzinski,
J. Duarte, R. Gerosa, D. Gilbert, B. Hashemi, D. Klein, V. Krutelyov, J.
Letts, M. Masciovecchio, S. May, S. Padhi, M. Pieri, V. Sharma, M. Tadel, F.
Würthwein, A. Yagil University of California, Santa Barbara - Department of
Physics, Santa Barbara, USA
N. Amin, R. Bhandari, C. Campagnari, M. Citron, A. Dorsett, V. Dutta, J.
Incandela, B. Marsh, H. Mei, A. Ovcharova, H. Qu, M. Quinnan, J. Richman, U.
Sarica, D. Stuart, S. Wang California Institute of Technology, Pasadena, USA
D. Anderson, A. Bornheim, O. Cerri, I. Dutta, J.M. Lawhorn, N. Lu, J. Mao,
H.B. Newman, T.Q. Nguyen, J. Pata, M. Spiropulu, J.R. Vlimant, S. Xie, Z.
Zhang, R.Y. Zhu Carnegie Mellon University, Pittsburgh, USA
J. Alison, M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, M. Sun, I.
Vorobiev, M. Weinberg University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, E. MacDonald, T. Mulholland, R. Patel, A. Perloff, K.
Stenson, K.A. Ulmer, S.R. Wagner Cornell University, Ithaca, USA
J. Alexander, Y. Cheng, J. Chu, D.J. Cranshaw, A. Datta, A. Frankenthal, K.
Mcdermott, J. Monroy, J.R. Patterson, D. Quach, A. Ryd, W. Sun, S.M. Tan, Z.
Tao, J. Thom, P. Wittich, M. Zientek Fermi National Accelerator Laboratory,
Batavia, USA
S. Abdullin, M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S.
Banerjee, L.A.T. Bauerdick, A. Beretvas, D. Berry, J. Berryhill, P.C. Bhat, K.
Burkett, J.N. Butler, A. Canepa, G.B. Cerati, H.W.K. Cheung, F. Chlebana, M.
Cremonesi, V.D. Elvira, J. Freeman, Z. Gecse, E. Gottschalk, L. Gray, D.
Green, S. Grünendahl, O. Gutsche, R.M. Harris, S. Hasegawa, R. Heller, T.C.
Herwig, J. Hirschauer, B. Jayatilaka, S. Jindariani, M. Johnson, U. Joshi, T.
Klijnsma, B. Klima, M.J. Kortelainen, S. Lammel, J. Lewis, D. Lincoln, R.
Lipton, M. Liu, T. Liu, J. Lykken, K. Maeshima, D. Mason, P. McBride, P.
Merkel, S. Mrenna, S. Nahn, V. O’Dell, V. Papadimitriou, K. Pedro, C. Pena50,
O. Prokofyev, F. Ravera, A. Reinsvold Hall, L. Ristori, B. Schneider, E.
Sexton-Kennedy, N. Smith, A. Soha, W.J. Spalding, L. Spiegel, S. Stoynev, J.
Strait, L. Taylor, S. Tkaczyk, N.V. Tran, L. Uplegger, E.W. Vaandering, M.
Wang, H.A. Weber, A. Woodard University of Florida, Gainesville, USA
D. Acosta, P. Avery, D. Bourilkov, L. Cadamuro, V. Cherepanov, F. Errico, R.D.
Field, D. Guerrero, B.M. Joshi, M. Kim, J. Konigsberg, A. Korytov, K.H. Lo, K.
Matchev, N. Menendez, G. Mitselmakher, D. Rosenzweig, K. Shi, J. Wang, S.
Wang, X. Zuo Florida International University, Miami, USA
Y.R. Joshi Florida State University, Tallahassee, USA
T. Adams, A. Askew, D. Diaz, R. Habibullah, S. Hagopian, V. Hagopian, K.F.
Johnson, R. Khurana, T. Kolberg, G. Martinez, H. Prosper, C. Schiber, R.
Yohay, J. Zhang Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, S. Butalla, T. Elkafrawy14, M. Hohlmann, D. Noonan, M. Rahmani,
M. Saunders, F. Yumiceva University of Illinois at Chicago (UIC), Chicago, USA
M.R. Adams, L. Apanasevich, H. Becerril Gonzalez, R. Cavanaugh, X. Chen, S.
Dittmer, O. Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, C. Mills, G. Oh,
T. Roy, M.B. Tonjes, N. Varelas, J. Viinikainen, H. Wang, X. Wang, Z. Wu The
University of Iowa, Iowa City, USA
M. Alhusseini, B. Bilki63, K. Dilsiz83, S. Durgut, R.P. Gandrajula, M.
Haytmyradov, V. Khristenko, O.K. Köseyan, J.-P. Merlo, A. Mestvirishvili84, A.
Moeller, J. Nachtman, H. Ogul85, Y. Onel, F. Ozok86, A. Penzo, C. Snyder, E.
Tiras, J. Wetzel, K. Yi87 Johns Hopkins University, Baltimore, USA
O. Amram, B. Blumenfeld, L. Corcodilos, M. Eminizer, A.V. Gritsan, S.
Kyriacou, P. Maksimovic, C. Mantilla, J. Roskes, M. Swartz, T.Á. Vámi The
University of Kansas, Lawrence, USA
C. Baldenegro Barrera, P. Baringer, A. Bean, A. Bylinkin, T. Isidori, S.
Khalil, J. King, G. Krintiras, A. Kropivnitskaya, C. Lindsey, N. Minafra, M.
Murray, C. Rogan, C. Royon, S. Sanders, E. Schmitz, J.D. Tapia Takaki, Q.
Wang, J. Williams, G. Wilson Kansas State University, Manhattan, USA
S. Duric, A. Ivanov, K. Kaadze, D. Kim, Y. Maravin, D.R. Mendis, T. Mitchell,
A. Modak, A. Mohammadi Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
E. Adams, A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, N.J. Hadley, S.
Jabeen, G.Y. Jeng, R.G. Kellogg, T. Koeth, A.C. Mignerey, S. Nabili, M.
Seidel, A. Skuja, S.C. Tonwar, L. Wang, K. Wong Massachusetts Institute of
Technology, Cambridge, USA
D. Abercrombie, B. Allen, R. Bi, S. Brandt, W. Busza, I.A. Cali, Y. Chen, M.
D’Alfonso, G. Gomez Ceballos, M. Goncharov, P. Harris, D. Hsu, M. Hu, M.
Klute, D. Kovalskyi, J. Krupa, Y.-J. Lee, P.D. Luckey, B. Maier, A.C. Marini,
C. Mcginn, C. Mironov, S. Narayanan, X. Niu, C. Paus, D. Rankin, C. Roland, G.
Roland, Z. Shi, G.S.F. Stephans, K. Sumorok, K. Tatar, D. Velicanu, J. Wang,
T.W. Wang, Z. Wang, B. Wyslouch University of Minnesota, Minneapolis, USA
R.M. Chatterjee, A. Evans, S. Guts${}^{\textrm{\textdagger}}$, P. Hansen, J.
Hiltbrand, Sh. Jain, M. Krohn, Y. Kubota, Z. Lesko, J. Mans, M. Revering, R.
Rusack, R. Saradhy, N. Schroeder, N. Strobbe, M.A. Wadud University of
Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
K. Bloom, S. Chauhan, D.R. Claes, C. Fangmeier, L. Finco, F. Golf, J.R.
González Fernández, I. Kravchenko, J.E. Siado, G.R.
Snow${}^{\textrm{\textdagger}}$, B. Stieger, W. Tabb State University of New
York at Buffalo, Buffalo, USA
G. Agarwal, C. Harrington, L. Hay, I. Iashvili, A. Kharchilava, C. McLean, D.
Nguyen, A. Parker, J. Pekkanen, S. Rappoccio, B. Roozbahani Northeastern
University, Boston, USA
G. Alverson, E. Barberis, C. Freer, Y. Haddad, A. Hortiangtham, G. Madigan, B.
Marzocchi, D.M. Morse, V. Nguyen, T. Orimoto, L. Skinnari, A. Tishelman-
Charny, T. Wamorkar, B. Wang, A. Wisecarver, D. Wood Northwestern University,
Evanston, USA
S. Bhattacharya, J. Bueghly, Z. Chen, A. Gilbert, T. Gunter, K.A. Hahn, N.
Odell, M.H. Schmitt, K. Sung, M. Velasco University of Notre Dame, Notre Dame,
USA
R. Bucci, N. Dev, R. Goldouzian, M. Hildreth, K. Hurtado Anampa, C. Jessop,
D.J. Karmgard, K. Lannon, W. Li, N. Loukas, N. Marinelli, I. Mcalister, F.
Meng, K. Mohrman, Y. Musienko44, R. Ruchti, P. Siddireddy, S. Taroni, M.
Wayne, A. Wightman, M. Wolf, L. Zygala The Ohio State University, Columbus,
USA
J. Alimena, B. Bylsma, B. Cardwell, L.S. Durkin, B. Francis, C. Hill, W. Ji,
A. Lefeld, B.L. Winer, B.R. Yates Princeton University, Princeton, USA
G. Dezoort, P. Elmer, B. Greenberg, N. Haubrich, S. Higginbotham, A.
Kalogeropoulos, G. Kopp, S. Kwan, D. Lange, M.T. Lucchini, J. Luo, D. Marlow,
K. Mei, I. Ojalvo, J. Olsen, C. Palmer, P. Piroué, D. Stickland, C. Tully
University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
V.E. Barnes, R. Chawla, S. Das, L. Gutay, M. Jones, A.W. Jung, B. Mahakud, G.
Negro, N. Neumeister, C.C. Peng, S. Piperov, H. Qiu, J.F. Schulte, N.
Trevisani, F. Wang, R. Xiao, W. Xie Purdue University Northwest, Hammond, USA
T. Cheng, J. Dolen, N. Parashar, M. Stojanovic Rice University, Houston, USA
A. Baty, S. Dildick, K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Kilpatrick, A.
Kumar, W. Li, B.P. Padley, R. Redjimi, J. Roberts${}^{\textrm{\textdagger}}$,
J. Rorie, W. Shi, A.G. Stahl Leiton, Z. Tu, A. Zhang University of Rochester,
Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, J.L. Dulemba, C. Fallon, T. Ferbel, M.
Galanti, A. Garcia-Bellido, O. Hindrichs, A. Khukhunaishvili, E. Ranken, R.
Taus Rutgers, The State University of New Jersey, Piscataway, USA
B. Chiarito, J.P. Chou, A. Gandrakota, Y. Gershtein, E. Halkiadakis, A. Hart,
M. Heindl, E. Hughes, S. Kaplan, O. Karacheban23, I. Laflotte, A. Lath, R.
Montalvo, K. Nash, M. Osherson, S. Salur, S. Schnetzer, S. Somalwar, R. Stone,
S.A. Thayil, S. Thomas University of Tennessee, Knoxville, USA
H. Acharya, A.G. Delannoy, S. Spanier Texas A&M University, College Station,
USA
O. Bouhali88, M. Dalchenko, A. Delgado, R. Eusebi, J. Gilmore, T. Huang, T.
Kamon89, H. Kim, S. Luo, S. Malhotra, R. Mueller, D. Overton, L. Perniè, D.
Rathjens, A. Safonov Texas Tech University, Lubbock, USA
N. Akchurin, J. Damgov, V. Hegde, S. Kunori, K. Lamichhane, S.W. Lee, T.
Mengke, S. Muthumuni, T. Peltola, S. Undleeb, I. Volobouev, Z. Wang, A.
Whitbeck Vanderbilt University, Nashville, USA
E. Appelt, S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H.
Ni, K. Padeken, F. Romeo, P. Sheldon, S. Tuo, J. Velkovska, M. Verweij
University of Virginia, Charlottesville, USA
L. Ang, M.W. Arenton, B. Cox, G. Cummings, J. Hakala, R. Hirosky, M. Joyce, A.
Ledovskoy, C. Neu, B. Tannenwald, Y. Wang, E. Wolfe, F. Xia Wayne State
University, Detroit, USA
P.E. Karchin, N. Poudyal, J. Sturdy, P. Thapa University of Wisconsin -
Madison, Madison, WI, USA
K. Black, T. Bose, J. Buchanan, C. Caillol, S. Dasu, I. De Bruyn, L. Dodd, C.
Galloni, H. He, M. Herndon, A. Hervé, U. Hussain, A. Lanaro, A. Loeliger, R.
Loveless, J. Madhusudanan Sreekala, A. Mallampalli, D. Pinna, T. Ruggles, A.
Savin, V. Shang, V. Sharma, W.H. Smith, D. Teague, S. Trembath-reichert, W.
Vetens †: Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at Department of Basic and Applied Sciences, Faculty of Engineering,
Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt
3: Also at Université Libre de Bruxelles, Bruxelles, Belgium
4: Also at IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
5: Also at Universidade Estadual de Campinas, Campinas, Brazil
6: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
7: Also at UFMS, Nova Andradina, Brazil
8: Also at Universidade Federal de Pelotas, Pelotas, Brazil
9: Also at University of Chinese Academy of Sciences, Beijing, China
10: Also at Institute for Theoretical and Experimental Physics named by A.I.
Alikhanov of NRC ‘Kurchatov Institute’, Moscow, Russia
11: Also at Joint Institute for Nuclear Research, Dubna, Russia
12: Also at Helwan University, Cairo, Egypt
13: Now at Zewail City of Science and Technology, Zewail, Egypt
14: Also at Ain Shams University, Cairo, Egypt
15: Also at Purdue University, West Lafayette, USA
16: Also at Université de Haute Alsace, Mulhouse, France
17: Also at Tbilisi State University, Tbilisi, Georgia
18: Also at Erzincan Binali Yildirim University, Erzincan, Turkey
19: Also at CERN, European Organization for Nuclear Research, Geneva,
Switzerland
20: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
21: Also at University of Hamburg, Hamburg, Germany
22: Also at Department of Physics, Isfahan University of Technology, Isfahan,
Iran, Isfahan, Iran
23: Also at Brandenburg University of Technology, Cottbus, Germany
24: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
25: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary,
Debrecen, Hungary
26: Also at Physics Department, Faculty of Science, Assiut University, Assiut,
Egypt
27: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary, Budapest, Hungary
28: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
29: Also at IIT Bhubaneswar, Bhubaneswar, India, Bhubaneswar, India
30: Also at Institute of Physics, Bhubaneswar, India
31: Also at G.H.G. Khalsa College, Punjab, India
32: Also at Shoolini University, Solan, India
33: Also at University of Hyderabad, Hyderabad, India
34: Also at University of Visva-Bharati, Santiniketan, India
35: Also at Indian Institute of Technology (IIT), Mumbai, India
36: Also at Deutsches Elektronen-Synchrotron, Hamburg, Germany
37: Also at Department of Physics, University of Science and Technology of
Mazandaran, Behshahr, Iran
38: Now at INFN Sezione di Bari a, Università di Bari b, Politecnico di Bari
c, Bari, Italy
39: Also at Italian National Agency for New Technologies, Energy and
Sustainable Economic Development, Bologna, Italy
40: Also at Centro Siciliano di Fisica Nucleare e di Struttura Della Materia,
Catania, Italy
41: Also at Riga Technical University, Riga, Latvia, Riga, Latvia
42: Also at Consejo Nacional de Ciencia y Tecnología, Mexico City, Mexico
43: Also at Warsaw University of Technology, Institute of Electronic Systems,
Warsaw, Poland
44: Also at Institute for Nuclear Research, Moscow, Russia
45: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
46: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
47: Also at University of Florida, Gainesville, USA
48: Also at Imperial College, London, United Kingdom
49: Also at P.N. Lebedev Physical Institute, Moscow, Russia
50: Also at California Institute of Technology, Pasadena, USA
51: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
52: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
53: Also at Università degli Studi di Siena, Siena, Italy
54: Also at Trincomalee Campus, Eastern University, Sri Lanka, Nilaveli, Sri
Lanka
55: Also at INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy,
Pavia, Italy
56: Also at National and Kapodistrian University of Athens, Athens, Greece
57: Also at Universität Zürich, Zurich, Switzerland
58: Also at Stefan Meyer Institute for Subatomic Physics, Vienna, Austria,
Vienna, Austria
59: Also at Laboratoire d’Annecy-le-Vieux de Physique des Particules,
IN2P3-CNRS, Annecy-le-Vieux, France
60: Also at Şırnak University, Sirnak, Turkey
61: Also at Department of Physics, Tsinghua University, Beijing, China,
Beijing, China
62: Also at Near East University, Research Center of Experimental Health
Science, Nicosia, Turkey
63: Also at Beykent University, Istanbul, Turkey, Istanbul, Turkey
64: Also at Istanbul Aydin University, Application and Research Center for
Advanced Studies (App. & Res. Cent. for Advanced Studies), Istanbul, Turkey
65: Also at Mersin University, Mersin, Turkey
66: Also at Piri Reis University, Istanbul, Turkey
67: Also at Adiyaman University, Adiyaman, Turkey
68: Also at Ozyegin University, Istanbul, Turkey
69: Also at Izmir Institute of Technology, Izmir, Turkey
70: Also at Necmettin Erbakan University, Konya, Turkey
71: Also at Bozok Universitetesi Rektörlügü, Yozgat, Turkey
72: Also at Marmara University, Istanbul, Turkey
73: Also at Milli Savunma University, Istanbul, Turkey
74: Also at Kafkas University, Kars, Turkey
75: Also at Istanbul Bilgi University, Istanbul, Turkey
76: Also at Hacettepe University, Ankara, Turkey
77: Also at Vrije Universiteit Brussel, Brussel, Belgium
78: Also at School of Physics and Astronomy, University of Southampton,
Southampton, United Kingdom
79: Also at IPPP Durham University, Durham, United Kingdom
80: Also at Monash University, Faculty of Science, Clayton, Australia
81: Also at Bethel University, St. Paul, Minneapolis, USA, St. Paul, USA
82: Also at Karamanoğlu Mehmetbey University, Karaman, Turkey
83: Also at Bingol University, Bingol, Turkey
84: Also at Georgian Technical University, Tbilisi, Georgia
85: Also at Sinop University, Sinop, Turkey
86: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey
87: Also at Nanjing Normal University Department of Physics, Nanjing, China
88: Also at Texas A&M University at Qatar, Doha, Qatar
89: Also at Kyungpook National University, Daegu, Korea, Daegu, Korea