EUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH (CERN)

 CERN-EP-2020-212 LHCb-PAPER-2020-027 December 4, 2020

Search for long-lived particles decaying to ${e^{\pm}}{\mu^{\mp}}{\nu}$

LHCb collaboration†††Authors are listed at the end of this paper.

Long-lived particles decaying to ${e^{\pm}}{\mu^{\mp}}{\nu}$, with masses
between 7 and $50\text{\,Ge\kern-1.00006ptV\\!/}c^{2}$ and lifetimes between 2
and $50\text{\,ps}$, are searched for by looking at displaced vertices
containing electrons and muons of opposite charges. The search is performed
using $5.4\text{\,fb}^{-1}$ of $p$ $p$ collisions collected with the LHCb
detector at a centre-of-mass energy of
$\sqrt{s}=13\text{\,Te\kern-1.00006ptV}$. Three mechanisms of production of
long-lived particles are considered: the direct pair production from quark
interactions, the pair production from the decay of a Standard-Model-like
Higgs boson with a mass of $125\text{\,Ge\kern-1.00006ptV\\!/}c^{2}$, and the
charged current production from an on-shell $W$ boson with an additional
lepton. No evidence of these long-lived states is obtained and upper limits on
the production cross-section times branching fraction are set on the different
production modes.

Submitted to Eur. Phys. J. C

© 2024 CERN for the benefit of the LHCb collaboration. CC BY 4.0 licence.

##  1 Introduction

A variety of models beyond the Standard Model (SM) feature the existence of
new massive particles with lifetimes that can be long, compared to the SM
particles at the weak scale. These so-called long-lived particles (LLP)
appear, for example, in Supersymmetry or extensions to the SM that predict
right-handed neutrinos [1]. The study presented in this paper focuses on the
search for decays of neutral LLPs using three production mechanisms: direct
pair production (DPP), pair production from the decay of a SM-like Higgs boson
with a mass of $125\text{\,Ge\kern-1.00006ptV\\!/}c^{2}$ (HIG), and from
charged current (CC) processes. Diagrams for each production mode are shown in
Fig. 1. The production of LLPs from the decay of a SM-like Higgs boson has
been studied in several searches conducted by the CMS, ATLAS and LHCb
experiments, using LLP decays to light-flavour jets [2, 3, 4, 5, 6], $b$-quark
jets [7] and light leptons [8, 9]. In this study the LLP can be a neutralino
$\tilde{\chi}^{1}_{0}$, in R-parity-violating supersymmetric models [10], or a
right-handed neutrino $N$ decaying to two charged leptons and a neutrino [11,
12, 13]. Searches for $\text{LLP}\\!\rightarrow{e^{\mp}}{\mu^{\pm}}{\nu}$
decays have been performed by the ATLAS experiment in the context of
Supersymmetry [14], and also with right-handed neutrinos [15].

The first direct $\text{LLP}\\!\rightarrow{e^{\mp}}{\mu^{\pm}}{\nu}$ search at
the LHCb experiment is presented in this paper. The LHCb detector probes the
forward rapidity region that is only partially covered by the other LHC
experiments, and triggers on particles with low transverse momenta, which
allows the experiment to explore relatively small LLP masses. In the present
study, displaced vertices consisting of an electron and a muon of opposite
charges are searched for in ${p}{p}$ collisions at a centre-of-mass energy of
$\sqrt{s}=13\text{\,Te\kern-1.00006ptV}$, using a data sample corresponding to
an integrated luminosity of $5.38\pm 0.11\text{\,fb}^{-1}$ collected with the
LHCb detector in $20162018$. The momentum of the neutrino in the final state
can be partly reconstructed from the misalignment between the LLP flight
direction and the momentum of the electron and muon system. The explored
masses of the LLP ($m_{\text{LLP}}$) range from 7 to
$50\text{\,Ge\kern-1.00006ptV\\!/}c^{2}$ and lifetimes ($\tau_{\text{LLP}}$)
range from 2 to $50\text{\,ps}$. This search enlarges the domain of searches
for heavy LLPs at LHCb, which previously probed for displaced jets [4, 5, 6]
or displaced dimuons [16, 17, 18].

Figure 1: Production modes of the LLP considered in this search. From left to
right: direct pair production (DPP), decay of a SM-like Higgs with a mass of
$125\text{\,Ge\kern-0.90005ptV\\!/}c^{2}$ produced by gluon-gluon fusion (HIG)
and production by charged current (CC).

##  2 Detector description

The LHCb detector [19, 20] is a single-arm forward spectrometer covering the
pseudorapidity range $2<\eta<5$, designed for the study of particles
containing $b$ or $c$ quarks. The detector includes a high-precision tracking
system consisting of a silicon-strip vertex detector surrounding the ${p}{p}$
interaction region (VELO), a large-area silicon-strip detector located
upstream of a dipole magnet with a bending power of about $4{\text{\,Tm}}$,
and three stations of silicon-strip detectors and straw drift tubes, placed
downstream of the magnet. The tracking system provides a measurement of
momentum, $p$, of charged particles with a relative uncertainty that varies
from $0.5\%$ at low momentum to $1.0\%$ at
$200\text{\,Ge\kern-1.00006ptV\\!/}c$. The minimum distance of a track to a
primary ${p}{p}$ collision vertex (PV), the impact parameter (IP), is measured
with a resolution of $(15+29/p_{\mathrm{T}})\,\upmu\text{m}$, where
$p_{\mathrm{T}}$ is the component of the momentum transverse to the beam axis,
in $\text{\,Ge\kern-1.00006ptV\\!/}c$. Different types of charged hadrons are
distinguished using information from two ring-imaging Cherenkov detectors.
Photons, electrons and hadrons are identified by a calorimeter system
consisting of scintillating-pad and preshower detectors, an electromagnetic
calorimeter (ECAL) and a hadronic calorimeter (HCAL). Muons are identified by
a system composed of alternating layers of iron and multiwire proportional
chambers.

The online event selection is performed by a trigger, which consists of a
hardware stage based on information from the calorimeter and muon systems,
followed by a software stage that carries out a full event reconstruction.
Events from ${p}{p}$ collisions fulfilling the muon or electron trigger are
studied. At the hardware level the muon trigger requires a muon track
identified by matching hits in the muon stations, for the electron trigger a
cluster in the ECAL with large transverse energy deposit is required. At the
software level the muon trigger selects muons with a minimum $p_{\mathrm{T}}$
of $10\text{\,Ge\kern-1.00006ptV\\!/}c$, the electron trigger selects
electrons with a minimum $p_{\mathrm{T}}$ of
$15\text{\,Ge\kern-1.00006ptV\\!/}c$.

##  3 Simulation

Simulated samples of $\text{LLP}\\!\rightarrow{e^{\mp}}{\mu^{\pm}}{\nu}$
events are used to design and optimise the signal selection and to estimate
the detection efficiency, but also for the construction of the signal model.
Parton-level events with LLPs are generated with MadGraph [21] using universal
Feynman outputs (UFO) [22] for long-lived particle searches following Ref.
[1]. For the DPP and HIG mechanisms, the UFO for the minimal supersymmetric
standard model with R-parity violation [10] is chosen, and in this framework
the signal is represented by the lightest neutralino $\tilde{\chi}^{1}_{0}$.
For the CC production the UFO of the Left-Right Symmetric model [23, 24, 25]
is used, and here the LLP is represented by a heavy neutrino produced from an
on-shell $W$ boson. For all three modes, the LLP is allowed to decay into an
electron and a muon with opposite charges, and a neutrino. The parton shower
of the events is simulated with Pythia8 [26, 27] using a specific LHCb
configuration [28] and using the CTEQ6 leading-order set of parton density
functions [29]. The interaction of the particles with the detector and its
response are implemented using the Geant4 toolkit [30, *Agostinelli:2002hh] as
described in Ref. [32]. Signal events with $m_{\text{LLP}}=7,10,15,20,30,38$
and $50\text{\,Ge\kern-1.00006ptV\\!/}c^{2}$ and $\tau_{\text{LLP}}=2,5,10,25$
and $50\text{\,ps}$ are generated.

Samples are also generated for background studies and cross checks, although
the background estimate in this study is based on data. The most relevant
background in this analysis is from ${b}{\overline{b}}$ events. Events
generated from ${gg/{q}{\overline{q}}\rightarrow{{b}{\overline{b}}}}$
processes with Pythia8, with at least one muon with
$p_{\mathrm{T}}>10\text{\,Ge\kern-1.00006ptV\\!/}c$ in the LHCb acceptance are
simulated.

##  4 Signal selection

The $\text{LLP}\\!\rightarrow{e^{\mp}}{\mu^{\pm}}{\nu}$ candidates are
reconstructed from the combination of a muon and an electron candidate of
opposite charges forming a good-quality vertex within the VELO detector. The
following selection of the candidates is developed and optimised using the DPP
samples for each pair of $m_{\text{LLP}}$ and $\tau_{\text{LLP}}$ values. This
selection is also adopted for the study of the HIG and CC processes.

The muon and electron candidates are required to have
$p_{\mathrm{T}}>1.6\text{\,Ge\kern-1.00006ptV\\!/}c$ and
$p>10\text{\,Ge\kern-1.00006ptV\\!/}c$. The measured momentum of the electron
candidates is corrected for the loss of energy due to bremsstrahlung [33]. The
muon and electron need to form a good-quality vertex displaced from any PV,
with a flight distance greater than 15 times its uncertainty. In addition, the
lifetime of the candidate is required to be greater than $0.5\text{\,ps}$. For
the estimate of the lifetime, the Lorentz boost is calculated from the
dilepton momentum, $p(e\mu)$, neglecting the contribution of the neutrino. The
mass of the candidate is obtained from the dilepton system with a correction
to account for not reconstructing the neutrino. The correction is inferred
from the misalignment of the dilepton reconstructed momentum and the flight
direction from the PV to the decay vertex. The corrected invariant mass is
computed as
${m_{\text{corr}}=\sqrt{m(e\mu)^{2}+p(e\mu)^{2}\sin^{2}\theta}+p(e\mu)\sin\theta}$
[34], where $\theta$ is the angle formed by the dilepton momentum and the LLP
flight direction. Candidates with
$m_{\text{corr}}<3.3\text{\,Ge\kern-1.00006ptV\\!/}c^{2}$ are discarded.

To suppress the heavy-flavour background the leptons are required to be
isolated from other charged particles. The isolation variable is defined as
$I=(\vec{p}-\vec{p}_{\text{cone}})_{\text{T}}\;/\;(\vec{p}+\vec{p}_{\text{cone}})_{\text{T}}$,
where $\vec{p}$ is the momentum of the lepton candidate and
$\vec{p}_{\text{cone}}$ is the sum of all the momenta of charged tracks,
excluding the lepton candidates, within a distance $\Delta
R=\sqrt{\Delta\eta^{2}+\Delta\phi^{2}}$ of 0.5 around the lepton, where
$\Delta\eta$ and $\Delta\phi$ are the pseudorapidity and azimuthal angle
differences between the lepton candidate and the charged tracks. The subscript
T indicates the momentum component in the transverse plane. A value of $I=1$
denotes a fully isolated lepton. Candidates with $I(\mu)>0$ and $I(e)>0.4$ are
selected. Particle identification criteria are applied to the muon and the
electron candidates, in particular for the electron a tighter criterion is
needed in order to reject the background due to misidentified pions or kaons.
This criterion is optimised to preserve signal efficiency while maximising the
rejection power over a data sample of same-sign candidates,
${e}^{\pm}{\mu}^{\pm}$, used as background proxy. Figure 2 compares
distributions of observables for data and simulated ${b}{\overline{b}}$
candidates, and examples of signals with different $m_{\text{LLP}}$ and
$\tau_{\text{LLP}}$ values, which survive the selection presented above.
Figures 2(a) and (b) show the candidates $m_{\text{corr}}$ and flight distance
distributions. These observables are used in the fit to determine the presence
of signal, as explained in Sect. 5. Figures 2(c) and (d) show the transverse
momentum distributions of the muon and electron, respectively. These muon and
electron $p_{\mathrm{T}}$ distributions show the effect of the
$p_{\mathrm{T}}$ threshold in the muon and the electron triggers. In Figs.
2(e) and (f) the distributions of the isolation variable, $I$, are displayed
for the muon and electron, respectively. The leptons from the signal are
expected to be more isolated than the ones from the ${b}{\overline{b}}$
background.

Figure 2:  Distributions in data (dashed black histogram) compared to
simulated ${{b}{\overline{b}}}\rightarrow{e}{\mu}X$ (green filled histogram),
showing, (a) $m_{\text{corr}}$, (b) the LLP flight distance, (c) the
transverse momentum of the muon, (d) the transverse momentum of the electron,
(e) the isolation of the muon, and (f) the isolation of the electron. LLP
signal distributions are also shown (coloured histograms) for different
$m_{\text{LLP}}$ and $\tau_{\text{LLP}}$ values, where the LLP is produced
through the DPP mechanism. The distributions from simulation are normalised to
the number of candidates in data. There are no simulated ${b}{\overline{b}}$
candidates for $p_{\mathrm{T}}(\mu)<10\text{\,Ge\kern-0.90005ptV\\!/}c^{2}$
due to a $p_{\mathrm{T}}$ requirement at the generation. For the same reason
there is a lack of simulated ${b}{\overline{b}}$ candidates for
$p_{\mathrm{T}}(e)>15\text{\,Ge\kern-0.90005ptV\\!/}c^{2}$ as candidates are
required to pass the muon or electron trigger.

A Boosted Decision Tree (BDT) classifier [35, 36] is used to further purify
the $\text{LLP}\\!\rightarrow{e^{\mp}}{\mu^{\pm}}{\nu}$ candidate sample. The
BDT is trained using 70k signal decays from a combination of DPP samples, and
background candidates drawn from the same-sign sample. The full signal sample
contains 2000 candidates for each set of ($m_{\text{LLP}}$,
$\tau_{\text{LLP}}$) parameters. Using all simulated signal samples for the
training phase allows to obtain a uniform BDT response across the
($m_{\text{LLP}}$, $\tau_{\text{LLP}}$) space. Furthermore, the uniformity is
enforced by using a special cost function described in Ref. [37]. This cost
function has the objective to provide the best classification between the
signal and the background, while keeping the BDT response uniform on
$m_{\text{LLP}}$ and $\tau_{\text{LLP}}$. The BDT input observables are: the
muon $p_{\mathrm{T}}$; the maximum between the momentum of the two leptons;
the two isolation variables; the angle between the muon momentum in the $e\mu$
rest frame and the $e\mu$ momentum; the ratio of the energy deposited by the
muon in the calorimeters and its momentum; the ratio of the energy deposited
by the electron in the HCAL and its momentum; the distance of closest approach
between the two lepton tracks; the $\chi^{2}$ of the LLP decay vertex; the
difference between the muon and electron impact parameters divided by the LLP
impact parameter; the impact parameter $\chi^{2}$ of the leptons,
$\chi^{2}_{\text{IP}}(l)$, divided by $\chi^{2}_{\text{IP}}(\text{LLP})$. For
a given particle, the impact parameter $\chi^{2}$ is defined as the difference
between the $\chi^{2}$ of the PV reconstructed with and without that particle.
The BDT response, shown in Fig. 3, is uniformly distributed between 0 and 1
for the signal, while peaking at zero for the background. Candidates with a
BDT value below $0.1$ are rejected, leaving 61116 signal candidates. The
observed BDT distribution is consistent with a ${b}{\overline{b}}$ composition
of the background. Using the ${b}{\overline{b}}$ cross-section at
$13\text{\,Te\kern-1.00006ptV}$ measured by LHCb, $144\pm 1\pm
21\,\upmu\text{b}$ [38], $(60\pm 14)\times 10^{3}$
${{b}{\overline{b}}}\rightarrow{e}{\mu}X$ candidates are predicted after
selection, consistent with the observed total yield.

Figure 3:  Distribution of the BDT response in data (dashed black histogram)
compared to simulated ${{b}{\overline{b}}}\rightarrow{e}{\mu}X$ (green filled
histogram) and LLP signal samples (coloured histograms) for different (a)
$m_{\text{LLP}}$ and (b) $\tau_{\text{LLP}}$ values, where the LLP is produced
through the DPP mechanism. The distributions from simulation are normalised to
the number of candidates in data.

##  5 Determination of the signal yield

The signal yield is determined from a simultaneous extended maximum likelihood
fit to the LLP corrected mass $m_{\text{corr}}$ and flight distance
distributions selected into two BDT intervals (0.1, 0.5] and (0.5, 1.0]. The
study of the simulated ${{b}{\overline{b}}}\rightarrow{e}{\mu}X$ background
indicates the presence of two components that depend on whether the two
leptons belong to the same heavy-flavour jet or two different jets. The two
components have different $m_{\text{corr}}$ and flight distance distributions,
and can be separated by the distance $\Delta R$ between the two leptons. When
leptons originate from the same heavy-flavour jet, they have relatively small
$\Delta R$, selected with $\Delta R<1$, while $\Delta R\geq 1$ selects the
complementary component. The background probability density functions of the
$m_{\text{corr}}$ and flight distance needed in the global fit are inferred
from the same-sign data. This choice has been validated by a comparison of the
distributions of $m_{\text{corr}}$ and the flight distance in simulated
${{b}{\overline{b}}}\rightarrow{e^{\pm}}{\mu^{\mp}}X$ and
${{b}{\overline{b}}}\rightarrow{e^{\pm}}{\mu^{\pm}}X$ candidates.

When $\Delta R<1$, the background $m_{\text{corr}}$ values are mostly found
below $6\text{\,Ge\kern-1.00006ptV\\!/}c^{2}$. This component is modelled
using a sum of a Gaussian and a Crystal Ball function [39]. The fraction
between the two distributions is fixed to the value obtained in the fit to the
same-sign data. The parameters describing the tail are free in each BDT bin,
the other parameters are free but common to all the BDT bins. For the $\Delta
R\geq 1$ region $m_{\text{corr}}$ is mostly above
$10\text{\,Ge\kern-1.00006ptV\\!/}c^{2}$. This region is modelled using a
Johnson SU distribution [40] with shape parameters free in each BDT bin. To
model the signal $m_{\text{corr}}$ distribution a sum of a modified Gaussian
distribution, where the left tail is exponential and the right tail a power
law, and another Gaussian distribution is used. The parameters of the model
are fixed to the values obtained from the fits to the simulated samples, for
each ($m_{\text{LLP}}$, $\tau_{\text{LLP}}$) hypothesis. The same signal
$m_{\text{corr}}$ models are used for each BDT bin and production mechanism.

The background candidates with $\Delta R<1$ have long flight distances, above
$10\text{\,mm}$. The opposite is true for $\Delta R\geq 1$. The two components
are modelled using a Johnson SU distribution, with all parameters kept free.
In the $\Delta R<1$ region the parameters of the model are not shared across
the BDT bins, while they are shared when $\Delta R\geq 1$. A kernel density
estimator algorithm is used to estimate the probability density function of
the flight distance distribution in simulated signal for each BDT bin. The
same signal flight distance model for a given ($m_{\text{LLP}}$,
$\tau_{\text{LLP}}$) hypothesis is used for each production mechanism.

In the final fit the fractions of signal yield in each BDT interval are
constrained by Gaussian functions to the values and uncertainties that are
estimated in the simulation. In order to explore a larger set of
$m_{\text{LLP}}$ values than the simulated set, signal templates for the
$m_{\text{corr}}$ and flight distance distributions are interpolated from the
simulated distributions using a moment morphing algorithm [41]. Distributions
of $m_{\text{corr}}$ and the flight distance in two BDT regions are shown in
Fig. 4, with an example of a fit result for a signal with
$m_{\text{LLP}}=47\text{\,Ge\kern-1.00006ptV\\!/}c^{2}$ and
$\tau_{\text{LLP}}=50\text{\,ps}$ overlaid. For each $m_{\text{LLP}}$ and
$\tau_{\text{LLP}}$ hypothesis the fitted yields are consistent with no signal
present.

Figure 4:  Distributions of $m_{\text{corr}}$ (top) and the flight distance
(bottom) of two BDT intervals (left and right), where a simultaneous fit
result for a LLP signal with
$m_{\text{LLP}}=47\text{\,Ge\kern-0.90005ptV\\!/}c^{2}$ and
$\tau_{\text{LLP}}=50\text{\,ps}$ is overlaid; the fitted signal yield in this
example is $14\pm 14$.

##  6 Signal efficiencies and systematic uncertainties

The determination of the signal detection efficiency relies on simulation.
Systematic effects are identified from differences between data and
simulation. Regarding the electron, samples of
${{J\mskip-3.0mu/\mskip-2.0mu\psi}}\rightarrow{e^{+}e^{-}}$ and
${Z}\rightarrow{e^{+}e^{-}}$ decays are considered, and
${{J\mskip-3.0mu/\mskip-2.0mu\psi}}\rightarrow{\mu^{+}\mu^{-}}$, ${\mathchar
28935\relax}\rightarrow{\mu^{+}\mu^{-}}$ and ${Z}\rightarrow{\mu^{+}\mu^{-}}$
decays are used for the muon. Samples of
${{b}{\overline{b}}}\rightarrow{e^{\pm}}{\mu^{\pm}}X$ candidates are used to
compare distributions of the reconstructed dilepton system such as the
corrected mass and the flight distance. Systematic uncertainties on the signal
efficiency have been evaluated. They are summarised in Table 1 and discussed
in more details below. Also reported in the table are the uncertainties on the
integrated luminosity, evaluated to be $2\%$ [42], on the signal fraction in
each BDT bin, and on the signal yield associated with the fit procedure,
discussed at the end of this section.

To account for the mismodelling in the simulation used to compute the signal
efficiency, a bias for each variable used in the selection is determined by
comparing simulated and experimental distributions of $Z$ and
${b}{\overline{b}}$ candidates. The correlations between the selection
variables are computed using the signal samples. The effect of imperfect
simulation is subsequently estimated by recomputing several times the signal
efficiency after changing the selection requirements on the variables by
factors drawn from a multivariate normal distribution, with biases and
correlations between the variables as input. The standard deviation of the
distribution of efficiencies is found in the range $4.9$ to $7.3\%$, depending
on the signal mass, lifetime and production mechanism, which is taken as a
contribution to the systematic uncertainty. In a similar way, systematic
uncertainties ranging from $0.5$ to $2.4\%$ are assigned to the identification
of the two leptons.

The systematic uncertainty due to the imprecision in the simulated signal
sample used to train the BDT classifier is estimated by applying the
classifier on modified signal distributions: each input variable is multiplied
by a scale factor drawn from a multivariate normal distribution built with the
variable biases and correlations, also inferred from the control samples. The
standard deviation of the efficiency distribution is used as systematic
uncertainty, ranging from $0.6$ to $1.0\%$ for the BDT $>0.1$ requirement, and
from $3.3$ to $4.0\%$ on the signal fraction in the BDT bins.

The contribution to the systematic uncertainty from the statistical precision
of the simulated signal samples is in the range $1.13.0$$\%$.

The theoretical uncertainties are dominated by the limited knowledge of the
partonic luminosity. This contribution is estimated following the procedure
explained in Ref. [43] and varies from $1.1\%$ up to $6.1\%$. The minimum
systematic contribution is found for the DPP and CC processes while the
maximum contribution is found for the gluon-gluon fusion process HIG.

Finally, the total systematic uncertainty is obtained as the sum in quadrature
of all contributions, where the different components of the detection
efficiency are assumed to be fully correlated. In order to uniformly cover the
full $m_{\text{LLP}}$ range, a third-order polynomial is fitted to the signal
detection efficiency as function of $m_{\text{LLP}}$ for each simulated
$\tau_{\text{LLP}}$ value. A second order polynomial is also fitted to the
efficiency. The difference between the two efficiencies is assigned as
systematic uncertainty, a contribution that is always less than $4\%$. The
interpolated signal efficiency for LLPs produced through the DPP mechanism is
shown in Fig. 5, accounting for the geometrical acceptance. The criteria on
the vertex displacement favour large lifetimes; however, above $10\text{\,ps}$
the probability that the LLP decays outside the VELO increases, leading to a
loss of efficiency. The selection efficiency increases with $m_{\text{LLP}}$,
however, this effect is counteracted by the loss of lepton candidates outside
the spectrometer acceptance, which is more likely for heavier LLPs. Therefore
the signal efficiencies are highest for masses between 20 and
$30\text{\,Ge\kern-1.00006ptV\\!/}c^{2}$ and lifetimes between 5 and
$10\text{\,ps}$.

Figure 5:  Total detection efficiency for LLP produced through the DPP mechanism as a function of $m_{\text{LLP}}$ (central line) and its uncertainty (coloured band), obtained for different values of $\tau_{\text{LLP}}$.  Table 1: Contributions to the relative systematic uncertainties in $\%$. The contributions are grouped in three categories, the integrated luminosity, the detection efficiency and the signal yield, separated by horizontal lines. The detection efficiency is affected by the parton luminosity model and depends upon the production process, with a maximum uncertainty of $6.1\%$ for the gluon-gluon fusion process HIG. Source | Contribution [$\%$]
---|---
Integrated luminosity | $2.0$
Reconstruction and selection | $4.97.3$
Particle identification | $0.52.4$
BDT | $0.61.0$
Simulation sample size | $1.13.0$
Parton luminosity | $1.16.1$
Efficiency interpolation | $0.14.0$
Signal fraction in the BDT bins | $3.34.0$
Signal model | $0.78.1$
Total | $10.617.7$

The choice of templates for the corrected mass and flight distance can affect
the result of the fit. The uncertainty due to the signal model accounts for
imperfect simulation of the scale and resolution of the $m_{\text{corr}}$ and
flight distance, and that of the finite size of the simulated signal samples
used to produce the probability density functions. Uncertainties of $0.2\%$ on
the $m_{\text{corr}}$ scale and $1.6\%$ on the $m_{\text{corr}}$ resolution
are estimated from the comparison between data and ${b}{\overline{b}}$
simulated candidates. For the flight distance a scale uncertainty of $1.2\%$
and a resolution uncertainty of $1.1\%$ are estimated. The propagation of
uncertainties is performed using pseudoexperiments generated from the
background model fitted to the same-sign data. Ten signal data points are
drawn from modified signal $m_{\text{corr}}$ and flight distance
distributions, modified by smearing or rescaling, and added to each
pseudoexperiment. The fitted signal yield is compared to the result with ten
signal data points drawn from a non-modified signal. Changing the
$m_{\text{corr}}$ scale leads to a relative change on the signal yield from
$0.1$ to $1.2\%$, and $0.1$ to $0.8\%$ for the flight distance, depending on
the signal hypothesis. A relative variation of the signal yield from $0.1$ to
$8.1\%$ is observed from an additional smearing of the signal
$m_{\text{corr}}$ distribution, $0.1$ to $0.8\%$ for the flight distance. The
effect of the limited sample size used to construct the signal model is
addressed by replacing the parameter values of the signal model by values
drawn from Gaussian distributions. For each parameter the mean of the Gaussian
distribution is equal to its fitted value, and the standard deviation is equal
to its uncertainty. A relative variation of the signal yield due to the
limited sample size is found to be between $0.1$ and $1.7\%$. A total
systematic uncertainty $0.78.1$$\%$ is accounted for the signal yield.

All the systematic uncertainties related to the integrated luminosity, the
signal efficiency and the signal yield are included as nuisance parameters in
the determination of the cross-section upper limits.

##  7 Results

The results of the simultaneous fits to the LLP corrected mass and flight
distance distributions in the two BDT intervals (0.1, 0.5] and (0.5, 1.0], are
found to be compatible with the background-only hypothesis for all signal
hypotheses considered. Upper limits at 95$\%$ Confidence Level (CL) on the
production cross-sections times branching fraction are computed for each
production mechanism,

$\displaystyle\sigma_{\text{DPP}}$
$\displaystyle=\sigma(q\bar{q}\rightarrow\tilde{\chi}^{0}_{1}\tilde{\chi}^{0}_{1})\times\mathcal{B}(\tilde{\chi}^{0}_{1}\rightarrow
e^{\pm}\mu^{\mp}\nu),$ $\displaystyle\sigma_{\text{HIG}}$
$\displaystyle=\sigma(gg\rightarrow
h)\times\mathcal{B}(h\rightarrow\tilde{\chi}^{0}_{1}\tilde{\chi}^{0}_{1})\times\mathcal{B}(\tilde{\chi}^{0}_{1}\rightarrow
e^{\pm}\mu^{\mp}\nu),\text{ and}$ $\displaystyle\sigma_{\text{CC}}$
$\displaystyle=\sigma({W}\rightarrow lN)\times\mathcal{B}(N\rightarrow
e^{\pm}\mu^{\mp}\nu),$

for each pair of $m_{\text{LLP}}$ and $\tau_{\text{LLP}}$ values using the CLs
approach [44]. Upper limits for selected $m_{\text{LLP}}$ and
$\tau_{\text{LLP}}$ values are shown in Figs. 6 to 8. Figure 6(a) gives
examples of observed upper limits on $\sigma_{\text{DPP}}$, along with the
range of limits expected for the background-only hypothesis, as a function of
$m_{\text{LLP}}$ for $\tau_{\text{LLP}}=10\text{\,ps}$. Figure 6(b) shows the
observed upper limits on $\sigma_{\text{DPP}}$ as a function of
$\tau_{\text{LLP}}$ for a selection of $m_{\text{LLP}}$ values, the best
limits are of the order of $0.06\text{\,pb}$. A comparison of observed upper
limits on $\sigma_{\text{DPP}}$, $\sigma_{\text{HIG}}$ and
$\sigma_{\text{CC}}$ as a function of $\tau_{\text{LLP}}$ for
$m_{\text{LLP}}=7$ and $29.8\text{\,Ge\kern-1.00006ptV\\!/}c^{2}$ is shown in
Fig. 7. The best and worst limits are obtained for the DPP and CC mechanisms,
respectively. The differences between the sensitivities for each production
mechanism are principally due to detection efficiency. The limits obtained by
the ATLAS experiment on the squark-antisquark production cross-section [14],
where the squark has a mass of 700 or
$1600\text{\,Ge\kern-1.00006ptV\\!/}c^{2}$ and decays to
${q}\,(\tilde{\chi}^{0}_{1}\rightarrow ee\nu/e\mu\nu/\mu\mu\nu)$, have values
from $1$ to $10\text{\,fb}$ for
$m(\tilde{\chi}^{0}_{1})=50\text{\,Ge\kern-1.00006ptV\\!/}c^{2}$ in the
lifetime range studied. These results are complementary to the results
obtained by the ATLAS experiment, extend to lower mass and lifetime regions
and explore different LLP production mechanisms.

Finally, the limits on $\sigma_{\text{HIG}}$ are compared to the value of the
SM Higgs boson production cross-section from gluon-gluon fusion of $46.5\pm
4.0\text{\,pb}$ [45], which is illustrated in Fig. 8. These limits are placed
on $(\sigma/\sigma^{SM}_{gg\rightarrow
H})\times\mathcal{B}({H^{0}}\rightarrow\tilde{\chi}^{0}_{1}\tilde{\chi}^{0}_{1})$,
assuming $\mathcal{B}(\tilde{\chi}^{0}_{1}\rightarrow e^{\pm}\mu^{\mp}\nu)=1$,
as a function of $\tau_{\text{LLP}}$ for a selection of $m_{\text{LLP}}$
values. Under this assumption the limits on
$\mathcal{B}({H^{0}}\rightarrow\tilde{\chi}^{0}_{1}\tilde{\chi}^{0}_{1})$ have
a minimum of $\sim 0.15\%$. Decays of $\text{LLP}\rightarrow{\mu^{+}\mu^{-}}$,
produced in pairs from SM Higgs bosons, were searched by the CMS experiment
[8]. Assuming $\mathcal{B}(\text{LLP}\rightarrow{\mu^{+}\mu^{-}})=1$, the
limits on $\mathcal{B}({H^{0}}\rightarrow\text{LLP}\,\text{LLP})$ for
$m_{\text{LLP}}=50\text{\,Ge\kern-1.00006ptV\\!/}c^{2}$ are the best for
lifetimes between $1\text{\,ps}$ and $10\text{\,ns}$ with a minimum of
$0.05\%$ [46], which is approximately 3 times lower than the minimum limits on
$\mathcal{B}({H^{0}}\rightarrow\tilde{\chi}^{0}_{1}\tilde{\chi}^{0}_{1})$
presented in this paper.

Figure 6:  (a) Expected (open circles and dotted line) and observed (filled
circles and solid line) upper limits of the cross-section as a function of
$m_{\text{LLP}}$ for $\tau_{\text{LLP}}=10\text{\,ps}$, for LLPs produced
through the DPP mechanism. The green and yellow bands indicate the quantiles
of the expected upper limit corresponding to $\pm 1\sigma$ and $\pm 2\sigma$
for a Gaussian distribution. (b) Observed limits on the cross-section as a
function of $\tau_{\text{LLP}}$ for different $m_{\text{LLP}}$ values for LLPs
produced through the DPP mechanism.  Figure 7:  Observed upper limits on the
production cross-sections times branching fraction for (a)
$m_{\text{LLP}}=7\text{\,Ge\kern-0.90005ptV\\!/}c^{2}$ and (b)
$m_{\text{LLP}}=29.8\text{\,Ge\kern-0.90005ptV\\!/}c^{2}$ as function of
$\tau_{\text{LLP}}$ for the DPP, HIG and CC production mechanisms.  Figure 8:
Observed limits on the $(\sigma/\sigma^{SM}_{gg\rightarrow
H})\times\mathcal{B}({H^{0}}\rightarrow\tilde{\chi}^{0}_{1}\tilde{\chi}^{0}_{1})$,
assuming $\mathcal{B}(\tilde{\chi}^{0}_{1}\rightarrow e^{\pm}\mu^{\mp}\nu)=1$
as a function of $\tau_{\text{LLP}}$ for different $m_{\text{LLP}}$ values.
The value of the gluon-gluon fusion production cross-section used is $46.5\pm
4.0\text{\,pb}$ [45].

##  8 Conclusion

A search for decays of long-lived massive particles, in the
${e^{\pm}}{\mu^{\mp}}{\nu}$ final state, is performed using ${p}{p}$
collisions at $\sqrt{s}=13\text{\,Te\kern-1.00006ptV}$ recorded with the LHCb
detector, for a total integrated luminosity of $5.38\pm 0.11\text{\,fb}^{-1}$.
The search covers LLP masses from 7 to
$50\text{\,Ge\kern-1.00006ptV\\!/}c^{2}$, lifetimes from 2 to $50\text{\,ps}$
and considers three production mechanisms: the direct pair production from the
interaction of quarks, the pair production from the decay of a SM-like Higgs
boson with a mass of $125\text{\,Ge\kern-1.00006ptV\\!/}c^{2}$, and the
charged current production from an on-shell $W$ boson with an additional
lepton.

Fully simulated signal events are used to define the signal selection criteria
and the signal detection efficiency. The background is dominated by
${{b}{\overline{b}}}$ candidates. A BDT, taking as input properties of the
leptons and displaced vertex of the LLP, is used to purify the signal from the
heavy hadron background. The signal yield is determined by a simultaneous fit
of the LLP corrected mass and flight distance, using signal templates derived
from simulation. All the results of the fits are compatible with the absence
of signal, and upper limits on the cross-section times branching fraction for
each production mechanism are computed. The best upper limits are achieved for
the pair production, from interaction of quarks or the decay of a SM-like
Higgs boson, for lifetimes below $10\text{\,ps}$ and masses above
$10\text{\,Ge\kern-1.00006ptV\\!/}c^{2}$, and are of the order of
$0.1\text{\,pb}$.

## Acknowledgements

We express our gratitude to our colleagues in the CERN accelerator departments
for the excellent performance of the LHC. We thank the technical and
administrative staff at the LHCb institutes. We acknowledge support from CERN
and from the national agencies: CAPES, CNPq, FAPERJ and FINEP (Brazil); MOST
and NSFC (China); CNRS/IN2P3 (France); BMBF, DFG and MPG (Germany); INFN
(Italy); NWO (Netherlands); MNiSW and NCN (Poland); MEN/IFA (Romania); MSHE
(Russia); MICINN (Spain); SNSF and SER (Switzerland); NASU (Ukraine); STFC
(United Kingdom); DOE NP and NSF (USA). We acknowledge the computing resources
that are provided by CERN, IN2P3 (France), KIT and DESY (Germany), INFN
(Italy), SURF (Netherlands), PIC (Spain), GridPP (United Kingdom), RRCKI and
Yandex LLC (Russia), CSCS (Switzerland), IFIN-HH (Romania), CBPF (Brazil), PL-
GRID (Poland) and OSC (USA). We are indebted to the communities behind the
multiple open-source software packages on which we depend. Individual groups
or members have received support from AvH Foundation (Germany); EPLANET, Marie
Skłodowska-Curie Actions and ERC (European Union); A*MIDEX, ANR, Labex P2IO
and OCEVU, and Région Auvergne-Rhône-Alpes (France); Key Research Program of
Frontier Sciences of CAS, CAS PIFI, Thousand Talents Program, and Sci. & Tech.
Program of Guangzhou (China); RFBR, RSF and Yandex LLC (Russia); GVA, XuntaGal
and GENCAT (Spain); the Royal Society and the Leverhulme Trust (United
Kingdom).

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Krokovny42,v, W. Krupa34, W. Krzemien35, W. Kucewicz33,l, M. Kucharczyk33, V.
Kudryavtsev42,v, H.S. Kuindersma31, G.J. Kunde66, T. Kvaratskheliya38, D.
Lacarrere47, G. Lafferty61, A. Lai26, A. Lampis26, D. Lancierini49, J.J.
Lane61, R. Lane53, G. Lanfranchi22, C. Langenbruch13, J. Langer14, O.
Lantwin49,80, T. Latham55, F. Lazzari28,t, R. Le Gac10, S.H. Lee84, R.
Lefèvre9, A. Leflat39, S. Legotin80, O. Leroy10, T. Lesiak33, B.
Leverington16, H. Li71, L. Li62, P. Li16, X. Li66, Y. Li6, Y. Li6, Z. Li67, X.
Liang67, T. Lin60, R. Lindner47, V. Lisovskyi14, R. Litvinov26, G. Liu71, H.
Liu5, S. Liu6, X. Liu3, A. Loi26, J. Lomba Castro45, I. Longstaff58, J.H.
Lopes2, G. Loustau49, G.H. Lovell54, Y. Lu6, D. Lucchesi27,m, S. Luchuk40, M.
Lucio Martinez31, V. Lukashenko31, Y. Luo3, A. Lupato61, E. Luppi20,g, O.
Lupton55, A. Lusiani28,r, X. Lyu5, L. Ma6, S. Maccolini19,e, F. Machefert11,
F. Maciuc36, V. Macko48, P. Mackowiak14, S. Maddrell-Mander53, O. Madejczyk34,
L.R. Madhan Mohan53, O. Maev37, A. Maevskiy81, D. Maisuzenko37, M.W.
Majewski34, J.J. Malczewski33, S. Malde62, B. Malecki47, A. Malinin79, T.
Maltsev42,v, H. Malygina16, G. Manca26,f, G. Mancinelli10, R. Manera
Escalero44, D. Manuzzi19,e, D. Marangotto25,o, J. Maratas9,u, J.F. Marchand8,
U. Marconi19, S. Mariani21,47,h, C. Marin Benito11, M. Marinangeli48, P.
Marino48, J. Marks16, P.J. Marshall59, G. Martellotti30, L. Martinazzoli47,j,
M. Martinelli24,j, D. Martinez Santos45, F. Martinez Vidal46, A. Massafferri1,
M. Materok13, R. Matev47, A. Mathad49, Z. Mathe47, V. Matiunin38, C.
Matteuzzi24, K.R. Mattioli84, A. Mauri31, E. Maurice11,b, J. Mauricio44, M.
Mazurek35, M. McCann60, L. Mcconnell17, T.H. Mcgrath61, A. McNab61, R.
McNulty17, J.V. Mead59, B. Meadows64, C. Meaux10, G. Meier14, N. Meinert75, D.
Melnychuk35, S. Meloni24,j, M. Merk31,78, A. Merli25, L. Meyer Garcia2, M.
Mikhasenko47, D.A. Milanes73, E. Millard55, M. Milovanovic47, M.-N. Minard8,
L. Minzoni20,g, S.E. Mitchell57, B. Mitreska61, D.S. Mitzel47, A. Mödden14,
R.A. Mohammed62, R.D. Moise60, T. Mombächer14, I.A. Monroy73, S. Monteil9, M.
Morandin27, G. Morello22, M.J. Morello28,r, J. Moron34, A.B. Morris74, A.G.
Morris55, R. Mountain67, H. Mu3, F. Muheim57, M. Mukherjee7, M. Mulder47, D.
Müller47, K. Müller49, C.H. Murphy62, D. Murray61, P. Muzzetto26, P. Naik53,
T. Nakada48, R. Nandakumar56, T. Nanut48, I. Nasteva2, M. Needham57, I.
Neri20,g, N. Neri25,o, S. Neubert74, N. Neufeld47, R. Newcombe60, T.D.
Nguyen48, C. Nguyen-Mau48, E.M. Niel11, S. Nieswand13, N. Nikitin39, N.S.
Nolte47, C. Nunez84, A. Oblakowska-Mucha34, V. Obraztsov43, D.P. O’Hanlon53,
R. Oldeman26,f, M.E. Olivares67, C.J.G. Onderwater77, A. Ossowska33, J.M.
Otalora Goicochea2, T. Ovsiannikova38, P. Owen49, A. Oyanguren46,47, B.
Pagare55, P.R. Pais47, T. Pajero28,47,r, A. Palano18, M. Palutan22, Y. Pan61,
G. Panshin82, A. Papanestis56, M. Pappagallo18,d, L.L. Pappalardo20,g, C.
Pappenheimer64, W. Parker65, C. Parkes61, C.J. Parkinson45, B. Passalacqua20,
G. Passaleva21, A. Pastore18, M. Patel60, C. Patrignani19,e, C.J. Pawley78, A.
Pearce47, A. Pellegrino31, M. Pepe Altarelli47, S. Perazzini19, D. Pereima38,
P. Perret9, K. Petridis53, A. Petrolini23,i, A. Petrov79, S. Petrucci57, M.
Petruzzo25, T.T.H. Pham67, A. Philippov41, L. Pica28, M. Piccini76, B.
Pietrzyk8, G. Pietrzyk48, M. Pili62, D. Pinci30, J. Pinzino47, F. Pisani47, A.
Piucci16, Resmi P.K10, V. Placinta36, S. Playfer57, J. Plews52, M. Plo
Casasus45, F. Polci12, M. Poli Lener22, M. Poliakova67, A. Poluektov10, N.
Polukhina80,c, I. Polyakov67, E. Polycarpo2, G.J. Pomery53, S. Ponce47, A.
Popov43, D. Popov5,47, S. Popov41, S. Poslavskii43, K. Prasanth33, L.
Promberger47, C. Prouve45, V. Pugatch51, A. Puig Navarro49, H. Pullen62, G.
Punzi28,n, W. Qian5, J. Qin5, R. Quagliani12, B. Quintana8, N.V. Raab17, R.I.
Rabadan Trejo10, B. Rachwal34, J.H. Rademacker53, M. Rama28, M. Ramos
Pernas55, M.S. Rangel2, F. Ratnikov41,81, G. Raven32, M. Reboud8, F. Redi48,
F. Reiss12, C. Remon Alepuz46, Z. Ren3, V. Renaudin62, R. Ribatti28, S.
Ricciardi56, K. Rinnert59, P. Robbe11, A. Robert12, G. Robertson57, A.B.
Rodrigues48, E. Rodrigues59, J.A. Rodriguez Lopez73, A. Rollings62, P.
Roloff47, V. Romanovskiy43, M. Romero Lamas45, A. Romero Vidal45, J.D. Roth84,
M. Rotondo22, M.S. Rudolph67, T. Ruf47, J. Ruiz Vidal46, A. Ryzhikov81, J.
Ryzka34, J.J. Saborido Silva45, N. Sagidova37, N. Sahoo55, B. Saitta26,f, D.
Sanchez Gonzalo44, C. Sanchez Gras31, R. Santacesaria30, C. Santamarina
Rios45, M. Santimaria22, E. Santovetti29,k, D. Saranin80, G. Sarpis61, M.
Sarpis74, A. Sarti30, C. Satriano30,q, A. Satta29, M. Saur5, D. Savrina38,39,
H. Sazak9, L.G. Scantlebury Smead62, S. Schael13, M. Schellenberg14, M.
Schiller58, H. Schindler47, M. Schmelling15, T. Schmelzer14, B. Schmidt47, O.
Schneider48, A. Schopper47, M. Schubiger31, S. Schulte48, M.H. Schune11, R.
Schwemmer47, B. Sciascia22, A. Sciubba30, S. Sellam45, A. Semennikov38, M.
Senghi Soares32, A. Sergi52,47, N. Serra49, J. Serrano10, L. Sestini27, A.
Seuthe14, P. Seyfert47, D.M. Shangase84, M. Shapkin43, I. Shchemerov80, L.
Shchutska48, T. Shears59, L. Shekhtman42,v, Z. Shen4, V. Shevchenko79, E.B.
Shields24,j, E. Shmanin80, J.D. Shupperd67, B.G. Siddi20, R. Silva Coutinho49,
G. Simi27, S. Simone18,d, I. Skiba20,g, N. Skidmore74, T. Skwarnicki67, M.W.
Slater52, J.C. Smallwood62, J.G. Smeaton54, A. Smetkina38, E. Smith13, M.
Smith60, A. Snoch31, M. Soares19, L. Soares Lavra9, M.D. Sokoloff64, F.J.P.
Soler58, A. Solovev37, I. Solovyev37, F.L. Souza De Almeida2, B. Souza De
Paula2, B. Spaan14, E. Spadaro Norella25,o, P. Spradlin58, F. Stagni47, M.
Stahl64, S. Stahl47, P. Stefko48, O. Steinkamp49,80, S. Stemmle16, O.
Stenyakin43, H. Stevens14, S. Stone67, M.E. Stramaglia48, M. Straticiuc36, D.
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1Centro Brasileiro de Pesquisas Físicas (CBPF), Rio de Janeiro, Brazil
2Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
3Center for High Energy Physics, Tsinghua University, Beijing, China
4School of Physics State Key Laboratory of Nuclear Physics and Technology,
Peking University, Beijing, China
5University of Chinese Academy of Sciences, Beijing, China
6Institute Of High Energy Physics (IHEP), Beijing, China
7Institute of Particle Physics, Central China Normal University, Wuhan, Hubei,
China
8Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, IN2P3-LAPP, Annecy,
France
9Université Clermont Auvergne, CNRS/IN2P3, LPC, Clermont-Ferrand, France
10Aix Marseille Univ, CNRS/IN2P3, CPPM, Marseille, France
11Université Paris-Saclay, CNRS/IN2P3, IJCLab, Orsay, France
12LPNHE, Sorbonne Université, Paris Diderot Sorbonne Paris Cité, CNRS/IN2P3,
Paris, France
13I. Physikalisches Institut, RWTH Aachen University, Aachen, Germany
14Fakultät Physik, Technische Universität Dortmund, Dortmund, Germany
15Max-Planck-Institut für Kernphysik (MPIK), Heidelberg, Germany
16Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg,
Germany
17School of Physics, University College Dublin, Dublin, Ireland
18INFN Sezione di Bari, Bari, Italy
19INFN Sezione di Bologna, Bologna, Italy
20INFN Sezione di Ferrara, Ferrara, Italy
21INFN Sezione di Firenze, Firenze, Italy
22INFN Laboratori Nazionali di Frascati, Frascati, Italy
23INFN Sezione di Genova, Genova, Italy
24INFN Sezione di Milano-Bicocca, Milano, Italy
25INFN Sezione di Milano, Milano, Italy
26INFN Sezione di Cagliari, Monserrato, Italy
27Universita degli Studi di Padova, Universita e INFN, Padova, Padova, Italy
28INFN Sezione di Pisa, Pisa, Italy
29INFN Sezione di Roma Tor Vergata, Roma, Italy
30INFN Sezione di Roma La Sapienza, Roma, Italy
31Nikhef National Institute for Subatomic Physics, Amsterdam, Netherlands
32Nikhef National Institute for Subatomic Physics and VU University Amsterdam,
Amsterdam, Netherlands
33Henryk Niewodniczanski Institute of Nuclear Physics Polish Academy of
Sciences, Kraków, Poland
34AGH - University of Science and Technology, Faculty of Physics and Applied
Computer Science, Kraków, Poland
35National Center for Nuclear Research (NCBJ), Warsaw, Poland
36Horia Hulubei National Institute of Physics and Nuclear Engineering,
Bucharest-Magurele, Romania
37Petersburg Nuclear Physics Institute NRC Kurchatov Institute (PNPI NRC KI),
Gatchina, Russia
38Institute of Theoretical and Experimental Physics NRC Kurchatov Institute
(ITEP NRC KI), Moscow, Russia
39Institute of Nuclear Physics, Moscow State University (SINP MSU), Moscow,
Russia
40Institute for Nuclear Research of the Russian Academy of Sciences (INR RAS),
Moscow, Russia
41Yandex School of Data Analysis, Moscow, Russia
42Budker Institute of Nuclear Physics (SB RAS), Novosibirsk, Russia
43Institute for High Energy Physics NRC Kurchatov Institute (IHEP NRC KI),
Protvino, Russia, Protvino, Russia
44ICCUB, Universitat de Barcelona, Barcelona, Spain
45Instituto Galego de Física de Altas Enerxías (IGFAE), Universidade de
Santiago de Compostela, Santiago de Compostela, Spain
46Instituto de Fisica Corpuscular, Centro Mixto Universidad de Valencia -
CSIC, Valencia, Spain
47European Organization for Nuclear Research (CERN), Geneva, Switzerland
48Institute of Physics, Ecole Polytechnique Fédérale de Lausanne (EPFL),
Lausanne, Switzerland
49Physik-Institut, Universität Zürich, Zürich, Switzerland
50NSC Kharkiv Institute of Physics and Technology (NSC KIPT), Kharkiv, Ukraine
51Institute for Nuclear Research of the National Academy of Sciences (KINR),
Kyiv, Ukraine
52University of Birmingham, Birmingham, United Kingdom
53H.H. Wills Physics Laboratory, University of Bristol, Bristol, United
Kingdom
54Cavendish Laboratory, University of Cambridge, Cambridge, United Kingdom
55Department of Physics, University of Warwick, Coventry, United Kingdom
56STFC Rutherford Appleton Laboratory, Didcot, United Kingdom
57School of Physics and Astronomy, University of Edinburgh, Edinburgh, United
Kingdom
58School of Physics and Astronomy, University of Glasgow, Glasgow, United
Kingdom
59Oliver Lodge Laboratory, University of Liverpool, Liverpool, United Kingdom
60Imperial College London, London, United Kingdom
61Department of Physics and Astronomy, University of Manchester, Manchester,
United Kingdom
62Department of Physics, University of Oxford, Oxford, United Kingdom
63Massachusetts Institute of Technology, Cambridge, MA, United States
64University of Cincinnati, Cincinnati, OH, United States
65University of Maryland, College Park, MD, United States
66Los Alamos National Laboratory (LANL), Los Alamos, United States
67Syracuse University, Syracuse, NY, United States
68School of Physics and Astronomy, Monash University, Melbourne, Australia,
associated to 55
69Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Rio de
Janeiro, Brazil, associated to 2
70Physics and Micro Electronic College, Hunan University, Changsha City,
China, associated to 7
71Guangdong Provencial Key Laboratory of Nuclear Science, Institute of Quantum
Matter, South China Normal University, Guangzhou, China, associated to 3
72School of Physics and Technology, Wuhan University, Wuhan, China, associated
to 3
73Departamento de Fisica , Universidad Nacional de Colombia, Bogota, Colombia,
associated to 12
74Universität Bonn - Helmholtz-Institut für Strahlen und Kernphysik, Bonn,
Germany, associated to 16
75Institut für Physik, Universität Rostock, Rostock, Germany, associated to 16
76INFN Sezione di Perugia, Perugia, Italy, associated to 20
77Van Swinderen Institute, University of Groningen, Groningen, Netherlands,
associated to 31
78Universiteit Maastricht, Maastricht, Netherlands, associated to 31
79National Research Centre Kurchatov Institute, Moscow, Russia, associated to
38
80National University of Science and Technology “MISIS”, Moscow, Russia,
associated to 38
81National Research University Higher School of Economics, Moscow, Russia,
associated to 41
82National Research Tomsk Polytechnic University, Tomsk, Russia, associated to
38
83DS4DS, La Salle, Universitat Ramon Llull, Barcelona, Spain, associated to 44
84University of Michigan, Ann Arbor, United States, associated to 67

aUniversidade Federal do Triângulo Mineiro (UFTM), Uberaba-MG, Brazil
bLaboratoire Leprince-Ringuet, Palaiseau, France
cP.N. Lebedev Physical Institute, Russian Academy of Science (LPI RAS),
Moscow, Russia
dUniversità di Bari, Bari, Italy
eUniversità di Bologna, Bologna, Italy
fUniversità di Cagliari, Cagliari, Italy
gUniversità di Ferrara, Ferrara, Italy
hUniversità di Firenze, Firenze, Italy
iUniversità di Genova, Genova, Italy
jUniversità di Milano Bicocca, Milano, Italy
kUniversità di Roma Tor Vergata, Roma, Italy
lAGH - University of Science and Technology, Faculty of Computer Science,
Electronics and Telecommunications, Kraków, Poland
mUniversità di Padova, Padova, Italy
nUniversità di Pisa, Pisa, Italy
oUniversità degli Studi di Milano, Milano, Italy
pUniversità di Urbino, Urbino, Italy
qUniversità della Basilicata, Potenza, Italy
rScuola Normale Superiore, Pisa, Italy
sUniversità di Modena e Reggio Emilia, Modena, Italy
tUniversità di Siena, Siena, Italy
uMSU - Iligan Institute of Technology (MSU-IIT), Iligan, Philippines
vNovosibirsk State University, Novosibirsk, Russia