EXO-19-008

EXO-19-008

# MUSiC: a model-unspecific search for new physics in proton-proton collisions
at $\sqrt{s}=13\TeV$

###### Abstract

Results of the Model Unspecific Search in CMS (MUSiC), using proton-proton
collision data recorded at the LHC at a centre-of-mass energy of 13,
corresponding to an integrated luminosity of 35.9, are presented. The MUSiC
analysis searches for anomalies that could be signatures of physics beyond the
standard model. The analysis is based on the comparison of observed data with
the standard model prediction, as determined from simulation, in several
hundred final states and multiple kinematic distributions. Events containing
at least one electron or muon are classified based on their final state
topology, and an automated search algorithm surveys the observed data for
deviations from the prediction. The sensitivity of the search is validated
using multiple methods. No significant deviations from the predictions have
been observed. For a wide range of final state topologies, agreement is found
between the data and the standard model simulation. This analysis complements
dedicated search analyses by significantly expanding the range of final states
covered using a model independent approach with the largest data set to date
to probe phase space regions beyond the reach of previous general searches.

##  0.1 Introduction

The CERN LHC has produced proton-proton ($\Pp\Pp$) collisions at an
unprecedented centre-of-mass energy of 13since 2015, providing an excellent
opportunity to search for new phenomena in regions that were previously
inaccessible to collider experiments. While the standard model (SM) of
particle physics is well established as the theory that describes the
fundamental particles and their interactions, it cannot explain certain
phenomena such as dark matter, neutrino oscillations, and the matter-
antimatter asymmetry in the universe. Several theories of physics beyond the
standard model (BSM) have been developed to address the inadequacies of the
SM, and a wide range of parameter and phase space regions of such theoretical
models is accessible for a direct search for the first time at the LHC. A
large number of searches for a range of BSM signatures have been conducted by
the experiments at the LHC, including the CMS experiment [1], but no direct
evidence for BSM physics has been found to date. Thus, it becomes imperative
to expand the scope of searches so that signs of new physics that are in
principle detectable by the CMS experiment are not missed.

Dedicated searches targeting specific BSM theories are often restricted in
their scope to a few final states that are sensitive to the particular models
probed. Practical constraints on the number of such analyses mean that there
are models and final states that remain unexplored, where BSM signatures could
possibly be hidden. Furthermore, new phenomena may exist that are not
described by any of the existing models. Hence, complementary to the existing
searches for specific BSM scenarios, a generalised model-independent approach
is employed in the analysis reported here: _Model Unspecific Search in CMS_
(MUSiC). The MUSiC analysis uses an automated approach to quantify deviations
between a Monte Carlo (MC) simulation of SM processes, as seen in the CMS
detector, and the observed data in a wide variety of final states, in order to
detect anomalies and identify discrepancies that could be hints of BSM physics
or other neglected or unknown phenomena. Following the MUSiC approach, events
from data and SM simulation are classified based on the so-called final-state
objects in an event, i.e. electrons (), muons (), photons (), jets originating
from light-flavour quarks or gluons, jets originating from quarks (jets), and
missing transverse momentum (), resulting in several hundred different event
classes. Then, an automated statistical method is used to scan the different
event classes and multiple kinematic distributions in each event class for
deviations between the data and simulation, identifying either excesses or
deficits. A deviation is considered significant if the measured significance
of the deviation is beyond the expectation of the SM-only hypothesis. The
discovery of significant deviations by MUSiC would lead to a detailed
investigation of both data and simulation in the final states of interest.
Such deviations or anomalies could result from a possible insufficient
description of the SM or detector effects in the simulation, from systematic
effects that are unknown or incorrectly modelled, or they could be the first
hints of BSM phenomena. This last interpretation cannot be the result of the
general search algorithm itself, since the algorithm uses a simplified
approach in order to probe a wide variety of diverse final states. Rather, it
would require additional study in the form of a dedicated analysis of the
final states of interest, ideally performed on statistically independent data
sets.

Since the analysis relies on the simulation to estimate the SM expectation,
only final-state objects that are well modelled in the simulation are
incorporated. In particular, leptons are not considered separately because of
challenges in modelling the effects of misidentification of hadronic jets as
leptons in the simulation. However, leptons enter the analysis in the form of
electrons or muons from leptonic decays, or as jets from hadronic lepton
decays. Other more complex objects, e.g. hadronic decays of highly boosted or
bosons, are not considered in the current analysis. Furthermore, because
beyond the leading order (LO) MC simulations of the quantum chromodymanics
(QCD) multijet and $\PGg$+jets processes of the SM are not available to this
analysis, the analysis is restricted to those final states that contain at
least one isolated lepton (electron or muon), since the contributions of these
processes are expected to be low in such final states. Finally, the electric
charges of the final-state objects are not considered in the analysis.

Dedicated analyses in specific final states with search strategies optimised
for particular signatures are expected to have greater sensitivity than the
present, more general approach. Moreover, further final-state objects,
kinematic distributions, and phase space regions remain to be explored.

General model-unspecific searches have been performed in the past by the D0
[2, 3, 4] and CDF experiments [5, 6] at the Tevatron, and by the H1 experiment
[7, 8] at HERA. Such searches have also been performed at the LHC by the ATLAS
Collaboration [9], and preliminary results have been reported by the CMS
Collaboration based on the MUSiC approach using the $\Pp\Pp$ collision data
set collected during the year 2010 at $\sqrt{s}=7\TeV$ [10], and during 2012
at 8[11].

This paper describes the MUSiC analysis that is performed with the full CMS
data set of $\Pp\Pp$ collisions at $\sqrt{s}=13\TeV$ collected during 2016,
corresponding to an integrated luminosity of $35.9\fbinv$. The increased
centre-of-mass energy and much larger amount of data analysed compared to the
previously reported results significantly extend the regions of BSM phase
space than can be probed.

We begin with the description of the CMS detector and object reconstruction in
Section 0.2, followed by a summary of the data set and simulated samples along
with the object and event selection in Sections 0.3 and 0.4. The MUSiC search
strategy is presented in Section 0.5, and systematic uncertainties are
discussed in Section 0.6. After selected sensitivity studies are presented in
Section 0.7, the results are shown in Section 0.8, before the paper is
summarised in Section 0.9. Additional figures complementing the results are
shown in Appendix .10.

##  0.2 The CMS detector and object reconstruction

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. Within the solenoid
volume are a silicon pixel and strip tracker, a lead tungstate crystal
electromagnetic calorimeter (ECAL), and a brass and scintillator hadron
calorimeter (HCAL), each composed of a barrel and two endcap sections. Forward
calorimeters extend the pseudorapidity ($\eta$) coverage provided by the
barrel and endcap detectors. Muons are detected in gas-ionisation chambers
embedded in the steel flux-return yoke outside the solenoid. A more detailed
description of the CMS detector, together with a definition of the coordinate
system used and the relevant kinematic variables, can be found in Ref. [1].

Events of interest are selected using a two-tiered trigger system [12]. The
first level, composed of custom hardware processors, uses information from the
calorimeters and muon detectors to select events at a rate of around 100kHz
within a time interval of less than 4. 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 optimised for fast processing, and reduces
the event rate to around 1kHz before data storage.

At CMS, the global event reconstruction (also called the particle-flow event
reconstruction [13]) aims to reconstruct and identify each individual particle
in an event, using an optimised combination of all subdetector information. In
this process, the identification of the particle type (muon, electron, photon,
charged or neutral hadron) plays an important role in the determination of the
particle direction and energy. Photons are identified as ECAL energy clusters
not linked to the extrapolation of any charged particle trajectory to the
ECAL. Electrons are identified as a charged-particle track and potentially
many ECAL energy clusters corresponding to this track extrapolation to the
ECAL and to possible bremsstrahlung photons emitted along the way through the
tracker material. Muons are identified as tracks in the central tracker
consistent with either a track or several hits in the muon system, and
associated with calorimeter deposits compatible with the muon hypothesis.
Charged hadrons are identified as charged-particle tracks neither identified
as electrons, nor as muons. Finally, neutral hadrons are identified as HCAL
energy clusters not linked to any charged-hadron trajectory, or as a combined
ECAL and HCAL energy excess with respect to the expected charged-hadron energy
deposit.

The energy of photons is obtained from the ECAL measurement. The energy of
electrons is determined from a combination of the track momentum at the main
interaction vertex, the corresponding ECAL cluster energy, and the energy sum
of all bremsstrahlung photons associated with the track. The candidate vertex
with the largest value of summed physics-object $\pt^{2}$ is taken to be the
primary $\Pp\Pp$ interaction vertex, where denotes the transverse momentum.
The physics objects are the jets, clustered using the jet finding algorithm
[14, 15] with the tracks assigned to candidate vertices as inputs, and the
associated missing transverse momentum, taken as the negative vector sum of
the of those jets. The energy of muons is obtained from the corresponding
track momentum. The energy of charged hadrons is determined from a combination
of the track momentum and the corresponding ECAL and HCAL energies, corrected
for zero-suppression effects and for the response function of the calorimeters
to hadronic showers. Finally, the energy of neutral hadrons is obtained from
the corresponding corrected ECAL and HCAL energies.

In the barrel section of the ECAL, an energy resolution of about 1% is
achieved for unconverted or late converting photons in the tens of GeV energy
range. Other photons detected in the barrel have a resolution of about 1.3% up
to $\abs{\eta}=1.0$, rising to about 2.5% at $\abs{\eta}=1.4$. In the endcaps,
the resolution of unconverted or late-converting photons is about 2.5%,
whereas other photons detected in the endcap have a resolution between 3 and
4% [16]. The momentum resolution for electrons with $\pt\approx 45\GeV$ from
$\PZ\to\Pe^{+}\Pe^{-}$ decays ranges from 1.7 to 4.5%. It is generally better
in the barrel region than in the endcaps, and also depends on the
bremsstrahlung energy emitted by the electron as it traverses the material in
front of the ECAL [17].

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 single muon trigger efficiency exceeds 90% over the full
$\eta$ range, and 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 100of 1% in the
barrel and 3% in the endcaps. The resolution in the barrel is better than 7%
for muons with up to 1 [18].

For each event, hadronic jets are clustered from these reconstructed particles
using the anti-algorithm [14, 15] with a distance parameter of 0.4. Jet
momentum is determined as the vectorial sum of all particle momenta in the
jet, and is found from simulation to be, on average, within 5 to 10% of the
true momentum over the whole spectrum and detector acceptance. Additional
$\Pp\Pp$ interactions within the same or nearby bunch crossings (pileup) can
contribute additional tracks and calorimetric energy depositions to the jet
momentum. To mitigate this effect, charged particles identified to be
originating from pileup vertices are discarded and an offset correction is
applied to correct for remaining contributions [13]. Jet energy corrections
are derived from simulation to bring, on average, the measured response of
jets to that of particle level jets. In situ measurements of the momentum
balance in dijet, $\text{photon}+\text{jet}$, $\PZ+\text{jet}$, and multijet
events are used to account for any residual differences between the jet energy
scale in data and in simulation [19]. The jet energy resolution amounts
typically to 15% at 10, 8% at 100, and 4% at 1. Additional selection criteria
are applied to each jet to remove jets potentially dominated by anomalous
contributions from various subdetector components or reconstruction failures
[20]. Jets originating from quarks are identified as -tagged jets using the
combined secondary vertex algorithm (v2) described in Ref. [21].

The missing transverse momentum vector is computed as the negative vector sum
of the transverse momenta of all the particle-flow candidates in an event, and
its magnitude is denoted as [22]. The is modified to account for corrections
to the energy scale of the reconstructed jets in the event.

##  0.3 Data set and simulated samples

The analysis presented in this paper is performed on the data sample collected
by the CMS experiment during 2016, based on $\Pp\Pp$ collisions at a
$\sqrt{s}=13\TeV$, corresponding to an integrated luminosity of $35.9\fbinv$.

The MUSiC analysis aims to find deviations in the data when compared to the SM
predictions, and hence an inclusive description of the SM with a full set of
simulated samples covering the entire range of SM processes that are expected
to be detected by the CMS experiment is required to have a good estimate of
the SM expectation in each final state. MC simulated events from the
generators 8.212 [23], version 2.2.2 [24] with MLM [25] or FxFx [26] matching
schemes, v2 [27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38], and 2.1.1 [39,
40] are combined to model each SM process of relevance in the studied energy
regime, with the NNPDF3.0 [41] parton distribution functions (PDFs) being used
for most of the simulated samples. Simulation of the parton shower and
hadronisation process is done with 8.205 [23], with the underlying event tune
CUETP8M1 [42]. The detector response is simulated using the package [43]. The
presence of pileup in data is incorporated in simulated events by using
additional inelastic events generated with with the same underlying event tune
as the main interaction that are superimposed on the hard-scattering events.

When available, higher order cross section estimates are used to normalise the
MC simulated samples. The cross sections of the inclusive
$\PW(\to\ell\PGn)+\text{jets}$ and $\PZ(\to\ell^{+}\ell^{-})+\text{jets}$
processes were obtained at next-to-next-to-LO (NNLO) in QCD using FEWZ 3.1.b2
[44] and at next-to-LO (NLO) electroweak (EW) precision using MCSANC 1.01
[45], while that for the $\PZ\to\PGn\PGn$ process was calculated at NLO in QCD
using 6.6 [46]. Cross sections for the $\PW\PW\to\ell\PGn\Pq\Pq$ and
$\PW\PW\to 2\ell 2\PGn$ processes were also obtained at NNLO in QCD using Ref.
[47]. The $\PQt\PAQt$ cross section was calculated at NNLO in QCD including
resummation of next-to-next-to-leading logarithmic soft-gluon terms with
Top++2.0 [48], and the single top quark cross section was obtained at NLO in
QCD with Hathor v2.1 [49, 50]. The cross sections for the SM Higgs boson ()
processes were obtained at NLO, NNLO, or next-to-NNLO (N3LO) from Ref. [51],
depending on the specific process.

A summary of the SM simulation samples can be found in Table 0.3. Considering
their perturbative accuracy, for the processes that are expected to be
dominant in several final states, such as the , , diboson, and top quark
processes, it is preferred to use samples generated at NLO or better. However,
simulated samples at LO are also used to improve the statistical precision in
the tails of the kinematic phase space. The choice of MC generators in some
cases also reflects the limited availability of simulated samples for this
analysis. Given the analysis requirement of the presence of at least a single
isolated lepton, the QCD multijet, $\PGg$+jets, and diphoton processes are not
expected to be the dominant processes although they have large cross sections.
For such processes, simulated samples generated at LO are used. Both the
$\PW$+jets and the $\PZ$+jets processes in leptonic final states are simulated
with at NLO precision with up to two additional partons, with the FxFx scheme
used for merging, and additional samples generated with at LO precision and
with at NLO precision are used to improve the statistical precision at high
masses for the $\PW$+jets process and the $\PZ$+jets process, respectively.
For the $\PZ$+jets process with neutrinos in the final state, samples produced
at LO are used. Simulated samples for the $\PGg$+jets process are generated at
LO using . The $\PQt\PAQt$ process is simulated using at NLO, and simulation
at NLO is used for top pair production in association with vector bosons. The
$\PQt\PAQt\PQt\PAQt$ process is simulated at NLO using . Single-top processes
are simulated at NLO using and single-top production in association with gauge
bosons is simulated at NLO using . At NLO in perturbative QCD, the $\PQt\PW$
single-top process interferes with the $\PQt\PAQt$ process, and this effect is
accounted for using the ”diagram removal” approach to correct the simulated
samples for the $\PQt\PW$ single-top production process [52]. Diboson
processes are simulated at NLO using a combination of and , with the exception
of the $\PW\PGg$ and $\PGg\PGg$ processes where simulated samples at LO
generated with and are used in certain phase space regions. QCD multijet
events are simulated by at LO precision. Triboson processes are simulated at
NLO with . The different Higgs boson production processes are simulated at NLO
using a combination of and .

For the samples listed in Table 0.3, kinematic overlaps, resulting from
additional samples used to increase the statistical precision, are removed.
For most of the SM processes, the statistical precision of the number of
simulated events corresponds to an integrated luminosity much larger than the
analysed data set. This is not the case, in general, for the QCD multijet and
the $\PGg$+jets MC samples; however, this analysis considers only final states
that contain at least one isolated electron or muon, and the contribution of
these SM processes is predicted to be small in such final states. For SM
processes that are expected to have significant contributions in several
different final states, such as the , , diboson, and top quark processes, the
number of simulated events correspond to a range of 3 to 10,000 times the
number of expected events based on the integrated luminosity of the data set
analysed.

Summary of standard model simulated samples. The generator described in the
table corresponds to the matrix element generator.  Process Details Generator
Generator Cross section order order $\PZ(\to\ell^{+}\ell^{-})$ \+ jets
$M_{\ell^{+}\ell^{-}}>10\GeV$ NLO NNLO $\pt(\PZ)>50\GeV$ NLO NNLO
$M_{\ell^{+}\ell^{-}}>120\GeV$ NLO NNLO $\PZ(\to\PGn\PGn)$ \+ jets LO NLO
$\PW(\to\ell\PGn)$ \+ jets Inclusive NLO NNLO $\pt(\PW)>100\GeV$ NLO NNLO
$M_{\ell\PGn}>200\GeV$ 8 LO NNLO $\PGg$ \+ jets LO LO $\PQt\PAQt$ Inclusive
NLO NNLO $M_{\ttbar}>700\GeV$ NLO NNLO $\PQt\PAQt\PGg$ NLO NLO $\PQt\PAQt\PW$
NLO NLO $\PQt\PAQt\PZ$ NLO NLO $\PQt\PAQt\PGg\PGg$ NLO NLO
$\PQt\PAQt\PQt\PAQt$ NLO NLO Top $\PQt$ ($\PQt\PW$-channel) NLO NLO $\PQt$
($t$-channel) NLO NLO $\PQt$ ($s$-channel) NLO NLO $\PQt\PGg$ NLO NLO
$\PQt\PZ\PQq$ NLO NLO $\PZ(\to 2\ell)\PGg$ NLO NLO $\PW(\to\ell\PGn)\PGg$
$\pt(\PGg)>40\GeV$ LO LO $\pt(\PGg)>130\GeV$ NLO NLO $\PZ\PZ$ $\PZ\PZ\to
4\ell$ NLO NLO $\PZ\PZ\to 2\ell 2\PQq$ NLO NLO $\PZ\PZ\to 2\ell 2\PGn$ NLO NLO
$\PW\PW$ $\PW\PW\to\ell\PGn\PQq\PQq$ NLO NNLO $\PW\PW\to 4\PQq$ NLO NLO
$\PW\PW\to 2\ell 2\PGn$ NLO NNLO $\PW\PZ$ $\PW\PZ\to\ell\PGn 2\PQq$ NLO NLO
$\PW\PZ\to 3\ell\PGn$ NLO NLO $\PW\PZ\to 2\ell 2\PQq$ NLO NLO $\PW\PZ\to 1\ell
3\PGn$ NLO NLO $\PW\PZ\to 1\ell 1\PGn 2\PQq$ NLO NLO $\PGg\PGg$
$M_{\PGg\PGg}>40\GeV$ LO LO $M_{\PGg\PGg}>80\GeV$ NLO NLO
$M_{\PGg\PGg}>200\GeV$, $p_{\text{T}}^{\PGg}>70\GeV$ LO LO QCD multijet LO LO
Triboson $\PZ\PZ\PZ$ NLO NLO $\PW\PGg\PGg$ NLO NLO $\PW\PZ\PZ$ NLO NLO
$\PW\PZ\PGg$ NLO NLO $\PW\PW\PW$ NLO NLO $\PW\PW\PZ$ NLO NLO $\PW\PW\PGg$ NLO
NLO Higgs boson $\Pg\Pg\PH\to\PQb\PAQb$, $\PGg\PGg$ NLO N3LO
$\Pg\Pg\PH\to\PGt\PAGt$, $\PZ\PZ(4\ell)$, $\PW\PW$($2\ell 2\PGn$), $\PZ\PGg$
NLO N3LO VBF ($\PH\to\PQb\PAQb$, $\PGt\PAGt$, $\PW\PW$, $\PZ\PZ$, $\PZ\PGg$)
NLO NNLO VBF ($\PH\to\PGg\PGg$) NLO NNLO VH (not $\PH\to\PQb\PAQb$) NLO NNLO
VH ($\PH\to\PQb\PAQb$) NLO NNLO $\PQt\PAQt\PH$ NLO NLO

##  0.4 Object and event selection

It is necessary to determine the physics object content of each event
unambiguously, which includes the identification of each reconstructed object
and the removal of overlap between the individual objects. Since the analysis
relies on simulation for the SM background prediction, tight selection
criteria for the different objects are used to minimise the effect of
misidentification while still retaining a reasonably high efficiency for
selecting the objects. A summary of the object selection criteria, discussed
in this Section, is shown in Table 0.4.

Summary of object selection criteria discussed in Section 0.4.  Object []
Pseudorapidity Muon $>$25 $\abs{\eta}<2.4$ Electron $>$25 $0<\abs{\eta}<1.44$
or $1.57<\abs{\eta}<2.50$ Photon $>$25 $\abs{\eta}<1.44$ Jet $>$50
$\abs{\eta}<2.4$ $\PQb$-tagged jet $>$50 $\abs{\eta}<2.4$ Missing transverse
momentum $>$100

Events are required to be triggered by one of several single-lepton, dilepton,
or single-photon triggers. The single-photon trigger improves the electron
trigger efficiency at high electron momenta when used in combination with the
single-electron trigger. Selected events are required to contain the
reconstructed objects that correspond to the associated trigger for the event,
and have a value of that is above the trigger requirement. Overlap between
triggers is removed, such that events triggered by two or more triggers are
not counted multiple times. Details of the trigger selection are given in
Table 0.4.

Summary of online and offline criteria.  Trigger Trigger level requirement
Analysis requirement Single-muon trigger $1\PGm$ with $\pt>50\GeV$
${\geq}1\PGm$ with $\pt>53\GeV$ Single-electron trigger $1\Pe$ with
$\pt>115\GeV$ ${\geq}1\Pe$ with $\pt>120\GeV$ Dimuon trigger $1\PGm$ with
$\pt>17\GeV$, ${\geq}2\PGm$, each with $\pt>20\GeV$ second $\PGm$ with
$\pt>8\GeV$ Dielectron trigger $2\Pe$, each with $\pt>33\GeV$ ${\geq}2\Pe$,
each with $\pt>40\GeV$ Single-photon trigger $1\PGg$ with $\pt>175\GeV$
${\geq}1\PGg$ with $\pt>200\GeV$

Muons are required to have $\pt>25\GeV$ and $\abs{\eta}<2.4$. Two dedicated
selection criteria are used to select well-reconstructed, isolated muons:
“Tight muon ID” for muons with up to 200, and “high-momentum muon ID” for
muons with $\pt>200\GeV$, as described in Refs. [18, 53]. The efficiency for
the selection of muons with such criteria has been measured to be between 96
and 98%, whereas the probability of pions (kaons) to be misidentified as muons
is about 0.1 (0.3)% [18].

Selected electrons need to fulfill $\pt>25\GeV$ and $\abs{\eta}<2.5$,
excluding electrons in the barrel endcap transition region of the CMS ECAL
($1.44<\abs{\eta}<1.57$). Two dedicated selection criteria are applied: the
“tight” selection criteria are used for electrons with up to 200, and the
“HEEP” electron selection is used for electrons with $\pt>200\GeV$ [17, 54].
Detailed studies of efficiency and misidentification probabilities for the
electron reconstruction are presented in Ref. [17].

Photons with $\pt>25\GeV$ and $\abs{\eta}<1.44$ in the barrel region of the
CMS ECAL, where the misidentification rate is low, are selected if they pass
the dedicated “tight” photon identification requirements that have been
introduced in Ref. [16] and adapted for the present data set.

Jets must have $\pt>50\GeV$ and $\abs{\eta}<2.4$. These criteria select well-
reconstructed jets within the coverage of the CMS tracking system in the high-
pileup environment of the 2016 data taking, with an average of 22 $\Pp\Pp$
interactions per bunch crossing. For $\PQb$-tagged jets, the chosen “tight”
working point corresponds to about 41% efficiency in identifying $\PQb$ jets
and about 0.1% misidentification rate for light-flavour and gluon jets [21].
The missing transverse momentum in the event is included as an object in the
event classification if $\ptmiss>100\GeV$. The distribution of at small values
is strongly affected by resolution effects, and for most cases the value of
associated with BSM phenomena is large. Thus, if $\ptmiss<100\GeV$, this
variable is not used in the selection process.

A reconstructed object may be identified as more than one particle. It is also
possible for some detector signals to be used for different reconstructed
objects, e.g. an electron and a photon overlapping in the calorimeters.
Possible ambiguities are resolved as follows. First, the list of particles is
prioritised in the order of muons, electrons, photons, and finally jets,
assumed to correspond to the order of purity. Then, in the case of an
ambiguity such as the reconstruction of multiple electrons or photons based on
the same calorimeter energy deposit, or in the case of an object overlapping
with a jet, the particle with the highest priority is selected. Other
particles close in $\Delta
R=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}$ are removed from the
event (where $\phi$ is the azimuthal angle in radians), using the threshold of
$\Delta R=0.5$ for jets and $\Delta R=0.4$ for all other particles.

Events passing the above criteria are then categorised into event classes
based on the event content. Event classes containing at least one lepton
(electron or muon) are considered in the analysis.

##  0.5 The MUSiC search algorithm

The MUSiC analysis is designed to be robust, unbiased by specific BSM physics
models, and as inclusive as possible. Every region is treated as a potential
signal region. The modelling of the known SM background processes is based
solely on MC simulation. No techniques based on control samples in data are
employed to estimate the background expectation, since this would result in
losing some kinematic regions in the data.

The main steps of the MUSiC search algorithm are described below, starting
with the classification of events, then the introduction of the kinematic
distributions of interest, followed by a description of the scanning procedure
and the strategy to account for the look-elsewhere effect (LEE) using pseudo-
data generation, and finally with the concept of the global overview of the
scan results.

###  0.5.1 Classification of events

Events in data and simulated samples are assigned to different classes (final
states) based on the physics object content of each event. To determine the
object content of an event unambiguously, all selection criteria described in
detail in Section 0.4 are applied both to the observed data and the
simulation, resulting in a defined number of well-reconstructed objects in the
final state of the event.

Each event is sorted into three different types of event classes:

  1. 1.

Exclusive event classes for events containing only those selected objects that
are specified for the event class and no additional final state objects. Thus
each event is assigned to just one exclusive class.

  2. 2.

Inclusive event classes contain events that include a nominal set of selected
objects, but may contain additional objects. An event is assigned to all
inclusive event classes that can be constructed from the selected objects. For
example, events containing two muons and any number of additional objects
would be classified into the $2\Pgm+X$ inclusive event class.

  3. 3.

Jet-inclusive event classes are defined as inclusive classes but restrict
additional allowed objects to jets. High jet multiplicities are not expected
to be accurately described in the simulation, and thus all exclusive classes
with five or more jets are instead assigned to the
$X+5\text{jets}+\text{Njets}$ class, which includes events with at least five
jets and is inclusive in terms of the number of additional jets that might be
present. The threshold of five jets was chosen based on studies described in
Section 0.8.1.

There is no explicit limit placed on the number of objects, and, consequently,
on the number of event classes, except for the case of jets, where it is set
to five. Events with greater than five jets can still enter the inclusive and
jet-inclusive event classes. The construction of event classes from the
physics object content of the final state, using the example of an event
containing $1\Pe+2\Pgm+1\text{jet}$, is illustrated in Fig. 1.

Figure 1: Illustrative example of classification of a single event (red
square) containing one electron, two muons, and one jet. This event will
contribute to precisely one exclusive (green), and several inclusive (blue)
and jet-inclusive (orange) event classes.

All exclusive event classes are statistically independent of each other and
can be regarded as uncorrelated (counting) experiments. This is not the case
for the inclusive event classes, where a single event will generally end up in
more than one event class. The resulting direct correlations are included
while performing the statistical analysis, with the exception of correlations
in the statistical uncertainties in the simulated events, which are assumed to
be negligible. In the presence of a possible signal, it is a priori unknown
how the same events populate different inclusive and jet-inclusive event
classes, and therefore further interpretation of the results of the
statistical analysis would need to include the possible consequences of such
an effect.

###  0.5.2 Kinematic distributions of interest

Although signs of new physics can become visible in the distributions of many
different kinematic variables, three are chosen for this analysis that seem
especially promising in terms of sensitivity to phenomena at high predicted by
a large number of BSM scenarios. This choice also prevents the analysis from
being overly complex, as might result from the addition of more kinematic
distributions. The three chosen kinematic distributions are:

  1. 1.

$S_{\mathrm{T}}$: The sum of all the physics objects that are considered for
that event class, defined as

$S_{\mathrm{T}}=\sum_{i}\abs{\vec{p}_{T,i}}$ (1)

where the sum is over the particles that make up the event class. It is the
most general variable of the three. It is calculated for every event passing
the analysis requirements, and includes when applicable. The BSM physics is
often expected to involve new heavy particles, the effects of which would show
up predominantly in the tails of the $S_{\mathrm{T}}$ distributions.

  2. 2.

$M$ or $M_{\mathrm{T}}$: The combined mass $M$ is the invariant mass
calculated from all physics objects considered for the event class. For
classes with , the transverse mass $M_{\mathrm{T}}$ is used instead of $M$,
because the longitudinal component of the missing momentum is unknown. Here,
$M_{\mathrm{T}}$ is defined as

$M_{\mathrm{T}}=\sqrt{\Bigl{(}\sum_{i}E_{i}\Bigr{)}^{2}-\Bigl{(}\sum_{i}p_{x,i}\Bigr{)}-\Bigl{(}\sum_{i}p_{y,i}\Bigr{)}}$
(2)

where the sum is over the particles that make up the event class, $E_{i}$ is
the energy, and $p_{x,i}$ and $p_{y,i}$ are the $x$ and $y$ components of the
momenta of the particle with index $i$. This distribution is important for
cases where a new massive particle is produced as a resonance and the mass
distribution of its decay products is a prominent place to look for a
deviation. All events in the event classes containing at least two objects are
used to evaluate the combined mass.

  3. 3.

: For classes with significant of at least 100, it is an indicator of the
energy of particles escaping detection. Only events with a substantial amount
of are considered here, since the low-region is dominated by detector
resolution effects and SM processes containing neutrinos. High values for can
be associated with new particles with large that do not interact with the
detector.

For a given exclusive event class, object types and multiplicities are
identical in all events. Variables for the distributions of interest are
calculated from the kinematic properties of all final-state objects. Since
inclusive and jet-inclusive event classes also include events with more
objects than those associated with the corresponding exclusive event class, an
ambiguity must be resolved to ensure that the same event property is evaluated
for all events in the distribution. Hence, only the objects stated explicitly
in the name of the event class are used to derive the kinematic properties.
For example, in the case of the $1\Pe+2\Pgm+\ptmiss+X$ event class, only the
four mentioned objects (one electron, two muons, and ) contribute to the
$S_{\mathrm{T}}$ distribution, in each case considering the ones with the
highest if more than the mentioned number of a particular object are present.

The bin widths for the kinematic distributions probed are chosen as a
compromise between a relatively large bin width, which is favorable in terms
of computation time but detrimental in terms of sensitivity to potential
narrow signals, and a small bin width, where random fluctuations will gain in
importance and possibly mask the actual deviations of interest. An optimal
choice is made in an automated way based on the typical total detector
resolution of all objects in each specific kinematic region, leading to a
larger value for the bin width at higher energies. All bin widths are integer
multiples of 10.

###  0.5.3 Scan for deviations: region of interest scan

A statistical analysis is performed to identify deviations between data and
the SM prediction by initially comparing the event yields in the event
classes, followed by a complete scan of the kinematic distributions in the
different event classes, referred to as the region of interest (RoI) scan. The
procedure is described in two parts below, beginning with a discussion of the
$p\text{-value}$ definition that is used to quantify any observed deviation,
followed by the description of the construction of the regions within which
the algorithm searches for deviations.

The measure for deviations is a $p\text{-value}$ that describes the agreement
between simulation and data using a hybrid Bayesian-frequentist approach,
where the statistical fluctuations are assumed to follow a Poisson
distribution, and nuisance parameters are modelled using a Gaussian prior
function. Both excesses and deficits are taken into account. The
$p\text{-value}$ $p_{\text{data}}$ is defined as:

$p_{\text{data}}=\begin{cases}\displaystyle\sum\limits^{\infty}_{i=N_{\text{data}}}C\int\limits^{\infty}_{0}\rd\lambda\,\exp{\left(-\frac{(\lambda-
N_{\text{SM}})^{2}}{2\,\sigma^{2}_{\text{SM}}}\right)}\frac{\re^{-\lambda}\,\lambda^{i}}{i!},&\qquad\text{if
$N_{\text{data}}\geq N_{\text{SM}}$},\\\
\displaystyle\sum\limits^{\hphantom{i}N_{\text{data}}\hphantom{=}}_{i=0}C\int\limits^{\infty}_{0}\rd\lambda\,\smash{\vphantom{\int\limits^{\infty}_{0}}\exp{\left(-\frac{(\lambda-
N_{\text{SM}})^{2}}{2\,\sigma^{2}_{\text{SM}}}\right)}}\smash{\vphantom{\int\limits^{\infty}_{0}}\frac{\re^{-\lambda}\,\lambda^{i}}{i!}},&\qquad\text{if
$N_{\text{data}}<N_{\text{SM}}$},\end{cases}$ (3)

where $N_{\text{data}}$ is the number of observed events, $N_{\text{SM}}$ is
the number of expected events from SM simulation, and $\sigma_{\text{SM}}$
denotes the uncertainty in $N_{\text{SM}}$, combining the statistical
uncertainty arising from the number of generated MC events and systematic
uncertainties. The probability distribution is summed up from
$i=N_{\text{data}}$ to infinity for the case of an excess in observed data
compared with the expectation, and from $i=0$ to $N_{\text{data}}$ for the
case of a deficit in observed data compared with the expectation. The Gaussian
distribution is truncated at zero and normalised to unity with a factor $C$.

A region is defined as any contiguous combination of bins. Since several
regions can contain the same bins, they are not disjoint, and a distribution
with $N_{\mathrm{bins}}$ bins will result in
$N_{\text{bins}}(N_{\text{bins}}+1)/2$ connected regions. All bins in question
are then successively combined into regions by adding up their individual
contributions, and a $p\text{-value}$ is calculated. The smallest
$p\text{-value}$ ($p^{\text{data}}_{\text{min}}$) defines the RoI. This
process is illustrated in Fig. 2. This procedure, referred to as the RoI
algorithm, is performed for all distributions in all classes.

Figure 2: Illustration for the calculation of $p\text{-values}$ in different
regions and the selection of the RoI as the region with the smallest
$p\text{-value}$. The dashed lines with arrowheads represent the different
possible continuous combinations of bins that are referred to as regions.

The minimum number of bins within a region is required to be three for the
$S_{\mathrm{T}}$ and distributions, since in these cases narrow deviations of
less than three bins would be indicative of statistical fluctuations. Regions
with a single bin are allowed for the mass distributions. Regions where
statistical accuracy is poor due to the limited number of simulated events
leading to an unreliable background estimate are removed, effectively
considering only regions composed of a larger number of bins in such cases.
Separate monitoring is in place to ensure that no potentially interesting
signal is missed because an event class that contains data events fails to
reach the required number of simulated events, particularly checking for event
classes with more than one event in the recorded data where the expectation
from simulation is below a threshold of 0.1 events.

###  0.5.4 Post-trial probability ($\tilde{p}$) and look-elsewhere effect

The chosen definition of the $p\text{-value}$ serves as a measure of the
probability of observing a deviation in a single region, while the algorithm
is intended to give a measure of finding a deviation of equal or lesser
compatibility anywhere in the distribution. We define the probability
$\tilde{p}$ to observe such a deviation in any of the considered regions
throughout the distribution. The transition from a per-region to a per-
distribution $p\text{-value}$, sometimes referred to as a post-trial
probability, is a requirement to allow the comparison of observed deviations
between many different distributions. A given $p\text{-value}$ can be
translated into a $\tilde{p}\text{-value}$ by the LEE effect correction, which
describes the increased probability to observe a significant deviation if a
large number of regions is considered.

An analytical calculation of the required correction is difficult because of
correlations between bins and the irregular shape of systematic uncertainties,
but the LEE correction can be determined using pseudo-experiments. Pseudo-
experiments are generated in a randomized manner according to the background-
only hypothesis, varying the prediction of the simulation according to the
associated uncertainties.

The RoI is not necessarily at the same position as the one found in the data.
The number of trials resulting in a local $p\text{-value}$ ($p_{\text{min}}$)
smaller or equal to the one found in the data to simulation comparison
($p^{\text{data}}_{\text{min}}$) is determined and divided by the full number
of trials to get the fraction $\tilde{p}$:

$\tilde{p}=\frac{N_{\text{pseudo}}(p_{\text{min}}<p^{\text{data}}_{\text{min}})}{N_{\text{pseudo}}}.$
(4)

This fraction is the post-trial $p\text{-value}$ ($\tilde{p}$), representing a
statistical estimate of how probable it is to see a deviation at least as
strong as the observed one in any region of the distribution. While optimising
for the computation time and also ensuring a robust measurement, and at the
same time taking into account correlations across the different event classes,
a total of 10,000 trials are conducted for each event class.

Pseudo-experiments for a single distribution in a single class require
generating randomised values for each bin $n$ to closely resemble the ensemble
of expected values given the background-only (null) hypothesis. The systematic
uncertainties in the null hypothesis are represented by a set of nuisance
parameters $\nu_{j}$, which are expected to be fully correlated across all
bins. This assumption requires that systematic uncertainties have been
separated to a level at which the underlying processes responsible for the
uncertainty remain similar for the complete range considered in a
distribution. The effect of each nuisance parameter is modelled with a
Gaussian centred on the mean expectation value for each bin $n$. To include
these correlation effects, a random number $\kappa_{j}$, following a standard
normal distribution, is generated for each nuisance parameter, excluding the
statistical uncertainty of the MC samples. The mean expectation value $\langle
N_{n}\rangle$ in each bin is then shifted according to

$\langle N_{n,{\text{shifted}}}\rangle=\langle
N_{n}\rangle+\sum_{j}\kappa_{j}\,\delta_{\nu_{j,n}},$ (5)

where $\langle N_{n,{\text{shifted}}}\rangle$ is the shifted mean in each bin,
and $\delta_{\nu_{j,n}}$ denotes the symmetrised 68% confidence interval for
$\nu_{j}$ in bin $n$. The value of $\langle N_{n,{\text{shifted}}}\rangle$ is
further spread using a Poisson distribution to model the expected statistical
variations.

The procedure employed here has been developed considering the limitations
arising from the absence of control regions and the requirement to keep the
algorithm consistent over the vast range of final states probed. The procedure
has been validated using toy simulations for the various statistical
configurations that are expected in the analysis of the data set.

###  0.5.5 Global overview of the RoI scan

Considering the large number of different event classes and kinematic
distributions scanned, a convenient global overview of the scan is required.
The RoI scanning algorithm gives a RoI and its associated
$\tilde{p}\text{-value}$ separately for each event class and each kinematic
distribution. To create the global overview, the results of the RoI scanning
algorithm for the different event classes are grouped and presented together,
separately for each of the three kinematic distributions scanned
($S_{\mathrm{T}}$, $M$, and ) for a particular type of event class (exclusive,
inclusive, or jet-inclusive). For each such grouping, the observed
distribution of deviations in the different event classes is compared with the
expectation for the same distribution from the SM-only hypothesis, obtained
from the pseudo-experiments. Any unexpected deviation from the scans would
become apparent in such a comparison. This would also probe certain BSM
signals that show smaller deviations spread out over several different final
states, in addition to such scenarios where the signature is a large deviation
in specific final states.

To produce a global overview of all event classes, the
$\tilde{p}\text{-values}$ calculated for each kinematic distribution are
summarised in a single histogram. A $\tilde{p}\text{-value}$ can be calculated
for any particular pseudo-experiment in the same way as is done for collision
data, by dividing the number of such experiments with $p\text{-values}$ that
are smaller than the $p\text{-value}$ for the particular pseudo-experiment
under consideration by their total number. This represents the
$\tilde{p}\text{-value}$ for one pseudo-experiment, and similarly
$\tilde{p}\text{-values}$ can be calculated for all the different pseudo-
experiments. The resulting histogram of $\tilde{p}\text{-values}$ from the
different pseudo-experiments shows the expected distribution of
$\tilde{p}\text{-values}$ for the simulation-only hypothesis. The
$\tilde{p}\text{-value}$ distribution obtained from the observed distribution
(from collision data) is then compared with the one obtained from pseudo-
experiments, taking the median of the distributions from the different pseudo-
experiments as the central value for the SM-only hypothesis. Furthermore, $\pm
1\sigma$ ($\pm 2\sigma$) uncertainty bands around the median expectation are
obtained corresponding to the bands within which the distributions from 68
(95)% of the pseudo-experiments are contained. This is summarised with an
illustrative example in Fig. 3.

Figure 3: Illustrative example of a $\tilde{p}\text{-value}$ distribution for
different event classes (final states) based on a RoI scan of an
$S_{\mathrm{T}}$ distribution. Histograms of the number of event classes
corresponding to a bin in $-\log_{10}(\tilde{p})$ for the different pseudo-
experiment iterations (shown on the left) are used to create the global
overview plot for a scan of each particular kinematic distribution for each
event class type (shown on the right for an $S_{\mathrm{T}}$ distribution scan
in exclusive event classes, without showing the observed deviations from data
here). The mean and the median distributions of $\tilde{p}\text{-values}$
obtained from the different pseudo-experiments are shown as solid cyan and
dotted grey lines. The distribution estimated from the analytic calculation is
shown as a green dashed line. The 68% ($\pm 1\sigma$) and 95% ($\pm 2\sigma$)
uncertainty bands are displayed as dark and light blue areas, respectively.

In addition to this numerical calculation, the $\tilde{p}\text{-value}$
distribution can also be determined analytically. Since
$\tilde{p}\text{-values}$ are distributed uniformly, the content of each bin
($N_{\text{bin}}$) can be evaluated from the edges of the bin and the number
of event class distributions ($N_{\mathrm{dist}}$) contributing to the bin.
For the double-logarithmic scale used to plot the $\tilde{p}\text{-value}$
distribution (as in the nominal distribution shown in Fig. 3), the content of
each bin is given by

$N_{\text{bin}}=\left(10^{-B_{\text{low}}}-10^{-B_{\text{up}}}\right)N_{\text{dist}},$
(6)

where $B_{\text{low}}$ and $B_{\text{up}}$ are the lower and upper bin edges,
respectively. This analytic description is depicted by a green dashed line in
the $\tilde{p}\text{-value}$ distribution shown in Fig. 3. The analytical and
numerical distributions are found to agree. The approach to estimate
systematic uncertainties, as implemented in this analysis and described later,
can affect the $\tilde{p}\text{-value}$ distribution with more event classes
showing smaller deviations and appearing in the bin with the smallest
deviations. The size of the uncertainty bands of the expected
$\tilde{p}\text{-value}$ distributions has been increased, motivated by
studies with pseudo-data that include the potential effect of overestimating
the systematic uncertainties by up to 50%. Since this effect is limited to the
first few bins in the $\tilde{p}\text{-value}$ distributions, which correspond
to small deviations, the modification does not have a significant impact on
the part of the distribution where possible statistically significant
deviations are expected to appear.

##  0.6 Systematic uncertainties

Estimates of all major known sources of systematic uncertainties are
incorporated. In particular, uncertainties in the following quantities are
included: integrated luminosity, contributions of pileup interactions, total
cross sections of SM processes, PDFs, energy and momentum scale of all
objects, reconstruction efficiencies, resolutions, misidentification
probabilities, and the number of simulated events. The uncertainties arising
from the finite size of the simulated samples are uncorrelated between bins.
The effect of each of the other sources of uncertainty is fully correlated
across all bins and event classes. Systematic uncertainties that influence
kinematic properties are evaluated by variations of such variables, which
might shift some particles in and out of the acceptance for the selection.
This effect is included by allowing uncertainty contributions to cause
migrations between different event classes. A summary of the systematic
uncertainties is presented in Table 0.6.

Summary of systematic uncertainties in the analysis.  Source of uncertainty
Typical values Integrated luminosity 2.5% Pileup $<$5% Cross sections of SM
processes For processes calculated at LO: 50% For higher-order calculations:
varies Parton distribution functions Varies, following PDF4LHC [55]
recommendations Value of $\alpS$ Varies, variations of $\pm 0.0015$ around
central value ($0.118$) Electron, muon, and photon energy scales 0.15–7.00%
Jet energy scale and resolution 3–5% Unclustered energy Varies, typically 0–15
Reconstruction and identification efficiency Varies, $<$10% Misidentification
uncertainties 50% MC statistical uncertainty Varies, up to 30%

The uncertainty in the value of the integrated luminosity is 2.5% [56], and
this uncertainty is propagated into the analysis as a normalisation
uncertainty in all simulated events in each region. Since the pileup
conditions assumed in the sample generation are not identical to the data,
simulated samples are corrected to reproduce the pileup distribution in data,
which has an average number of $\Pp\Pp$ interactions per bunch crossing of
approximately 22 for the 2016 data sample. The associated uncertainties
because of the estimation of the pileup distribution in data are propagated
through the individual event weights for the MC samples in the analysis.

The uncertainties in the total cross sections for individual SM physics
processes are included, although not all cross sections are known to the same
order of perturbation theory. Uncertainties in individual simulated samples of
a single physics process, samples of different phase space regions or QCD
multijet samples enriched in heavy flavours, are assumed to be fully
correlated. The total cross section uncertainty is evaluated from all
contributing physics processes assuming their separate uncertainties are
uncorrelated. For processes generated at LO we apply an uncertainty of 50% in
the value of the cross section. For higher-order calculations the effect of
missing higher-order corrections is estimated using coherent variations in the
factorisation and renormalisation scales in the MC simulation by factors of
$2.0$ and $0.5$ up and down, respectively.

To estimate the uncertainties corresponding to the PDFs, the procedure
outlined in the PDF4LHC recommendations for the LHC Run 2 [55] is used. These
uncertainties are treated as arising from a single source, and hence are fully
correlated over all bins and event classes. For the assumed central value of
the strong coupling $\alpS=0.118$, variations of $\pm 0.0015$ are used. This
simplified approach is used here, considering the complexities associated with
the variety of event classes and the number of bins corresponding to the
different kinematic observables that are used for the scan in each event class
probed. While the correlations are treated in a simplistic manner in this
approach, the limitations of this approach are not expected to have a
significant impact on the results considering the impact of other systematic
and statistical uncertainties that are taken into account in this analysis.

Uncertainties in the energy or momentum measurement of the different physics
objects are estimated by varying the measured kinematic observables such as
and $\eta$ within their uncertainties. For all variations, the full analysis
is performed and the uncertainty in the event yield is derived from the
resulting difference in each kinematic distribution. The effect of these
variations in the measured is also included. The uncertainty in the muon
momentum scale has a dependence on and $\eta$ that is taken into account. For
$1\TeV$ muons in the central region of the detector, the uncertainty is 7%
[18]. Uncertainties in the energy scale for electrons and photons have been
estimated separately for the barrel and endcap regions, and are 0.2% (barrel)
and 0.3% (endcap) for low-energy electrons [17], 0.15% (barrel) and 0.30%
(endcap) for low-energy photons [16], and 2% for high-energy electrons and
photons [17]. Corrections are applied to the energy scale of reconstructed
jets to account for effects from pileup, simulated true jet response, and
residual data and simulation scale factors, as summarised in Ref. [19]. The
associated uncertainties range from 3–5%, depending on the jet and $\eta$.
Although the jet energy corrections are not constant throughout the entire
detector, they will be similar for jets close to each other. For this analysis
it is assumed that jet energy scale uncertainties are fully correlated. All
energy deposits measured in the CMS detector and not assigned to a
reconstructed physics object are summed up and referred to as unclustered
energy, with its uncertainty propagated to the uncertainty [22].

Scale factors, in general close to one and depending on and $\eta$, correct
for differences in the efficiencies for reconstruction and identification of
the objects in data and simulation. The uncertainties arising from the
employed methods or limited size of the analysed data sets used to calculate
the efficiencies are included. Uncertainties are assumed to be fully
correlated for objects of the same type and uncorrelated for objects of
different type. For $\PQb$ tagging uncertainties we use and jet hadron
flavour-dependent scale factors along with their uncertainties, which are
derived from data [21].

Although the selection criteria have been chosen to minimise
misidentification, residual amounts of misidentified objects remain. The
fraction of misidentified objects is determined in the simulation with
generator-level information by matching generated particles to the
reconstructed objects. To cover the uncertainty in misidentified objects (i.e.
those not matched to a generated particle of the same type), we apply a 50%
uncertainty in the simulation. Misidentification is mainly relevant when jets
are wrongly identified as charged leptons or photons, whereas the uncertainty
in the inverse process, i.e. leptons misidentified as jets, is usually
negligible compared to the reconstruction efficiency and scale factor
uncertainties. The uncertainty assigned has been validated in the studies
described later in Section 0.8.1. Misidentification uncertainties are assumed
to be fully correlated for objects of the same type, and uncorrelated for
objects of different types.

The statistical uncertainty in the number of generated events and the total
event weight, arising from the limited number of events produced for each
simulated process, is included. Contributions from different simulated data
sets are uncorrelated when constructing regions.

##  0.7 Sensitivity studies

To illustrate the capability of the MUSiC analysis to identify deviations in
the comparison of measured data and the SM simulation, two separate approaches
are followed. In the first approach, a simulated BSM signal in addition to the
SM simulation is injected into the analysis and compared to the SM simulation
alone. In the second approach, the measured data are used, but a particular
process is removed from the SM simulation. In both cases the MUSiC algorithm
is shown to find event classes in significant tension with the SM expectation.
Examples for both approaches are discussed here.

A dedicated BSM signal is used to test the ability of the MUSiC algorithm to
identify possible new physics signals. A simulation of the BSM signal is added
to the SM simulation, and pseudo-data are generated that can be scanned with
the MUSiC algorithm against the SM-only background. The signal described here
is expected to introduce a localised excess of events in individual final
states: a new heavy vector boson $\PWpr$ is produced and promptly decays into
a charged lepton and a neutrino, as predicted by the sequential standard model
(SSM) [57]. In the CMS detector, such a signature is reconstructed as an event
containing a single isolated, energetic charged lepton, substantial , and any
number of jets originating from initial- or final-state radiation. Simulated
samples for the $\PWpr$ process are produced at LO using 8.212, and the cross
sections are obtained at NNLO QCD using FEWZ for different values of the
$\PWpr$ boson mass. Distributions of pseudo-data with an additional $\PWpr$
boson signal are generated 200 times per event class and kinematic variable.
This ensures a statistically stable outcome. The $p\text{-value}$ between
signal-induced pseudo-data and SM expectation is calculated for each of the
pseudo-experiments, and the pseudo-data generation corresponding to the median
$p\text{-value}$ is chosen as the representative event class distribution. Up
to 10,000 pseudo-experiments are generated under the SM-only hypothesis to
account for the LEE in each distribution and event class.

As a representative example, a scan of the (transverse) invariant mass
distribution in exclusive event classes is presented here. The distribution of
a signal and the SM background for the $1\Pgm+\ptmiss$ event class is shown in
Fig. 4 for a $\PWpr$ boson with mass of 3 . For $\PWpr$ masses of 2, 3, 4, and
5, the final $\tilde{p}$ distributions for the scan of the invariant
(transverse) mass in exclusive event classes are shown in Fig. 5. Two or more
final states with significant deviations from the SM simulation beyond the
expectation are found, as seen in the entries in the final bin of each
distribution that lie outside of the uncertainty bands of the SM-only
expectation, thus illustrating the ability of MUSiC to identify deviations
arising from a signal. The observation of such deviations would prompt
dedicated studies in the event classes of interest probing the possibility of
BSM physics phenomena. The signal corresponding to 4leads to significant
deviations only in the $1\Pe+\ptmiss$ and $1\Pgm+\ptmiss$ event classes. As
shown in Fig. 5, the scan performed for the $\PWpr$ boson with a mass of 5did
not show any significant deviations. These results are consistent with the
dedicated analysis of the same data set [58], where stronger exclusion limits
for the mass of the $\PWpr$ boson of 4.9were placed at 95% confidence level
based on the individual analyses in the $1\Pe+\ptmiss$ and $1\Pgm+\ptmiss$
channels, respectively, and 5.2for the combination of both channels.

Figure 4: Distribution of the transverse mass for the $1\Pgm+\ptmiss$
exclusive class with a hypothetical SSM $\PWpr$ boson (with mass of 3 ) along
with the SM simulation.

Figure 5: Distribution of $\tilde{p}\text{-values}$ for the RoI scan in
exclusive classes for the invariant mass (transverse mass for classes with )
with assumed values for the mass of the SSM $\PWpr$ boson of 2 (upper left), 3
(upper right), 4 (lower left), and 5(lower right). The uncertainty in the
distribution of $\tilde{p}\text{-values}$ for the signal is obtained from the
variations in the pseudo-data performed with the $\PWpr$ signal simulation.

Another hypothetical BSM signal that has been used to test the capabilities of
MUSiC is the EW production of sphalerons [59, 60, 61, 62]. This model is based
on a possible nonperturbative solution to the SM Lagrangian of the EW sector,
which includes a vacuum transition referred to as a “sphaleron”. It plays an
important role in the EW baryogenesis theory [63], which can explain the
matter-antimatter asymmetry of the universe. The CMS experiment has published
the results of a search for sphalerons in inclusive final states that are
dominated by jets associated with the QCD multijet process [64]. No analysis
targeting leptonic final states has been performed to date. The sphaleron
signal sample used for the sensitivity study is generated at LO with the
BaryoGEN v1.0 generator [65] with the CT10 LO PDF set [66] using a threshold
energy $E_{\text{sph}}$ = 8for the sphaleron transition. The cross section for
sphaleron production is given by $\sigma$ = PEF $\sigma_{0}$ [62], where
$\sigma_{0}=121$ fb for $E_{\text{sph}}=8\TeV$, and PEF denotes the pre-
exponential factor, defined as the fraction of all quark-quark interactions
above the sphaleron energy threshold $E_{\text{sph}}$ that undergo the
sphaleron transition. The result of the MUSiC RoI scan for $S_{\mathrm{T}}$
distributions in inclusive event classes is shown in Fig. 6, where the
simulation of the sphaleron production with PEF = 0.05 is used as the signal.
Several event classes with large deviations beyond the expectation from the
SM-only hypothesis are identified in the final bins of the
$\tilde{p}\text{-values}$ distribution. Among the most deviating event classes
are the $1\Pgm+5\text{jets}+\ptmiss\ +X$, $1\Pe+5\text{jets}+\ptmiss\ +X$,
$1\Pgm+1\PQb+2\text{jets}+\ptmiss\ +X$, and $1\Pe+1\Pgm+3\text{jets}+\ptmiss\
+X$ event classes. In the inclusive CMS analysis [64] based on the same data
set, an upper limit of PEF = 0.002 was set at the 95% confidence level. This
result demonstrates the sensitivity of MUSiC to an example of BSM physics in
final states where no previous search has been conducted by the CMS
experiment.

Figure 6: Distribution of $\tilde{p}\text{-values}$ for the RoI scan in
inclusive classes for the $S_{\mathrm{T}}$ distributions for a sphaleron
signal with $E_{\text{sph}}=8\TeV$ and PEF = 0.05. The uncertainty in the
distribution of $\tilde{p}\text{-values}$ for the signal is obtained from the
variations in the pseudo-data performed with the sphaleron signal simulation.

In a second approach to evaluate the sensitivity of the MUSiC analysis, a
single SM process is removed from the SM simulation, and the scanning
algorithm is applied against the recorded CMS data using the modified SM
simulation. In the example shown here, the process of $\PW\PZ$ diboson
production is removed. Several final states show large and significant
deviations with $\tilde{p}<0.0002$, compared to the prediction of having no
final states showing such a deviation based on the simulation. The most
significant final states concern classes with three leptons, as well as three
leptons and , corresponding to event classes where the $\PW\PZ$ process is
expected to contribute, confirming the ability of the MUSiC analysis to detect
deviations corresponding to the missing $\PW\PZ$ process. Figure 7 shows the
event class $3\Pgm+\ptmiss$ with and without the $\PW\PZ$ process as part of
the SM simulation. The sensitivity has also been verified by removing other SM
processes with smaller cross sections, such as $\PZ\PZ$ and $\PQt\PAQt\PZ$
production, from the SM simulation, leading to similar conclusions. For the
case where the $\PQt\PAQt\PZ$ process is removed from the SM background, the
$3\Pe+1\PQb+2\text{jets}+\text{Njets}$ jet-inclusive event class shows the
most significant deviation with $\tilde{p}<0.0002$ for the RoI scan of the
$S_{\mathrm{T}}$ distributions in jet-inclusive event classes.

Figure 7: Distributions of $S_{\mathrm{T}}$ for the $3\Pgm+\ptmiss$ exclusive
class without (left) and with (right) $\PW\PZ$ production as part of the SM
simulation. The data events are shown in black and the simulations of the SM
processes are shown as coloured histograms. The region enclosed within the red
dashed lines is the region of interest.

These sensitivity studies emphasize the ability of the MUSiC algorithm to
identify deviations of the data from the simulated background.

##  0.8 Results

Using the MUSiC classification procedure we observe 498 exclusive event
classes and 571 (530) inclusive (jet-inclusive) event classes with at least
one data event. For the number of classes in the simulation, we use a lower
threshold of 0.1 on the expected total yield to make the number of classes
stable against small changes in the total number of simulated events, and to
ensure well-defined statistical properties for the comparison of deviations.
We did not find any event class that contained data but no simulated events at
all. No event class with a total expected yield below 0.1 events from the
simulation was found that contained more than one data event, which would have
required further investigation.

Before the results of the scan algorithm are presented in detail, the overall
performance to reconstruct and identify objects and their multiplicities is
discussed, based on a set of final states where a single SM process is
expected to dominate, and where contributions from a potential signal are
unlikely, based on previously published search and precision measurement
results.

###  0.8.1 Commissioning studies and vetoed event classes

The final state $\PZ\to\ell\ell+X$ is defined by the presence of at least two
same-flavour leptons (or ) and any additional number of particles. For the
total inclusive selection, the invariant mass of the lepton pair in the event
is studied along with the distribution of the number of jets and the $\Delta
R$ distribution between the leading lepton and a jet, to verify the event
cleaning introduced in Section 0.4. The distribution of the number of $\PQb$
jets is checked for $\PZ\to\ell\ell+X$ with at least two jets. We choose
events with electron or muon pairs within a $20\GeV$ window around the mass of
the boson to further validate our ability to reliably reconstruct the lepton
kinematic properties, using the $\pt$ distribution and angular distributions
for $\eta$ and $\phi$ of the two leading leptons. The distributions are in
agreement with the SM simulation within the uncertainties. In addition, the
global event properties , $S_{\mathrm{T}}$, and (defined as the sum of $\pt$
of all jets and $\PQb$ jets in an event) are checked for events in the mass
window, and are in agreement with the SM simulation. Events in the boson mass
window along with one additional lepton of a different flavour and without
substantial ($<100\GeV$) are selected, which form a region dominated by SM
processes with relatively small cross sections and sensitive to possible
misidentification of charged leptons. The $S_{\mathrm{T}}$ distribution for
that selection is in agreement with the prediction within the uncertainties.

The final state $\PQt\PAQt\to\ell+2\text{jets}+2\PQb+X$ is defined by the
presence of at least one lepton (or ), two jets, two $\PQb$-tagged jets, and
any additional number of final-state objects (i.e. additional leptons, jets,
or $\PQb$ jets). We use this final state to validate our ability to describe
the kinematic properties in events with a complicated event topology and a
larger contribution from misidentified objects. An overall good agreement is
observed between the data and simulation. In addition to the inclusive
selection, we require the mass of the jet pair to be within a $30\GeV$ window
centered on the boson mass, and the $M_{\mathrm{T}}$ of the lepton plus system
to be larger than $60\GeV$. Within this selected region we check the hadronic
activity and find no significant deviations of the data from the expectation.

Kinematic distributions of photons are studied in photon-triggered events,
using $\PGg$+jets events with one photon, one jet, and no leptons nor
substantial . The kinematic distributions of the photons, such as the $\pt$,
$\eta$, and $\phi$, are in reasonable agreement with the SM simulation.

A few final states have been found to be unsuitable for the present analysis,
since they require special treatment of simulated samples in these specific
final states, which cannot be applied generally, and are therefore removed
from the analysis. This is the case for the event classes containing two same-
flavour leptons and one photon, but no additional leptons or photons. These
classes are affected by the overlap between simulated samples for the
inclusive $\PZ(\to\ell^{+}\ell^{-})+\text{jets}$ process and specific samples
for the SM production of a boson in association with a photon, leading to an
overestimation of the background by the simulation. Since no consistent
overlap removal could be performed in these event classes, they are removed
from further analysis. Dedicated analyses of the same data set target such
final states [67].

###  0.8.2 Total yield scans and object group representation

Scans are performed based on the total event yield in the different event
classes between data and SM expectation, calculating the $p\text{-value}$ for
each event class based on the total yield. Broad agreement is observed between
the data and simulation, with no particular event class being found to have a
significant discrepancy between the data and the SM simulation. Selected
results for the exclusive event classes are shown in Fig. 8, where the 20 most
significant event classes are displayed, along with the $p\text{-value}$ for
each event class calculated based on the total event yield. The
$p\text{-values}$ for the most significant classes are within the expectations
of the SM considering the number of classes.

Further results are presented of the comparison of the total event yield in
event classes between data and the SM expectation, grouped by their object
content. The term “object group” is used to describe a set of classes based on
the composition of its event content, e.g. the double electron object group
consists of all classes with exactly two electrons and any number of jets (or
$\PQb$ jets). Two examples are shown in Fig. 9, for the double electron object
group and for the single-muon + group. For a quantitative comparison of data
and simulation, the event classes are displayed along with the corresponding
$p\text{-value}$ for each event class. Different jet multiplicities are
overall well described, and the total event yields agree with the SM
simulation within their uncertainties for different dominating processes,
where and boson decays dominate when additional light-flavour jets are
present, whereas final states with additional $\PQb$ jets are dominated by
$\PQt\PAQt$ production. Figures for additional object groups can be found in
Appendix .10.

Figure 8: Data and SM predictions for the most significant exclusive event
classes, where the significance of an event class is calculated in a single
aggregated bin. Measured data are shown as black markers, contributions from
SM processes are represented by coloured histograms, and the shaded region
represents the uncertainty in the SM background. The values above the plot
indicate the observed $p\text{-value}$ for each event class.

Figure 9: Overview of total event yields for event classes corresponding to
the double-electron (upper) and for the single-muon + object groups (lower).
Measured data are shown as black markers, contributions from SM processes are
represented by coloured histograms, and the shaded region represents the
uncertainty in the SM background. The numbers above each plot indicate the
observed $p\text{-value}$ for the agreement of data and simulation for the
corresponding event class.

###  0.8.3 Results of the RoI scans

Some typical examples of kinematic distributions are shown. The distributions
in Fig. 10 for $S_{\mathrm{T}}$ and $M$ belong to the $2\Pgm$ exclusive event
class, and the distribution is from the $2\Pgm+\ptmiss+X$ inclusive event
class. No significant deviations are found with respect to the SM
expectations. The aforementioned distributions illustrate the variable binning
depending on the resolution, and the contributions of the different physics
processes. They also show experimental features arising from a combination of
the threshold effects, such as the trigger and the minimum of the selected
objects, along with effects related to the underlying physics, such as the
peak associated with the boson. In the distribution, a global offset between
data and SM simulation is observed, covered by the uncertainties, which are
mostly related to and dominated by the uncertainties in the jet energy scale
and resolution. In general, the observed differences between data and SM
simulation are covered by the systematic uncertainties over the entire
kinematic ranges, and the resulting $\tilde{p}\text{-values}$ for the regions
of interest indicate agreement between the two.

Figure 10: Example $S_{\mathrm{T}}$ (upper left) and $M$ (upper right)
distributions for the $2\Pgm$ exclusive event class, and the distribution for
the $2\Pgm+\ptmiss+X$ inclusive event class (lower). Measured data are shown
as black markers, contributions from SM processes are represented by coloured
histograms, and the region enclosed by red dashed lines in each figure
corresponds to the region of interest determined by the RoI algorithm
described in Section 0.5.

The global overview plots for the $M$, $S_{\mathrm{T}}$, and RoI scans for the
exclusive event classes are shown in Fig. 11. The corresponding plots for the
inclusive and the jet-inclusive classes are shown in Figs. 12 and 13,
respectively. The distributions observed based on the scans of the data are
consistent with the expectations based on simulation within the uncertainty
bands. In general, slightly fewer event classes are observed in data in the
second bin of the distributions compared to the expectation, while there are
more event classes in data in the first bin, where the observed deviation is
smaller. This is a consequence of a possible overestimation of systematic
uncertainties (see Section 0.5.5).

Figure 11: Distribution of $\tilde{p}\text{-values}$ for the RoI scan in
exclusive classes for the $M$ (upper), $S_{\mathrm{T}}$ (middle), and (lower)
distributions.

Figure 12: Distribution of $\tilde{p}\text{-values}$ for the RoI scan in
inclusive classes for the $M$ (upper), $S_{\mathrm{T}}$ (middle), and (lower)
distributions.

Figure 13: Distribution of $\tilde{p}\text{-values}$ for the RoI scan in jet-
inclusive classes for the $M$ (upper), $S_{\mathrm{T}}$ (middle), and (lower)
distributions.

No event classes with an outstanding deviation from the SM simulation beyond
the expectation, which could be studied for signs of BSM physics, have been
found in the analysed data set. The largest deviations seen are consistent
with the statistical analysis based on the SM-only hypothesis. The exact
number of event classes and the corresponding values of $\tilde{p}$ required
to be considered significant deviations beyond the SM-only hypothesis depend
on the kinematic distributions and the type of event class being probed, and
can be inferred from the global overview plots (Figs. 11, 12 and 13). The two
most significant event classes from the RoI scan for each kinematic variable
are described in Table 0.8.3, separately for exclusive, inclusive, and jet-
inclusive event classes, respectively. The event classes showing the most
significant deviations have been studied in detail, and no systematic trend in
related or neighboring event classes has been found. Since the individual
event classes do not show a deviation that is statistically significant
compared to the expectation, a deeper inspection for possible signs of BSM
physics is not required as a part of this analysis.

Overview of the two most significant event classes in each RoI scan. Details
of the RoI, the expectation from the SM simulation, and the number of data
events within the RoI are shown along with the $p$\- and
$\tilde{p}\text{-values}$.  Event class RoI $N_{\text{MC}}$ $N_{\text{Data}}$
$p$ $\tilde{p}$ [] Exclusive event classes: $M$ $1\Pe+1\PGm+1\PGg+\ptmiss$
380–560 2.7 $\pm$ 2.5 14 0.0026 0.0061 $4\PGm+1\PQb+1\text{jet}+\ptmiss$
590–950 0.092 $\pm$ 0.044 2 0.0048 0.0072 Exclusive event classes:
$S_{\mathrm{T}}$ $3\Pe+1\PQb+2\text{jets}$ 340–540 0.84 $\pm$ 0.27 6 0.00053
0.0038 $4\PGm+1\PQb+1\text{jet}+\ptmiss$ 590–950 0.092 $\pm$ 0.047 2 0.0052
0.0082 Exclusive event classes:  $4\PGm+1\PQb+1\text{jet}+\ptmiss$ 100–390
0.16 $\pm$ 0.12 2 0.018 0.022 $1\PGm+4\PQb+1\text{jet}+\ptmiss$ 140–330 0.57
$\pm$ 0.50 4 0.014 0.027 Inclusive event classes: $M$
$4\PGm+1\PQb+1\text{jet}+\ptmiss+X$ 590–860 0.16 $\pm$ 0.10 2 0.016 0.022
$4\PGm+1\text{jet}+\ptmiss+X$ 560–770 0.60 $\pm$ 0.24 4 0.0055 0.026 Inclusive
event classes: $S_{\mathrm{T}}$ $2\Pe+1\PGm+1\PQb+5\text{jets}+X$ 740–890
0.062 $\pm$ 0.043 2 0.0028 0.0063 $2\PGm+X$ 1050–6110 95.8 $\pm$ 6.8 58
0.00036 0.012 Inclusive event classes:  $4\PGm+1\text{jet}+\ptmiss+X$ 130–160
0.46 $\pm$ 0.32 4 0.0045 0.012 $3\PGm+4\text{jets}+\ptmiss+X$ 170–570 2.5
$\pm$ 1.3 8 0.021 0.048 Jet-inclusive event classes: $M$
$2\Pe+1\PGm+5\text{jets}+\text{Njets}$ 1370–2030 0.37 $\pm$ 0.29 4 0.0028
0.0063 $1\Pe+1\PGm+3\PQb+2\text{jets}+\ptmiss+\text{Njets}$ 1140–1700 0.79
$\pm$ 0.46 5 0.0050 0.0071 Jet-inclusive event classes: $S_{\mathrm{T}}$
$2\Pe+1\PGm+5\text{jets}+\text{Njets}$ 990–1780 0.39 $\pm$ 0.34 4 0.0039
0.0060 $2\Pe+1\PGm+1\PQb+3\text{jets}+\text{Njets}$ 430–650 0.52 $\pm$ 0.26 5
0.00066 0.0070 Jet-inclusive event classes:
$4\PGm+1\PQb+1\text{jet}+\ptmiss+\text{Njets}$ 100–150 0.19 $\pm$ 0.12 2 0.022
0.022 $4\PGm+1\text{jet}+\ptmiss+\text{Njets}$ 130–160 0.36 $\pm$ 0.24 3 0.012
0.032

Some of these event classes have low numbers of events and high object
multiplicity, such as the $4\Pgm+1\PQb+1\text{jet}+\ptmiss$ event class with
two data events compared to an overall expectation of $0.16\pm 0.11$ events in
the entire event class (the numbers displayed in Table 0.8.3 refer to the data
events and simulated expectation within the RoI), where the deviation can be
attributed to a fluctuation. The events in this event class also contribute to
the $4\Pgm+1\PQb+1\text{jet}+\ptmiss\ +X$, $4\Pgm+1\text{jet}+\ptmiss\ +X$,
and $4\Pgm+1\PQb+1\text{jet}+\ptmiss\ +\textrm{Njets}$ event classes, which
also appear among the event classes with the largest deviations for the
inclusive and jet-inclusive categories. There are high jet multiplicity event
classes with relatively low numbers of events, particularly
$2\Pe+1\Pgm+1\PQb+5\text{jets}+X$, $2\Pe+1\Pgm+5\text{jets}+\text{Njets}$,
$1\Pgm+4\PQb+1\text{jet}+\ptmiss$,
$1\Pe+1\Pgm+3\PQb+2\text{jets}+\ptmiss+\text{Njets}$, and
$2\Pe+1\Pgm+1\PQb+3\text{jets}+\text{Njets}$, most of which are also inclusive
at least in terms of the number of jets. The $\tilde{p}\text{-values}$ of
these deviations are not very significant, and they can be ascribed either to
fluctuations or to inadequate modelling of the data by the simulation at high
jet multiplicities.

The $3\Pe+1\PQb+2\text{jets}$ event class is the event class with the smallest
$\tilde{p}\text{-value}$, and it appears in the scan of the $S_{\mathrm{T}}$
distribution for exclusive event classes. The entire event class has seven
data events compared to the expectation of $2.7\pm 1.8$ from the simulation.
The major contribution of SM processes in this event class is $\PQt\PAQt$
production in association with a vector boson. Related event classes were
studied, including the corresponding inclusive and jet-inclusive event
classes, the flavour counterpart $3\Pgm+1\PQb+2\text{jets}$, and event classes
with one object removed. None of those event classes show a significant
deviation in the data from the simulated SM background predictions. Similar
studies were performed also for the $1\Pe+1\Pgm+1\PGg+\ptmiss$ event class
that shows the second-smallest $\tilde{p}\text{-value}$ in exclusive event
classes, as a result of the scan of the $M$ distribution. Related event
classes, such as the corresponding inclusive and jet-inclusive classes, and
event classes where the number of physics objects has been reduced by one,
were checked. Again, none of the related event classes show a large deviation
from the simulated SM background predictions. The largest SM contribution in
this event class corresponds to the $\PQt\PAQt$ process, and other event
classes dominated by the same process are described well. The low
$\tilde{p}\text{-value}$ in the $2\Pgm+X$ event class identified by the scan
of the $S_{\mathrm{T}}$ distribution for inclusive event classes corresponds
to a deficit in the tail of the distribution. It is not found as a prominent
deviation in the corresponding exclusive or jet-inclusive categories. The
observed effect is not very significant, and was also seen during a dedicated
analysis targeting this final state [54]. The remaining event classes detailed
in Table 0.8.3 show smaller deviations from the simulated SM background
predictions.

In summary, the low $\tilde{p}\text{-values}$ observed in the aforementioned
individual event classes are not beyond the expectations from SM, and no
systematic trends are observed.

##  0.9 Summary

The Model Unspecific Search in CMS (MUSiC) analysis has been presented. The
analysis is based on data recorded by the CMS detector at the LHC during
proton-proton collisions at a centre-of-mass energy of $13\TeV$ in 2016 and
corresponding to an integrated luminosity of $35.9\fbinv$. The MUSiC analysis
searches for anomalies and possible hints of physics beyond the standard model
in the data using a model-independent approach, relying solely on the
assumptions of the well-tested standard model.

Events from data and simulation containing at least one electron or muon have
been sorted into event classes based on their final-state topology, defined by
the number of electrons, muons, photons, jets and $\PQb$-tagged jets, and
missing transverse momentum. The event yields were compared between the data
and the expectation in a wide range of event classes. The kinematic
distributions corresponding to the sum of transverse momenta, invariant (or
transverse) mass, and missing transverse momentum in each of the event classes
have been scanned using a region of interest algorithm. The algorithm
identifies deviations of the data from the simulated standard model
predictions, calculating a $p\text{-value}$ of any observed deviation after
correcting for the look-elsewhere effect. A global overview of the results
from the different event classes and distributions has been presented.

The sensitivity and robustness of the analysis has been shown in a variety of
different studies. No significant deviations from the standard model
expectations were found in the data analysed by the MUSiC algorithm. A wide
range of final-state topologies has been studied, and there is agreement
between data and the standard model simulation given the experimental and
theoretical uncertainties. This analysis complements dedicated search analyses
by significantly expanding the range of final states covered using a model
independent approach with the largest data set to date to probe phase space
regions beyond the reach of previous general searches.

###### 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 centres 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 programme 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”) Programme
and the János Bolyai Research Scholarship of the Hungarian Academy of
Sciences, the New National Excellence Program ÚNKP, the NKFIA research grants
123842, 123959, 124845, 124850, 125105, 128713, 128786, and 129058 (Hungary);
the Council of Science and Industrial Research, India; the HOMING PLUS
programme of the Foundation for Polish Science, cofinanced from European
Union, Regional Development Fund, the Mobility Plus programme 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 Tomsk Polytechnic University Competitiveness Enhancement Program; 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 programmes cofinanced by
EU-ESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral
Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its
2nd Century Project Advancement Project (Thailand); the Kavli Foundation; the
Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract
C-1845; and the Weston Havens Foundation (USA).

## References

  * [1] CMS Collaboration, “The CMS experiment at the CERN LHC”,  JINST 3 (2008) S08004, 10.1088/1748-0221/3/08/S08004.
  * [2] D0 Collaboration, “Search for new physics in e$\mu$x data at D0 using SLEUTH: A quasi-model-independent search strategy for new physics”,  Phys. Rev. D 62 (2000) 092004, 10.1103/PhysRevD.62.092004, arXiv:hep-ex/0006011.
  * [3] D0 Collaboration, “Quasi-model-independent search for new physics at large transverse momentum”,  Phys. Rev. D 64 (2001) 012004, 10.1103/PhysRevD.64.012004, arXiv:hep-ex/0011067.
  * [4] D0 Collaboration, “Quasi-model-independent search for new high ${p}_{T}$ physics at D0”,  Phys. Rev. Lett. 86 (2001) 3712, 10.1103/PhysRevLett.86.3712, arXiv:hep-ex/0011071.
  * [5] CDF Collaboration, “Model-independent and quasi-model-independent search for new physics at CDF”,  Phys. Rev. D 78 (2008) 012002, 10.1103/PhysRevD.78.012002, arXiv:0712.1311.
  * [6] CDF Collaboration, “Global search for new physics with 2.0 fb-1 at CDF”,  Phys. Rev. D 79 (2009) 011101, 10.1103/PhysRevD.79.011101, arXiv:0809.3781.
  * [7] H1 Collaboration, “A general search for new phenomena in ep scattering at HERA”,  Phys. Lett. B 602 (2004) 14, 10.1016/j.physletb.2004.09.057, arXiv:hep-ex/0408044.
  * [8] H1 Collaboration, “A general search for new phenomena at HERA”,  Phys. Lett. B 674 (2009) 257, 10.1016/j.physletb.2009.03.034, arXiv:0901.0507.
  * [9] ATLAS Collaboration, “A strategy for a general search for new phenomena using data-derived signal regions and its application within the ATLAS experiment”,  Eur. Phys. J. C 79 (2019) 120, 10.1140/epjc/s10052-019-6540-y, arXiv:1807.07447.
  * [10] CMS Collaboration, “Model unspecific search for new physics in pp collisions at $\sqrt{s}=7\,\mathrm{TeV}$”, CMS Physics Analysis Summary CMS-PAS-EXO-10-021, 2011.
  * [11] CMS Collaboration, “MUSiC, a model unspecific search for new physics, in pp collisions at $\sqrt{s}=8\,\mathrm{TeV}$”, CMS Physics Analysis Summary CMS-PAS-EXO-14-016, 2017.
  * [12] CMS Collaboration, “The CMS trigger system”,  JINST 12 (2017) P01020, 10.1088/1748-0221/12/01/P01020, arXiv:1609.02366.
  * [13] 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.
  * [14] M. Cacciari, G. P. Salam, and G. Soyez, “The anti-jet clustering algorithm”,  JHEP 04 (2008) 063, 10.1088/1126-6708/2008/04/063, arXiv:0802.1189.
  * [15] M. Cacciari, G. P. Salam, and G. Soyez, “FastJet user manual”,  Eur. Phys. J. C 72 (2012) 1896, 10.1140/epjc/s10052-012-1896-2, arXiv:1111.6097.
  * [16] CMS Collaboration, “Performance of photon reconstruction and identification with the CMS detector in proton-proton collisions at $\sqrt{s}=8$ TeV”,  JINST 10 (2015) P08010, 10.1088/1748-0221/10/08/P08010, arXiv:1502.02702.
  * [17] CMS Collaboration, “Performance of electron reconstruction and selection with the CMS detector in proton-proton collisions at $\sqrt{s}=8$”,  JINST 10 (2015) P06005, 10.1088/1748-0221/10/06/P06005, arXiv:1502.02701.
  * [18] CMS Collaboration, “Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at $\sqrt{s}=13$”,  JINST 13 (2018) P06015, 10.1088/1748-0221/13/06/P06015, arXiv:1804.04528.
  * [19] CMS Collaboration, “Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV”,  JINST 12 (2017) P02014, 10.1088/1748-0221/12/02/P02014, arXiv:1607.03663.
  * [20] CMS Collaboration, “Jet algorithms performance in 13 TeV data”, CMS Physics Analysis Summary CMS-PAS-JME-16-003, 2017.
  * [21] CMS Collaboration, “Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV”,  JINST 13 (2018) P05011, 10.1088/1748-0221/13/05/P05011, arXiv:1712.07158.
  * [22] CMS Collaboration, “Performance of missing transverse momentum reconstruction in proton-proton collisions at $\sqrt{s}=13$ TeV using the CMS detector”,  JINST 14 (2019) P07004, 10.1088/1748-0221/14/07/P07004, arXiv:1903.06078.
  * [23] 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.
  * [24] J. Alwall et al., “The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations”,  JHEP 07 (2014) 079, 10.1007/JHEP07(2014)079, arXiv:1405.0301.
  * [25] J. Alwall et al., “Comparative study of various algorithms for the merging of parton showers and matrix elements in hadronic collisions”,  Eur. Phys. J. C 53 (2008) 473, 10.1140/epjc/s10052-007-0490-5, arXiv:0706.2569.
  * [26] R. Frederix and S. Frixione, “Merging meets matching in MC@NLO”,  JHEP 12 (2012) 061, 10.1007/JHEP12(2012)061, arXiv:1209.6215.
  * [27] P. Nason, “A new method for combining NLO QCD with shower Monte Carlo algorithms”,  JHEP 11 (2004) 040, 10.1088/1126-6708/2004/11/040, arXiv:hep-ph/0409146.
  * [28] S. Frixione, P. Nason, and C. Oleari, “Matching NLO QCD computations with parton shower simulations: the powheg method”,  JHEP 11 (2007) 070, 10.1088/1126-6708/2007/11/070, arXiv:0709.2092.
  * [29] S. Alioli, P. Nason, C. Oleari, and E. Re, “A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX”,  JHEP 06 (2010) 043, 10.1007/JHEP06(2010)043, arXiv:1002.2581.
  * [30] S. Alioli, P. Nason, C. Oleari, and E. Re, “NLO vector-boson production matched with shower in POWHEG”,  JHEP 07 (2008) 060, 10.1088/1126-6708/2008/07/060, arXiv:0805.4802.
  * [31] E. Re, “Single-top Wt-channel production matched with parton showers using the POWHEG method”,  Eur. Phys. J. C 71 (2011) 1547, 10.1140/epjc/s10052-011-1547-z, arXiv:1009.2450.
  * [32] S. Alioli, P. Nason, C. Oleari, and E. Re, “NLO single-top production matched with shower in POWHEG: s- and t-channel contributions”,  JHEP 09 (2009) 111, 10.1088/1126-6708/2009/09/111, arXiv:0907.4076. [Erratum: 10.1007/JHEP02(2010)011].
  * [33] S. Alioli, P. Nason, C. Oleari, and E. Re, “NLO Higgs boson production via gluon fusion matched with shower in POWHEG”,  JHEP 04 (2009) 002, 10.1088/1126-6708/2009/04/002, arXiv:0812.0578.
  * [34] P. Nason and C. Oleari, “NLO Higgs boson production via vector-boson fusion matched with shower in POWHEG”,  JHEP 02 (2010) 037, 10.1007/JHEP02(2010)037, arXiv:0911.5299.
  * [35] T. Melia, P. Nason, R. Rontsch, and G. Zanderighi, “W+W-, WZ and ZZ production in the POWHEG BOX”,  JHEP 11 (2011) 078, 10.1007/JHEP11(2011)078, arXiv:1107.5051.
  * [36] P. Nason and G. Zanderighi, “W+W-, WZ and ZZ production in the POWHEG-BOX-V2”,  Eur. Phys. J. C 74 (2014) 2702, 10.1140/epjc/s10052-013-2702-5, arXiv:1311.1365.
  * [37] J. M. Campbell, R. K. Ellis, P. Nason, and E. Re, “Top-pair production and decay at NLO matched with parton showers”,  JHEP 04 (2015) 114, 10.1007/JHEP04(2015)114, arXiv:1412.1828.
  * [38] E. Bagnaschi, G. Degrassi, P. Slavich, and A. Vicini, “Higgs production via gluon fusion in the POWHEG approach in the SM and in the MSSM”,  JHEP 02 (2012) 088, 10.1007/JHEP02(2012)088, arXiv:1111.2854.
  * [39] Sherpa Collaboration, “Event generation with Sherpa 2.2”,  SciPost Phys. 7 (2019) 034, 10.21468/SciPostPhys.7.3.034, arXiv:1905.09127.
  * [40] T. Gleisberg et al., “Event generation with SHERPA 1.1”,  JHEP 02 (2009) 007, 10.1088/1126-6708/2009/02/007, arXiv:0811.4622.
  * [41] NNPDF Collaboration, “Parton distributions for the LHC Run II”,  JHEP 04 (2015) 040, 10.1007/JHEP04(2015)040, arXiv:1410.8849.
  * [42] CMS Collaboration, “Event generator tunes obtained from underlying event and multiparton scattering measurements”,  Eur. Phys. J. C 76 (2016) 155, 10.1140/epjc/s10052-016-3988-x, arXiv:1512.00815.
  * [43] GEANT4 Collaboration, “—a simulation toolkit”,  Nucl. Instrum. Meth. A 506 (2003) 250, 10.1016/S0168-9002(03)01368-8.
  * [44] Y. Li and F. Petriello, “Combining QCD and electroweak corrections to dilepton production in FEWZ”,  Phys. Rev. D 86 (2012) 094034, 10.1103/PhysRevD.86.094034, arXiv:1208.5967.
  * [45] S. G. Bondarenko and A. A. Sapronov, “NLO EW and QCD proton-proton cross section calculations with mcsanc-v1.01”,  Comput. Phys. Commun. 184 (2013) 2343, 10.1016/j.cpc.2013.05.010, arXiv:1301.3687.
  * [46] J. M. Campbell and R. K. Ellis, “MCFM for the Tevatron and the LHC”,  Nucl. Phys. Proc. Suppl. 205–206 (2010) 10, 10.1016/j.nuclphysbps.2010.08.011, arXiv:1007.3492.
  * [47] T. Gehrmann et al., “W+W- production at hadron colliders in next to next to leading order QCD”,  Phys. Rev. Lett. 113 (2014) 212001, 10.1103/PhysRevLett.113.212001, arXiv:1408.5243.
  * [48] M. Czakon and A. Mitov, “Top++: A program for the calculation of the top-pair cross-section at hadron colliders”,  Comput. Phys. Commun. 185 (2014) 2930, 10.1016/j.cpc.2014.06.021, arXiv:1112.5675.
  * [49] M. Aliev et al., “HATHOR: HAdronic Top and Heavy quarks crOss section calculatoR”,  Comput. Phys. Commun. 182 (2011) 1034, 10.1016/j.cpc.2010.12.040, arXiv:1007.1327.
  * [50] P. Kant et al., “HATHOR for single top-quark production: Updated predictions and uncertainty estimates for single top-quark production in hadronic collisions”,  Comput. Phys. Commun. 191 (2015) 74, 10.1016/j.cpc.2015.02.001, arXiv:1406.4403.
  * [51] LHC Higgs Cross Section Working Group, “Handbook of LHC Higgs cross sections: 4. deciphering the nature of the Higgs sector”,  CERN (2016) 10.23731/CYRM-2017-002, arXiv:1610.07922.
  * [52] S. Frixione et al., “Single-top hadroproduction in association with a W boson”,  JHEP 07 (2008) 029, 10.1088/1126-6708/2008/07/029, arXiv:0805.3067.
  * [53] CMS Collaboration, “Performance of the reconstruction and identification of high-momentum muons in proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  JINST 15 (2020) P02027, 10.1088/1748-0221/15/02/P02027, arXiv:1912.03516.
  * [54] CMS Collaboration, “Search for high-mass resonances in dilepton final states in proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  JHEP 06 (2018) 120, 10.1007/JHEP06(2018)120, arXiv:1803.06292.
  * [55] J. Butterworth et al., “PDF4LHC recommendations for LHC Run II”,  J. Phys. G 43 (2016) 023001, 10.1088/0954-3899/43/2/023001, arXiv:1510.03865.
  * [56] CMS Collaboration, “CMS luminosity measurements for the 2016 data taking period”, CMS Physics Analysis Summary CMS-PAS-LUM-17-001, 2017.
  * [57] G. Altarelli, B. Mele, and M. Ruiz-Altaba, “Searching for new heavy vector bosons in $p\bar{p}$ colliders”,  Z. Phys. C 45 (1989) 109, 10.1007/BF01556677. [Erratum: 10.1007/BF01552335].
  * [58] CMS Collaboration, “Search for high-mass resonances in final states with a lepton and missing transverse momentum at $\sqrt{s}=13$ TeV”,  JHEP 06 (2018) 128, 10.1007/JHEP06(2018)128, arXiv:1803.11133.
  * [59] G. ’t Hooft, “Symmetry breaking through Bell-Jackiw anomalies”,  Phys. Rev. Lett. 37 (1976) 8, 10.1103/PhysRevLett.37.8.
  * [60] F. R. Klinkhamer and N. S. Manton, “A saddle-point solution in the Weinberg-Salam theory”,  Phys. Rev. D 30 (1984) 2212, 10.1103/PhysRevD.30.2212.
  * [61] A. Ringwald, “High-energy breakdown of perturbation theory in the electroweak instanton sector”,  Nucl. Phys. B 330 (1990) 1, 10.1016/0550-3213(90)90300-3.
  * [62] J. Ellis and K. Sakurai, “Search for sphalerons in proton-proton collisions”,  JHEP 04 (2016) 086, 10.1007/JHEP04(2016)086, arXiv:1601.03654.
  * [63] M. Trodden, “Electroweak baryogenesis”,  Rev. Mod. Phys. 71 (1999) 1463, 10.1103/RevModPhys.71.1463, arXiv:hep-ph/9803479.
  * [64] CMS Collaboration, “Search for black holes and sphalerons in high-multiplicity final states in proton-proton collisions at $\sqrt{s}=13$ TeV”,  JHEP 11 (2018) 042, 10.1007/JHEP11(2018)042, arXiv:1805.06013.
  * [65] C. Bravo and J. Hauser, “BaryoGEN, a Monte Carlo generator for sphaleron-like transitions in proton-proton collisions”,  JHEP 11 (2018) 041, 10.1007/JHEP11(2018)041, arXiv:1805.02786.
  * [66] H.-L. Lai et al., “New parton distributions for collider physics”,  Phys. Rev. D 82 (2010) 074024, 10.1103/PhysRevD.82.074024, arXiv:1007.2241.
  * [67] CMS Collaboration, “Search for Z$\gamma$ resonances using leptonic and hadronic final states in proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  JHEP 09 (2018) 148, 10.1007/JHEP09(2018)148, arXiv:1712.03143.

##  .10 Total event yield distributions by object group for exclusive event
classes

10.1.Figure: Overview of total event yields for the event classes of the
single-electron (upper) and single-muon (lower) object groups. Measured data
are shown as black markers, contributions from SM processes are represented by
coloured histograms, and the shaded region represents the uncertainty in the
SM background. The numbers above the plot indicate the observed
$p\text{-value}$ for the agreement of data and simulation.

10.2.Figure: Overview of total event yields for the event classes of the
double-muon (upper) and the electron $+$ muon (lower) object groups. Measured
data are shown as black markers, contributions from SM processes are
represented by coloured histograms, and the shaded region represents the
uncertainty in the SM background. The numbers above the plot indicate the
observed $p\text{-value}$ for the agreement of data and simulation.

10.3.Figure: Overview of total event yields for the event classes of the
single-electron $+$ (upper) and the single-electron $+$ single-muon $+$
(lower) object groups. Measured data are shown as black markers, contributions
from SM processes are represented by coloured histograms, and the shaded
region represents the uncertainty in the SM background. The numbers above the
plot indicate the observed $p\text{-value}$ for the agreement of data and
simulation.

10.4.Figure: Overview of total event yields for the event classes of the
single-electron $+$ photons (upper) and the single-muon $+$ photons (lower)
object groups. Measured data are shown as black markers, contributions from SM
processes are represented by coloured histograms, and the shaded region
represents the uncertainty in the SM background. The numbers above the plot
indicate the observed $p\text{-value}$ for the agreement of data and
simulation.

10.5.Figure: Overview of total event yields for the event classes of the
double-electron $+$ (upper) and the double-muon $+$ (lower) object groups.
Measured data are shown as black markers, contributions from SM processes are
represented by coloured histograms, and the shaded region represents the
uncertainty in the SM background. The numbers above the plot indicate the
observed $p\text{-value}$ for the agreement of data and simulation.

10.6.Figure: Overview of total event yields for the event classes of the
single-electron $+$ photons $+$ (upper) and the single-muon $+$ photons $+$
(lower) object groups. Measured data are shown as black markers, contributions
from SM processes are represented by coloured histograms, and the shaded
region represents the uncertainty in the SM background. The numbers above the
plot indicate the observed $p\text{-value}$ for the agreement of data and
simulation.

10.7.Figure: Overview of total event yields for the event classes of the
three-lepton object groups with same flavour (upper) and different flavour
(lower). Measured data are shown as black markers, contributions from SM
processes are represented by coloured histograms, and the shaded region
represents the uncertainty in the SM background. The numbers above the plot
indicate the observed $p\text{-value}$ for the agreement of data and
simulation.

10.8.Figure: Overview of total event yields for the event classes of the
three-lepton (same flavour) $+$ object group (upper), and the three-lepton
(different flavour) $+$ object group (lower). Measured data are shown as black
markers, contributions from SM processes are represented by coloured
histograms, and the shaded region represents the uncertainty in the SM
background. The numbers above the plot indicate the observed $p\text{-value}$
for the agreement of data and simulation.

10.9.Figure: Overview of total event yields for the event classes of the
${\geq}4$ leptons object group (upper), and the ${\geq}4$ leptons $+$ object
group (lower). Measured data are shown as black markers, contributions from SM
processes are represented by coloured histograms, and the shaded region
represents the uncertainty in the SM background. The numbers above the plot
indicate the observed $p\text{-value}$ for the agreement of data and
simulation.

10.10.Figure: Overview of total event yields for the event classes of the $>1$
lepton $+$ photons object group (upper), and the electron $+$ muon $+$ photons
$+$ object group (lower). Measured data are shown as black markers,
contributions from SM processes are represented by coloured histograms, and
the shaded region represents the uncertainty in the SM background. The numbers
above the plot indicate the observed $p\text{-value}$ for the agreement of
data and simulation.

##  .11 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, 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, 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, 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. Elgammal14, A. Ellithi Kamel15 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
C. Amendola, M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F.
Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
B. Lenzi, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin, A. Savoy-
Navarro16, M. Titov, G.B. Yu Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole
Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
S. Ahuja, F. Beaudette, M. Bonanomi, A. Buchot Perraguin, P. Busson, C.
Charlot, O. Davignon, B. Diab, G. Falmagne, R. Granier de Cassagnac, A.
Hakimi, I. Kucher, A. Lobanov, C. Martin Perez, M. Nguyen, C. Ochando, P.
Paganini, J. Rembser, R. Salerno, J.B. Sauvan, Y. Sirois, A. Zabi, A. Zghiche
Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France
J.-L. Agram17, J. Andrea, D. Bloch, G. Bourgatte, J.-M. Brom, E.C. Chabert, C.
Collard, J.-C. Fontaine17, 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. Toriashvili18, 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, L. Vigilante, S. Wiedenbeck, S. Zaleski RWTH Aachen
University, III. Physikalisches Institut B, Aachen, Germany
C. Dziwok, G. Flügge, W. Haj Ahmad19, O. Hlushchenko, T. Kress, A. Nowack, C.
Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl20, 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. Borras21, 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, L.I. Estevez Banos, E. Gallo22, A. Geiser,
A. Giraldi, A. Grohsjean, M. Guthoff, A. Harb, A. Jafari23, N.Z. Jomhari, H.
Jung, A. Kasem21, M. Kasemann, H. Kaveh, C. Kleinwort, J. Knolle, D. Krücker,
W. Lange, T. Lenz, J. Lidrych, K. Lipka, W. Lohmann24, 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, A. Singh, R.E. Sosa Ricardo, N. Tonon, O. Turkot, A.
Vagnerini, M. Van De Klundert, R. Walsh, D. Walter, Y. Wen, K. Wichmann, C.
Wissing, S. Wuchterl, O. Zenaiev, R. Zlebcik University of Hamburg, Hamburg,
Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, K. De Leo, T. Dreyer, A.
Ebrahimi, M. Eich, F. Feindt, A. Fröhlich, C. Garbers, E. Garutti, P.
Gunnellini, J. Haller, A. Hinzmann, A. Karavdina, G. Kasieczka, R. Klanner, R.
Kogler, V. Kutzner, J. Lange, T. Lange, A. Malara, C.E.N. Niemeyer, A.
Nigamova, K.J. Pena Rodriguez, O. Rieger, P. Schleper, 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. Hartmann20, C. Heidecker, U. Husemann, M.A. Iqbal, I.
Katkov25, P. Keicher, R. Koppenhöfer, S. Maier, M. Metzler, S. Mitra, 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, 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. Bartók26, R. Chudasama, M. Csanad, M.M.A. Gadallah27, S. Lökös28, 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. Horvath29, F. Sikler, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary
S. Czellar, J. Karancsi26, 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. Bahinipati30, D. Dash, C. Kar, P. Mal, T. Mishra, V.K. Muraleedharan Nair
Bindhu, A. Nayak31, D.K. Sahoo30, N. Sur, S.K. Swain Panjab University,
Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, N. Dhingra32, R. Gupta, 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. Bharti33, R. Bhattacharya, S. Bhattacharya, D. Bhowmik, S. Dutta, S. Ghosh,
B. Gomber34, M. Maity35, S. Nandan, P. Palit, A. Purohit, P.K. Rout, G. Saha,
S. Sarkar, M. Sharan, B. Singh33, S. Thakur33 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. Kumar, K. Naskar36, 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. Bakhshiansohi37 Institute for Research in Fundamental Sciences (IPM),
Tehran, Iran
S. Chenarani38, S.M. Etesami, M. Khakzad, M. Mohammadi Najafabadi University
College Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, R. Alya,b,39, 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, A. Ranieria, G. Selvaggia,b, L.
Silvestrisa, F.M. Simonea,b, R. Vendittia, P. Verwilligena INFN Sezione di
Bologna a, Università di Bologna b, Bologna, Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia,b, S. Braibant-
Giacomellia,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
C. Cioccaa, M. Cuffiania,b, G.M. Dallavallea, T. Diotalevia,b, F. Fabbria, A.
Fanfania,b, E. Fontanesia,b, P. Giacomellia, L. Giommia,b, C. Grandia, L.
Guiduccia,b, F. Iemmia,b, S. Lo Meoa,40, 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,41, S. Costaa,b, A. Di Mattiaa, R. Potenzaa,b, A. Tricomia,b,41,
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,20,
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,20, 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,20, P. Paoluccia,20, 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, M. Margonia,b, A.T. Meneguzzoa,b, M. Presillab,
P. Ronchesea,b, R. Rossina,b, G. Strong, A. Tikoa, M. Tosia,b, H. YARARa,b, M.
Zanettia,b, P. Zottoa,b, A. Zucchettaa,b, G. Zumerlea,b INFN Sezione di Pavia
a, Università di Pavia b, Pavia, Italy
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, B.C. Radburn-Smith, S. Sekmen, Y.C. Yang Chonnam National
University, Institute for Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon Hanyang University, Seoul, Korea
B. Francois, T.J. Kim, J. Park Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, 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, 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, Y. Lee, I. Yu College of Engineering and
Technology, American University of the Middle East (AUM), Kuwait
Y. Maghrbi Riga Technical University, Riga, Latvia
V. Veckalns42 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 Cruz43, 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, 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. Byszuk44, 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, P. Faccioli, M. Gallinaro, J. Hollar, N.
Leonardo, T. Niknejad, J. Seixas, K. Shchelina, O. Toldaiev, J. Varela Joint
Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, P. Bunin, I. Golutvin, I. Gorbunov, A. Kamenev, V. Karjavine, I.
Kashunin, A. Lanev, A. Malakhov, V. Matveev45,46, V.V. Mitsyn, P. Moisenz, V.
Palichik, V. Perelygin, M. Savina, S. Shmatov, S. Shulha, V. Smirnov, O.
Teryaev, V. Trofimov, N. Voytishin, B.S. Yuldashev47, A. Zarubin Petersburg
Nuclear Physics Institute, Gatchina (St. Petersburg), Russia