TAU-20-001

TAU-20-001

[inst1]CMS Collaboration

# Identification of hadronic tau lepton decays using a deep neural network

###### Abstract

A new algorithm is presented to discriminate reconstructed hadronic decays of
tau leptons () that originate from genuine tau leptons in the CMS detector
against candidates that originate from quark or gluon jets, electrons, or
muons. The algorithm inputs information from all reconstructed particles in
the vicinity of a candidate and employs a deep neural network with
convolutional layers to efficiently process the inputs. This algorithm leads
to a significantly improved performance compared with the previously used one.
For example, the efficiency for a genuine to pass the discriminator against
jets increases by 10–30% for a given efficiency for quark and gluon jets.
Furthermore, a more efficient reconstruction is introduced that incorporates
additional hadronic decay modes. The superior performance of the new algorithm
to discriminate against jets, electrons, and muons and the improved
reconstruction method are validated with LHC proton-proton collision data at
$\sqrt{s}=13\TeV$.

##  0.1 Introduction

Tau leptons play a crucial role in determining the nature of the 125Higgs
boson [1, 2, 3, 4, 5], in searching for additional Higgs bosons [6, 7, 8, 9,
10, 11, 12, 13], and in searching for other new particles [14, 15, 16]. Since
tau leptons most frequently decay to hadrons and neutrinos, all these analyses
require the efficient reconstruction and identification of hadronic tau lepton
decays (). In this paper, we introduce a new algorithm to identify candidates
coming from hadronic tau lepton decays in the CMS detector and measure its
performance with collision data from the CERN LHC. The new algorithm is based
on a deep neural network (DNN) that significantly improves the performance of
the identification. Furthermore, we discuss an optimized reconstruction
algorithm that includes additional decay modes.

Hadronic tau lepton decays are reconstructed and identified as follows. First,
candidates are reconstructed in one of their decay modes with the “hadrons-
plus-strips” algorithm [17, 18, 19], which reconstructs its decay mode and the
visible four-momentum, the four-momentum of all decay products except for the
neutrinos. Second, the candidates are identified as coming from genuine decays
rather than from jets, electrons, and muons. Quark and gluon jets often
constitute the most important source of background since they are produced
abundantly in proton-proton ($\Pp\Pp$) collisions at the LHC.

Previously, candidates were reconstructed only in their main decay modes: one-
prong decays with and without $\Pgpz$s, and three-prong decays without
$\Pgpz$s. Now, we also consider three-prong decays with $\Pgpz$s, which
account for 7.4% of all decays. Furthermore, three-prong decays are recovered
in which one of the charged hadrons is either not reconstructed as a charged
hadron or not even as a track.

The identification of candidates in CMS previously proceeded with separate
discriminators against jets, electrons, and muons [18, 19]. The discriminators
against jets and electrons combined information from high-level input
variables (derived quantities, such as the transverse momentum sum of
particles near the axis) using a multivariate analysis (MVA) classifier based
on an ensemble of boosted decision trees. A similar approach is used for
identification by the ATLAS Collaboration [20, 21]. Our previous discriminator
against muons was based on explicit thresholds of a few discriminating
observables. Here, we introduce a new identification algorithm, DeepTau, based
on a DNN that simultaneously discriminates against jets, electrons, and muons.
The DNN uses a combination of high-level inputs, similar to previous
algorithms, and information from all reconstructed particles in the vicinity
of the candidate. The information from all reconstructed particles near the
axis is processed with convolutional layers [22, 23, 24] in pseudorapidity-
azimuth ($\eta$-$\phi$) space. Convolutional layers have been developed in the
context of image recognition and are based on the premise that a fragment of
the input, an image, can be processed independently of its position,
exploiting the translational invariance of the problem. In a given physics
analysis, the superior performance of such an algorithm leads to an increase
of the signal efficiency for a given target background rate from jets,
electrons, or muons misidentified as , which typically translates directly
into superior sensitivity or precision of the analysis.

Similar approaches have been proposed and employed in the context of
reconstruction and identification as well as in the classification of jets.
Identification algorithms for using shallow neural networks were studied at
LEP [25]. The ATLAS Collaboration developed an algorithm to identify visible
decay products with a recurrent DNN [26]. The ATLAS and CMS Collaborations
also use DNNs to identify jets as coming from decays of quarks [27, 28, 29,
30]. In particular, the DeepJet algorithm [30] employs multiclass
classification to identify jets as coming from gluons, leptons, quarks,
quarks, or light quarks. Convolutional layers have been used in algorithms
that identify large-radius jets as originating from the decays of high-heavy
particles, in the DeepAK8 algorithm that identifies jets with $\pt>200\GeV$ as
coming from decays of top quarks or decays of , , or Higgs bosons [31].

This paper is structured as follows. After an overview of the CMS detector and
the event reconstruction in Section 0.2, the recent advances in the
reconstruction are discussed in Section 0.3. Section 0.4 describes the new
identification algorithm based on neural networks and gives an overview of its
performance with simulated events. The performance of the algorithm with LHC
collision events is then discussed in Section 0.5. The paper is summarized in
Section 0.6. Tabulated results are provided in the HEPData record for this
analysis [32].

##  0.2 The CMS detector

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. Within the solenoid
volume are a silicon pixel and strip tracker, a lead tungstate crystal
electromagnetic calorimeter (ECAL), and a brass and scintillator hadron
calorimeter (HCAL), each composed of a barrel and two endcap sections. Forward
calorimeters extend the $\eta$ coverage provided by the barrel and endcap
detectors. Muons are detected in gas-ionization chambers embedded in the steel
flux-return yoke outside the solenoid. A more detailed description of the CMS
detector, together with a definition of the coordinate system used and the
relevant kinematic variables, is reported in Ref. [33].

###  0.2.1 Event reconstruction

The particle-flow (PF) algorithm [34] reconstructs and identifies each
individual particle in an event, with an optimized combination of information
from the various elements of the CMS detector. The energy of photons is
obtained from the ECAL measurement. The energy of electrons is determined from
a combination of the electron momentum at the primary interaction vertex, as
determined by the tracker, the energy of the corresponding ECAL cluster, and
the energy sum of all bremsstrahlung photons spatially compatible with
originating from the electron track. The energy of muons is obtained from the
curvature of the corresponding track. The energy of charged hadrons is
determined from a combination of their momentum measured in the tracker and
the matching ECAL and HCAL energy deposits, corrected 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.

For each event, hadronic jets are clustered from all the PF candidates using
the infrared- and collinear-safe anti-algorithm [35, 36] with a distance
parameter of 0.4. Jet momentum is determined as the vectorial sum of all
particle momenta in the jet, and the simulation is, 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,
increasing the apparent jet momentum. To mitigate this effect, charged
particles identified as originating from pileup vertices are discarded and an
offset correction is applied to correct for remaining contributions [37]. Jet
energy corrections are derived from simulation to bring the measured response
of jets to that of particle-level jets on average. In situ measurements of the
momentum balance in dijet, photon+jet, $\PZ$+jet, and multijet events are used
to correct any residual differences in the jet energy scale between data and
simulation [38]. Additional selection criteria are applied to each jet to
remove jets potentially dominated by instrumental effects or reconstruction
failures [37].

The missing transverse momentum vector is computed as the negative vector sum
of the transverse momenta of all the PF candidates in an event, and its
magnitude is denoted as [39]. The is modified to include corrections to the
energy scale of the reconstructed jets in the event.

The candidate vertex with the largest value of summed physics-object $\pt^{2}$
is the primary $\Pp\Pp$ interaction vertex. The physics objects specifically
used for this purpose are jets, clustered using the jet finding algorithm [35,
36] 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, which include tracks from leptons.

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 resolution for muons with $\pt<100\GeV$ of 1% in the barrel and 3%
in the endcaps. The resolution in the barrel is better than 7% for muons with
$\pt<1\TeV$ [40].

The electron momentum is estimated by combining the energy measurement in the
ECAL with the momentum measurement in the tracker. 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 [41].

Events of interest are selected using a two-tiered trigger system. 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 fixed latency of about 4 [42]. The second level, known as the high-
level trigger, consists of a farm of processors running a version of the full
event reconstruction software optimized for fast processing, and reduces the
event rate to around 1kHz before data storage [43].

###  0.2.2 Simulated event samples

Monte Carlo simulated event samples are used to optimize the reconstruction,
as input for the training of the neural network, and to validate the
performance of the reconstruction and identification. Unless explicitly
mentioned, the simulated event samples include decays to all leptons (), to
electrons, muons, and tau leptons. The samples listed in the following are
produced separately for conditions corresponding to the 2016, 2017, and 2018
data-taking periods; a range of versions of the different generators is used
as specified below.

Events from the production of $\PZ/\gamma^{*}$ and bosons in association with
jets ($\PZ$+jets and $\PW$+jets) are generated with v2.2.2 or v2.4.2 [44] at
leading order (LO) in perturbative quantum chromodynamics (QCD) with the MLM
jet merging scheme [45]. A separate $\PZ$+jets sample for the training, as
well as diboson event samples ($\PW\PW$, $\PW\PZ$, and $\PZ\PZ$), are
generated with at next-to-leading order (NLO) in perturbative QCD with the
FxFx merging scheme [46]. Events from top quark-antiquark pair () and single
top quark production are generated with v2.0 at NLO in perturbative QCD [47,
48, 49, 50, 51, 52]. Events from multijet production via the strong
interaction, referred to as QCD multijet production, are generated at LO using
8.223, 8.226, or 8.230 [53]. The event generator is also used to produce heavy
additional gauge boson event samples at LO, $\zp\to\Pell\Pell$, with $m(\zp)$
ranging from 1 to 5. The generation of 125Higgs boson () events via gluon
fusion at NLO with decays to tau leptons ($\PH\to\Pgt\Pgt$) is also performed
with the 2.0 generator [54]. The NNPDF 3.0 or 3.1 parton distribution
functions are used as input in all the calculations [55, 56].

The program is used to simulate tau lepton decays, parton showering,
hadronization, and multiparton interactions, with version 8.223, 8.226, or
8.230 and tune CUETP8M1 or CP5 [57]. For the $\zp\to\Pell\Pell$ samples, is
used instead to simulate tau lepton decays [58]. Pileup interactions are
generated with and are overlaid on all simulated events according to the
luminosity profile of the considered data. All generated events are passed
through a detailed simulation of the CMS detector with [59]. The simulated and
recorded events are reconstructed with the same CMS reconstruction software.

##  0.3 The reconstruction

The reconstruction of candidates is carried out with the hadrons-plus-strips
algorithm. The reconstruction proceeds in four steps, as summarized in the
following and described in detail in Refs. [17, 18, 19].

First, seed regions are defined, with the goal of reconstructing one candidate
per seed region. Each seed region is defined by a reconstructed hadronic jet.
The jets for the seeding are clustered from all particles reconstructed by the
PF algorithm using the anti-algorithm with a distance parameter of $R=0.4$.
All particles in an $\eta$-$\phi$ cone of radius $\Delta
R\equiv\sqrt{\smash[b]{\Delta\eta\,^{2}+\Delta\phi\,^{2}}}=0.5$ around the jet
axis are considered for the next steps of the reconstruction.

Second, candidates are reconstructed using “strips” in $\eta$-$\phi$ space in
which the four-momenta of electrons and photons are added, and charged-hadron
($\mathrm{h}^{\pm}$) candidates are selected using charged particles from the
PF algorithm as input.

Third, all possible candidates are reconstructed in a number of decay modes
from the reconstructed charged hadrons and strips. An overview of the decay
modes and their branching fractions is given in Table 0.3. There are seven
different reconstructed decay modes, including three new ones (with respect to
the algorithm documented in Ref. [19]) that target
$\Pgt^{-}\to\mathrm{h}^{-}\mathrm{h}^{+}\mathrm{h}^{-}\Pgpz\Pgngt$ decays and
decays with three charged hadrons of which one is not reconstructed as a
charged particle:

  1. 1.

$\mathrm{h}^{\pm}$, targeting $\Pgt^{-}\to\mathrm{h}^{-}\Pgngt$ decays
(charge-conjugate decays are implied);

  2. 2.

$\mathrm{h}^{\pm}\Pgpz$, targeting $\Pgt^{-}\to\mathrm{h}^{-}\Pgpz\Pgngt$
decays;

  3. 3.

$\mathrm{h}^{\pm}\Pgpz\Pgpz$, targeting
$\Pgt^{-}\to\mathrm{h}^{-}\Pgpz\Pgpz\Pgngt$ decays;

  4. 4.

$\mathrm{h}^{\pm}\mathrm{h}^{\mp}\mathrm{h}^{\pm}$, targeting
$\Pgt^{-}\to\mathrm{h}^{-}\mathrm{h}^{+}\mathrm{h}^{-}\Pgngt$ decays;

  5. 5.

$\mathrm{h}^{\pm}\mathrm{h}^{\mp}\mathrm{h}^{\pm}\Pgpz$ (new), targeting
$\Pgt^{-}\to\mathrm{h}^{-}\mathrm{h}^{+}\mathrm{h}^{-}\Pgpz\Pgngt$ decays;

  6. 6.

$\mathrm{h}^{\pm}\mathrm{h}^{\pm/\mp}$ (new), targeting
$\Pgt^{-}\to\mathrm{h}^{-}\mathrm{h}^{+}\mathrm{h}^{-}\Pgngt$ decays;

  7. 7.

$\mathrm{h}^{\pm}\mathrm{h}^{\pm/\mp}\Pgpz$ (new), targeting
$\Pgt^{-}\to\mathrm{h}^{-}\mathrm{h}^{+}\mathrm{h}^{-}\Pgpz\Pgngt$ decays.

[Tau lepton decays and their branching fractions.] Decays of leptons and their
branching fractions ($\mathcal{B}$) in % [60]. The known intermediate
resonances of all the listed hadrons are indicated where appropriate. Charged
hadrons are denoted by the symbol $\mathrm{h}^{\pm}$. Although only $\Pgt^{-}$
decays are shown, the decays and values of the branching fractions are
identical for charge-conjugate decays.  Decay mode Resonance $\mathcal{B}$ (%)
Leptonic decays 35.2 $\Pgt^{-}\to\Pem\Pagne\Pgngt$ 17.8
$\Pgt^{-}\to\Pgmm\Pagngm\Pgngt$ 17.4 Hadronic decays 64.8
$\Pgt^{-}\to\mathrm{h}^{-}\Pgngt$ 11.5 $\Pgt^{-}\to\mathrm{h}^{-}\Pgpz\Pgngt$
25.9 $\Pgt^{-}\to\mathrm{h}^{-}\Pgpz\Pgpz\Pgngt$ 9.5
$\Pgt^{-}\to\mathrm{h}^{-}\mathrm{h}^{+}\mathrm{h}^{-}\Pgngt$ 9.8
$\Pgt^{-}\to\mathrm{h}^{-}\mathrm{h}^{+}\mathrm{h}^{-}\Pgpz\Pgngt$ 4.8 Other
3.3

Fourth, a single candidate is chosen among all possible reconstructed
candidates within a seed region. Reconstructed candidates are subject to the
following constraints:

  * •

The mass of the reconstructed candidate is required to be loosely compatible
with: (i) the resonance if reconstructed in the $\mathrm{h}^{\pm}\Pgpz$ mode;
(ii) the resonance if reconstructed in either of the
$\mathrm{h}^{\pm}\Pgpz\Pgpz$ and
$\mathrm{h}^{\pm}\mathrm{h}^{\mp}\mathrm{h}^{\pm}$ modes [19]; or (iii) the
expected mass spectrum including the resonance if reconstructed in the
$\mathrm{h}^{\pm}\mathrm{h}^{\mp}\mathrm{h}^{\pm}\Pgpz$ modes. The mass of all
$\mathrm{h}^{\pm}$ candidates is assumed to be the charged-pion mass, in line
with the output from the PF algorithm.

  * •

The charge needs to correspond to $\pm 1$, unless the candidate is
reconstructed in a mode with a missed charged hadron, in which case the charge
is set to correspond to the charge of the charged hadron with higher .

  * •

All reconstructed $\mathrm{h}^{\pm}$ and need to be in the signal cone,
defined with radius $\Delta R=3.0/\pt(\GeVns)$ (with $\Delta R$ limited to the
range 0.05–0.1) with respect to the momentum.

Among the selected candidates, the one with the highest is chosen. This
candidate can subsequently be discriminated against quark or gluon jets,
electrons, and muons.

Figure 1: Decay mode confusion matrix. For a given generated decay mode, the
fractions of reconstructed in different decay modes are given, as well as the
fraction of generated that are not reconstructed. Both the generated and
reconstructed need to fulfil $\pt>20\GeV$ and $\abs{\eta}<2.3$. The candidates
come from a $\PZ\to\Pgt\Pgt$ event sample with $m_{\Pgt\Pgt}>50\GeV$. Decay
modes with the same numbers of charged hadrons and one or two $\Pgpz$s are
combined and labelled as “$\Pgpz$s”.

A large fraction of decays are reconstructed in their targeted decay modes, as
inferred from Fig. 1, which shows the fractions of reconstructed decay modes
for generated with a given decay mode that fulfil $\pt>20\GeV$ and
$\abs{\eta}<2.3$. The fraction of generated not reconstructed in any of the
modes ranges from 11% for $\mathrm{h}^{\pm}$ to 25% for
$\mathrm{h}^{\pm}\Pgpz$. The overall reconstruction efficiency is mostly
limited by the ability to reconstruct tracks from charged hadrons of around
90% [34]. For the decay modes without missing charged hadrons, the charge
assignment is 99% correct for an average $\PZ\to\Pgt\Pgt$ event sample, 98%
for with $\pt\approx 200\GeV$, and 92% for with $\pt\approx 1\TeV$.

The decay modes with missing charged hadrons recover 19% of the
$\Pgt^{-}\to\mathrm{h}^{-}\mathrm{h}^{+}\mathrm{h}^{-}\Pgngt$ decays and 13%
of the $\Pgt^{-}\to\mathrm{h}^{-}\mathrm{h}^{+}\mathrm{h}^{-}\Pgpz\Pgngt$
decays. However, because of the missing charged hadron, the charge assignment
is only correct in ${\approx}$70% of the cases, as opposed to an average of
99% for the other decay modes. Since most physics analyses apply requirements
on the reconstructed charge to suppress background events, these decay modes
are only useful for analyses that are not limited by background events. None
of the current analyses within the CMS Collaboration fall into this category.
Although we include the decay modes with missing charged hadrons in the new
identification algorithm, most of the subsequent results are shown only for
reconstructed candidates in one of the other decay modes, unless explicitly
stated.

##  0.4 The identification with a deep neural network

The newly developed identification algorithm uses a deep neural network
structure. Its architecture is based on the following three premises:

  * •

Multiclass: Previously, separate dedicated algorithms were used to reject
electrons, muons, or quark and gluon jets reconstructed as a candidate, either
based on tree ensembles (jets and electrons) or on a number of selection
criteria (muons) [19]. Including electrons, muons, and jets in the same
algorithm is expected to both improve identification performance and to reduce
maintenance efforts.

  * •

Usage of lower-level information: The MVA discriminators used previously were
built on higher-level input variables and showed improved performance with
respect to cutoff-based criteria. However, jets hadronize and fragment in
complex patterns, and particles from concurrent interactions lead to similarly
complex detector patterns. We expect that a machine-learned algorithm, which
uses a sufficiently large data set for the training and is able to exploit
lower-level information, can lead to improved performance. Therefore,
information about all reconstructed particles near the candidate is directly
used as input to the algorithm.

  * •

Domain knowledge: Sets of handcrafted higher-level input variables used
previously are utilized as additional inputs to the information about the
single reconstructed particles. Although it should, in principle, be possible
to achieve the same performance with and without using these variables given a
sufficiently large set of events for the training and a suitable network
architecture, the usage of the higher-level inputs may reduce the number of
training events needed and improve the convergence of the training, as seen in
other studies [30].

Various network architectures were considered under the constraints that they
satisfy these premises with manageable complexity. In particular, the
algorithm must be trainable on the available hardware, and both its memory
footprint and evaluation time are subject to constraints when used in the CMS
reconstruction. To process the information on all reconstructed particles near
the axis, convolutional layers are employed. Compared with other approaches
like fully connected layers or graph-based structures, convolutional layers
have two main advantages: they have a smaller computational complexity and
they are implemented efficiently in current deep learning software. On the
other hand, they require a partitioning of inputs, in the case at hand a two-
dimensional one in $\eta$-$\phi$ space. As explained in the following, this
partitioning both leads to many empty input cells and cells in which
information is lost since multiple particles are found. The details of the
architecture and the inputs are given in the following.

###  0.4.1 Inputs

#### Particle-level inputs

Two grids are defined in $\eta$-$\phi$ space, centered around the axis, as
displayed in Fig. 2: an inner grid with $11{\times}11$ cells and a grid size
of $0.02{\times}0.02$, and an outer grid with $21{\times}21$ cells and grid
size of $0.05{\times}0.05$. These two grids overlap, the information in the
inner grid will also be part of the information that is processed by the outer
grid. For each grid cell, properties of contained reconstructed particles of
seven different types are used as inputs. If there is more than one particle
of a given type, only the highest particle is used as input. The various types
are the five kinds of particles reconstructed by the PF algorithm in the
central detector, muons, electrons, photons, charged hadrons, and neutral
hadrons. The muons and electrons are reconstructed by dedicated standalone
reconstruction algorithms, which provide more specific information to
distinguish them from the other particles. For each particle, various types of
information are taken into account, as listed in Table 0.4.1. The major
tradeoffs defining the grid size are the computational cost and the loss of
information when more than one particle of a specific type is present in a
grid cell. On average, 1.7% of the inner and 7.1% of the outer grid cells are
occupied. Up to approximately 10% of the occupied cells contain more than one
particle of a specific type.

Figure 2: Layout of the grids in $\eta$-$\phi$ space around the reconstructed
axis used to process the particle-level inputs for the convolutional layers of
the DNN. The inner grid comprises $11{\times}11$ cells with a grid size of
$0.02{\times}0.02$ and contains the signal cone with a radius of 0.05–0.1,
which is defined in the reconstruction (the charged hadrons and candidates
used to reconstruct the candidate need to be within the signal cone). For
high-quark and gluon jets, the finer grid is also able to resolve the dense
core of the jet. The outer grid comprises $21{\times}21$ cells with a grid
size of $0.05{\times}0.05$ and contains the isolation cone with a radius of
0.5 that is used to define higher-level observables that correlate with quark
or gluon jet activity.

The finer binning near the axis is motivated as follows: First, the decay
products occur predominantly in an $\eta$-$\phi$ cone with radius 0.1 around
the reconstructed axis. Second, high-jets are more collimated, so a finer grid
is needed to capture all particles within them.

[Per-particle input variables.] Input variables used for the various kinds of
particles that are contained in a given cell. For each type of particle, basic
kinematic quantities ($\pt,\eta,\phi$) are included but not listed below.
Similarly, the reconstructed charge is included for all charged particles. An
estimated per-particle probability for the particle to come from a pileup
interaction using the pileup identification (PUPPI) algorithm [61] is labelled
as PUPPI. A number of input variables that give the compatibility of the track
with the primary interaction vertex (PV) or a possible secondary vertex (SV)
from the reconstruction are denoted as “Track PV” and “Track SV”.  Particle
$N_{\text{var}}$ Inputs PF charged hadron 27 Track PV/SV/quality, PUPPI, HCAL
energy fraction PF neutral hadron 7 PUPPI, HCAL energy fraction Electron 37
Electron track quality, track/cluster matching, cluster shape PF electron 22
Track PV/SV/quality, PUPPI PF photon 23 Track PV/SV/quality, PUPPI Muon 37
Track quality, muon station hits, ECAL deposits PF muon 23 Track
PV/SV/quality, PUPPI

#### High-level inputs

The high-level input variables correspond primarily to those proven to be
useful in the previous MVA classifier [19]. These variables include: (i) the
four-momentum and charge; (ii) the number of charged and neutral particles
used to reconstruct the candidate; (iii) isolation variables; (iv) the
compatibility of the leading track with coming from the PV; (v) the properties
of a secondary vertex in case of a multiprong ; (vi) observables related to
the $\eta$ and $\phi$ distributions of energy reconstructed in the strips;
(vii) observables related to the compatibility of the candidate with being an
electron; and (viii) the estimated pileup density in the event. In all, there
are a total of 47 high-level input variables.

#### Transformations

Input variables that are either integer in nature or have a significantly
skewed distribution are modified with a linear transformation such that the
values lie in the intervals $[-1,1]$ or $[0,1]$. Most of the other input
variables are subject to a standardization procedure, $x\to(x-\mu)/\sigma$,
with $\mu$ denoting the mean of the distribution of $x$ and $\sigma$ its
standard deviation. The standardized inputs are truncated to $[-5,5]$ to
protect against outliers.

###  0.4.2 Architecture

The overall DNN architecture is visualized in Fig. 3. The goal of the DNN is
to use and process the inputs to optimally predict the target classes, which
correspond to determining whether the reconstructed originates from a genuine
, a muon, an electron, or a quark or gluon jet. Three different subnetworks
are created that independently process the inputs from the high-level
variables, the outer cells, and the inner cells. The outputs from these three
subnetworks are then concatenated and passed through four fully connected
layers with 200 nodes each and then to a final layer with four nodes that
yield outputs $x_{\alpha}$, with $\alpha\in\\{\text{jet},\Pgm,\Pe\\}$. A
softmax activation function [62],

$y_{\alpha}=\exp(x_{\alpha})/\Sigma_{\beta}\exp(x_{\beta}),$ (1)

is then applied to yield estimates $y_{\alpha}$ of the probabilities for the
candidate to come from each of the four target classes (, jet, , ).

Figure 3: The DNN architecture. The three sets of input variables (inner
cells, outer cells, and high-level features) are first processed separately
through different subnetworks, whose outputs are then concatenated and
processed through five fully connected layers before the output is calculated
that gives the probabilities for a candidate to be either a , an electron, a
muon, or a quark or gluon jet. The subnetwork for the high-level inputs
consists of three fully connected layers with decreasing numbers of nodes,
taking 47 inputs and yielding 57 outputs. The features of both the inner and
outer cells are input to complex subnetworks. In the first part, the
observables in each grid cell are processed through a set of fully connected
layers, first separately for electrons/photons (containing both the features
for PF electrons and electrons from the standalone reconstruction), muons
(similarly containing both features from PF and standalone muons), and
charged/neutral hadrons, passing through three fully connected layers each.
The outputs are concatenated and passed through four additional fully
connected layers, yielding 64 outputs for each cell. The grids are then
processed with convolutional layers, which successively reduce the size of the
grid. For the inner cells, there are hence 5 convolutional layers that reduce
the grid from $11{\times}11$ to a single cell; for the outer cells, there are
10 convolutional layers that reduce the grid from $21{\times}21$ to a single
cell. The numbers of trainable parameters (TP) for the different subnetworks
are also given for the different subnetworks.

The two subnetworks that process the inputs from the inner and outer cells
have similar structures. As explained above, the main idea is to process the
grids with convolutional layers [22, 23, 24]. In our case, the large number of
input parameters for each cell makes it computationally too expensive to
directly process the grid cells. To reduce the dimensionality, the inputs from
each cell of the inner and outer grid are first sent through a number of fully
connected layers. Since these layers act on single cells of the grids, with
the full grid later on processed with convolutional layers, these fully
connected layers can also be considered as one-dimensional convolutional
layers, and the nodes can be considered as filters. For each cell, inputs from
the electron, muon, and hadron blocks are first processed separately in three
fully connected layers with a decreasing number of nodes. The outputs from the
three separate processing blocks are then concatenated and passed through four
additional fully connected layers with a decreasing number of nodes. The 188
input parameters are reduced to 64 output parameters (filters) for each cell
that are input to the convolutional layers.

The convolutional layers each make use of 64 filters with size $3{\times}3$.
These filters reduce the size of the grid with each step by two in each
dimension since no padding is applied (the grid is not artificially extended
with additional cells beyond the dimension of the grid). For both the inner
cells with a dimension of $11{\times}11$ and the outer cells with a dimension
of $21{\times}21$, the convolutional layers are applied until the grid is
reduced to a single cell. As a consequence, there are 5 convolutional layers
for the inner cells and 10 for the outer cells. For both the inner and the
outer cells, the final single cell implies that there are 64 outputs.

The 47 high-level input variables are processed by three fully connected
layers with 113, 80, and 57 nodes, yielding 57 outputs. Taken together, the
three subnetworks yield 185 output parameters that are processed through a
number of fully connected layers as explained above.

After each of the fully connected and convolutional layers, batch
normalization [63] and dropout regularization [64, 65] with a dropout rate of
0.2 are applied. Furthermore, nonlinearity is introduced by applying the
parametric rectified linear unit activation function, which is given by
$\alpha x$ for $x<0$ and by $x$ for $x\geq 0$, with $\alpha$ being a trainable
parameter [66].

A few alternative approaches were tried. First, networks using only high-level
inputs and an overall lower number of trainable parameters were tested. These
showed significantly inferior performance. Second, alternative approaches were
tried that were based on the idea of having two-dimensional grids for the
particle-level inputs, but with different input cell sizes and with
differently organized convolutional layers, convolutional layers with padding
and pooling (combining the information from a number of nearby cells). The
chosen architecture yielded the best performance among the ones tested within
the predefined computing constraints.

###  0.4.3 Loss function and classifier training

The loss function is a sum of three different terms, given the four target
classes (jet, , , ):

  1. 1.

a regular cross-entropy term [62] for the target class,

  2. 2.

a term implementing focal loss [67] for the binary classification of against
all backgrounds combined, and

  3. 3.

another focal-loss term with three components for the classification as , , or
jet, each combined with a smoothened step function that is nonzero only for
candidates that are sufficiently likely to be classified as .

The introduction of the two focal-loss terms gives superior performance
compared with using cross-entropy only. It is motivated by two considerations.
First, analyses with candidates in the final state show the highest
sensitivity for efficiencies in the range 50–80%, whereas performance for the
highest efficiencies and purities is less important. Second, the
classification performance as is more important than the classification as , ,
or jet; particularly in the high-purity regime where it is less important to
distinguish between the three other classes. The full definition of the loss
function is given in Appendix .7.

For the training, events from the following simulated processes are used:
$\PZ$+jets (NLO), $\PW$+jets, , $\zp\to\Pgt\Pgt$, $\zp\to\Pe\Pe$,
$\zp\to\Pgm\Pgm$ (with $m(\zp)$ ranging from 1 to 5), and QCD multijet
production. For testing, additional event samples are used, including
$\PH\to\Pgt\Pgt$ and $\PZ$+jets (LO) event samples, and more event samples
corresponding to the processes used for the training. The event samples are
simulated and reconstructed according to the 2017 data-taking conditions. The
prospective candidates need to fulfil $\pt>20\GeV$ and $\abs{\eta}<2.3$. The
candidates are sampled from the various event samples such that the
contributions of the different classes (jet, , , ) and in different
$(\pt,\eta)$ bins are the same. Additional weights are added to to make the
distributions from the different classes uniform within each $(\pt,\eta)$ bin.
In total, around 140 million candidates are used for the training, whereas 10
million are assigned as the validation samples.

The loss function is minimized with Nesterov-accelerated adaptive momentum
estimation [68], which combines the _Adam_ algorithm [69] with Nesterov
momentum. The training setup uses the Keras library [70] with TensorFlow [71]
as backend. The training was carried out with a single GeForce RTX™ 2080
graphics processing unit. The training of the final classifier was run for 10
epochs, with the training for an epoch lasting 69 hours on average. The best
performance on the validation samples has been obtained after 7 epochs.

The final discriminators against jets, muons, and electrons are given by

$D_{\alpha}(\boldsymbol{y})=\frac{y_{\Pgt}}{y_{\Pgt}+y_{\alpha}},$ (2)

with $y_{\alpha}$ taken from Eq. (1) and $\alpha\in\\{\text{jet},\Pgm,\Pe\\}$.
In the following, these discriminators are labelled as DeepTau discriminators
against jets ($D_{\text{jet}}$), muons ($D_{\Pgm}$), and electrons
($D_{\Pe}$).

###  0.4.4 Expected performance

The performance of the DeepTau discriminators is evaluated with the validation
samples. Working points are defined to guide usage in physics analyses and to
derive suitable data-to-simulation corrections. The target efficiencies for
the various working points used in the wide range of physics analyses with tau
leptons are presented in Table 0.4.4. The target efficiencies are defined as
the efficiency for genuine in the $\PH\to\Pgt\Pgt$ event sample that are
reconstructed as candidates with $30<\pt<70\GeV$ to pass the given
discriminator. The target efficiencies range from 40 to 98% for
$D_{\text{jet}}$, from 99.5 to 99.95% for $D_{\Pgm}$, and from 60 to 99.5% for
$D_{\Pe}$.

Target identification efficiencies for the different working points defined
for the three different discriminators. The target efficiencies are evaluated
with the $\PH\to\Pgt\Pgt$ event sample for with $\pt\in[30,70]\GeV$.  VVTight
VTight Tight Medium Loose VLoose VVLoose VVVLoose $D_{\Pe}$ 60% 70% 80% 90%
95% 98% 99% 99.5% $D_{\Pgm}$ 99.5% 99.8% 99.9% 99.95% $D_{\text{jet}}$ 40% 50%
60% 70% 80% 90% 95% 98%

Figure 4: Efficiency for quark and gluon jets to pass different tau
identification discriminators versus the efficiency for genuine . The upper
two plots are obtained with jets from the $\PW$+jets simulated sample and the
lower two plots with jets from the sample. The left two plots include jets and
genuine with $\pt<100\GeV$, whereas the right two plots include those with
$\pt>100\GeV$. The working points are indicated as full circles. The
efficiency for jets from the $\PW$+jets event sample, enriched in quark jets,
to pass the discriminators is higher compared with jets from the event sample,
which has a larger fraction of gluon and -quark jets. The jet efficiency for a
given efficiency is larger for jets and with $\pt<100\GeV$ than for those with
$\pt>100\GeV$. Compared with the previously used MVA discriminator, the
DeepTau discriminator reduces the jet efficiency for a given efficiency by
consistently more than a factor of 1.8, and by more at high efficiency. The
additional gain at high comes from the inclusion of updated decay modes in the
reconstruction, as illustrated by the curves for the previously used MVA
discriminator but including reconstructed candidates with additional decay
modes.

In Fig. 4, the performance of the $D_{\text{jet}}$ discriminator is evaluated
by studying the efficiencies for quark and gluon jets and genuine to pass the
discriminator and comparing it with the previously used MVA classifier. All
generated and reconstructed candidates in this figure and those that follow
are required to pass $\pt>20\GeV$ and $\abs{\eta}<2.3$. For the previously
used MVA classifier, two curves are shown: The first curve corresponds to the
decay mode reconstruction as used in the previous publication and in previous
physics analyses, whereas the second curve includes the additional decay modes
introduced in Section 0.3, leading to an overall increase of the MVA
classifier performance, in particular for higher efficiency values. The
efficiency is evaluated separately for the $\PW$+jets and event samples. The
$\PW$+jets sample is enriched in quark jets, whereas the event sample has a
larger fraction of quark and gluon jets and has a busier event topology. The
efficiencies are also evaluated separately for candidates with $\pt<100\GeV$
and $\pt>100\GeV$ to test the performance in different regimes. For all
considered scenarios, the $D_{\text{jet}}$ discriminator shows a large
improvement of the signal efficiency at a given background efficiency compared
with the previously used MVA classifier. At a given efficiency for jets to
pass the discriminator, there is a relative increase in signal efficiency of
more than 30% for the tightest working points and more than 10% for the
loosest working points. The larger gain at high is a consequence of the
inclusion of additional decay modes in the reconstruction.

Figure 5: Efficiencies for simulated decays with $\abs{\eta}<2.3$ to pass the
following reconstruction and identification requirements: to be reconstructed
in any decay mode with $\pt>20\GeV$ and $\abs{\eta}<2.3$ (black dashed line),
to be reconstructed in a decay mode except for those with missing charged
hadrons (labelled “2-prong” and shown as full black line), and to be
reconstructed in a decay mode except the 2-prong ones and to pass the Loose,
Medium, or Tight working point of the $D_{\text{jet}}$ discriminator (blue
lines), obtained with a $\PZ\to\Pgt\Pgt$ event sample. The efficiencies are
shown as a function of the visible genuine obtained from simulated decay
products.

The dependence of the efficiency for genuine generated with $\pt>20\GeV$ to be
reconstructed with $\pt>20\GeV$ and to pass the $D_{\text{jet}}$ discriminator
is shown in Fig. 5. The reconstruction efficiency exceeds 80% for $\pt>30\GeV$
and is close to 90% for $\pt>100\GeV$, and is mostly limited by the charged-
hadron reconstruction efficiency. If decay modes with missing charged hadrons
(the so-called two-prong decay modes) are excluded, the efficiency is reduced
by around 10%. The efficiency to additionally pass either of the shown working
points of the $D_{\text{jet}}$ discriminator (for reconstructed candidates
without missing charged hadrons) is in line with the target efficiencies
listed in Table 0.4.4.

Figure 6: Efficiency for electrons against efficiency for genuine to pass the
MVA and $D_{\Pe}$ discriminators, separately for electrons and with
$20<\pt<100\GeV$ (left) and $\pt>100\GeV$ (right). Vertical bars correspond to
the statistical uncertainties. The candidates are reconstructed in one of the
decay modes without missing charged hadrons. Compared with the MVA
discriminator, the $D_{\Pe}$ discriminator reduces the electron efficiency by
more than a factor of two for a efficiency of 70% and by more than a factor of
10 for efficiencies larger than 88%. Furthermore, working points (indicated as
full circles) are now provided for previously inaccessible efficiencies larger
than 90%, for a misidentification efficiency between 0.3 and 8%.

The $D_{\Pe}$ discriminator leads to a significantly improved rejection of
electrons compared with the MVA discriminator, as can be inferred from Fig. 6.
The efficiencies for candidates and electrons to pass the two discriminators
are shown separately for and electrons with $20<\pt<100\GeV$ and
$\pt>100\GeV$. Within the range of applicability of the previously used MVA
discriminator, the $D_{\Pe}$ discriminator increases the efficiency by
consistently more than 10% for a constant misidentification probability. For
example, the efficiency increases from 87 to 99% for the loosest working point
of the two discriminators with an electron efficiency of around 7% for
$\pt<100\GeV$. Besides increasing the reach in identification efficiency, the
new discriminator also provides the possibility to reject electrons with an
efficiency of $10^{-4}$ for a efficiency of 55%. Similar, albeit slightly
smaller, gains are observed for $\pt>100\GeV$.

Figure 7: Efficiency for muons against efficiency for simulated to pass the
cutoff-based and $D_{\Pgm}$ discriminators, separately for muons and with
$20<\pt<100\GeV$ (left) and $\pt>100\GeV$ (right). The four working points are
indicated as full circles. Vertical bars correspond to the statistical
uncertainties. In both regimes, the $D_{\Pgm}$ discriminator rejects up to a
factor of 10 more muons at efficiencies around 99%, and it leads to an
increase of the efficiency for a similar background rejection by about 0.5%.

Gains are also observed in terms of the discrimination of candidates against
muons, as shown in Fig. 7. Compared with the cutoff-based discriminator, the
$D_{\Pgm}$ discriminator leads to an increase of the efficiency of around 0.5%
for a given prompt muon efficiency. For the efficiency range of 99.1–99.4%
addressed by the cutoff-based discriminator, the $D_{\Pgm}$ discriminator
leads to a factor of 3–10 larger prompt muon rejection, and thereby provides a
significantly improved rejection of prompt muons for analyses with
hadronically decaying tau leptons, while leading to only a percent-level loss
of identification efficiency.

The performance results discussed so far have been obtained with simulated
samples that are consistent with the 2017 data-taking conditions. Moreover,
after applying the discriminators to simulated samples that correspond to the
2016 and 2018 data-taking conditions, we see consistent performance, both in
absolute terms and relative to other discriminators. Based on these findings,
the discriminators $D_{\Pe}$, $D_{\Pgm}$, and $D_{\text{jet}}$ have been
recommended for all CMS analyses of LHC data at $\sqrt{s}=13\TeV$.

To summarize, the DeepTau discriminators against jets, electrons, and muons
lead to large gains in discrimination performance against various backgrounds.
The gains in efficiency are largest for jets and electrons, and amount to more
than 30% for jets and more than 10% for electrons for typical jet and electron
rejection criteria used in physics analyses. Therefore, a large gain in reach
is expected in physics analyses with tau leptons, in particular for final
states with two candidates. These improvements are confirmed by physics
analyses that already used the DeepTau discriminators [72, 73, 74].

##  0.5 Performance with $\sqrt{s}=13\TeV$ data

The performance of the reconstruction and of the DeepTau discriminators is
validated with collision data at $\sqrt{s}=13\TeV$. The total integrated
luminosity for the 2016–2018 data collected with the CMS detector and
validated for use in analysis amounts to 138, of which 36were recorded in
2016, 42in 2017, and 60in 2018 [75, 76, 77]. The reconstruction and
identification efficiencies for are measured separately for the three data-
taking years using a $\PZ\to\Pgt\Pgt$ event sample in the $\Pgm\tauh$ final
state, following methods similar to those established previously [19]. In
addition, measurements are made of the misidentification efficiency with which
jets, electrons, and muons are reconstructed and identified as candidates. All
of these measurements are essential ingredients for physics analyses that use
candidates.

In this paper, we present a subset of the measurements that were carried out,
mostly based on the largest data set recorded in 2018. The identification
efficiencies are generally measured for all working points introduced above.
We discuss a subset of measurements here only for representative working
points. Consistent results have been obtained for the other working points,
and for the 2016 and 2017 data sets. The full set of efficiency and energy
scale corrections together with their corresponding uncertainties is available
for data analyses in the CMS Collaboration.

###  0.5.1 Reconstruction and identification efficiency

The measurement of the reconstruction and identification efficiencies uses a
$\Pgm\tauh$ event sample that is selected in the same way as in the previous
measurement [19], with the following updates. Events are recorded with a
single-muon trigger with a nominal threshold of 24and are required to have at
least one reconstructed and isolated muon with $\pt>25\GeV$. The candidate is
required to be reconstructed with the decay mode finding algorithm, to have
$\pt>20\GeV$, and to pass a given threshold on the $D_{\text{jet}}$
discriminator. In addition, the candidate needs to pass the VVLoose working
point of the $D_{\Pe}$ discriminator and the Tight working point of the
$D_{\Pgm}$ discriminator, to reject background from muons or electrons
misidentified as a candidate. Additional criteria are applied to increase the
purity of events with genuine candidates, including a requirement on the
transverse mass of the muon and
($\sqrt{2\pt^{\mu}\ptmiss(1-\cos\Phi)}<60\GeV$, where $\Phi$ is the angle in
the transverse plane between the muon momentum and ) and a maximum difference
in $\eta$ between the reconstructed muon and the candidate
($\abs{\Delta\eta}<1.5$). In addition to the $\Pgm\tauh$ event sample, a
$\Pgm\Pgm$ event sample is defined to normalize the $\PZ\to\Pgt\Pgt$ event
yields. This event sample is subject to the same trigger and muon selection
criteria as the $\Pgm\tauh$ event sample, such that related uncertainties
partially cancel in the normalization scale factor.

The efficiencies are extracted from a maximum likelihood fit to the
distribution of the reconstructed visible invariant mass of the $\Pgm\tauh$
system, $m_{\text{vis}}$, and to the expected and observed event yields in the
$\Pgm\Pgm$ region. In the fit, the ratio of the observed and expected
efficiency for a genuine to pass the selection is incorporated as a free
parameter, such that the fit yields a scale factor w.r.t. the simulated
efficiency. All known sources of systematic uncertainties are incorporated
into the fit. Uncertainties include the uncertainty in the integrated
luminosity, uncertainties in the muon trigger, identification, and isolation
efficiencies, uncertainties due to the limited number of simulated events, and
uncertainties in the normalizations of , QCD multijet, and
$\PZ/\gamma^{*}$+jets background. Since the scale factors are extracted after
the full event selection, they incorporate any differences between data and
simulation of the full product of all reconstruction and identification
efficiencies. The scale factors are extracted from the maximum likelihood fit
together with their corresponding uncertainties using the asymptotic
properties of the likelihood function [78].

The scale factors are extracted in different bins, $\pt\in\\{[20,25]$,
$[25,30]$, $[30,35]$, $[35,40]$, $[40,50]$, $[50,70]$, ${>}70\GeV\\}$, to
account for a possible dependence of the extracted scale factors, which would
affect analyses that use different minimum thresholds or that analyze final
states from processes with different distributions. While analyses usually use
all reconstructed decay modes, except for the modes with missing charged
hadrons so a parametrization in terms of decay modes is not necessary,
analyses that select events using the di-trigger algorithms observe a
different proportion of candidates reconstructed in the various decay modes.
This is a consequence of the dependency of trigger efficiency on the various
decay modes. Therefore, scale factors are separately determined in four bins
of reconstructed decay modes ($\mathrm{h}^{\pm}$; $\mathrm{h}^{\pm}\Pgpz$ or
$\mathrm{h}^{\pm}\Pgpz\Pgpz$;
$\mathrm{h}^{\pm}\mathrm{h}^{\mp}\mathrm{h}^{\pm}$; and
$\mathrm{h}^{\pm}\mathrm{h}^{\mp}\mathrm{h}^{\pm}\Pgpz$). These scale factors
are shown in Fig. 8 for $\pt>40\GeV$, which corresponds to the minimum
recommended threshold in analyses that use events recorded with the di-
trigger.

Since the reach in is limited in the $\PZ\to\Pgt\Pgt$ sample, a separate off-
shell boson ($\PW^{*}$) event sample is used to measure additional scale
factors for $\pt>100\GeV$ in a -plus-final state without an additional
hadronic jet, following closely the method discussed in Ref. [19]. The
requirement of high and high combined with the hadronic jet veto leads to an
event sample dominated by off-shell $\PW^{*}\to\Pgt\Pgngt$ boson decays. The
events are recorded with a trigger that requires a large . The efficiencies
are obtained from a maximum likelihood fit to the transverse mass distribution
of the candidate and . To constrain the fiducial $\PW^{*}$ cross section in
the signal region, a $\PW^{*}\to\Pgm\Pgngm$ control region is also included in
the fit.

Figure 8: Data-to-simulation scale factors for genuine to be reconstructed as
candidates and to pass the Loose, Medium, and Tight working points of the
$D_{\text{jet}}$ discriminator as a function of the candidate (left). Vertical
bars correspond to the combined statistical and systematic uncertainties in
the individual scale factors. The red hatched bands indicate the uncertainties
for $\pt>40\GeV$, obtained from a combination of the individual measurements.
The right plot shows the data-to-simulation scale factors for the candidates
with $\pt>40\GeV$ to pass the Medium $D_{\text{jet}}$ working point as a
function of reconstructed decay mode. The efficiencies are obtained with 2018
data and the according simulated events using a likelihood fit to the
distribution of the reconstructed $m_{\text{vis}}(\Pgm,\tauh)$. The scale
factors are shown separately for data taken in 2016, 2017, and 2018 (and the
corresponding simulated events) and for the four main decay modes.

Figure 8 (left) shows the scale factors as a function of the reconstructed
candidate separately for the Loose, Medium, and Tight working points. The
plots also show the uncertainties for the candidates with $\pt>40\GeV$, which
are obtained from a constant fit up to values of 500, assuming there is no
statistically significant dependence of the scale factors. Beyond 500, the
uncertainties are enlarged because of the lack of candidates with such high
values, with the uncertainty at 1000twice as large as at 500.

The -dependent uncertainties in the scale factors range from 2 to 5%. For the
spectrum in the $\PZ\to\Pgt\Pgt$ sample, the combined scale factor uncertainty
amounts to ${\approx}$2%. These uncertainties are considerably smaller than in
previous measurements, which reported combined uncertainties of ${\approx}$6%
[19]. The previous measurements used the “tag-and-probe” technique [79] that
only measured the identification efficiency for the discriminator under
consideration and added additional uncertainties in the reconstruction
efficiency from independent measurements of single charged-hadron
reconstruction efficiencies. The measurement of the product of all
reconstruction and identification efficiencies with a maximum likelihood fit
hence represents another important improvement for physics analyses with final
states, as it leads to a considerable reduction of the systematic
uncertainties related to the reconstruction and identification.

The data-to-simulation scale factors as a function of the decay mode are shown
in Fig. 8 (right) for $\pt>40\GeV$ for the three data-taking years (2016,
2017, and 2018). The scale factors are generally a bit smaller than one but
consistent with unity within 10% (20% for the candidates reconstructed in the
$\mathrm{h}^{\pm}\mathrm{h}^{\mp}\mathrm{h}^{\pm}\Pgpz$ decay mode). The scale
factors below one can be traced back to different origins, including the
imperfect modelling of hadronization and an imperfect simulation of the
detector alignment and track hit reconstruction efficiencies.

###  0.5.2 The energy scale

The energy scale is obtained from the same $\Pgm\tauh$ event samples as the
scale factors discussed in the previous section, following the same method
described in previous publications [19]. For the main measurement discussed
here, candidates are required to pass the Medium working point of the
$D_{\text{jet}}$ discriminator. Consistent results are obtained with cross-
check measurements for various working points of the $D_{\text{jet}}$
discriminator. To improve the a priori data-simulation agreement, scale
factors as described in the previous section are already applied to simulated
samples with final states.

Two different observables are used to measure the energy scale: the
$\Pgm\tauh$ invariant mass ($m_{\text{vis}}$) and the reconstructed mass
($m(\tauh)$). For candidates reconstructed in the $\mathrm{h}^{\pm}$ decay
mode, $m(\tauh)$ is constant, so the measurement is only performed with the
$m_{\text{vis}}$ observable.

Simulated templates of the $m_{\text{vis}}$ and $m(\tauh)$ distributions are
created for energy scale shifts between $\pm 3\%$ in steps of 0.2%. For each
shift, a maximum likelihood fit is performed, using the combined expectation
from the $\PZ\to\Pgt\Pgt$ in the $\Pgm\tauh$ final state and the background
events, using the set of systematic uncertainties discussed above. The
observed value of the energy scale shift is obtained from the distribution of
the likelihood ratio together with the corresponding uncertainty.

Figure 9: Relative difference between energy obtained in data and simulated
events for the four main reconstructed decay modes for the 2018 data set. The
results are obtained from fits to the distribution of either the reconstructed
$m_{\text{vis}}(\Pgm,\tauh)$ (blue lines) or $m_{\tauh}$ (black lines). The
horizontal bars represent the uncertainties in the measurements. The measured
values are consistent with no shift of the energy scale between data and
simulation, with the largest difference amounting to 1.5 standard deviations.

The resulting relative differences between the energy scale in data and
simulation are shown in Fig. 9 separately for the two different measurements.
The relative difference is given as a correction, in percent, to a unitary
multiplicative factor applied to the four-momentum in the simulation. The
results obtained with the two methods are consistent with each other, and the
energy scale shifts are consistent with no data-simulation differences, with
the largest difference amounting to 1.5 standard deviations. The relative
uncertainties range from 0.6% for the $\mathrm{h}^{\pm}\Pgpz$ and
$\mathrm{h}^{\pm}\mathrm{h}^{\mp}\mathrm{h}^{\pm}$ decay modes to 0.8% for the
$\mathrm{h}^{\pm}$ mode.

Figure 10: Distribution of the reconstructed visible invariant mass of the
$\Pgm\tauh$ system, $m_{\text{vis}}(\Pgm,\tauh)$ (left) and of the visible
invariant mass (right). Vertical bars correspond to the statistical
uncertainties. The event selection corresponds to the one in the measurement
of the reconstruction and identification efficiencies and uses the Tight
working point of the $D_{\text{jet}}$ discriminator. The distributions
incorporate all measured scale factors and energy corrections and are scaled
to the outcome of a maximum likelihood to the observed data with the
$\PZ\to\Pgt\Pgt$ contribution freely floating. The electroweak background
combines contributions from single top quark, diboson, and $\PW$+jets
processes as well as $\PZ(\to\Pgt\Pgt)$+jets events where the reconstructed
originates from a jet misidentified as a candidate instead of one of the
produced tau leptons. In the $m(\tauh)$ distribution, the $\PZ\to\Pgt\Pgt$
contributions are shown separately for the different decay modes.

To validate the extracted scale factors and to illustrate the distributions
used for the extraction of the scale factors and the energy corrections,
distributions of $m_{\text{vis}}$ and $m(\tauh)$ are investigated, requiring
the to pass the Tight working point of the $D_{\text{jet}}$ discriminator
(Fig. 10). These distributions are obtained from a maximum likelihood fit to
the $\Pgm\Pgt$ data, applying the same selection used in the extraction of the
scale factors. This fit incorporates the derived scale factors and energy
corrections with their uncertainties, together with the full set of systematic
uncertainties used in the scale factor extractions. In the fit to data, the
$\PZ\to\Pgt\Pgt$ normalization is a freely floating parameter. Data and
predictions agree within the uncertainties, indicating that the derived scale
factors and energy corrections lead to a good and complete description of the
data, including the description of the different decay modes.

###  0.5.3 Lepton and jet misidentification efficiencies

The efficiencies for electrons, muons, and jets to pass the respective DeepTau
discriminators are measured with dedicated event samples, following closely
the measurements explained in detail in Ref. [19]. The efficiencies for
electrons and muons to be reconstructed and misidentified as candidates are
measured with $\PZ\to\Pell\Pell$ events ($\Pell\in\\{\Pe,\Pgm\\}$) where one
is misreconstructed as a candidate; the event is reconstructed as an
$\Pell\tauh$ final state. The efficiencies are extracted with the tag-and-
probe method, where candidates that pass a given working point of the
$D_{\Pgm}$ or $D_{\Pe}$ discriminator end up in the pass region and those that
do not pass in the fail region. A maximum likelihood fit is performed to
obtain the efficiencies. The distribution used in the pass region is the
invariant $\Pell\tauh$ mass ($m_{\text{vis}}$), whereas the fail region is
included as a single bin. Similar to the extraction of the efficiencies, all
relevant uncertainties are included in the maximum likelihood fit. To reject
background from jets reconstructed as candidates, the candidates are also
required to pass the Medium working point of the $D_{\text{jet}}$
discriminator. An additional parameter is introduced in the fit that scales
the contributions in the pass and fail regions simultaneously and corresponds
to the scale factor for the efficiency to pass the $D_{\text{jet}}$
discriminator. This approach is based on the assumption that the probability
for electrons or muons to pass the $D_{\text{jet}}$ discriminator is
independent of the probability to pass the $D_{\Pe}$ or $D_{\Pgm}$
discriminator. Another parameter is introduced for electrons misreconstructed
as candidates that allows for a shifted energy scale.

Figure 11: Observed and expected efficiencies for electrons to pass different
$D_{\Pe}$ working points. Vertical bars correspond to the combined statistical
and systematic uncertainties in the individual scale factors. The electrons
are required to be reconstructed as a candidate and to pass the medium
$D_{\text{jet}}$ working point. The efficiencies are shown separately for
electrons with $\abs{\eta}<1.46$ (left), corresponding to the ECAL barrel
region, and $\abs{\eta}>1.56$ (right), corresponding to the ECAL endcap
regions.

The $\Pe\tauh$ events for the measurement of the scale factors for electrons
to pass different working points of the $D_{\Pe}$ discriminator are recorded
with a single-electron trigger with a threshold of 35. The efficiencies for
electrons to pass the different working points in data and simulated events as
well as the ratio of the efficiencies are shown in Fig. 11. The efficiencies
are derived separately in the ECAL barrel region ($\abs{\eta}<1.46$) and the
ECAL endcap region ($\abs{\eta}>1.56$). The observed and expected efficiencies
agree within a few percent for the Loose and VLoose working points in the
barrel and within 11% in the endcap region. For tighter working points, scale
factors are obtained that are significantly different from one and that are
generally greater than one in the barrel, with a maximum scale factor of
$1.61\pm 0.28$ for the VTight working point, and smaller in the endcap.

Figure 12: Observed and expected efficiencies for muons to pass the loose
(left) and tight (right) $D_{\Pgm}$ working points. Vertical bars correspond
to the combined statistical and systematic uncertainties in the individual
scale factors. The muons are required to be reconstructed as candidates and to
pass the Medium $D_{\text{jet}}$ working point. The efficiencies are shown for
several bins in $\abs{\eta}$.

The $\Pgm\tauh$ events for the measurement of the scale factors for muons to
be misreconstructed and misidentified as candidates are recorded with a
single-muon trigger with a threshold of 27. The leading muon is required to
have $\pt>28\GeV$, and the transverse mass of the muon and is required to be
smaller than 30. The resulting data-to-simulation scale factors are shown in
Fig. 12. The scale factors are shown separately for the Loose and Tight
working points, and for five different bins in $\abs{\eta}$. Although the
scale factors are consistent for the different working points and for four of
the $\abs{\eta}$ bins, they differ significantly from one for
$\abs{\eta}>1.7$. As a result of the high rejection power of $D_{\Pgm}$, muons
that are not discarded belong to atypical regions of the phase space, far from
the bulk of the distributions. For such uncommon muons, we observe that the
simulation does not model the data well, especially for observables related to
the muon track quality at large $\abs{\eta}$; this is compatible with being
the cause of the sizeable data-to-simulation difference at $\abs{\eta}>1.7$.
The large $D_{\Pgm}$ scale factors at $\abs{\eta}>1.7$ have no notable impact
on physics analyses because of the small probability for muons to pass any
$D_{\Pgm}$ working point and the limited extent of the region presenting large
data-to-simulation differences.

Figure 13: Observed and expected efficiencies for quark and gluon jets with
$\abs{\eta}<2.3$ to be reconstructed as candidates with $\pt>20\GeV$ and to
pass the loose (left) and tight (right) $D_{\text{jet}}$ working points. The
efficiencies are shown as a function of reconstructed jet and are obtained
with data recorded in 2018 and the corresponding simulated events. The shaded
uncertainty band includes contributions from the limited number of simulated
events and from uncertainties in the jet energy scale and the description.
Besides the statistical uncertainty in the observed events, the error bars in
the ratio of data to simulation also include uncertainties from the
subtraction of events with genuine candidates, electrons, or muons. The
efficiency first rises with near the 20threshold because it becomes more
likely for a jet to give rise to a reconstructed candidate that passes this
threshold. For higher , the particle multiplicity in a quark or gluon jet
increases with . Therefore, the jets become easier to distinguish from genuine
candidates and the probability for quark or gluon jets to pass the
$D_{\text{jet}}$ discriminator decreases with .

For a measurement of the efficiency for jets to pass the $D_{\text{jet}}$
discriminator, events are selected that are consistent with a
$\PW(\to\Pgm\Pgngm)$+jet topology. Similar to the previously discussed
measurement, the events are recorded with a single-muon trigger with a
threshold of 27, and the reconstructed muon is required to have $\pt>28\GeV$.
Furthermore, there must be exactly one central jet with $\pt>20\GeV$ and
$\abs{\eta}<2.3$, and events with additional reconstructed muons or a
reconstructed electron passing loose identification criteria are rejected. To
select a pure sample of $\PW$+jets events, the transverse mass of the muon and
is required to be greater than 60. The reconstructed jet serves as a probe for
the measurement of the jet-to-misidentification efficiency. The efficiencies
for jets to be reconstructed as candidates and to pass the Loose and Tight
$D_{\text{jet}}$ working points are shown in Fig. 13 as a function of
reconstructed jet . In the measurement of the efficiency, background events
with genuine candidates are subtracted. The observed and expected efficiencies
agree within the uncertainties.

##  0.6 Summary

A new algorithm has been introduced to discriminate hadronic tau lepton decays
() against jets, electrons, and muons. The algorithm is based on a deep neural
network and combines fully connected and convolutional layers. As input, the
algorithm combines information from all individual reconstructed particles
near the axis with information about the reconstructed candidate and other
high-level variables. In addition, an improved reconstruction algorithm is
introduced that increases the overall efficiency of the reconstruction by
explicitly considering the decay mode with three charged hadrons and a neutral
pion, and by applying looser quality criteria for the charged hadrons in the
case of three-prong decays. The performance of the new identification and
reconstruction algorithms significantly improves over the previously used
algorithms, in particular in terms of discrimination against the background
from jets and electrons. For a given jet rejection level, the efficiency for
genuine candidates increases by 10–30%. Similarly, the efficiency for genuine
candidates to pass the discriminator against electrons increases by 14% for
the loosest working point that is employed in many analyses. Following its
superior performance, CMS physics analyses with tau leptons will significantly
increase their sensitivities when using the new algorithm. The superior
performance of the algorithm is validated with collision data. The observed
efficiencies for genuine , jets, and electrons to be identified as typically
agree within 10% with the expected efficiencies from simulated events. The
agreement is similar to the one observed with previous algorithms and confirms
the improvements.

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing centers and personnel of the Worldwide LHC Computing Grid and other
centers for delivering so effectively the computing infrastructure essential
to our analyses. Finally, we acknowledge the enduring support for the
construction and operation of the LHC, the CMS detector, and the supporting
computing infrastructure provided by the following funding agencies: BMBWF and
FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and
FAPESP (Brazil); MES and BNSF (Bulgaria); CERN; CAS, MoST, and NSFC (China);
MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT
(Ecuador); MoER, ERC PUT and ERDF (Estonia); Academy of Finland, MEC, and HIP
(Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRI
(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); MCIN/AEI and PCTI (Spain); MOSTR (Sri Lanka); Swiss
Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR, and
NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU (Ukraine); STFC (United
Kingdom); DOE and NSF (USA). Individuals have received support from the Marie-
Curie program and the European Research Council and Horizon 2020 Grant,
contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 884104, and COST
Action CA16108 (European Union); the Leventis Foundation; the Alfred 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, and under project number 400140256 -
GRK2497; the Lendület (“Momentum”) Program and the János Bolyai Research
Scholarship of the Hungarian Academy of Sciences, the New National Excellence
Program ÚNKP, the NKFIA research grants 123842, 123959, 124845, 124850,
125105, 128713, 128786, and 129058 (Hungary); the Council of Science and
Industrial Research, India; the Latvian Council of Science; the Ministry of
Science and Higher Education and the National Science Center, contracts Opus
2014/15/B/ST2/03998 and 2015/19/B/ST2/02861 (Poland); the Fundação para a
Ciência e a Tecnologia, grant CEECIND/01334/2018 (Portugal); the National
Priorities Research Program by Qatar National Research Fund; the Ministry of
Science and Higher Education, projects no. 0723-2020-0041 and no.
FSWW-2020-0008, and the Russian Foundation for Basic Research, project
No.19-42-703014 (Russia); MCIN/AEI/10.13039/501100011033, ERDF “a way of
making Europe”, and the Programa Estatal de Fomento de la Investigación
Científica y Técnica de Excelencia María de Maeztu, grant MDM-2017-0765 and
Programa Severo Ochoa del Principado de Asturias (Spain); the Stavros Niarchos
Foundation (Greece); 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, “Observation of the Higgs boson decay to a pair of $\tau$ leptons with the CMS detector”,  Phys. Lett. B 779 (2018) 283, 10.1016/j.physletb.2018.02.004, arXiv:1708.00373.
  * [2] CMS Collaboration, “Search for Higgs boson pair production in events with two bottom quarks and two tau leptons in proton–proton collisions at $\sqrt{s}=$ 13 TeV”,  Phys. Lett. B 778 (2018) 101, 10.1016/j.physletb.2018.01.001, arXiv:1707.02909.
  * [3] ATLAS Collaboration, “Cross-section measurements of the Higgs boson decaying into a pair of $\tau$-leptons in proton-proton collisions at $\sqrt{s}=13$ TeV with the ATLAS detector”,  Phys. Rev. D 99 (2019) 072001, 10.1103/PhysRevD.99.072001, arXiv:1811.08856.
  * [4] ATLAS Collaboration, “Search for resonant and non-resonant Higgs boson pair production in the ${\text{b}\bar{\text{b}}\tau^{+}\tau^{-}}$ decay channel in pp collisions at $\sqrt{s}=13$ TeV with the ATLAS detector”,  Phys. Rev. Lett. 121 (2018) 191801, 10.1103/PhysRevLett.121.191801, arXiv:1808.00336. [Erratum: 10.1103/PhysRevLett.122.089901].
  * [5] ATLAS Collaboration, “Test of CP invariance in vector-boson fusion production of the Higgs boson in the $\text{H}\rightarrow\tau\tau$ channel in proton$-$proton collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector”,  Phys. Lett. B 805 (2020) 135426, 10.1016/j.physletb.2020.135426, arXiv:2002.05315.
  * [6] CMS Collaboration, “Search for additional neutral MSSM Higgs bosons in the $\tau\tau$ final state in proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  JHEP 09 (2018) 007, 10.1007/JHEP09(2018)007, arXiv:1803.06553.
  * [7] ATLAS Collaboration, “Search for charged Higgs bosons decaying via H${}^{\pm}\to\tau^{\pm}\nu_{\tau}$ in the $\tau$+jets and $\tau$+lepton final states with 36 fb-1 of pp collision data recorded at $\sqrt{s}=13$ TeV with the ATLAS experiment”,  JHEP 09 (2018) 139, 10.1007/JHEP09(2018)139, arXiv:1807.07915.
  * [8] CMS Collaboration, “Search for an exotic decay of the Higgs boson to a pair of light pseudoscalars in the final state with two b quarks and two $\tau$ leptons in proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  Phys. Lett. B 785 (2018) 462, 10.1016/j.physletb.2018.08.057, arXiv:1805.10191.
  * [9] CMS Collaboration, “Search for a heavy pseudoscalar Higgs boson decaying into a 125 GeV Higgs boson and a Z boson in final states with two tau and two light leptons at $\sqrt{s}=$ 13 TeV”,  JHEP 03 (2020) 065, 10.1007/JHEP03(2020)065, arXiv:1910.11634.
  * [10] CMS Collaboration, “Search for lepton flavour violating decays of a neutral heavy Higgs boson to $\mu\tau$ and e$\tau$ in proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  JHEP 03 (2020) 103, 10.1007/JHEP03(2020)103, arXiv:1911.10267.
  * [11] CMS Collaboration, “Search for a low-mass $\tau^{+}\tau^{-}$ resonance in association with a bottom quark in proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  JHEP 05 (2019) 210, 10.1007/JHEP05(2019)210, arXiv:1903.10228.
  * [12] CMS Collaboration, “Search for charged Higgs bosons in the H± $\to$ $\tau^{\pm}\nu_{\tau}$ decay channel in proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  JHEP 07 (2019) 142, 10.1007/JHEP07(2019)142, arXiv:1903.04560.
  * [13] ATLAS Collaboration, “Search for heavy Higgs bosons decaying into two tau leptons with the ATLAS detector using pp collisions at $\sqrt{s}=13$ TeV”,  Phys. Rev. Lett. 125 (2020) 051801, 10.1103/PhysRevLett.125.051801, arXiv:2002.12223.
  * [14] CMS Collaboration, “Search for direct pair production of supersymmetric partners to the $\tau$ lepton in proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  Eur. Phys. J. C 80 (2020) 189, 10.1140/epjc/s10052-020-7739-7, arXiv:1907.13179.
  * [15] CMS Collaboration, “Search for heavy neutrinos and third-generation leptoquarks in hadronic states of two $\tau$ leptons and two jets in proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  JHEP 03 (2019) 170, 10.1007/JHEP03(2019)170, arXiv:1811.00806.
  * [16] ATLAS Collaboration, “Searches for third-generation scalar leptoquarks in $\sqrt{s}$ = 13 TeV pp collisions with the ATLAS detector”,  JHEP 06 (2019) 144, 10.1007/JHEP06(2019)144, arXiv:1902.08103.
  * [17] CMS Collaboration, “Performance of tau-lepton reconstruction and identification in CMS”,  JINST 7 (2012) P01001, 10.1088/1748-0221/7/01/P01001, arXiv:1109.6034.
  * [18] CMS Collaboration, “Reconstruction and identification of $\tau$ lepton decays to hadrons and $\nu_{\tau}$ at CMS”,  JINST 11 (2016) P01019, 10.1088/1748-0221/11/01/P01019, arXiv:1510.07488.
  * [19] CMS Collaboration, “Performance of reconstruction and identification of $\tau$ leptons decaying to hadrons and $\nu_{\tau}$ in pp collisions at $\sqrt{s}=$ 13 TeV”,  JINST 13 (2018) P10005, 10.1088/1748-0221/13/10/P10005, arXiv:1809.02816.
  * [20] ATLAS Collaboration, “Identification and energy calibration of hadronically decaying tau leptons with the ATLAS experiment in pp collisions at $\sqrt{s}=8$ TeV”,  Eur. Phys. J. C 75 (2015) 303, 10.1140/epjc/s10052-015-3500-z, arXiv:1412.7086.
  * [21] ATLAS Collaboration, “Reconstruction of hadronic decay products of tau leptons with the ATLAS experiment”,  Eur. Phys. J. C 76 (2016) 295, 10.1140/epjc/s10052-016-4110-0, arXiv:1512.05955.
  * [22] Y. LeCun et al., “Backpropagation applied to handwritten zip code recognition”,  Neural Comput. 1 (1989) 541, 10.1162/neco.1989.1.4.541.
  * [23] Y. LeCun et al., “Handwritten digit recognition with a back-propagation network”, in  Advances in Neural Information Processing Systems 2, p. 396.  Morgan Kaufmann, 1990.
  * [24] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition”,  Proc. IEEE 86 (1998) 2278, 10.1109/5.726791.
  * [25] V. Innocente, Y. F. Wang, and Z. P. Zhang, “Identification of tau decays using a neural network”,  Nucl. Instrum. Meth. A 323 (1992) 647, 10.1016/0168-9002(92)90011-R.
  * [26] ATLAS Collaboration, “Identification of hadronic tau lepton decays using neural networks in the ATLAS experiment”, ATLAS PUB Note ATL-PHYS-PUB-2019-033, 2019.
  * [27] 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.
  * [28] ATLAS Collaboration, “Identification of jets containing b-hadrons with recurrent neural networks at the ATLAS experiment”, ATLAS PUB Note ATL-PHYS-PUB-2017-003, 2017.
  * [29] ATLAS Collaboration, “Deep sets based neural networks for impact parameter flavour tagging in ATLAS”, ATLAS PUB Note ATL-PHYS-PUB-2020-014, 2020.
  * [30] E. Bols et al., “Jet flavour classification using DeepJet”,  JINST 15 (2020) P12012, 10.1088/1748-0221/15/12/P12012, arXiv:2008.10519.
  * [31] CMS Collaboration, “Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques”,  JINST 15 (2020) P06005, 10.1088/1748-0221/15/06/P06005, arXiv:2004.08262.
  * [32] HEPData record for this analysis, 2022.  10.17182/hepdata.116281.
  * [33] CMS Collaboration, “The CMS experiment at the CERN LHC”,  JINST 3 (2008) S08004, 10.1088/1748-0221/3/08/S08004.
  * [34] 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.
  * [35] 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.
  * [36] M. Cacciari, G. P. Salam, and G. Soyez, “user manual”,  Eur. Phys. J. C 72 (2012) 1896, 10.1140/epjc/s10052-012-1896-2, arXiv:1111.6097.
  * [37] CMS Collaboration, “Pileup mitigation at CMS in 13 TeV data”,  JINST 15 (2020) P09018, 10.1088/1748-0221/15/09/P09018, arXiv:2003.00503.
  * [38] CMS Collaboration, “Jet energy scale and resolution in the CMS experiment in pp collisions at 8”,  JINST 12 (2017) P02014, 10.1088/1748-0221/12/02/P02014, arXiv:1607.03663.
  * [39] 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.
  * [40] CMS Collaboration, “Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at $\sqrt{s}=13\TeV$”,  JINST 13 (2018) P06015, 10.1088/1748-0221/13/06/P06015, arXiv:1804.04528.
  * [41] CMS Collaboration, “Performance of electron reconstruction and selection with the CMS detector in proton-proton collisions at $\sqrt{s}=8\TeV$”,  JINST 10 (2015) P06005, 10.1088/1748-0221/10/06/P06005, arXiv:1502.02701.
  * [42] CMS Collaboration, “Performance of the CMS Level-1 trigger in proton-proton collisions at $\sqrt{s}=13$ TeV”,  JINST 15 (2020) P10017, 10.1088/1748-0221/15/10/P10017, arXiv:2006.10165.
  * [43] CMS Collaboration, “The CMS trigger system”,  JINST 12 (2017) P01020, 10.1088/1748-0221/12/01/P01020, arXiv:1609.02366.
  * [44] 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.
  * [45] 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.
  * [46] R. Frederix and S. Frixione, “Merging meets matching in MC@NLO”,  JHEP 12 (2012) 061, 10.1007/JHEP12(2012)061, arXiv:1209.6215.
  * [47] 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.
  * [48] 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.
  * [49] 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].
  * [50] 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.
  * [51] 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.
  * [52] 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.
  * [53] 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.
  * [54] 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.
  * [55] NNPDF Collaboration, “Parton distributions for the LHC Run II”,  JHEP 04 (2015) 040, 10.1007/JHEP04(2015)040, arXiv:1410.8849.
  * [56] NNPDF Collaboration, “Parton distributions from high-precision collider data”,  Eur. Phys. J. C 77 (2017) 663, 10.1140/epjc/s10052-017-5199-5, arXiv:1706.00428.
  * [57] CMS Collaboration, “Extraction and validation of a new set of CMS PYTHIA8 tunes from underlying-event measurements”,  Eur. Phys. J. C 80 (2020) 4, 10.1140/epjc/s10052-019-7499-4, arXiv:1903.12179.
  * [58] N. Davidson et al., “Universal interface of TAUOLA technical and physics documentation”,  Comput. Phys. Commun. 183 (2012) 821, 10.1016/j.cpc.2011.12.009, arXiv:1002.0543.
  * [59] GEANT4 Collaboration, “—a simulation toolkit”,  Nucl. Instrum. Meth. A 506 (2003) 250, 10.1016/S0168-9002(03)01368-8.
  * [60] Particle Data Group, P. A. Zyla et al., “Review of particle physics”,  Prog. Theor. Exp. Phys. 2020 (2020) 083C01, 10.1093/ptep/ptaa104.
  * [61] D. Bertolini, P. Harris, M. Low, and N. Tran, “Pileup per particle identification”,  JHEP 10 (2014) 059, 10.1007/JHEP10(2014)059, arXiv:1407.6013.
  * [62] I. J. Goodfellow, Y. Bengio, and A. Courville, “Deep Learning”.  MIT Press, Cambridge, MA, USA, 2016.  http://www.deeplearningbook.org.
  * [63] S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift”, 2015. arXiv:1502.03167.
  * [64] G. E. Hinton et al., “Improving neural networks by preventing co-adaptation of feature detectors”, 2012\. arXiv:1207.0580.
  * [65] N. Srivastava et al., “Dropout: A simple way to prevent neural networks from overfitting”, J. Mach. Learn. Res. 15 (2014) 1929.
  * [66] K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification”, 2015\. arXiv:1502.01852.
  * [67] T.-Y. Lin et al., “Focal loss for dense object detection”,  TPAMI 42 (2018) 318, 10.1109/TPAMI.2018.2858826, arXiv:1708.02002.
  * [68] T. Dozat, “Incorporating Nesterov momentum into ADAM”, in  Conference Track Proceedings, 4th International Conference on Learning Representations (ICLR 2016).  2016\.
  * [69] D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization”, in  Conference Track Proceedings, 3rd International Conference on Learning Representations (ICLR 2015).  2015\.  arXiv:1412.6980.
  * [70] F. Chollet et al., “Keras”. https://keras.io, 2015.
  * [71] M. Abadi et al., “TensorFlow: Large-scale machine learning on heterogeneous systems”, 2015\.  Software available from tensorflow.org. https://www.tensorflow.org/.
  * [72] CMS Collaboration, “Measurement of Higgs boson production in the decay channel with a pair of $\tau$ leptons”, CMS Physics Analysis Summary CMS-PAS-HIG-19-010, 2020.
  * [73] CMS Collaboration, “Measurement of the Higgs boson production rate in association with top quarks in final states with electrons, muons, and hadronically decaying tau leptons at $\sqrt{s}=$ 13 TeV”,  Eur. Phys. J. C 81 (2021) 378, 10.1140/epjc/s10052-021-09014-x, arXiv:2011.03652.
  * [74] CMS Collaboration, “Search for singly and pair-produced leptoquarks coupling to third-generation fermions in proton-proton collisions at $\sqrt{s}=$ 13 TeV”,  Phys. Lett. B 819 (2021) 136446, 10.1016/j.physletb.2021.136446, arXiv:2012.04178.
  * [75] CMS Collaboration, “Precision luminosity measurement in proton-proton collisions at $\sqrt{s}=$ 13 TeV in 2015 and 2016 at CMS”,  Eur. Phys. J. C 81 (2021) 800, 10.1140/epjc/s10052-021-09538-2, arXiv:2104.01927.
  * [76] CMS Collaboration, “CMS luminosity measurement for the 2017 data-taking period at $\sqrt{s}=13$ TeV”, CMS Physics Analysis Summary CMS-PAS-LUM-17-004, 2018.
  * [77] CMS Collaboration, “CMS luminosity measurement for the 2018 data-taking period at $\sqrt{s}=13$ TeV”, CMS Physics Analysis Summary CMS-PAS-LUM-18-002, 2019.
  * [78] G. Cowan, K. Cranmer, E. Gross, and O. Vitells, “Asymptotic formulae for likelihood-based tests of new physics”,  Eur. Phys. J. C 71 (2011) 1554, 10.1140/epjc/s10052-011-1554-0, arXiv:1007.1727. [Erratum: 10.1140/epjc/s10052-013-2501-z].
  * [79] CMS Collaboration, “Measurements of inclusive W and Z cross sections in pp collisions at $\sqrt{s}=7$ TeV”,  JHEP 01 (2011) 080, 10.1007/JHEP01(2011)080, arXiv:1012.2466.

##  .7 Loss function

The loss function used for the training of the DeepTau algorithm has the form

$\begin{split}&L(\mathbf{y}^{\text{true}},\mathbf{y};\boldsymbol{\kappa},\boldsymbol{\gamma},\boldsymbol{\omega})=\underbrace{\vphantom{Bigl(\kappa_{\Pe}+\kappa_{\Pgm}+\kappa_{\text{jet}}\Bigr{)}}\kappa_{\Pgt}\,H_{\Pgt}(\mathbf{y}^{\text{true}},\mathbf{y};\boldsymbol{\omega})}+\underbrace{\Bigl{(}\kappa_{\Pe}+\kappa_{\Pgm}+\kappa_{\text{jet}}\Bigr{)}\overline{F}_{\text{cmb}}(1-y_{\Pgt}^{\text{true}},1-y_{\Pgt};\gamma_{\text{cmb}})}\\\
&\hskip 88.2037pt\text{(a) Separation of all }\alpha\hskip 34.14322pt\text{(b)
Focused separation of}\\\ &\hskip 233.3125pt\Pe,\,\Pgm,\,\text{jet from
}\tauh\\\ &\\\
&\hphantom{L(\mathbf{y}^{\text{true}},\mathbf{y};\mathbf{\kappa},\mathbf{\gamma},\mathbf{\omega})=}+\underbrace{\kappa_{F}\sum\limits_{i\in\\{\Pe,\,\Pgm,\,\text{jet}\\}}\kappa_{i}\,\hat{\theta}(y_{\Pgt}-0.1)\,\overline{F}_{i}(y_{i}^{\text{true}},y_{i};\gamma_{i}).}\\\
&\hskip 99.58464pt\text{(c) Focused separation of }\tauh\text{ from
}\Pe,\,\Pgm,\\\ &\hskip 99.58464pt\text{jet for }y_{\Pgt}>0.1\\\ &\\\
&\textbf{Categorical cross entropy:}\\\ &\\\
&H(\mathbf{y}^{\text{true}},\mathbf{y};\boldsymbol{\omega})=-\sum\limits_{\alpha}\omega_{\alpha}\,y_{\alpha}^{\text{true}}\,y_{\alpha}\,;\qquad\alpha\in\\{\tau,\,\Pe,\,\Pgm,\,\text{jet}\\}.\\\
&\\\ &\textbf{Focal loss function:}\\\ &\\\
&F(y^{\text{true}},y;\gamma)=-y^{\text{true}}\,\left(1-y\right)^{\gamma}\log(y)\,\qquad\overline{F}(y^{\text{true}},y;\gamma)=\mathcal{N}\,F(y^{\text{true}},y;\gamma)\,,\\\
&\\\ \end{split}$ (.7.1)

where bold fonts indicate groups of parameters; $H(\cdot,\cdot;\cdot)$
corresponds to the categorical cross entropy and $F(\cdot,\cdot;\cdot)$ to the
focal loss function [67]; $\mathcal{N}$ is a factor to normalize
$F(\cdot,\cdot;\cdot)$ to unity in the interval 0–1; and $\hat{\theta}(\cdot)$
is a smoothened step function that approaches 1 for $y_{\Pgt}>0.1$. The focal
loss terms in Eq. .7.1 (b) and (c) put more emphasis on the part of parameter
space in which it is more difficult to separate candidates from , , and jets.
The step function in Eq. .7.1 (c) disregards the part of parameter space in
which the probability for the candidate to correspond to a genuine is low, for
which we are not interested in optimal separation between , , and jets.

The values and meanings of the parameters $\boldsymbol{\kappa}$,
$\boldsymbol{\gamma}$, and $\boldsymbol{\omega}$ are given in Table .7.1. The
values of the $\kappa$-factors are chosen such that the numerical values of
the different components of the loss function are close to each other, while
putting more emphasis on the discrimination against jets, since jets are the
dominant source of background.

7.1.Table:  Parameters used in the definition of the loss function for the training of the DeepTau algorithm.  |  | Location in Eq. .7.1 |  |  |
---|---|---|---|---|---
Parameter | Value | (a) | (b) | (c) | Meaning
$\kappa_{\Pgt}$ | $2.0$ | $\checkmark$ |  |  | Emphasis on |  | separation
$\kappa_{\Pe}$ | $0.4$ | $\checkmark$ | $\checkmark$ | $\checkmark$ |
$\kappa_{\Pgm}$ | $1.0$ | $\checkmark$ | $\checkmark$ | $\checkmark$ |
$\kappa_{\text{jet}}$ | $0.6$ | $\checkmark$ | $\checkmark$ | $\checkmark$ | jet
$\kappa_{F}$ | $5.0$ |  |  | $\checkmark$ | Emphasis on high efficiency
$\gamma_{\Pe}$ | $2.0$ |  |  | $\checkmark$ | Focus on |  | separation
$\gamma_{\Pgm}$ | $2.0$ |  |  | $\checkmark$ |
$\gamma_{\text{jet}}$ | $0.5$ |  |  | $\checkmark$ | jet
$\gamma_{\text{cmb}}$ | $2.0$ |  | $\checkmark$ |  | $\Pe,\,\Pgm,\,\text{jet}$
$\omega_{\alpha}$ | varying | $\checkmark$ |  |  | Sample normalization

##  .8 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia A. Tumasyan Institut für
Hochenergiephysik, Vienna, Austria W. Adam0000-0001-9099-4341, J.W.
Andrejkovic, T. Bergauer0000-0002-5786-0293, S. Chatterjee0000-0003-2660-0349,
M. Dragicevic0000-0003-1967-6783, A. Escalante Del Valle0000-0002-9702-6359,
R. Frühwirth1, M. Jeitler10000-0002-5141-9560, N. Krammer, L.
Lechner0000-0002-3065-1141, D. Liko, I. Mikulec, P. Paulitsch, F.M. Pitters,
J. Schieck10000-0002-1058-8093, R. Schöfbeck0000-0002-2332-8784, D. Schwarz,
S. Templ0000-0003-3137-5692, W. Waltenberger0000-0002-6215-7228, C.-E.
Wulz10000-0001-9226-5812 Institute for Nuclear Problems, Minsk, Belarus V.
Chekhovsky, A. Litomin, V. Makarenko0000-0002-8406-8605 Universiteit
Antwerpen, Antwerpen, Belgium M.R. Darwish2, E.A. De Wolf, T.
Janssen0000-0002-3998-4081, T. Kello3, A. Lelek0000-0001-5862-2775, H. Rejeb
Sfar, P. Van Mechelen0000-0002-8731-9051, S. Van Putte, N. Van
Remortel0000-0003-4180-8199 Vrije Universiteit Brussel, Brussel, Belgium F.
Blekman0000-0002-7366-7098, E.S. Bols0000-0002-8564-8732, J.
D’Hondt0000-0002-9598-6241, M. Delcourt, H. El Faham0000-0001-8894-2390, S.
Lowette0000-0003-3984-9987, S. Moortgat0000-0002-6612-3420, A.
Morton0000-0002-9919-3492, D. Müller0000-0002-1752-4527, A.R.
Sahasransu0000-0003-1505-1743, S. Tavernier0000-0002-6792-9522, W. Van
Doninck, P. Van Mulders Université Libre de Bruxelles, Bruxelles, Belgium D.
Beghin, B. Bilin0000-0003-1439-7128, B. Clerbaux0000-0001-8547-8211, G. De
Lentdecker, L. Favart0000-0003-1645-7454, A. Grebenyuk, A.K.
Kalsi0000-0002-6215-0894, K. Lee, M. Mahdavikhorrami, I.
Makarenko0000-0002-8553-4508, L. Moureaux0000-0002-2310-9266, L. Pétré, A.
Popov0000-0002-1207-0984, N. Postiau, E. Starling0000-0002-4399-7213, L.
Thomas0000-0002-2756-3853, M. Vanden Bemden, C. Vander
Velde0000-0003-3392-7294, P. Vanlaer0000-0002-7931-4496, L. Wezenbeek Ghent
University, Ghent, Belgium T. Cornelis0000-0001-9502-5363, D. Dobur, J.
Knolle0000-0002-4781-5704, L. Lambrecht, G. Mestdach, M.
Niedziela0000-0001-5745-2567, C. Roskas, A. Samalan, K.
Skovpen0000-0002-1160-0621, M. Tytgat0000-0002-3990-2074, B. Vermassen, M. Vit
Université Catholique de Louvain, Louvain-la-Neuve, Belgium A. Benecke, A.
Bethani0000-0002-8150-7043, G. Bruno, F. Bury0000-0002-3077-2090, C.
Caputo0000-0001-7522-4808, P. David0000-0001-9260-9371, C.
Delaere0000-0001-8707-6021, I.S. Donertas0000-0001-7485-412X, A.
Giammanco0000-0001-9640-8294, K. Jaffel, Sa. Jain0000-0001-5078-3689, V.
Lemaitre, K. Mondal0000-0001-5967-1245, J. Prisciandaro, A. Taliercio, M.
Teklishyn0000-0002-8506-9714, T.T. Tran, P. Vischia0000-0002-7088-8557, S.
Wertz0000-0002-8645-3670 Centro Brasileiro de Pesquisas Fisicas, Rio de
Janeiro, Brazil G.A. Alves0000-0002-8369-1446, C. Hensel, A.
Moraes0000-0002-5157-5686 Universidade do Estado do Rio de Janeiro, Rio de
Janeiro, Brazil W.L. Aldá Júnior0000-0001-5855-9817, M. Alves Gallo
Pereira0000-0003-4296-7028, M. Barroso Ferreira Filho, H. Brandao Malbouisson,
W. Carvalho0000-0003-0738-6615, J. Chinellato4, E.M. Da
Costa0000-0002-5016-6434, G.G. Da Silveira50000-0003-3514-7056, D. De Jesus
Damiao0000-0002-3769-1680, S. Fonseca De Souza0000-0001-7830-0837, D. Matos
Figueiredo, C. Mora Herrera0000-0003-3915-3170, K. Mota Amarilo, L.
Mundim0000-0001-9964-7805, H. Nogima, P. Rebello Teles0000-0001-9029-8506, A.
Santoro, S.M. Silva Do Amaral0000-0002-0209-9687, A.
Sznajder0000-0001-6998-1108, M. Thiel, F. Torres Da Silva De
Araujo60000-0002-4785-3057, A. Vilela Pereira0000-0003-3177-4626 Universidade
Estadual Paulista (a), Universidade Federal do ABC (b), São Paulo, Brazil C.A.
Bernardes50000-0001-5790-9563, L. Calligaris0000-0002-9951-9448, T.R.
Fernandez Perez Tomei0000-0002-1809-5226, E.M. Gregores0000-0003-0205-1672,
D.S. Lemos0000-0003-1982-8978, P.G. Mercadante0000-0001-8333-4302, S.F.
Novaes0000-0003-0471-8549, Sandra S. Padula0000-0003-3071-0559 Institute for
Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia,
Bulgaria A. Aleksandrov, G. Antchev0000-0003-3210-5037, R. Hadjiiska, P.
Iaydjiev, M. Misheva, M. Rodozov, M. Shopova, G. Sultanov University of Sofia,
Sofia, Bulgaria A. Dimitrov, T. Ivanov, L. Litov0000-0002-8511-6883, B.
Pavlov, P. Petkov, A. Petrov Beihang University, Beijing, China T.
Cheng0000-0003-2954-9315, T. Javaid7, M. Mittal, L. Yuan Department of
Physics, Tsinghua University, Beijing, China M. Ahmad0000-0001-9933-995X, G.
Bauer, C. Dozen80000-0002-4301-634X, Z. Hu0000-0001-8209-4343, J.
Martins90000-0002-2120-2782, Y. Wang, K. Yi10,11 Institute of High Energy
Physics, Beijing, China E. Chapon0000-0001-6968-9828, G.M.
Chen70000-0002-2629-5420, H.S. Chen70000-0001-8672-8227, M.
Chen0000-0003-0489-9669, F. Iemmi, A. Kapoor0000-0002-1844-1504, D. Leggat, H.
Liao, Z.-A. Liu70000-0002-2896-1386, V. Milosevic0000-0002-1173-0696, F.
Monti0000-0001-5846-3655, R. Sharma0000-0003-1181-1426, J.
Tao0000-0003-2006-3490, J. Thomas-Wilsker, J. Wang0000-0002-4963-0877, H.
Zhang0000-0001-8843-5209, J. Zhao0000-0001-8365-7726 State Key Laboratory of
Nuclear Physics and Technology, Peking University, Beijing, China A. Agapitos,
Y. An, Y. Ban, C. Chen, A. Levin0000-0001-9565-4186, Q. Li0000-0002-8290-0517,
X. Lyu, Y. Mao, S.J. Qian, D. Wang0000-0002-9013-1199, Q.
Wang0000-0003-1014-8677, J. Xiao Sun Yat-Sen University, Guangzhou, China M.
Lu, Z. You0000-0001-8324-3291 Institute of Modern Physics and Key Laboratory
of Nuclear Physics and Ion-beam Application (MOE) - Fudan University,
Shanghai, China X. Gao3, H. Okawa0000-0002-2548-6567 Zhejiang University,
Hangzhou, China, Zhejiang, China Z. Lin0000-0003-1812-3474, M.
Xiao0000-0001-9628-9336 Universidad de Los Andes, Bogota, Colombia C.
Avila0000-0002-5610-2693, A. Cabrera0000-0002-0486-6296, C.
Florez0000-0002-3222-0249, J. Fraga Universidad de Antioquia, Medellin,
Colombia J. Mejia Guisao, F. Ramirez, J.D. Ruiz Alvarez0000-0002-3306-0363,
C.A. Salazar González0000-0002-0394-4870 University of Split, Faculty of
Electrical Engineering, Mechanical Engineering and Naval Architecture, Split,
Croatia D. Giljanovic, N. Godinovic0000-0002-4674-9450, D.
Lelas0000-0002-8269-5760, I. Puljak0000-0001-7387-3812 University of Split,
Faculty of Science, Split, Croatia Z. Antunovic, M. Kovac, T.
Sculac0000-0002-9578-4105 Institute Rudjer Boskovic, Zagreb, Croatia V.
Brigljevic0000-0001-5847-0062, D. Ferencek0000-0001-9116-1202, D.
Majumder0000-0002-7578-0027, M. Roguljic, A. Starodumov120000-0001-9570-9255,
T. Susa0000-0001-7430-2552 University of Cyprus, Nicosia, Cyprus A.
Attikis0000-0002-4443-3794, K. Christoforou, E. Erodotou, A. Ioannou, G.
Kole0000-0002-3285-1497, M. Kolosova, S. Konstantinou, J.
Mousa0000-0002-2978-2718, C. Nicolaou, F. Ptochos0000-0002-3432-3452, P.A.
Razis, H. Rykaczewski, H. Saka0000-0001-7616-2573 Charles University, Prague,
Czech Republic M. Finger13, M. Finger Jr.130000-0003-3155-2484, A. Kveton
Escuela Politecnica Nacional, Quito, Ecuador E. Ayala Universidad San
Francisco de Quito, Quito, Ecuador E. Carrera Jarrin0000-0002-0857-8507
Academy of Scientific Research and Technology of the Arab Republic of Egypt,
Egyptian Network of High Energy Physics, Cairo, Egypt A. Ellithi Kamel14, E.
Salama15,16 Center for High Energy Physics (CHEP-FU), Fayoum University, El-
Fayoum, Egypt A. Lotfy0000-0003-4681-0079, M.A. Mahmoud0000-0001-8692-5458
National Institute of Chemical Physics and Biophysics, Tallinn, Estonia S.
Bhowmik0000-0003-1260-973X, R.K. Dewanjee0000-0001-6645-6244, K. Ehataht, M.
Kadastik, S. Nandan, C. Nielsen, J. Pata, M. Raidal0000-0001-7040-9491, L.
Tani, C. Veelken Department of Physics, University of Helsinki, Helsinki,
Finland P. Eerola0000-0002-3244-0591, L. Forthomme0000-0002-3302-336X, H.
Kirschenmann0000-0001-7369-2536, K. Osterberg0000-0003-4807-0414, M.
Voutilainen0000-0002-5200-6477 Helsinki Institute of Physics, Helsinki,
Finland S. Bharthuar, E. Brücken0000-0001-6066-8756, F.
Garcia0000-0002-4023-7964, J. Havukainen0000-0003-2898-6900, M.S.
Kim0000-0003-0392-8691, R. Kinnunen, T. Lampén, K. Lassila-
Perini0000-0002-5502-1795, S. Lehti0000-0003-1370-5598, T. Lindén, M. Lotti,
L. Martikainen, M. Myllymäki, J. Ott0000-0001-9337-5722, H. Siikonen, E.
Tuominen0000-0002-7073-7767, J. Tuominiemi Lappeenranta University of
Technology, Lappeenranta, Finland P. Luukka0000-0003-2340-4641, H. Petrow, T.
Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France C.
Amendola0000-0002-4359-836X, M. Besancon, F. Couderc0000-0003-2040-4099, M.
Dejardin, D. Denegri, J.L. Faure, F. Ferri0000-0002-9860-101X, S. Ganjour, A.
Givernaud, P. Gras, G. Hamel de Monchenault0000-0002-3872-3592, P. Jarry, B.
Lenzi0000-0002-1024-4004, E. Locci, J. Malcles, J. Rander, A.
Rosowsky0000-0001-7803-6650, M.Ö. Sahin0000-0001-6402-4050, A. Savoy-
Navarro17, M. Titov0000-0002-1119-6614, G.B. Yu0000-0001-7435-2963 Laboratoire
Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut Polytechnique de
Paris, Palaiseau, France S. Ahuja0000-0003-4368-9285, F.
Beaudette0000-0002-1194-8556, M. Bonanomi0000-0003-3629-6264, A. Buchot
Perraguin, P. Busson, A. Cappati, C. Charlot, O. Davignon, B. Diab, G.
Falmagne0000-0002-6762-3937, S. Ghosh, R. Granier de
Cassagnac0000-0002-1275-7292, A. Hakimi, I. Kucher0000-0001-7561-5040, J.
Motta, M. Nguyen0000-0001-7305-7102, C. Ochando0000-0002-3836-1173, P.
Paganini0000-0001-9580-683X, J. Rembser, R. Salerno0000-0003-3735-2707, U.
Sarkar0000-0002-9892-4601, J.B. Sauvan0000-0001-5187-3571, Y.
Sirois0000-0001-5381-4807, A. Tarabini, A. Zabi, A. Zghiche0000-0002-1178-1450
Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France J.-L.
Agram180000-0001-7476-0158, J. Andrea, D. Apparu, D. Bloch0000-0002-4535-5273,
G. Bourgatte, J.-M. Brom, E.C. Chabert, C. Collard0000-0002-5230-8387, D.
Darej, J.-C. Fontaine18, U. Goerlach, C. Grimault, A.-C. Le Bihan, E.
Nibigira0000-0001-5821-291X, P. Van Hove0000-0002-2431-3381 Institut de
Physique des 2 Infinis de Lyon (IP2I ), Villeurbanne, France E.
Asilar0000-0001-5680-599X, S. Beauceron0000-0002-8036-9267, C.
Bernet0000-0002-9923-8734, G. Boudoul, C. Camen, A. Carle, N.
Chanon0000-0002-2939-5646, D. Contardo, P. Depasse0000-0001-7556-2743, H. El
Mamouni, J. Fay, S. Gascon0000-0002-7204-1624, M.
Gouzevitch0000-0002-5524-880X, B. Ille, I.B. Laktineh, H.
Lattaud0000-0002-8402-3263, A. Lesauvage0000-0003-3437-7845, M.
Lethuillier0000-0001-6185-2045, L. Mirabito, S. Perries, K. Shchablo, V.
Sordini0000-0003-0885-824X, L. Torterotot0000-0002-5349-9242, G. Touquet, M.
Vander Donckt, S. Viret Georgian Technical University, Tbilisi, Georgia G.
Adamov, I. Lomidze, Z. Tsamalaidze13 RWTH Aachen University, I. Physikalisches
Institut, Aachen, Germany V. Botta, L. Feld0000-0001-9813-8646, K. Klein, M.
Lipinski, D. Meuser, A. Pauls, N. Röwert, J. Schulz, M.
Teroerde0000-0002-5892-1377 RWTH Aachen University, III. Physikalisches
Institut A, Aachen, Germany A. Dodonova, D. Eliseev, M.
Erdmann0000-0002-1653-1303, P. Fackeldey0000-0003-4932-7162, B. Fischer, S.
Ghosh0000-0001-6717-0803, T. Hebbeker0000-0002-9736-266X, K. Hoepfner, F.
Ivone, L. Mastrolorenzo, M. Merschmeyer0000-0003-2081-7141, A.
Meyer0000-0001-9598-6623, G. Mocellin, S. Mondal, S.
Mukherjee0000-0001-6341-9982, D. Noll0000-0002-0176-2360, A. Novak, T.
Pook0000-0002-9635-5126, A. Pozdnyakov0000-0003-3478-9081, Y. Rath, H.
Reithler, J. Roemer, A. Schmidt0000-0003-2711-8984, S.C. Schuler, A.
Sharma0000-0002-5295-1460, L. Vigilante, S. Wiedenbeck, S. Zaleski RWTH Aachen
University, III. Physikalisches Institut B, Aachen, Germany C. Dziwok, G.
Flügge, W. Haj Ahmad190000-0003-1491-0446, O. Hlushchenko, T. Kress, A.
Nowack0000-0002-3522-5926, C. Pistone, O. Pooth, D. Roy0000-0002-8659-7762, H.
Sert0000-0003-0716-6727, A. Stahl200000-0002-8369-7506, T.
Ziemons0000-0003-1697-2130, A. Zotz Deutsches Elektronen-Synchrotron, Hamburg,
Germany H. Aarup Petersen, M. Aldaya Martin, P. Asmuss, S. Baxter, M.
Bayatmakou, O. Behnke, A. Bermúdez Martínez, S. Bhattacharya, A.A. Bin
Anuar0000-0002-2988-9830, K. Borras21, D. Brunner, A.
Campbell0000-0003-4439-5748, A. Cardini0000-0003-1803-0999, C. Cheng, F.
Colombina, S. Consuegra Rodríguez0000-0002-1383-1837, G. Correia Silva, V.
Danilov, M. De Silva, L. Didukh, G. Eckerlin, D. Eckstein, L.I. Estevez
Banos0000-0001-6195-3102, O. Filatov0000-0001-9850-6170, E. Gallo22, A.
Geiser, A. Giraldi, A. Grohsjean0000-0003-0748-8494, M. Guthoff, A.
Jafari230000-0001-7327-1870, N.Z. Jomhari0000-0001-9127-7408, H.
Jung0000-0002-2964-9845, A. Kasem210000-0002-6753-7254, M.
Kasemann0000-0002-0429-2448, H. Kaveh0000-0002-3273-5859, C.
Kleinwort0000-0002-9017-9504, D. Krücker0000-0003-1610-8844, W. Lange, T.
Lenz, J. Lidrych0000-0003-1439-0196, K. Lipka, W. Lohmann24, R. Mankel, I.-A.
Melzer-Pellmann0000-0001-7707-919X, M. Mendizabal Morentin, J. Metwally, A.B.
Meyer0000-0001-8532-2356, M. Meyer0000-0003-2436-8195, J.
Mnich0000-0001-7242-8426, A. Mussgiller, Y. Otarid, D. Pérez
Adán0000-0003-3416-0726, D. Pitzl, A. Raspereza, B. Ribeiro Lopes, J.
Rübenach, A. Saggio0000-0002-7385-3317, A. Saibel0000-0002-9932-7622, M.
Savitskyi0000-0002-9952-9267, M. Scham25, V. Scheurer, P. Schütze, C.
Schwanenberger220000-0001-6699-6662, A. Singh, R.E. Sosa
Ricardo0000-0002-2240-6699, D. Stafford, N. Tonon0000-0003-4301-2688, M. Van
De Klundert0000-0001-8596-2812, R. Walsh0000-0002-3872-4114, D. Walter, Y.
Wen0000-0002-8724-9604, K. Wichmann, L. Wiens, C. Wissing, S.
Wuchterl0000-0001-9955-9258 University of Hamburg, Hamburg, Germany R.
Aggleton, S. Albrecht0000-0002-5960-6803, S. Bein0000-0001-9387-7407, L.
Benato0000-0001-5135-7489, P. Connor0000-0003-2500-1061, K. De
Leo0000-0002-8908-409X, M. Eich, F. Feindt, A. Fröhlich, C.
Garbers0000-0001-5094-2256, E. Garutti0000-0003-0634-5539, P. Gunnellini, M.
Hajheidari, J. Haller0000-0001-9347-7657, A. Hinzmann0000-0002-2633-4696, G.
Kasieczka, R. Klanner0000-0002-7004-9227, R. Kogler0000-0002-5336-4399, T.
Kramer, V. Kutzner, J. Lange0000-0001-7513-6330, T. Lange0000-0001-6242-7331,
A. Lobanov0000-0002-5376-0877, A. Malara0000-0001-8645-9282, A. Nigamova, K.J.
Pena Rodriguez, O. Rieger, P. Schleper, M. Schröder0000-0001-8058-9828, J.
Schwandt0000-0002-0052-597X, J. Sonneveld0000-0001-8362-4414, H. Stadie, G.
Steinbrück, A. Tews, I. Zoi0000-0002-5738-9446 Karlsruher Institut fuer
Technologie, Karlsruhe, Germany J. Bechtel0000-0001-5245-7318, S. Brommer, M.
Burkart, E. Butz0000-0002-2403-5801, R. Caspart0000-0002-5502-9412, T.
Chwalek, W. De Boer${}^{\textrm{\textdagger}}$, A. Dierlamm, A. Droll, K. El
Morabit, N. Faltermann0000-0001-6506-3107, M. Giffels, J.o. Gosewisch, A.
Gottmann, F. Hartmann200000-0001-8989-8387, C. Heidecker, U.
Husemann0000-0002-6198-8388, P. Keicher, R. Koppenhöfer, S. Maier, M. Metzler,
S. Mitra0000-0002-3060-2278, Th. Müller, M. Neukum, A. Nürnberg, G.
Quast0000-0002-4021-4260, K. Rabbertz0000-0001-7040-9846, J. Rauser, D.
Savoiu0000-0001-6794-7475, M. Schnepf, D. Seith, I. Shvetsov, H.J. Simonis, R.
Ulrich0000-0002-2535-402X, J. Van Der Linden, R.F. Von Cube, M. Wassmer, M.
Weber0000-0002-3639-2267, S. Wieland, R. Wolf0000-0001-9456-383X, S.
Wozniewski, S. Wunsch Institute of Nuclear and Particle Physics (INPP), NCSR
Demokritos, Aghia Paraskevi, Greece G. Anagnostou, G. Daskalakis, T.
Geralis0000-0001-7188-979X, A. Kyriakis, D. Loukas, A.
Stakia0000-0001-6277-7171 National and Kapodistrian University of Athens,
Athens, Greece M. Diamantopoulou, D. Karasavvas, G. Karathanasis, P.
Kontaxakis0000-0002-4860-5979, C.K. Koraka, A. Manousakis-Katsikakis, A.
Panagiotou, I. Papavergou, N. Saoulidou0000-0001-6958-4196, K.
Theofilatos0000-0001-8448-883X, E. Tziaferi0000-0003-4958-0408, K. Vellidis,
E. Vourliotis National Technical University of Athens, Athens, Greece G.
Bakas, K. Kousouris0000-0002-6360-0869, I. Papakrivopoulos, G. Tsipolitis, A.
Zacharopoulou University of Ioánnina, Ioánnina, Greece K. Adamidis, I.
Bestintzanos, I. Evangelou0000-0002-5903-5481, C. Foudas, P. Gianneios, P.
Katsoulis, P. Kokkas, N. Manthos, I. Papadopoulos0000-0002-9937-3063, J.
Strologas0000-0002-2225-7160 MTA-ELTE Lendület CMS Particle and Nuclear
Physics Group, Eötvös Loránd University, Budapest, Hungary M.
Csanad0000-0002-3154-6925, K. Farkas, M.M.A. Gadallah260000-0002-8305-6661, S.
Lökös270000-0002-4447-4836, P. Major, K. Mandal0000-0002-3966-7182, A.
Mehta0000-0002-0433-4484, G. Pasztor0000-0003-0707-9762, A.J. Rádl, O.
Surányi, G.I. Veres0000-0002-5440-4356 Wigner Research Centre for Physics,
Budapest, Hungary M. Bartók280000-0002-4440-2701, G. Bencze, C.
Hajdu0000-0002-7193-800X, D. Horvath290000-0003-0091-477X, F.
Sikler0000-0001-9608-3901, V. Veszpremi0000-0001-9783-0315 Institute of
Nuclear Research ATOMKI, Debrecen, Hungary S. Czellar, J.
Karancsi280000-0003-0802-7665, J. Molnar, Z. Szillasi, D. Teyssier Institute
of Physics, University of Debrecen, Debrecen, Hungary P. Raics, Z.L.
Trocsanyi300000-0002-2129-1279, B. Ujvari Karoly Robert Campus, MATE Institute
of Technology, Gyongyos, Hungary T. Csorgo310000-0002-9110-9663, F. Nemes31,
T. Novak Indian Institute of Science (IISc), Bangalore, India S. Choudhury,
J.R. Komaragiri0000-0002-9344-6655, D. Kumar, L. Panwar0000-0003-2461-4907,
P.C. Tiwari0000-0002-3667-3843 National Institute of Science Education and
Research, HBNI, Bhubaneswar, India S. Bahinipati320000-0002-3744-5332, C.
Kar0000-0002-6407-6974, P. Mal, T. Mishra0000-0002-2121-3932, V.K.
Muraleedharan Nair Bindhu33, A. Nayak330000-0002-7716-4981, P. Saha, N.
Sur0000-0001-5233-553X, S.K. Swain, D. Vats33 Panjab University, Chandigarh,
India S. Bansal0000-0003-1992-0336, S.B. Beri, V.
Bhatnagar0000-0002-8392-9610, G. Chaudhary0000-0003-0168-3336, S.
Chauhan0000-0001-6974-4129, N. Dhingra340000-0002-7200-6204, R. Gupta, A.
Kaur, M. Kaur0000-0002-3440-2767, S. Kaur, P. Kumari0000-0002-6623-8586, M.
Meena, K. Sandeep0000-0002-3220-3668, J.B. Singh0000-0001-9029-2462, A.K.
Virdi0000-0002-0866-8932 University of Delhi, Delhi, India A. Ahmed, A.
Bhardwaj0000-0002-7544-3258, B.C. Choudhary0000-0001-5029-1887, M. Gola, S.
Keshri0000-0003-3280-2350, A. Kumar0000-0003-3407-4094, M.
Naimuddin0000-0003-4542-386X, P. Priyanka0000-0002-0933-685X, K. Ranjan, A.
Shah0000-0002-6157-2016 Saha Institute of Nuclear Physics, HBNI, Kolkata,
India M. Bharti35, R. Bhattacharya, S. Bhattacharya0000-0002-8110-4957, D.
Bhowmik, S. Dutta, S. Dutta, B. Gomber360000-0002-4446-0258, M. Maity37, P.
Palit0000-0002-1948-029X, P.K. Rout0000-0001-8149-6180, G. Saha, B.
Sahu0000-0002-8073-5140, S. Sarkar, M. Sharan, B. Singh35, S. Thakur35 Indian
Institute of Technology Madras, Madras, India P.K. Behera0000-0002-1527-2266,
S.C. Behera, P. Kalbhor0000-0002-5892-3743, A. Muhammad, R. Pradhan, P.R.
Pujahari, A. Sharma0000-0002-0688-923X, A.K. Sikdar Bhabha Atomic Research
Centre, Mumbai, India D. Dutta0000-0002-0046-9568, V. Jha, V.
Kumar0000-0001-8694-8326, D.K. Mishra, K. Naskar38, P.K. Netrakanti, L.M.
Pant, P. Shukla0000-0001-8118-5331 Tata Institute of Fundamental Research-A,
Mumbai, India T. Aziz, S. Dugad, M. Kumar Tata Institute of Fundamental
Research-B, Mumbai, India S. Banerjee0000-0002-7953-4683, R. Chudasama, M.
Guchait, S. Karmakar, S. Kumar, G. Majumder, K. Mazumdar, S.
Mukherjee0000-0003-3122-0594 Indian Institute of Science Education and
Research (IISER), Pune, India K. Alpana, S. Dube0000-0002-5145-3777, B.
Kansal, A. Laha, S. Pandey0000-0003-0440-6019, A. Rane0000-0001-8444-2807, A.
Rastogi0000-0003-1245-6710, S. Sharma0000-0001-6886-0726 Isfahan University of
Technology, Isfahan, Iran H. Bakhshiansohi390000-0001-5741-3357, E. Khazaie,
M. Zeinali40 Institute for Research in Fundamental Sciences (IPM), Tehran,
Iran S. Chenarani41, S.M. Etesami0000-0001-6501-4137, M.
Khakzad0000-0002-2212-5715, M. Mohammadi Najafabadi0000-0001-6131-5987
University College Dublin, Dublin, Ireland M. Grunewald0000-0002-5754-0388
INFN Sezione di Bari a, Bari, Italy, Università di Bari b, Bari, Italy,
Politecnico di Bari c, Bari, Italy M. Abbresciaa,b0000-0001-8727-7544, R.
Alya,b,420000-0001-6808-1335, C. Arutaa,b, A. Colaleoa0000-0002-0711-6319, D.
Creanzaa,c0000-0001-6153-3044, N. De Filippisa,c0000-0002-0625-6811, M. De
Palmaa,b0000-0001-8240-1913, A. Di Florioa,b, A. Di
Pilatoa,b0000-0002-9233-3632, W. Elmetenaweea,b0000-0001-7069-0252, L.
Fiorea0000-0002-9470-1320, A. Gelmia,b0000-0002-9211-2709, M.
Gula0000-0002-5704-1896, G. Iasellia,c0000-0003-2546-5341, M.
Incea,b0000-0001-6907-0195, S. Lezkia,b0000-0002-6909-774X, G.
Maggia,c0000-0001-5391-7689, M. Maggia0000-0002-8431-3922, I. Margjekaa,b, V.
Mastrapasquaa,b0000-0002-9082-5924, J.A. Merlina, S. Mya,b0000-0002-9938-2680,
S. Nuzzoa,b0000-0003-1089-6317, A. Pellecchiaa,b, A.
Pompilia,b0000-0003-1291-4005, G. Pugliesea,c0000-0001-5460-2638, D. Ramos, A.
Ranieria0000-0001-7912-4062, G. Selvaggia,b0000-0003-0093-6741, L.
Silvestrisa0000-0002-8985-4891, F.M. Simonea,b0000-0002-1924-983X, R.
Vendittia0000-0001-6925-8649, P. Verwilligena0000-0002-9285-8631 INFN Sezione
di Bologna a, Bologna, Italy, Università di Bologna b, Bologna, Italy G.
Abbiendia0000-0003-4499-7562, C. Battilanaa,b0000-0002-3753-3068, D.
Bonacorsia,b0000-0002-0835-9574, L. Borgonovia, L. Brigliadoria, R.
Campaninia,b0000-0002-2744-0597, P. Capiluppia,b0000-0003-4485-1897, A.
Castroa,b0000-0003-2527-0456, F.R. Cavalloa0000-0002-0326-7515, M.
Cuffiania,b0000-0003-2510-5039, G.M. Dallavallea0000-0002-8614-0420, T.
Diotalevia,b0000-0003-0780-8785, F. Fabbria0000-0002-8446-9660, A.
Fanfania,b0000-0003-2256-4117, P. Giacomellia0000-0002-6368-7220, L.
Giommia,b0000-0003-3539-4313, C. Grandia0000-0001-5998-3070, L. Guiduccia,b,
S. Lo Meoa,43, L. Lunertia,b, S. Marcellinia0000-0002-1233-8100, G.
Masettia0000-0002-6377-800X, F.L. Navarriaa,b0000-0001-7961-4889, A.
Perrottaa0000-0002-7996-7139, F. Primaveraa,b0000-0001-6253-8656, A.M.
Rossia,b0000-0002-5973-1305, T. Rovellia,b0000-0002-9746-4842, G.P.
Sirolia,b0000-0002-3528-4125 INFN Sezione di Catania a, Catania, Italy,
Università di Catania b, Catania, Italy S. Albergoa,b,440000-0001-7901-4189,
S. Costaa,b,440000-0001-9919-0569, A. Di Mattiaa0000-0002-9964-015X, R.
Potenzaa,b, A. Tricomia,b,440000-0002-5071-5501, C. Tuvea,b0000-0003-0739-3153
INFN Sezione di Firenze a, Firenze, Italy, Università di Firenze b, Firenze,
Italy G. Barbaglia0000-0002-1738-8676, A. Cassesea0000-0003-3010-4516, R.
Ceccarellia,b, V. Ciullia,b0000-0003-1947-3396, C.
Civininia0000-0002-4952-3799, R. D’Alessandroa,b0000-0001-7997-0306, E.
Focardia,b0000-0002-3763-5267, G. Latinoa,b0000-0002-4098-3502, P.
Lenzia,b0000-0002-6927-8807, M. Lizzoa,b, M. Meschinia0000-0002-9161-3990, S.
Paolettia0000-0003-3592-9509, R. Seiditaa,b, G. Sguazzonia0000-0002-0791-3350,
L. Viliania0000-0002-1909-6343 INFN Laboratori Nazionali di Frascati,
Frascati, Italy L. Benussi0000-0002-2363-8889, S. Bianco0000-0002-8300-4124,
D. Piccolo0000-0001-5404-543X INFN Sezione di Genova a, Genova, Italy,
Università di Genova b, Genova, Italy M. Bozzoa,b0000-0002-1715-0457, F.
Ferroa0000-0002-7663-0805, R. Mulargiaa,b, E. Robuttia0000-0001-9038-4500, S.
Tosia,b0000-0002-7275-9193 INFN Sezione di Milano-Bicocca a, Milano, Italy,
Università di Milano-Bicocca b, Milano, Italy A. Benagliaa0000-0003-1124-8450,
G. Boldrini0000-0001-5490-605X, F. Brivioa,b, F. Cetorellia,b, F. De
Guioa,b0000-0001-5927-8865, M.E. Dinardoa,b0000-0002-8575-7250, P.
Dinia0000-0001-7375-4899, S. Gennaia0000-0001-5269-8517, A.
Ghezzia,b0000-0002-8184-7953, P. Govonia,b0000-0002-0227-1301, L.
Guzzia,b0000-0002-3086-8260, M.T. Lucchinia,b0000-0002-7497-7450, M.
Malbertia, S. Malvezzia0000-0002-0218-4910, A. Massironia0000-0002-0782-0883,
D. Menascea0000-0002-9918-1686, L. Moronia0000-0002-8387-762X, M.
Paganonia,b0000-0003-2461-275X, D. Pedrinia0000-0003-2414-4175, B.S. Pinolini,
S. Ragazzia,b0000-0001-8219-2074, N. Redaellia0000-0002-0098-2716, T.
Tabarelli de Fatisa,b0000-0001-6262-4685, D. Valsecchia,b,20, D.
Zuoloa,b0000-0003-3072-1020 INFN Sezione di Napoli a, Napoli, Italy,
Università di Napoli ’Federico II’ b, Napoli, Italy, Università della
Basilicata c, Potenza, Italy, Università G. Marconi d, Roma, Italy S.
Buontempoa0000-0001-9526-556X, F. Carnevalia,b, N.
Cavalloa,c0000-0003-1327-9058, A. De Iorioa,b0000-0002-9258-1345, F.
Fabozzia,c0000-0001-9821-4151, A.O.M. Iorioa,b0000-0002-3798-1135, L.
Listaa,b0000-0001-6471-5492, S. Meolaa,d,200000-0002-8233-7277, P.
Paoluccia,200000-0002-8773-4781, B. Rossia0000-0002-0807-8772, C.
Sciaccaa,b0000-0002-8412-4072 INFN Sezione di Padova a, Padova, Italy,
Università di Padova b, Padova, Italy, Università di Trento c, Trento, Italy
P. Azzia0000-0002-3129-828X, N. Bacchettaa0000-0002-2205-5737, D.
Biselloa,b0000-0002-2359-8477, P. Bortignona0000-0002-5360-1454, A.
Bragagnoloa,b0000-0003-3474-2099, R. Carlina,b0000-0001-7915-1650, P.
Checchiaa0000-0002-8312-1531, T. Dorigoa0000-0002-1659-8727, U.
Dossellia0000-0001-8086-2863, F. Gasparinia,b0000-0002-1315-563X, U.
Gasparinia,b0000-0002-7253-2669, G. Grosso, S.Y. Hoha,b0000-0003-3233-5123, L.
Layera,45, E. Lusiani0000-0001-8791-7978, M. Margonia,b0000-0003-1797-4330,
A.T. Meneguzzoa,b0000-0002-5861-8140, J. Pazzinia,b0000-0002-1118-6205, M.
Presillaa,b0000-0003-2808-7315, P. Ronchesea,b0000-0001-7002-2051, R.
Rossina,b, F. Simonettoa,b0000-0002-8279-2464, G. Stronga0000-0002-4640-6108,
M. Tosia,b0000-0003-4050-1769, H. Yarara,b, M. Zanettia,b0000-0003-4281-4582,
P. Zottoa,b0000-0003-3953-5996, A. Zucchettaa,b0000-0003-0380-1172, G.
Zumerlea,b0000-0003-3075-2679 INFN Sezione di Pavia a, Pavia, Italy,
Università di Pavia b, Pavia, Italy C. Aime‘a,b, A.
Braghieria0000-0002-9606-5604, S. Calzaferria,b, D.
Fiorinaa,b0000-0002-7104-257X, P. Montagnaa,b, S.P. Rattia,b, V.
Rea0000-0003-0697-3420, C. Riccardia,b0000-0003-0165-3962, P.
Salvinia0000-0001-9207-7256, I. Vaia0000-0003-0037-5032, P.
Vituloa,b0000-0001-9247-7778 INFN Sezione di Perugia a, Perugia, Italy,
Università di Perugia b, Perugia, Italy P. Asenova,460000-0003-2379-9903, G.M.
Bileia0000-0002-4159-9123, D. Ciangottinia,b0000-0002-0843-4108, L.
Fanòa,b0000-0002-9007-629X, P. Laricciaa,b, M. Magherinib, G. Mantovania,b, V.
Mariania,b, M. Menichellia0000-0002-9004-735X, F.
Moscatellia,460000-0002-7676-3106, A. Piccinellia,b0000-0003-0386-0527, A.
Rossia,b0000-0002-2031-2955, A. Santocchiaa,b0000-0002-9770-2249, D.
Spigaa0000-0002-2991-6384, T. Tedeschia,b0000-0002-7125-2905 INFN Sezione di
Pisa a, Pisa, Italy, Università di Pisa b, Pisa, Italy, Scuola Normale
Superiore di Pisa c, Pisa, Italy, Università di Siena d, Siena, Italy P.
Azzurria0000-0002-1717-5654, G. Bagliesia0000-0003-4298-1620, V.
Bertacchia,c0000-0001-9971-1176, L. Bianchinia0000-0002-6598-6865, T.
Boccalia0000-0002-9930-9299, E. Bossinia,b0000-0002-2303-2588, R.
Castaldia0000-0003-0146-845X, M.A. Cioccia,b0000-0003-0002-5462, V.
D’Amantea,d0000-0002-7342-2592, R. Dell’Orsoa0000-0003-1414-9343, M.R. Di
Domenicoa,d0000-0002-7138-7017, S. Donatoa0000-0001-7646-4977, A.
Giassia0000-0001-9428-2296, F. Ligabuea,c0000-0002-1549-7107, E.
Mancaa,c0000-0001-8946-655X, G. Mandorlia,c0000-0002-5183-9020, A.
Messineoa,b0000-0001-7551-5613, F. Pallaa0000-0002-6361-438X, S. Paroliaa,b,
G. Ramirez-Sancheza,c, A. Rizzia,b0000-0002-4543-2718, G.
Rolandia,c0000-0002-0635-274X, S. Roy Chowdhurya,c, A. Scribanoa, N.
Shafieia,b0000-0002-8243-371X, P. Spagnoloa0000-0001-7962-5203, R.
Tenchinia0000-0003-2574-4383, G. Tonellia,b0000-0003-2606-9156, N.
Turinia,d0000-0002-9395-5230, A. Venturia0000-0002-0249-4142, P.G.
Verdinia0000-0002-0042-9507 INFN Sezione di Roma a, Rome, Italy, Sapienza
Università di Roma b, Rome, Italy P. Barriaa0000-0002-3924-7380, M.
Campanaa,b, F. Cavallaria0000-0002-1061-3877, D. Del Rea,b0000-0003-0870-5796,
E. Di Marcoa0000-0002-5920-2438, M. Diemoza0000-0002-3810-8530, E.
Longoa,b0000-0001-6238-6787, P. Meridiania0000-0002-8480-2259, G.
Organtinia,b0000-0002-3229-0781, F. Pandolfia, R.
Paramattia,b0000-0002-0080-9550, C. Quarantaa,b, S.
Rahatloua,b0000-0001-9794-3360, C. Rovellia0000-0003-2173-7530, F.
Santanastasioa,b0000-0003-2505-8359, L. Soffia0000-0003-2532-9876, R.
Tramontanoa,b INFN Sezione di Torino a, Torino, Italy, Università di Torino b,
Torino, Italy, Università del Piemonte Orientale c, Novara, Italy N.
Amapanea,b0000-0001-9449-2509, R. Arcidiaconoa,c0000-0001-5904-142X, S.
Argiroa,b0000-0003-2150-3750, M. Arneodoa,c0000-0002-7790-7132, N.
Bartosika0000-0002-7196-2237, R. Bellana,b0000-0002-2539-2376, A.
Belloraa,b0000-0002-2753-5473, J. Berenguer Antequeraa,b0000-0003-3153-0891,
C. Biinoa0000-0002-1397-7246, N. Cartigliaa0000-0002-0548-9189, S.
Comettia0000-0001-6621-7606, M. Costaa,b0000-0003-0156-0790, R.
Covarellia,b0000-0003-1216-5235, N. Demariaa0000-0003-0743-9465, B.
Kiania,b0000-0001-6431-5464, F. Leggera0000-0003-1400-0709, C.
Mariottia0000-0002-6864-3294, S. Masellia0000-0001-9871-7859, E.
Migliorea,b0000-0002-2271-5192, E. Monteila,b0000-0002-2350-213X, M.
Montenoa0000-0002-3521-6333, M.M. Obertinoa,b0000-0002-8781-8192, G.
Ortonaa0000-0001-8411-2971, L. Pachera,b0000-0003-1288-4838, N.
Pastronea0000-0001-7291-1979, M. Pelliccionia0000-0003-4728-6678, G.L. Pinna
Angionia,b, M. Ruspaa,c0000-0002-7655-3475, K. Shchelinaa0000-0003-3742-0693,
F. Sivieroa,b0000-0002-4427-4076, V. Solaa0000-0001-6288-951X, A.
Solanoa,b0000-0002-2971-8214, D. Soldia,b0000-0001-9059-4831, A.
Staianoa0000-0003-1803-624X, M. Tornagoa,b, D. Trocinoa0000-0002-2830-5872, A.
Vagnerinia,b INFN Sezione di Trieste a, Trieste, Italy, Università di Trieste
b, Trieste, Italy S. Belfortea0000-0001-8443-4460, V.
Candelisea,b0000-0002-3641-5983, M. Casarsaa0000-0002-1353-8964, F.
Cossuttia0000-0001-5672-214X, A. Da Rolda,b0000-0003-0342-7977, G. Della
Riccaa,b0000-0003-2831-6982, G. Sorrentinoa,b, F.
Vazzolera,b0000-0001-8111-9318 Kyungpook National University, Daegu, Korea S.
Dogra0000-0002-0812-0758, C. Huh0000-0002-8513-2824, B. Kim, D.H.
Kim0000-0002-9023-6847, G.N. Kim0000-0002-3482-9082, J. Kim, J. Lee, S.W.
Lee0000-0002-1028-3468, C.S. Moon0000-0001-8229-7829, Y.D.
Oh0000-0002-7219-9931, S.I. Pak, B.C. Radburn-Smith, S.
Sekmen0000-0003-1726-5681, Y.C. Yang Chonnam National University, Institute
for Universe and Elementary Particles, Kwangju, Korea H.
Kim0000-0001-8019-9387, D.H. Moon0000-0002-5628-9187 Hanyang University,
Seoul, Korea B. Francois0000-0002-2190-9059, T.J. Kim0000-0001-8336-2434, J.
Park0000-0002-4683-6669 Korea University, Seoul, Korea S. Cho, S.
Choi0000-0001-6225-9876, Y. Go, B. Hong0000-0002-2259-9929, K. Lee, K.S.
Lee0000-0002-3680-7039, J. Lim, J. Park, S.K. Park, J. Yoo Kyung Hee
University, Department of Physics, Seoul, Republic of Korea, Seoul, Korea J.
Goh0000-0002-1129-2083, A. Gurtu Sejong University, Seoul, Korea H.S.
Kim0000-0002-6543-9191, 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.
Lee0000-0002-1138-3700, S. Lee, B.H. Oh, M. Oh0000-0003-2618-9203, S.B. Oh, H.
Seo0000-0002-3932-0605, U.K. Yang, I. Yoon0000-0002-3491-8026 University of
Seoul, Seoul, Korea W. Jang, D.Y. Kang, Y. Kang, S. Kim, B. Ko, J.S.H.
Lee0000-0002-2153-1519, Y. Lee, I.C. Park, Y. Roh, M.S. Ryu, D. Song, I.J.
Watson0000-0003-2141-3413, S. Yang Yonsei University, Department of Physics,
Seoul, Korea S. Ha, H.D. Yoo Sungkyunkwan University, Suwon, Korea M. Choi, H.
Lee, Y. Lee, I. Yu0000-0003-1567-5548 College of Engineering and Technology,
American University of the Middle East (AUM), Egaila, Kuwait, Dasman, Kuwait
T. Beyrouthy, Y. Maghrbi Riga Technical University, Riga, Latvia T. Torims, V.
Veckalns470000-0003-3676-9711 Vilnius University, Vilnius, Lithuania M.
Ambrozas, A. Carvalho Antunes De Oliveira0000-0003-2340-836X, A.
Juodagalvis0000-0002-1501-3328, A. Rinkevicius0000-0002-7510-255X, G.
Tamulaitis0000-0002-2913-9634 National Centre for Particle Physics, Universiti
Malaya, Kuala Lumpur, Malaysia N. Bin Norjoharuddeen0000-0002-8818-7476,
W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli Universidad de Sonora (UNISON),
Hermosillo, Mexico J.F. Benitez0000-0002-2633-6712, A. Castaneda
Hernandez0000-0003-4766-1546, M. León Coello, J.A. Murillo
Quijada0000-0003-4933-2092, A. Sehrawat, L. Valencia Palomo0000-0002-8736-440X
Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
G. Ayala, H. Castilla-Valdez, E. De La Cruz-Burelo0000-0002-7469-6974, I.
Heredia-De La Cruz480000-0002-8133-6467, R. Lopez-Fernandez, C.A. Mondragon
Herrera, D.A. Perez Navarro, A. Sánchez Hernández0000-0001-9548-0358
Universidad Iberoamericana, Mexico City, Mexico S. Carrillo Moreno, C. Oropeza
Barrera0000-0001-9724-0016, F. Vazquez Valencia Benemerita Universidad
Autonoma de Puebla, Puebla, Mexico I. Pedraza, H.A. Salazar Ibarguen, C. Uribe
Estrada University of Montenegro, Podgorica, Montenegro J. Mijuskovic49, N.
Raicevic University of Auckland, Auckland, New Zealand D.
Krofcheck0000-0001-5494-7302 University of Canterbury, Christchurch, New
Zealand P.H. Butler0000-0001-9878-2140 National Centre for Physics, Quaid-I-
Azam University, Islamabad, Pakistan A. Ahmad, M.I. Asghar, A. Awais, M.I.M.
Awan, H.R. Hoorani, W.A. Khan, M.A. Shah, M. Shoaib0000-0001-6791-8252, M.
Waqas0000-0002-3846-9483 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. Bluj0000-0003-1229-1442, B. Boimska0000-0002-4200-1541, M.
Górski, M. Kazana, M. Szleper0000-0002-1697-004X, P. Zalewski Institute of
Experimental Physics, Faculty of Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, K. Doroba, A. Kalinowski0000-0002-1280-5493, M.
Konecki0000-0001-9482-4841, J. Krolikowski0000-0002-3055-0236, M.
Walczak0000-0002-2664-3317 Laboratório de Instrumentação e Física Experimental
de Partículas, Lisboa, Portugal M. Araujo, P. Bargassa0000-0001-8612-3332, D.
Bastos, A. Boletti0000-0003-3288-7737, P. Faccioli0000-0003-1849-6692, M.
Gallinaro0000-0003-1261-2277, J. Hollar0000-0002-8664-0134, N.
Leonardo0000-0002-9746-4594, T. Niknejad, M. Pisano, J.
Seixas0000-0002-7531-0842, O. Toldaiev0000-0002-8286-8780, J.
Varela0000-0003-2613-3146 Joint Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, D. Budkouski, I. Golutvin, I. Gorbunov0000-0003-3777-6606, V.
Karjavine, V. Korenkov0000-0002-2342-7862, A. Lanev, A. Malakhov, V.
Matveev50,51, V. Palichik, V. Perelygin, M. Savina, D. Seitova, V. Shalaev, S.
Shmatov, S. Shulha, V. Smirnov, O. Teryaev, N. Voytishin, B.S. Yuldashev52, A.
Zarubin, I. Zhizhin Petersburg Nuclear Physics Institute, Gatchina (St.
Petersburg), Russia G. Gavrilov0000-0003-3968-0253, V. Golovtcov, Y. Ivanov,
V. Kim530000-0001-7161-2133, E. Kuznetsova54, V. Murzin, V. Oreshkin, I.
Smirnov, D. Sosnov0000-0002-7452-8380, V. Sulimov, L. Uvarov, S. Volkov, A.
Vorobyev Institute for Nuclear Research, Moscow, Russia Yu.
Andreev0000-0002-7397-9665, A. Dermenev, S. Gninenko0000-0001-6495-7619, N.
Golubev, A. Karneyeu0000-0001-9983-1004, D. Kirpichnikov0000-0002-7177-077X,
M. Kirsanov, N. Krasnikov, A. Pashenkov, G. Pivovarov0000-0001-6435-4463, A.
Toropin Institute for Theoretical and Experimental Physics named by A.I.
Alikhanov of NRC ‘Kurchatov Institute’, Moscow, Russia V. Epshteyn, V.