EXO-18-007

$HeadURL:svn+ssh://svn.cern.ch/reps/tdr2/papers/EXO-18-007/trunk/EXO-18-007.tex$
$Id:EXO-18-007.tex4899732019-02-2321:01:02Zjingyu$ EXO-18-007

# Search for long-lived particles decaying into displaced jets in proton-
proton collisions at $\sqrt{s}=13\TeV$

###### Abstract

A search for long-lived particles decaying into jets is presented. Data were
collected with the CMS detector at the LHC from proton-proton collisions at a
center-of-mass energy of $13\TeV$ in 2016, corresponding to an integrated
luminosity of $35.9\fbinv$. The search examines the distinctive topology of
displaced tracks and secondary vertices. The selected events are found to be
consistent with standard model predictions. For a simplified model in which
long-lived neutral particles are pair produced and decay to two jets, pair
production cross sections larger than $0.2\unit{fb}$ are excluded at $95\%$
confidence level for a long-lived particle mass larger than $1000\GeV$ and
proper decay lengths between 3 and $130\mm$. Several supersymmetry models with
gauge-mediated supersymmetry breaking or $R$-parity violation, where pair-
produced long-lived gluinos or top squarks decay to several final-state
topologies containing displaced jets, are also tested. For these models, in
the mass ranges above $200\GeV$, gluino masses up to 2300–2400$\GeV$ and top
squark masses up to 1350–1600$\GeV$ are excluded for proper decay lengths
approximately between 10 and $100\mm$. These are the most restrictive limits
to date on these models.

##  0.1 Introduction

A large number of extensions to the standard model (SM) predict the production
of long-lived particles at the CERN LHC that can further decay into final
states containing jets. The theoretical motivations are extremely rich [1];
examples include split supersymmetry (SUSY) [2, 3, 4, 5, 6, 7], SUSY with weak
$R$-parity violation (RPV) [8, 9, 10, 11], SUSY with gauge-mediated
supersymmetry breaking (GMSB) [12, 13, 14], “stealth SUSY” [15, 16], “Hidden
Valley” models [17, 18, 19], baryogenesis triggered by weakly interacting
massive particles (WIMPs) [20, 21, 22] and twin Higgs models [23, 24, 25].

In this paper, we search for long-lived particles decaying into jets, with
each long-lived particle having a decay vertex displaced from the production
vertex by up to $55\cm$ in the transverse plane. Events used in this analysis
were collected with the CMS detector [26] at the LHC from proton-proton
($\Pp\Pp$) collisions at a center-of-mass energy of $13\TeV$ in 2016,
corresponding to an integrated luminosity of 35.9. The analysis examines
dijets formed by jets clustered from energy deposits in the calorimeters. For
the displaced-jet signal, the tracks left by charged particles originating
from the decay of a long-lived particle will usually exhibit large
displacements with respect to the primary vertex, allowing the reconstruction
of a secondary vertex within the associated dijet. The properties of the
secondary vertex can be utilized to discriminate between the long-lived
signatures and the SM backgrounds. Although the objects studied here are
dijets, two separate displaced single jets can pass the selection criteria,
even when each displaced vertex contains only one jet. A variety of models
predict long-lived particles decaying into displaced jets and we test several
of them, including SUSY models with GMSB or RPV, as will be discussed in
detail in Section 0.3.

Results of searches for similar long-lived particle signatures at
$\sqrt{s}=8$have been reported by ATLAS [27, 28], CMS [29, 30, 31], and LHCb
[32, 33]. The ATLAS Collaboration has reported on a search at $\sqrt{s}=13$,
which includes a missing transverse momentum requirement [34]. The CMS
Collaboration has reported several long-lived particle searches at
$\sqrt{s}=13\TeV$; one doesn’t utilize secondary vertex information [35], and
another searches for a pair of displaced vertices within the beam pipe [36].
The search presented in this paper is designed to be sensitive to multiple
final-state topologies containing displaced jets, and is therefore sensitive
to a wide range of long-lived particle signatures.

##  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 detectors. Muons
are detected in gas-ionization chambers embedded in the steel flux-return yoke
outside the solenoid.

The silicon tracker measures charged particles in the pseudorapidity range
$\abs{\eta}<2.5$. It consists of 1440 silicon pixel and 15 148 silicon strip
detector modules. For nonisolated particles of $1<\pt<10\GeV$ and
$\abs{\eta}<1.4$, the track resolutions are typically 1.5$\%$ in $\pt$, and
25–90 (45–150)$\mum$ in the transverse (longitudinal) impact parameter [37].

In the region $\abs{\eta}<1.74$, the HCAL cells have widths of $0.087$ in
pseudorapidity and $0.087$ in azimuth. In the $\eta$-$\phi$ plane, and for
$\abs{\eta}<1.48$, the HCAL cells map on to $5{\times}5$ arrays of ECAL
crystals to form calorimeter towers projecting radially outward from the
nominal interaction point. For $\abs{\eta}>1.74$, the coverage of the towers
increases progressively to a maximum of $0.174$ in $\Delta\eta$ and
$\Delta\phi$. Within each tower, the energy deposits in ECAL and HCAL cells
are summed to define the calorimeter tower energies, and are subsequently used
to provide the energies and directions of hadronic jets.

Events of interest are selected using a two-tiered trigger system [38]. The
first level (L1), composed of custom hardware processors, uses information
from the calorimeters and muon detectors to select events at a rate of around
$100\unit{kHz}$ within a time interval of less than 4$\mus$. The second level,
known as the high-level trigger (HLT), consists of a farm of processors
running a version of the full event reconstruction software optimized for fast
processing, and reduces the event rate to around $1\unit{kHz}$ before data
storage.

A more detailed description of the CMS detector, together with a definition of
the coordinate system used and the relevant kinematic variables, can be found
in Ref. [26].

##  0.3 Data sets and simulated samples

Data were collected with a dedicated HLT displaced-jet trigger. At the trigger
level, jets are reconstructed from the energy deposits in the calorimeter
towers, clustered using the anti-$\kt$ algorithm [39, 40] with a distance
parameter of 0.4. In this process, the contribution from each calorimeter
tower is assigned a momentum, the absolute value and the direction of which
are given by the energy measured in the tower and the coordinates of the
tower. The raw jet energy is obtained from the sum of the tower energies, and
the raw jet momentum from the vector sum of the tower momenta, which results
in a nonzero jet mass. The raw jet energies are then corrected [41] to
establish a relative uniform response of the calorimeter in $\eta$ and a
calibrated absolute response in transverse momentum $\pt$.

Events may contain multiple primary vertices, corresponding to multiple
$\Pp\Pp$ collisions occurring in the same bunch crossing. The reconstructed
vertex with the largest value of summed physics-object $\pt^{2}$ is taken to
be the primary $\Pp\Pp$ interaction vertex, referred to as the leading primary
vertex. The physics objects are the “jets,” clustered using the jet finding
algorithm [39, 40] with the tracks assigned to the vertex as inputs, and the
associated missing transverse momentum, taken as the negative vector sum of
the $\pt$ of those jets. More details are given in Section 9.4.1 of Ref. [42].

The displaced-jet trigger requires an $\HT$ larger than 350$\GeV$, where $\HT$
is defined as the scalar sum of the transverse momenta of all jets satisfying
$\pt>40\GeV$ and $\abs{\eta}<2.5$ in the event. The trigger also requires the
presence of at least two jets, each of them satisfying the following
requirements:

  * •

$\pt>40\GeV$ and $\abs{\eta}<2.0$;

  * •

at most two associated prompt tracks, which are tracks having a transverse
impact parameter (with respect to the leading primary vertex) smaller than
1.0$\unit{mm}$; and

  * •

at least one associated displaced track, defined as a track with a transverse
impact parameter (with respect to the leading primary vertex) larger than 0.5,
and an impact parameter significance larger than 5.0, where the significance
is the ratio of the impact parameter to its uncertainty.

The main background of this analysis arises from the SM events comprised
uniquely of jets produced through the strong interaction, referred to as
quantum chromodynamics (QCD) multijet events. The QCD multijet sample is
simulated with $\MGvATNLO$ 2.2.2 [43] at leading order, which is interfaced
with $\PYTHIA$ 8.212 [44] for parton showering, hadronization, and
fragmentation. Jets from the matrix element calculations are matched to parton
shower jets using the MLM algorithm [45]. The CUETP8M1 tune [46] is used for
modeling the underlying event. For parton distribution function (PDF)
modeling, the NNPDF3.0 PDF set [47] is used.

One of the benchmark signal models is a simplified model, referred to as the
jet-jet model, where long-lived scalar neutral particles X are pair-produced
through a $2\to 2$ scattering process, mediated by an off-shell $\PZ$ boson
propagator. Each X particle decays to a quark-antiquark pair, and is assumed
to do so with equal branching fractions to $\cPqu$, $\cPqd$, $\cPqs$, $\cPqc$,
and $\cPqb$ quark pairs. Although X could decay to top quark pairs, we chose a
signature with a simple topology, such that the analysis strategy would be
sensitive to a variety of models. Simulation shows that exclusion of the top
quark pair decay mode leads to only small changes in the signal efficiency.
The chosen signature has two displaced vertices, each of them the origin of
one displaced jet pair. The samples are produced with different resonance
masses ranging from 50 to 3000$\GeV$, and with different proper decay lengths
ranging from $1\mm$ to 10m.

Several SUSY models with long-lived particles are considered, where we mainly
focus on testing SUSY particles with masses larger than $200\GeV$. The first
is a GMSB SUSY model [1], in which the gluino is long lived and then decays to
a gluon and a gravitino, referred to as the $\sGlu\to\Pg\sGra$ model. The
gravitino is assumed to be the lightest supersymmetric particle (LSP) and
manifests itself as missing transverse momentum. The signature is two
displaced vertices, each of them the origin of a single displaced jet and
missing transverse momentum. The samples are produced with gluino masses from
800 to 2500$\GeV$, and a proper decay length varying from 1$\mm$ to 10m.

The second is an RPV SUSY model [48] with minimum flavor violation, where the
gluino is long lived and decays to a top quark and a top squark, the top
squark is assumed to be virtual and decays to a strange antiquark and a bottom
antiquark through the RPV interaction with strength given by the coupling
$\lambda^{\prime\prime}_{323}$ [11], effectively resulting in a three-body
decay with a “multijet” final-state topology. This model is referred to as the
$\sGlu\to\cPqt\cPqb\cPqs$ model. The samples are produced with gluino masses
from 1200 to 3000$\GeV$, and a proper decay length varying from 1$\mm$ to 10m.

Other signal models considered include an RPV SUSY model [49], in which the
long-lived top squark decays to a bottom quark and a charged lepton via RPV
interactions with strengths given by couplings $\Lamp_{331}$,
$\lambda^{\prime}_{332}$, and $\lambda^{\prime}_{333}$ [11], assuming the
decay rate to each of the three lepton flavors to be equal, referred to as the
$\sTop\to\cPqb\ell$ model. The samples are produced with different top squark
masses from 200 to 1600$\GeV$, and a proper decay length varying from 1$\mm$
to 1m.

We also consider another SUSY model motivated by dynamical $R$-parity
violation (dRPV) [50, 51], where the long-lived top squark decays to two down
antiquarks via RPV interaction with strength given by a nonholomorphic RPV
coupling $\eta^{\prime\prime}_{311}$ [52], referred to as the
$\sTop\to\cPaqd\cPaqd$ model. The samples are produced with different top
squark masses from 800 to 1800$\GeV$, and proper decay length varying from
1$\mm$ to 10m.

All signal samples are produced with $\PYTHIA$ 8.212, and NNPDF2.3QED [53] is
used for PDF modeling. When a gluino or top squark is long lived, it will have
enough time to form a hadronic state, an $R$-hadron [9, 54, 55], which is
simulated with $\PYTHIA$. For underlying event modeling the CUETP8M1 tune is
utilized.

Both the background and the signal events are processed with a
$\GEANTfour$-based [56] simulation for detailed CMS detector response. To take
account of the effects of additional $\Pp\Pp$ interactions within the same or
nearby bunch crossings (“pileup”), additional minimum bias events are overlaid
on the simulated events to match the pileup distribution observed in the data.

##  0.4 Event reconstruction and preselection

The offline jet reconstruction and primary vertex selection follow the same
procedures applied at the trigger level (as described in Section 0.3), except
that the full offline information is used. After the trigger selection, events
are selected offline requiring $\HT>400\GeV$; dijet candidates are formed from
all possible pairs of jets in the event, where the jets are required to have
transverse momenta $\pt>50\GeV$ and pseudorapidity $\abs{\eta}<2.0$. These
selection criteria are chosen so that the online $\HT$ and jet $\pt$
requirements in the trigger are fully efficient. The track candidates used in
this analysis are required to have “high purity” and to have transverse
momenta $\pt>1\GeV$. The “high-purity” selection utilizes track information
(including the normalized $\chi^{2}$ of the track fit, the impact parameters,
and the hits in different layers) to reduce the fake rate and is optimized
separately for each iteration of the track reconstruction, so that it is
efficient for selecting tracks with different displacements. More details of
the “high-purity” selection can be found in Section 4.4 of Ref. [37]. The
$\eta$ and $\phi$ of the track are determined by the direction of the momentum
vector at the closest point to the leading primary vertex. The tracks are then
associated with the jets by requiring $\Delta R<0.5$, where $\Delta
R=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}$ and $\Delta\eta$
($\Delta\phi$) is the difference in $\eta$ ($\phi$) between the jet axis and
the track direction. If a track satisfies $\Delta R<0.5$ for more than one
jet, it is associated with the jet with smaller $\Delta R$.

To reconstruct secondary vertices, displaced tracks associated with each dijet
candidate are selected by requiring transverse impact parameters (with respect
to the leading primary vertex) larger than $0.5\mm$ and transverse impact
parameter significances larger than 5. An adaptive vertex fitter algorithm
[57] is then used for reconstructing a possible secondary vertex (containing
at least 2 tracks) with the displaced tracks in each dijet. The adaptive
vertex fitter utilizes an annealing algorithm in which the outlier tracks are
down-weighted for each step, and thus exhibits robustness against outlier
tracks. Only secondary vertices with a $\chi^{2}$ per degree-of-freedom
($\chi^{2}/\mathrm{n_{dof}}$) of less than 5.0 are selected. Also, the four-
momentum of the vertex is reconstructed assuming the pion mass for all
assigned tracks; the invariant mass of the vertex is required to be larger
than $4\GeV$, and the transverse momentum of the vertex is required to be
larger than $8\GeV$, in order to suppress long-lived SM mesons and baryons.

Each dijet candidate is required to have one reconstructed secondary vertex
satisfying the above selection criteria. Furthermore, we select the track with
the second-highest transverse (two-dimensional) impact parameter (IP)
significance among the tracks that are assigned to the secondary vertex (the
highest two-dimensional IP significance is usually more sensitive to the tail
of impact parameter distribution in the background process, and is therefore
less powerful). For displaced-jet signatures, where tracks tend to be more
displaced, the two-dimensional IP significance of this selected track will be
large. If it is smaller than 15, the dijet candidate is rejected. We also
compute the ratio between the sum of energy for all the tracks assigned to the
secondary vertex and the sum of the energy for all the tracks associated with
the two jets. This ratio is expected to be large for displaced-jet signatures,
therefore dijet candidates with a ratio smaller than 0.15 are rejected.

An additional variable, $\zeta$, is defined to characterize the contribution
of prompt activity to the jets. For each track associated with a jet, the
primary vertex (including the leading primary vertex and the pileup vertices)
with the minimum three-dimensional impact parameter significance to the track
is identified. If this minimum three-dimensional impact parameter significance
is smaller than 5, we assign the track to this primary vertex. Then for each
jet, we compute the track energy contribution from each primary vertex, and
the primary vertex with the largest track energy contribution to the jet is
chosen. Finally, we define $\zeta$ as

$\zeta=\frac{\sum_{\mathrm{track\in
PV_{1}}}E_{\mathrm{track}}^{\mathrm{Jet_{1}}}+\sum_{\mathrm{track\in
PV_{2}}}E_{\mathrm{track}}^{\mathrm{Jet_{2}}}}{E_{\mathrm{Jet_{1}}}+E_{\mathrm{Jet_{2}}}},$
(1)

which is the charged energy fraction of the dijet associated with the most
compatible primary vertices. For displaced-jet signatures, $\zeta$ tends to be
small since the jets are not compatible with primary vertices. Dijet
candidates with $\zeta$ larger than 0.2 are rejected.

We do not require the secondary vertex to contain tracks from both jets in the
dijet candidate. Two displaced single jets originating from two separate
displaced vertices can be paired together and pass the selection, thus the
search can be sensitive to long-lived particles decaying to a single jet (as
in the $\sGlu\to\Glu\sGra$ model).

The preselection criteria of the analysis are summarized in Table 0.4. The
variables used in the preselection are checked in data and QCD multijet MC
events, and are found to be well-modeled in the MC events.

Summary of the preselection criteria lc Secondary-vertex/dijet variable &
Requirement
Vertex $\chi^{2}/\mathrm{n_{dof}}$ $<$5.0
Vertex invariant mass $>$$4\GeV$
Vertex transverse momentum $>$$8\GeV$
Second largest two-dimensional IP significance $>$15
Vertex track energy fraction in the dijet $>$0.15
$\zeta$ (charged energy fraction associated with compatible primary vertices)
$<$0.20

##  0.5 Event selection and background prediction

In addition to the secondary vertex reconstruction based on the adaptive
vertex fitter, an auxiliary algorithm is explored. For each displaced track
(as defined in Section 0.3) associated with the dijet, an expected decay point
consistent with the displaced dijet hypothesis is determined by finding the
crossing point between the track helix and the dijet direction in the
transverse plane. The displaced tracks associated with the dijet are then
clustered based on the expected transverse decay length with respect to the
leading primary vertex $L_{xy}^{\mathrm{exp}}$ using a hierarchical clustering
algorithm [58], in which two clusters are merged together when the smallest
expected transverse decay length difference between the two clusters is
smaller than $15\%$ of the transverse decay length ($L_{xy}$) of the secondary
vertex. When more than one cluster is formed after the final step of the
hierarchical clustering, the one closest to the secondary vertex is selected.
The cluster root-mean-square (RMS), which is a relative RMS of individual
tracks $L_{xy}^{\mathrm{exp}}$ with respect to the secondary vertex $L_{xy}$,
is computed to provide signal-background discrimination:

$\mathrm{RMS}_{\mathrm{cluster}}=\sqrt{\frac{1}{N_{\mathrm{tracks}}}\sum_{i=1}^{N_{\mathrm{tracks}}}\frac{(L_{xy}^{\mathrm{exp}}(i)-L_{xy})^{2}}{L_{xy}^{2}}}.$
(2)

We then construct a likelihood discriminant based on three variables:

  * •

vertex track multiplicity;

  * •

vertex $L_{xy}$ significance;

  * •

cluster RMS.

The three variables are chosen so that the correlations between them are
small. The likelihood discriminant, $\mathcal{L}$, is defined as

$\mathcal{L}=\frac{p_{\mathrm{S}}}{p_{\mathrm{S}}+p_{\mathrm{B}}}=\frac{1}{1+p_{\mathrm{B}}/p_{\mathrm{S}}}=\frac{1}{1+\prod_{i}p_{\mathrm{Bi}}/p_{\mathrm{Si}}},$
(3)

where $p_{\mathrm{S}}$($p_{\mathrm{B}}$) is the probability distribution
function of the signal (background), and $i$ is the label for different
variables. Simulated jet-jet model events and simulated QCD multijet events
are used to derive the probability distribution functions, where jet-jet model
events with $m_{\mathrm{X}}=300$ and $1000\GeV$, and with $c\tau_{0}=1$, 3,
10, 30, 100, 300, and $1000\mm$ are added together to derive $p_{\mathrm{S}}$.
When building the likelihood discriminant the trigger requirement is removed,
since the number of simulated events is limited. Figure 1 shows the
distributions of the three variables used to build the likelihood
discriminant, as well as the discriminant itself, with selections on $\HT$ and
jet kinematic variables applied. Simulated signal events for the jet-jet model
with $m_{\mathrm{X}}=300\GeV$ at different proper decay lengths $c\tau_{0}$
are also shown for comparison.

Figure 1:  The distributions of vertex track multiplicity (upper left), vertex
$L_{\mathrm{xy}}$ significance (upper right), cluster RMS (lower left), and
likelihood discriminant (lower right), for data, simulated QCD multijet
events, and simulated signal events. The lower panel of each plot shows the
ratio between the data and the simulated QCD multijet events. Data and
simulated events are selected with the displaced-jet trigger. The offline
$\HT$ is required to be larger than 400, and the jets are required to have
$\pt>50\GeV$ and $\abs{\eta}<2.0$. The error bars and bands represent the
statistical uncertainties of each distribution. Three benchmark signal
distributions are shown (dashed lines) for the jet-jet model with
$m_{X}=300\GeV$ and varying lifetimes. For visualization each signal process
is given a cross section, $\sigma$, such that $\sigma\ 35.9\fbinv=1\times
10^{6}$.

Two other variables are utilized in the event selection. One is the number of
three-dimensional prompt tracks in a single jet, where three-dimensional
prompt tracks are the tracks with three-dimensional impact parameters (with
respect to the leading primary vertex) smaller than $0.3\mm$. The other is the
jet energy fraction carried by two-dimensional prompt tracks, referred to as
the charged prompt energy fraction, where two-dimensional prompt tracks are
those having transverse impact parameters (with respect to the leading primary
vertex) smaller than $0.5\mm$.

If more than one dijet candidate passes the preselections described in Section
0.4, the one with the largest track multiplicity is selected. When the track
multiplicities are the same, the one with the smallest $\chi^{2}$ per degree-
of-freedom is selected. The candidate is then required to pass three final
selection criteria. The first makes a selection on the number of three-
dimensional prompt tracks and on the charged prompt energy fraction for the
leading jet, while the second places a similar requirement on the same
variables for the subleading jet. The third makes a selection on the
discriminant variable $\mathcal{L}$. The three selection criteria are chosen
such that the correlations between them are small for background events. The
numerical values of the selection criteria are chosen by optimizing the signal
sensitivity for the jet-jet model across different proper decay lengths
(1–1000$\mm$) and different X masses (100–1000$\GeV$). The final selection
criteria are determined to be

  * •

Selection 1: for the leading jet in the dijet candidate, the number of three-
dimensional prompt tracks is smaller than 2, the charged prompt energy
fraction is smaller than 15%;

  * •

Selection 2: for the subleading jet in the dijet candidate, the number of
three-dimensional prompt tracks is smaller than 2, the charged prompt energy
fraction is smaller than 13%; and

  * •

Selection 3: $\mathcal{L}$ is larger than 0.9993.

The definition of the different regions used in the background estimation.
cccc Region Selection 1 Selection 2 Selection 3
A Fail Fail Fail
B Pass Fail Fail
C Fail Pass Fail
D Fail Fail Pass
E Fail Pass Pass
F Pass Fail Pass
G Pass Pass Fail
H Pass Pass Pass

For the jet-jet model, when $m_{\mathrm{X}}=1000\GeV$ and after all the
selection criteria are applied, the signal efficiencies for proper decay
lengths $c\tau_{0}=1$, 10, 100, and 1000$\mm$ are 9.7, 57, 45, and 7.8$\%$,
respectively. When $m_{\mathrm{X}}=100\GeV$, the signal efficiencies for
$c\tau_{0}=1$, 10, 100 and, 1000$\mm$ are 0.9, 4.4, 1.6, and 0.2$\%$,
respectively. More details of the signal efficiencies for different signal
models can be found in Tables .9–.9 of Appendix .9.

Based on the three selections above, eight nonoverlapping regions are defined
(regions A–H), as shown in Table 0.5. The signal region (region H) is defined
for events passing all three selections. The rest of the regions (A–G) are
when events fail one or more of the three selections. The background estimate
relies on the three selection criteria having little correlation between them.
The background yield in the signal region H is predicted by different ratios
of event counts in regions A–G, where the ratio G(D+E+F)/(A+B+C) uses the
fraction of events passing to those failing the likelihood discriminant
selection (selection 3) and is taken as the central value of the predicted
background events. Three additional ratios are evaluated using the events
failing one or both of the other two selections (selections 1 and 2):

  * •

cross-check 1: G(D+E)/(A+C), uses events that fail selection 1;

  * •

cross-check 2: G(D+F)/(A+B), uses events that fail selection 2; and

  * •

cross-check 3: G(E+F)/(B+C), uses events that fail either selection 1 or
selection 2.

These cross-checks provide an important test of the robustness of the
background prediction and the assumption that the three selection criteria are
minimally correlated. Differences between the predictions obtained with the
nominal method and the cross-checks are also used to estimate the systematic
uncertainty in the background prediction.

The nominal background prediction and the cross-checks are first tested with
simulated QCD multijet events, and are found to be robust against different
numerical values for the selection criteria. The method is also checked in
data by using a control region defined to be independent to the signal region.
This is achieved by inverting the selection on the vertex track energy
fraction in the dijet, requiring this fraction to be less than 0.15. In
addition, in order to improve the statistical precision in the control region,
the following two requirements are relaxed relative to the baseline selection:

  * •

number of three-dimensional prompt tracks smaller than 4; and

  * •

charged prompt energy fraction smaller than 0.4.

The nominal background prediction and cross-checks are then tested in the
control region for different threshold values of the likelihood discriminant.
The numbers of predicted and observed background events for the nominal
background method and the three cross-checks in the control region are
summarized in Fig. 2 and Table 0.5. The $p$-value of each observation is
computed based on the lower-tail of a Poisson distribution convolved with a
normalized Gaussian function for statistical and systematic uncertainties. The
$p$-value is then converted to a $Z$-value using the error function,

$Z=\sqrt{2}\erf^{-1}[2p-1],$ (4)

which represents the observed significance, expressed as an equivalent number
of standard deviations. The $Z$-values are also listed in the Table 0.5 for
different threshold values of the likelihood discriminant, where the
magnitudes of the $Z$-values are smaller than 1.5 standard deviations.

Figure 2: Numbers of predicted and observed background events for the nominal
background method and the three cross-checks in the control region. Shown are
the comparisons for likelihood discriminant thresholds of 0.3, 0.4, 0.5, 0.6,
0.7, 0.8, and 0.9 (left); and for thresholds of 0.95, 0.96, 0.97, 0.98, 0.99,
and 0.9993 (right). The error bars represent the statistical uncertainties of
the predictions and the observations. The data points at different likelihood
discriminant thresholds are correlated, since the events passing higher
likelihood discriminant thresholds also pass lower likelihood discriminant
thresholds.

The predicted and observed background in the control region for different
likelihood discriminant thresholds. The background predictions are shown
together with their statistical (first) and systematic (second) uncertainties
(the systematic uncertainties in the background predictions are described in
Section 0.6). The observed significances are also shown in terms of
$Z$-values, and are smaller than 1.5 standard deviations. cccc Discriminant
threshold Predicted background Observed background $Z$-value
0.3 $33.9\pm 3.3\pm 4.1$ 27 $-0.80$
0.5 $22.8\pm 2.6\pm 4.6$ 21 $-0.14$
0.7 $18.5\pm 2.3\pm 4.1$ 18 $0.05$
0.9 $14.5\pm 2.0\pm 3.4$ 15 $0.24$
0.99 $8.3\pm 1.5\pm 3.1$ 3 $-1.12$
0.9993 $3.0\pm 0.9\pm 0.5$ 0 $-1.40$

##  0.6 Systematic uncertainties

The systematic uncertainties considered include the uncertainty in the
background prediction, and the uncertainties in the signal yields. The
integrated luminosity uncertainty for the 2016 13$\Pp\Pp$ collision data
recorded by the CMS detector is determined to be 2.5% [59], and is applied as
a systematic uncertainty in the signal yields.

The systematic uncertainty in the background prediction is taken to be the
largest deviation from the nominal background prediction (G(D+E+F)/(A+B+C)) to
the three cross-checks described in Section 0.5, and is found to be 11% for
the background yields in the signal region.

The signal efficiencies are calculated with simulated signal samples. The
uncertainty in the efficiency of the online $\HT$ requirement for the trigger
emulation is determined by measuring the efficiency with the events collected
with an isolated single-muon trigger. The deviation from full efficiency as a
function of offline $\HT$ for events above the offline $\HT$ threshold is
taken as a correction and applied to the signal samples. Half of each of the
corrections for the signal yields are taken as systematic uncertainties, and
are calculated for different masses and proper decay lengths. The largest
correction is 5$\%$, thus a systematic uncertainty of 2.5$\%$ is assigned for
all the signal points.

The uncertainty in the efficiency of the online jet $\pt$ requirement is
obtained by comparing the per-jet efficiency measured using the data collected
with a prescaled $\HT$ trigger that requires $\HT>325\GeV$, with the
efficiency determined from simulated multijet events. Above the offline $\pt$
threshold, both efficiencies are close to 100$\%$, and the difference between
them is negligible, thus no corresponding systematic uncertainty is assigned.

Similarly, the uncertainty in the efficiency of the online tracking
requirement for the trigger emulation is obtained by comparing the per-jet
efficiency measured using the data collected with the prescaled $\HT$ trigger
with the efficiency determined from simulated multijet events. The differences
in the efficiencies between data and simulation are parameterized as functions
of the number of offline prompt and displaced tracks, where the convention of
“prompt” and “displaced” follows the same definitions described in Section
0.3. The difference in the efficiencies is treated as a bias for the
probability of a single jet passing the online tracking requirement, and is
applied to the simulated signal samples. The systematic uncertainty is then
determined by computing the variation of the efficiency for signal events to
have at least two jets passing the online tracking requirement. The largest
variation is 9$\%$–10$\%$ for the considered signal models in the studied
mass-lifetime range, which is taken as the corresponding systematic
uncertainty.

To estimate the uncertainty in the offline vertex reconstruction, the events
selected with the prescaled $\HT$ trigger are utilized, from which dijet
candidates are reconstructed using the same vertex reconstruction procedure
and the same jet kinematics selections as in the offline analysis. We then
compare the data with simulated multijet events in the secondary vertex
transverse decay length and vertex track multiplicity distributions. We find
that the main inconsistency between data and multijet simulation lies in the
vertex track multiplicity. A reweighting factor is therefore extracted as a
function of the number of tracks in the secondary vertex, and is interpreted
as the correction for the vertex survival probabilities. The correction is
then applied to simulated signal samples vertex-by-vertex, and the systematic
uncertainty is obtained by computing the variations of signal efficiencies
after the correction. The uncertainty is found to be 2$\%$–15$\%$ for
different signal models in the tested mass-lifetime range.

The uncertainty in the track reconstruction is estimated by studying the track
impact parameter measurement in the data and in the multijet simulation, using
the events selected with the prescaled $\HT$ trigger. The possible mismodeling
of the impact parameters is taken into account by varying the impact
parameters in the signal samples by the same magnitude. The largest variation
in the signal efficiency is taken as the corresponding uncertainty, and is
found to be 14$\%$–20$\%$ for different signal models.

The jet energy scale uncertainty is obtained by varying the jet energy
correction [41] by one standard deviation. The resulting uncertainty is
2$\%$–4$\%$ for the considered signal models.

The uncertainty in the choice of PDF sets is estimated by reweighting the
signal events using NNPDF3.0, CT14 [60] and MMHT14 [61] PDF sets, and their
associated uncertainty sets [62, 63]. The uncertainty in signal efficiencies
is quantified by comparing the efficiencies calculated with alternative PDF
sets and the ones with the nominal NNPDF set, and is found to be 4$\%$–6$\%$
for the considered signal models.

The uncertainty in the selection of the primary vertex is estimated by
replacing the leading primary vertex with the subleading vertex when
calculating impact parameters and vertex displacement, where the primary
vertices are ordered based on their values of summed physics-object $\pt^{2}$
as described in Section 0.3. The resulting uncertainty in signal efficiency is
found to be 6$\%$–15$\%$ for different signal models in the tested mass-
lifetime range.

A summary of different sources of systematic uncertainties in the signal
yields can be found in Table 0.6. For each signal model, the largest
variations due to each source across the tested mass-lifetime points are taken
as the corresponding systematic uncertainties.

Systematic uncertainties in the signal yields, for each signal model studied.
The quoted values reflect the largest variations due to each source for each
signal model, in the studied range of masses and proper decay lengths. lccccc
Source Jet-jet model $\sGlu\to\Pg\sGra$ $\sGlu\to\cPqt\cPqb\cPqs$
$\sTop\to\cPqb\ell$ $\sTop\to\cPaqd\cPaqd$
Integrated luminosity 2.5$\%$ 2.5% 2.5% 2.5% 2.5%
Online $\HT$ requirement 2.5$\%$ 2.5% 2.5% 2.5% 2.5%
Online tracking requirement 9% 9% 9% 9% 10%
Offline vertexing 15% 2% 6% 5% 2%
Track impact parameter modeling 14% 16% 20% 10% 20%
Jet energy scale 4% 2% 2% 2% 2%
PDF 5$\%$ 6% 4% 5% 4%
Primary vertex selection 6$\%$ 10% 15% 8% 12%
Total 24$\%$ 22% 28% 18% 26%

##  0.7 Results

###  0.7.1 Data in the signal region

We divide the signal region in bins of $\HT$ and the number of dijets passing
the preselection criteria in order to gain sensitivity to long-lived particles
with different masses. After applying all the selection criteria described in
Sections 0.4 and 0.5, we observe one event in the data, in accord with the
total background prediction of $1.03\pm 0.19\stat\pm 0.11\syst$ events. This
observed event has an $\HT$ of 590$\GeV$; and yields a secondary vertex
candidate, with a transverse decay length of 3.5$\cm$ and a track multiplicity
of 10. This is consistent with the presence of a quark jet, where the bottom
hadron travels in the tracker for an extremely long distance before it decays.

Table 0.7.1 shows the predicted background and observations in the different
bins of the signal region, where the sum of the predicted background in the
four bins is consistent with the total background prediction quoted earlier.
We find the observed yield is consistent with the predicted background in all
bins, and we use the results in the four bins to set limits on a variety of
models.

Summary of predicted and observed events in the signal region, for different
$\HT$ and number of dijet candidates values. cccc Selection on $\HT$ Number of
dijets Expected Observed
$400<\HT<450\GeV$ 1 $0.42\pm 0.14\stat\pm 0.01\syst$ 0
$450<\HT<550\GeV$ 1 $0.23\pm 0.08\stat\pm 0.07\syst$ 0
$\HT>550\GeV$ 1 $0.19\pm 0.07\stat\pm 0.05\syst$ 1
—$>$1 $0.16\pm 0.11\stat\pm 0.06\syst$ 0

###  0.7.2 Interpretation of results

We set upper limits on the production cross section versus mass or lifetime
for a given model by computing the 95$\%$ confidence level ($\CL$) associated
with each signal point according to the $\CLs$ prescription [64, 65, 66, 67],
using an LHC-style profile likelihood ratio [66, 67] as the test statistics.
The $\CLs$ values are calculated using the asymptotic approximation [66], and
are verified with full-frequentist results for representative signal points.
The signal yields in the four bins in Table 0.7.1 are utilized to compute the
$\CLs$ values, and the systematic uncertainties are taken to be fully
correlated across the four bins. The bin where more than one dijet candidate
passes the preselection criteria usually brings most of the sensitivity in a
given model since it often has the largest signal efficiency.

Figure 3 presents the expected and observed upper limits (at $95\%$ $\CL$) on
the pair production cross section for the jet-jet model at different scalar
particle X masses and proper decay lengths, assuming a $100\%$ branching
fraction. The limits are most stringent for $c\tau_{0}$ between 3 and
$100\mm$. For smaller decay lengths, the limits become less restrictive
because of the vetoes on prompt activity. Since the tracking efficiency
decreases with larger displacement, the limits also become less stringent for
larger decay lengths when $c\tau_{0}>100\mm$. Pair production cross sections
larger than 0.2fb are excluded at high mass ($m_{\rm{X}}>1000\GeV$) for proper
decay lengths between 3 and 130$\mm$. The lowest pair production cross section
excluded is 0.13fb, at $c\tau_{0}=30\mm$ and long-lived particle mass
$m_{\rm{X}}>1000\GeV$.

Figure 3: The expected and observed 95$\%$ $\CL$ upper limits on the pair
production cross section of the long-lived particle X, assuming a $100\%$
branching fraction for X to decay to a quark-antiquark pair, shown at
different particle X masses and proper decay lengths for the jet-jet model.
The solid (dashed) lines represent the observed (median expected) limits. The
shaded bands represent the regions containing $68\%$ of the distributions of
the expected limits under the background-only hypothesis.

Figure 4 presents the expected and observed upper limits on the pair
production cross section of long-lived gluino in the GMSB $\sGlu\to\Glu\sGra$
model, assuming a $100\%$ branching fraction for the gluino to decay into a
gluon and a gravitino. Although in the $\sGlu\to\Glu\sGra$ signature each
displaced vertex is associated with only one jet, the two separate displaced
single jets can be paired together and pass the selections, therefore the
analysis is sensitive to this kind of signature. When the gluino mass is
2400$\GeV$, gluino pair production cross sections larger than 0.25fb are
excluded for proper decay lengths between 10 and 210$\mm$. When the proper
decay length $c\tau_{0}=1\mm$, the upper limit is insensitive to the gluino
mass in the tested range since the signal acceptance is mainly limited by the
online prompt track requirement in the displaced-jet trigger. The upper limits
on the pair production cross section are then translated into upper limits on
the gluino mass for different proper decay lengths, based on a calculation at
the next-to-leading logarithmic accuracy matched to next-to-leading order
predictions (NLO+NLL) of the gluino pair production cross section at
$\sqrt{s}=13\TeV$ [68, 69, 70, 71, 72] in the limit where all the other SUSY
particles are much heavier and decoupled. Gluino masses up to $2300\GeV$ are
excluded for proper decay lengths between 20 and $110\mm$. The bounds are the
most stringent to date on this model in the tested proper decay length range.

Figure 4: Left: the expected and observed $95\%$ $\CL$ upper limits on the
pair production cross section of the long-lived gluino, assuming a $100\%$
branching fraction for $\sGlu\to\Glu\sGra$ decays. The horizontal lines
indicate the NLO+NLL gluino pair production cross sections for
$m_{\sGlu}=2400\GeV$ and $m_{\sGlu}=1600\GeV$, as well as their variations due
to the uncertainties in the choices of renormalization scales, factorization
scales, and PDF sets. The solid (dashed) lines represent the observed (median
expected) limits, the bands show the regions containing $68\%$ of the
distributions of the expected limits under the background-only hypothesis.
Right: the expected and observed $95\%$ $\CL$ limits for the long-lived gluino
model in the mass-lifetime plane, assuming a 100% branching fraction for
$\sGlu\to\Pg\sGra$ decays, based on the NLO+NLL calculation of the gluino pair
production cross section at $\sqrt{s}=13\TeV$. The thick solid black (dashed
red) line represents the observed (median expected) limits at 95$\%$ CL. The
thin black lines represent the change in the observed limit due to the
variation of the signal cross sections within their theoretical uncertainties.
The thin red lines indicate the region containing $68\%$ of the distribution
of the expected limits under the background-only hypothesis.

Figure 5 presents the expected and observed upper limits on the pair
production cross section of the long-lived gluino in the RPV
$\sGlu\to\cPqt\cPqb\cPqs$ model, assuming a $100\%$ branching fraction for the
gluino to decay to top, bottom, and strange antiquarks. The upper limits on
the pair production cross section are translated into upper limits on the
gluino mass for different proper decay lengths, based on the NLO+NLL
calculation of the gluino pair production cross section at $\sqrt{s}=13\TeV$
[68, 69, 70, 71, 72] in the limit where all the other SUSY particles are much
heavier and decoupled. Gluino masses up to $2400\GeV$ are excluded for proper
decay lengths between 10 and $250\mm$. The bounds are currently the most
stringent on this model for proper decay lengths between $10\mm$ and 10m. A
comparison on this model with the existing CMS search for displaced vertices
within the beam pipe [36] can be found in Fig. 8 of Appendix .9.

Figure 5: Left: the expected and observed $95\%$ $\CL$ upper limits on the
pair production cross section of the long-lived gluino, assuming a $100\%$
branching fraction for $\sGlu\to\cPqt\cPqb\cPqs$ decays. The horizontal lines
indicate the NLO+NLL gluino pair production cross sections for
$m_{\sGlu}=2400\GeV$ and $m_{\sGlu}=1600\GeV$, as well as their variations due
to the uncertainties in the choices of renormalization scales, factorization
scales, and PDF sets. The solid (dashed) lines represent the observed (median
expected) limits, the bands show the regions containing $68\%$ of the
distributions of the expected limits under the background-only hypothesis.
Right: the expected and observed $95\%$ $\CL$ limits for the long-lived gluino
model in the mass-lifetime plane, assuming a $100\%$ branching fraction for
$\sGlu\to\cPqt\cPqb\cPqs$ decays, based on the NLO+NLL calculation of the
gluino pair production cross section at $\sqrt{s}=13\TeV$. The thick solid
black (dashed red) line represents the observed (median expected) limits at
$95\%$ CL. The thin black lines represent the change in the observed limit due
to the variation of the signal cross sections within their theoretical
uncertainties. The thin red lines indicate the region containing $68\%$ of the
distributions of the expected limits under the background-only hypothesis.

Figure 6 presents the expected and observed upper limits on the pair
production cross section of the long-lived top squark in the RPV
$\sTop\to\cPqb\ell$ model, assuming a $100\%$ branching fraction for the top
squark to decay to a bottom quark and a charged lepton. The upper limits on
the pair production cross section are then translated into upper limits on the
top squark mass for different proper decay lengths, based on an NLO+NLL
calculation of the top squark pair production cross section at
$\sqrt{s}=13\TeV$ [68, 69, 70, 71, 72] in the limit where all the other SUSY
particles are much heavier and decoupled. Top squark masses up to $1350\GeV$
are excluded for proper decay lengths between 7 and $110\mm$. The bounds are
currently the most stringent on this model for proper decay lengths between
$3\mm$ and 1m.

Figure 6: Left: the expected and observed $95\%$ $\CL$ upper limits on the
pair production cross section of the long-lived top squark, assuming a $100\%$
branching fraction for $\sTop\to\cPqb\ell$ decays. The horizontal lines
indicate the NLO+NLL top squark pair production cross sections for
$m_{\sTop}=1600\GeV$ and $m_{\sTop}=1000\GeV$, as well as their variations due
to the uncertainties in the choices of renormalization scales, factorization
scales, and PDF sets. The solid (dashed) lines represent the observed (median
expected) limits, the bands show the regions containing $68\%$ of the
distributions of the expected limits under the background-only hypothesis.
Right: the expected and observed $95\%$ limits for the long-lived top squark
model in the mass-lifetime plane, assuming a $100\%$ branching fraction for
$\sTop\to\cPqb\ell$ decays, based on the NLO+NLL calculation of the top squark
pair production cross section at $\sqrt{s}=13\TeV$. The thick solid black
(dashed red) line represents the observed (median expected) limits at $95\%$
CL. The thin black lines represent the change in the observed limit due to the
variation of the signal cross sections within their theoretical uncertainties.
The thin red lines indicate the region containing $68\%$ of the distributions
of the expected limits under the background-only hypothesis.

Figure 7 presents the expected and observed upper limits on the pair
production cross section of the long-lived top squark in the dRPV
$\sTop\to\cPaqd\cPaqd$ model, assuming a $100\%$ branching fraction for the
top squark to decay to two down antiquarks. The upper limits on the pair
production cross section are translated into upper limits on the top squark
mass for different proper decay lengths assuming a $100\%$ branching fraction,
based on the NLO+NLL calculation of the top squark pair production cross
section at $\sqrt{s}=13\TeV$ [68, 69, 70, 71, 72] in the limit where all the
other SUSY particles are much heavier and decoupled. Top squark masses up to
$1600\GeV$ are excluded for proper decay lengths between 10 and $100\mm$. The
bounds are currently the most stringent on this model for proper decay lengths
between $10\mm$ and 10m. A comparison on this model with the existing CMS
search for displaced vertices within the beam pipe [36] can be found in Fig. 8
of Appendix .9.

Figure 7: Left: the expected and observed $95\%$ $\CL$ upper limits on the the
pair production cross section of the long-lived top squark, assuming a $100\%$
branching fraction for $\sTop\to\cPaqd\cPaqd$ decays. The horizontal lines
indicate the NLO+NLL top squark pair production cross sections for
$m_{\sTop}=1600\GeV$ and $m_{\sTop}=1000\GeV$, as well as their variations due
to the uncertainties in the choices of renormalization scales, factorization
scales, and PDF sets. The solid (dashed) lines represent the observed (median
expected) limits, the bands show the regions containing $68\%$ of the
distributions of the expected limits under the background-only hypothesis.
Right: the expected and observed $95\%$ limits for the long-lived top squark
model in the mass-lifetime plane, assuming a $100\%$ branching fraction for
$\sTop\to\cPaqd\cPaqd$ decays, based on an NLO+NLL calculation of the top
squark pair production cross section at $\sqrt{s}=13\TeV$. The thick solid
black (dashed red) line represents the observed (median expected) limits at
$95\%$ CL. The thin black lines represent the change in the observed limit due
to the variation of the signal cross sections within their theoretical
uncertainties. The thin red lines indicate the region containing $68\%$ of the
distribution of the expected limits under the background-only hypothesis.

##  0.8 Summary

A search for long-lived particles decaying to jets is presented, based on
proton-proton collision data collected with the CMS experiment at a center-of-
mass energy of $13\TeV$ in 2016, corresponding to an integrated luminosity of
35.9. The analysis utilizes a dedicated trigger to capture events with
displaced-jet signatures, and exploits jet, track, and secondary vertex
information to discriminate displaced-jet candidate events from those produced
by the standard model and instrumental backgrounds. The observed yields in
data are in agreement with the background predictions. For a variety of
models, we set the best limits to date for long-lived particles with proper
decay lengths approximately between $5\mm$ and 10m. Upper limits are set at
95% confidence level on the pair production cross section of long-lived
neutral particles decaying to two jets, for different masses and proper
lifetimes, and are as low as 0.2fb at high mass ($m_{\mathrm{X}}>1000\GeV$)
for proper decay lengths between 3 and 130. A supersymmetric (SUSY) model with
gauge-mediated supersymmetry breaking (GMSB) is also tested, in which the
long-lived gluino can decay to one jet and a lightest SUSY particle. Upper
limits are set on the pair production cross section of the gluino with
different masses and proper decay lengths $c\tau_{0}$. Pair-produced long-
lived gluinos lighter than $2300\GeV$ are excluded for proper decay lengths
between 20 and $110\mm$. For an $R$-parity violating (RPV) SUSY model, where
the long-lived gluino can decay to top, bottom, and strange antiquarks, pair-
produced gluinos lighter than $2400\GeV$ are excluded for decay lengths
between 10 and $250\mm$. For a second RPV SUSY model, in which the long-lived
top squark can decay to one bottom quark and a charged lepton, pair-produced
long-lived top squarks lighter than $1350\GeV$ are excluded for decay lengths
between 7 and $110\mm$. For another RPV SUSY model where the long-lived top
squark decays to two down antiquarks, pair-produced long-lived top squarks
lighter than $1600\GeV$ are excluded for decay lengths between 10 and
$110\mm$. These are the most stringent limits to date on these models.

###### Acknowledgements.

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

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##  .9 Supplemental information

Tables .9–.9 summarize the signal efficiencies for representative signal
points in jet-jet, $\sGlu\to\Glu\sGra$, $\sGlu\to\cPqt\cPqb\cPqs$,
$\sTop\to\cPqb\ell$, and $\sTop\to\cPaqd\cPaqd$ models. Figure 8 shows the
comparison with the search for displaced vertices in multijet events at
$\sqrt{s}=13\TeV$ with the CMS detector [36], for $\sGlu\to\cPqt\cPqb\cPqs$
and $\sTop\to\cPaqd\cPaqd$ models.

Signal efficiencies (in %) for the jet-jet model at different proper decay
lengths $c\tau_{0}$ and different masses $m_{\mathrm{X}}$. Selection
requirements are cumulative from the first row to the last. Uncertainties are
statistical only.

lccccc Efficiency ($\%$)$m_{\mathrm{X}}$ ($\GeV$) c$\tau_{0}$
$1\mm$ $10\mm$ $100\mm$ $1000\mm$
Trigger 1000 $20.32\pm 0.45$ $82.96\pm 0.91$ 64.58 $\pm$ 0.80 12.94 $\pm$ 0.36
Preselection 17.99 $\pm$ 0.42 80.54 $\pm$ 0.90 61.40 $\pm$ 0.78 11.29 $\pm$
0.34
Final selection 9.69 $\pm$ 0.31 57.23 $\pm$ 0.76 44.86 $\pm$ 0.67 7.79 $\pm$
0.28

Trigger 300 18.86 $\pm$ 0.43 69.00 $\pm$ 0.83 42.44 $\pm$ 0.65 6.27 $\pm$ 0.25
Preselection 14.22 $\pm$ 0.37 60.94 $\pm$ 0.78 36.53 $\pm$ 0.60 4.83 $\pm$
0.22
Final selection 6.98 $\pm$ 0.26 39.51 $\pm$ 0.63 22.0 $\pm$ 0.47 2.82 $\pm$
0.17

Trigger 100 3.10 $\pm$ 0.10 10.3 $\pm$ 0.18 4.91 $\pm$ 0.13 0.50 $\pm$ 0.04
Preselection 2.13 $\pm$ 0.08 7.91 $\pm$ 0.16 3.48 $\pm$ 0.11 0.34 $\pm$ 0.03
Final selection 0.92 $\pm$ 0.06 4.41 $\pm$ 0.12 1.64 $\pm$ 0.07 0.17 $\pm$
0.02

Signal efficiencies (in %) for pair produced long-lived gluinos decaying to a
gluon and a gravitino at different proper decay lengths $c\tau_{0}$ and
different gluino masses $m_{\sGlu}$. Selection requirements are cumulative
from the first row to the last. Uncertainties are statistical only. lccccc
Efficiency ($\%$)$m_{\sGlu}$ ($\GeV$) c$\tau_{0}$
$1\mm$ $10\mm$ $100\mm$ $1000\mm$
Trigger 2400 6.58 $\pm$ 0.12 62.49 $\pm$ 0.35 75.45 $\pm$ 0.39 27.55 $\pm$
0.25
Preselection 4.80 $\pm$ 0.10 57.62 $\pm$ 0.34 68.36 $\pm$ 0.37 22.58 $\pm$
0.21
Final selection 2.02 $\pm$ 0.06 31.73 $\pm$ 0.25 43.45 $\pm$ 0.29 14.18 $\pm$
0.17

Trigger 1800 7.43 $\pm$ 0.12 61.48 $\pm$ 0.35 70.12 $\pm$ 0.37 22.31 $\pm$
0.21
Preselection 5.56 $\pm$ 0.11 56.05 $\pm$ 0.33 62.05 $\pm$ 0.35 18.05 $\pm$
0.19
Final selection 2.19 $\pm$ 0.07 29.91 $\pm$ 0.24 37.73 $\pm$ 0.27 10.88 $\pm$
0.15

Trigger 1000 7.42 $\pm$ 0.13 55.92 $\pm$ 0.34 57.58 $\pm$ 0.34 13.71 $\pm$
0.16
Preselection 5.47 $\pm$ 0.11 48.55 $\pm$ 0.31 47.13 $\pm$ 0.31 $10.52\pm 0.15$
Final selection 1.96 $\pm$ 0.06 23.48 $\pm$ 0.22 25.78 $\pm$ 0.23 5.52 $\pm$
0.11

Signal efficiencies (in %) for pair produced long-lived gluinos decaying to
top, bottom and strange antiquarks at different proper decay lengths
$c\tau_{0}$ and different gluino masses $m_{\sGlu}$. Selection requirements
are cumulative from the first row to the last. Uncertainties are statistical
only. lccccc Efficiency ($\%$)$m_{\sGlu}$ ($\GeV$) c$\tau_{0}$
$1\mm$ $10\mm$ $100\mm$ $1000\mm$
Trigger 2400 12.53 $\pm$ 0.41 80.30 $\pm$ 4.90 85.00 $\pm$ 1.10 33.83 $\pm$
0.41
Preselection 10.48 $\pm$ 0.37 79.70 $\pm$ 4.88 84.57 $\pm$ 1.10 32.01 $\pm$
0.40
Final selection 4.42 $\pm$ 0.24 51.04 $\pm$ 3.90 60.35 $\pm$ 0.93 21.55 $\pm$
0.33

Trigger 1800 14.95 $\pm$ 0.28 78.94 $\pm$ 0.64 82.93 $\pm$ 0.65 28.81 $\pm$
0.38
Preselection 10.48 $\pm$ 0.37 79.70 $\pm$ 4.88 84.57 $\pm$ 1.10 32.01 $\pm$
0.40
Final selection 4.42 $\pm$ 0.24 51.04 $\pm$ 3.90 60.35 $\pm$ 0.93 21.55 $\pm$
0.33

Trigger 1200 18.30 $\pm$ 0.30 78.32 $\pm$ 0.63 77.75 $\pm$ 0.63 23.39 $\pm$
0.34
Preselection 15.21 $\pm$ 0.28 76.92 $\pm$ 0.62 76.94 $\pm$ 0.63 $21.45\pm
0.33$
Final selection 5.21 $\pm$ 0.16 43.01 $\pm$ 0.47 48.40 $\pm$ 0.50 12.03 $\pm$
0.24

Signal efficiencies (in %) for pair produced long-lived top squarks decaying
to a bottom quark and a lepton at different proper decay lengths $c\tau_{0}$
and different top squark masses $m_{\sTop}$. Selection requirements are
cumulative from the first row to the last. Uncertainties are statistical only.
lccccc Efficiency ($\%$)$m_{\sTop}$ ($\GeV$) c$\tau_{0}$
$1\mm$ $10\mm$ $100\mm$ $1000\mm$
Trigger 1500 6.44 $\pm$ 0.19 48.12 $\pm$ 0.52 46.33 $\pm$ 0.53 12.02 $\pm$
0.26
Preselection 3.79 $\pm$ 0.14 37.11 $\pm$ 0.45 32.39 $\pm$ 0.45 6.24 $\pm$ 0.19
Final selection 1.57 $\pm$ 0.09 20.53 $\pm$ 0.34 17.47 $\pm$ 0.33 3.02 $\pm$
0.13

Trigger 1200 6.68 $\pm$ 0.09 46.25 $\pm$ 0.23 42.98 $\pm$ 0.21 8.71$\pm$ 0.10
Preselection 4.34 $\pm$ 0.07 38.28 $\pm$ 0.21 33.62 $\pm$ 0.19 5.83 $\pm$ 0.08
Final selection 1.55 $\pm$ 0.04 18.41 $\pm$ 0.14 16.54 $\pm$ 0.14 2.63 $\pm$
0.05

Trigger 600 6.99 $\pm$ 0.08 41.12 $\pm$ 0.21 32.65 $\pm$ 0.19 5.28 $\pm$ 0.08
Preselection 3.53 $\pm$ 0.06 29.69 $\pm$ 0.18 22.34 $\pm$ 0.16 3.06 $\pm$ 0.06
Final selection 0.88 $\pm$ 0.03 11.63 $\pm$ 0.11 8.71 $\pm$ 0.10 1.10 $\pm$
0.03

Signal efficiencies (in %) for pair produced long-lived top squarks decaying
to two down antiquarks at different proper decay lengths $c\tau_{0}$ and
different top squark masses $m_{\sTop}$. Selection requirements are cumulative
from the first row to the last. Uncertainties are statistical only. lccccc
Efficiency ($\%$)$m_{\sTop}$ ($\GeV$) c$\tau_{0}$
$1\mm$ $10\mm$ $100\mm$ $1000\mm$
Trigger 1600 12.05 $\pm$ 0.25 74.66 $\pm$ 0.62 77.19 $\pm$ 0.98 24.03 $\pm$
0.54
Preselection 10.27 $\pm$ 0.23 72.70 $\pm$ 0.61 75.01 $\pm$ 0.97 21.43 $\pm$
0.51
Final selection 5.36 $\pm$ 0.16 48.75 $\pm$ 0.50 53.59 $\pm$ 0.81 14.65 $\pm$
0.42

Trigger 1200 12.31 $\pm$ 0.25 73.74 $\pm$ 0.61 73.92 $\pm$ 0.61 $20.26\pm
0.48$
Preselection 10.46 $\pm$ 0.23 71.55 $\pm$ 0.60 71.36 $\pm$ 0.60 18.04 $\pm$
0.45
Final selection 5.13 $\pm$ 0.16 48.04 $\pm$ 0.49 49.44 $\pm$ 0.50 12.27 $\pm$
0.37

Trigger 600 12.02 $\pm$ 0.37 71.75 $\pm$ 0.89 67.03 $\pm$ 0.92 16.27 $\pm$
0.29
Preselection 9.97 $\pm$ 0.33 69.08 $\pm$ 0.88 63.60 $\pm$ 0.89 14.38 $\pm$
0.26
Final selection 4.90 $\pm$ 0.23 45.68 $\pm$ 0.71 42.61 $\pm$ 0.72 9.08 $\pm$
0.21

Figure 8: Comparison with search for displaced vertices in multijet events at
$\sqrt{s}=13\TeV$ with the CMS detector [36] (referred to as the CMS DV
search) for $\sGlu\to\cPqt\cPqb\cPqs$ (left) and $\sTop\to\cPaqd\cPaqd$
(right) models. The CMS DV search looks for a pair of displaced vertices
within the beam pipe. The observed limits obtained by the CMS DV search
(purple curves) are overlaid with the limits obtained by the search presented
in this paper in the mass-lifetime plane, and are good complements for proper
decay length $c\tau_{0}<10\mm$ in these two signal models.

##  .10 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan, A. Tumasyan Institut für Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, E. Asilar, T. Bergauer, J. Brandstetter, M. Dragicevic,
J. Erö, A. Escalante Del Valle, M. Flechl, R. Frühwirth1, V.M. Ghete, J.
Hrubec, M. Jeitler1, N. Krammer, I. Krätschmer, D. Liko, T. Madlener, I.
Mikulec, N. Rad, H. Rohringer, J. Schieck1, R. Schöfbeck, M. Spanring, D.
Spitzbart, W. Waltenberger, J. Wittmann, C.-E. Wulz1, M. Zarucki Institute for
Nuclear Problems, Minsk, Belarus
V. Chekhovsky, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
E.A. De Wolf, D. Di Croce, X. Janssen, J. Lauwers, A. Lelek, M. Pieters, H.
Van Haevermaet, P. Van Mechelen, N. Van Remortel Vrije Universiteit Brussel,
Brussel, Belgium
S. Abu Zeid, F. Blekman, J. D’Hondt, J. De Clercq, K. Deroover, G. Flouris, D.
Lontkovskyi, S. Lowette, I. Marchesini, S. Moortgat, L. Moreels, Q. Python, K.
Skovpen, S. Tavernier, W. Van Doninck, P. Van Mulders, I. Van Parijs
Université Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, B.
Dorney, G. Fasanella, L. Favart, A. Grebenyuk, A.K. Kalsi, T. Lenzi, J.
Luetic, N. Postiau, E. Starling, L. Thomas, C. Vander Velde, P. Vanlaer, D.
Vannerom, Q. Wang Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, A. Fagot, M. Gul, I. Khvastunov2, D. Poyraz, C. Roskas,
D. Trocino, M. Tytgat, W. Verbeke, B. Vermassen, M. Vit, N. Zaganidis
Université Catholique de Louvain, Louvain-la-Neuve, Belgium
H. Bakhshiansohi, O. Bondu, S. Brochet, G. Bruno, C. Caputo, P. David, C.
Delaere, M. Delcourt, A. Giammanco, G. Krintiras, V. Lemaitre, A. Magitteri,
K. Piotrzkowski, A. Saggio, M. Vidal Marono, P. Vischia, S. Wertz, J. Zobec
Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
F.L. Alves, G.A. Alves, G. Correia Silva, C. Hensel, A. Moraes, M.E. Pol, P.
Rebello Teles Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, C. De Oliveira Martins, S.
Fonseca De Souza, H. Malbouisson, D. Matos Figueiredo, M. Melo De Almeida, C.
Mora Herrera, L. Mundim, H. Nogima, W.L. Prado Da Silva, L.J. Sanchez Rosas,
A. Santoro, A. Sznajder, M. Thiel, E.J. Tonelli Manganote3, F. Torres Da Silva
De Araujo, A. Vilela Pereira Universidade Estadual Paulista a, Universidade
Federal do ABC b, São Paulo, Brazil
S. Ahujaa, C.A. Bernardesa, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M.
Gregoresb, P.G. Mercadanteb, S.F. Novaesa, SandraS. Padulaa Institute for
Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia,
Bulgaria
A. Aleksandrov, R. Hadjiiska, P. Iaydjiev, A. Marinov, M. Misheva, M. Rodozov,
M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
A. Dimitrov, L. Litov, B. Pavlov, P. Petkov Beihang University, Beijing, China
W. Fang5, X. Gao5, L. Yuan Institute of High Energy Physics, Beijing, China
M. Ahmad, J.G. Bian, G.M. Chen, H.S. Chen, M. Chen, Y. Chen, C.H. Jiang, D.
Leggat, H. Liao, Z. Liu, S.M. Shaheen6, A. Spiezia, J. Tao, E. Yazgan, H.
Zhang, S. Zhang6, J. Zhao State Key Laboratory of Nuclear Physics and
Technology, Peking University, Beijing, China
Y. Ban, G. Chen, A. Levin, J. Li, L. Li, Q. Li, Y. Mao, S.J. Qian, D. Wang
Tsinghua University, Beijing, China
Y. Wang Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C.A. Carrillo Montoya, L.F. Chaparro Sierra, C. Florez,
C.F. González Hernández, M.A. Segura Delgado University of Split, Faculty of
Electrical Engineering, Mechanical Engineering and Naval Architecture, Split,
Croatia
B. Courbon, N. Godinovic, D. Lelas, I. Puljak, T. Sculac University of Split,
Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, K. Kadija, B. Mesic, M. Roguljic, A. Starodumov7,
T. Susa University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, M. Kolosova, G. Mavromanolakis, J. Mousa, C. Nicolaou,
F. Ptochos, P.A. Razis, H. Rykaczewski Charles University, Prague, Czech
Republic
M. Finger8, M. Finger Jr.8 Escuela Politecnica Nacional, Quito, Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
S. Elgammal9, A. Mahrous10, Y. Mohammed11 National Institute of Chemical
Physics and Biophysics, Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, M. Raidal, C. Veelken Department of Physics, University of Helsinki,
Helsinki, Finland
P. Eerola, H. Kirschenmann, J. Pekkanen, M. Voutilainen Helsinki Institute of
Physics, Helsinki, Finland
J. Havukainen, J.K. Heikkilä, T. Järvinen, V. Karimäki, R. Kinnunen, T.
Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, P. Luukka, T.
Mäenpää, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta University of
Technology, Lappeenranta, Finland
T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F. Ferri, S.
Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry, C. Leloup,
E. Locci, J. Malcles, G. Negro, J. Rander, A. Rosowsky, M.Ö. Sahin, M. Titov
Laboratoire Leprince-Ringuet, Ecole polytechnique, CNRS/IN2P3, Université
Paris-Saclay, Palaiseau, France
A. Abdulsalam12, C. Amendola, I. Antropov, F. Beaudette, P. Busson, C.
Charlot, R. Granier de Cassagnac, I. Kucher, A. Lobanov, J. Martin Blanco, C.
Martin Perez, M. Nguyen, C. Ochando, G. Ortona, P. Paganini, J. Rembser, R.
Salerno, J.B. Sauvan, Y. Sirois, A.G. Stahl Leiton, A. Zabi, A. Zghiche
Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France
J.-L. Agram13, J. Andrea, D. Bloch, J.-M. Brom, E.C. Chabert, V. Cherepanov,
C. Collard, E. Conte13, J.-C. Fontaine13, D. Gelé, U. Goerlach, M. Jansová,
A.-C. Le Bihan, N. Tonon, P. Van Hove Centre de Calcul de l’Institut National
de Physique Nucleaire et de Physique des Particules, CNRS/IN2P3, Villeurbanne,
France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, N. Chanon, R. Chierici, D. Contardo, P.
Depasse, H. El Mamouni, J. Fay, L. Finco, S. Gascon, M. Gouzevitch, G.
Grenier, B. Ille, F. Lagarde, I.B. Laktineh, H. Lattaud, M. Lethuillier, L.
Mirabito, S. Perries, A. Popov14, V. Sordini, G. Touquet, M. Vander Donckt, S.
Viret Georgian Technical University, Tbilisi, Georgia
T. Toriashvili15 Tbilisi State University, Tbilisi, Georgia
D. Lomidze RWTH Aachen University, I. Physikalisches Institut, Aachen, Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, M. Preuten, M.P.
Rauch, C. Schomakers, J. Schulz, M. Teroerde, B. Wittmer RWTH Aachen
University, III. Physikalisches Institut A, Aachen, Germany
A. Albert, D. Duchardt, M. Erdmann, S. Erdweg, T. Esch, R. Fischer, S. Ghosh,
A. Güth, T. Hebbeker, C. Heidemann, K. Hoepfner, H. Keller, L. Mastrolorenzo,
M. Merschmeyer, A. Meyer, P. Millet, S. Mukherjee, T. Pook, M. Radziej, H.
Reithler, M. Rieger, A. Schmidt, D. Teyssier, S. Thüer RWTH Aachen University,
III. Physikalisches Institut B, Aachen, Germany
G. Flügge, O. Hlushchenko, T. Kress, T. Müller, A. Nehrkorn, A. Nowack, C.
Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl16 Deutsches Elektronen-
Synchrotron, Hamburg, Germany
M. Aldaya Martin, T. Arndt, C. Asawatangtrakuldee, I. Babounikau, K.
Beernaert, O. Behnke, U. Behrens, A. Bermúdez Martínez, D. Bertsche, A.A. Bin
Anuar, K. Borras17, V. Botta, A. Campbell, P. Connor, C. Contreras-Campana, V.
Danilov, A. De Wit, M.M. Defranchis, C. Diez Pardos, D. Domínguez Damiani, G.
Eckerlin, T. Eichhorn, A. Elwood, E. Eren, E. Gallo18, A. Geiser, J.M. Grados
Luyando, A. Grohsjean, M. Guthoff, M. Haranko, A. Harb, H. Jung, M. Kasemann,
J. Keaveney, C. Kleinwort, J. Knolle, D. Krücker, W. Lange, T. Lenz, J.
Leonard, K. Lipka, W. Lohmann19, R. Mankel, I.-A. Melzer-Pellmann, A.B. Meyer,
M. Meyer, M. Missiroli, G. Mittag, J. Mnich, V. Myronenko, S.K. Pflitsch, D.
Pitzl, A. Raspereza, A. Saibel, M. Savitskyi, P. Saxena, P. Schütze, C.
Schwanenberger, R. Shevchenko, A. Singh, H. Tholen, O. Turkot, A. Vagnerini,
M. Van De Klundert, G.P. Van Onsem, R. Walsh, Y. Wen, K. Wichmann, C. Wissing,
O. Zenaiev University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, T. Dreyer, A. Ebrahimi, E.
Garutti, D. Gonzalez, P. Gunnellini, J. Haller, A. Hinzmann, A. Karavdina, G.
Kasieczka, R. Klanner, R. Kogler, N. Kovalchuk, S. Kurz, V. Kutzner, J. Lange,
D. Marconi, J. Multhaup, M. Niedziela, C.E.N. Niemeyer, D. Nowatschin, A.
Perieanu, A. Reimers, O. Rieger, C. Scharf, P. Schleper, S. Schumann, J.
Schwandt, J. Sonneveld, H. Stadie, G. Steinbrück, F.M. Stober, M. Stöver, B.
Vormwald, I. Zoi Karlsruher Institut fuer Technologie, Karlsruhe, Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, E. Butz, R. Caspart, T. Chwalek, F.
Colombo, W. De Boer, A. Dierlamm, K. El Morabit, N. Faltermann, B. Freund, M.
Giffels, M.A. Harrendorf, F. Hartmann16, S.M. Heindl, U. Husemann, I.
Katkov14, S. Kudella, S. Mitra, M.U. Mozer, Th. Müller, M. Musich, M. Plagge,
G. Quast, K. Rabbertz, M. Schröder, I. Shvetsov, H.J. Simonis, R. Ulrich, S.
Wayand, M. Weber, T. Weiler, C. Wöhrmann, R. Wolf Institute of Nuclear and
Particle Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas, G. Paspalaki
National and Kapodistrian University of Athens, Athens, Greece
A. Agapitos, G. Karathanasis, P. Kontaxakis, A. Panagiotou, I. Papavergou, N.
Saoulidou, K. Vellidis National Technical University of Athens, Athens, Greece
K. Kousouris, I. Papakrivopoulos, G. Tsipolitis University of Ioánnina,
Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, N.
Manthos, I. Papadopoulos, E. Paradas, J. Strologas, F.A. Triantis, D.
Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
M. Bartók20, M. Csanad, N. Filipovic, P. Major, M.I. Nagy, G. Pasztor, O.
Surányi, G.I. Veres Wigner Research Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath21, Á. Hunyadi, F. Sikler, T.Á. Vámi, V.
Veszpremi, G. Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear
Research ATOMKI, Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi20, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
P. Raics, Z.L. Trocsanyi, B. Ujvari Indian Institute of Science (IISc),
Bangalore, India
S. Choudhury, J.R. Komaragiri, P.C. Tiwari National Institute of Science
Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati23, C. Kar, P. Mal, K. Mandal, A. Nayak24, S. Roy Chowdhury, D.K.
Sahoo23, S.K. Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, R. Chawla, N. Dhingra, R.
Gupta, A. Kaur, M. Kaur, S. Kaur, P. Kumari, M. Lohan, M. Meena, A. Mehta, K.
Sandeep, S. Sharma, J.B. Singh, A.K. Virdi, G. Walia University of Delhi,
Delhi, India
A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, Ashok Kumar, S.
Malhotra, M. Naimuddin, P. Priyanka, K. Ranjan, Aashaq Shah, R. Sharma Saha
Institute of Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj25, M. Bharti25, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep25,
D. Bhowmik, S. Dey, S. Dutt25, S. Dutta, S. Ghosh, M. Maity26, K. Mondal, S.
Nandan, A. Purohit, P.K. Rout, A. Roy, G. Saha, S. Sarkar, T. Sarkar26, M.
Sharan, B. Singh25, S. Thakur25 Indian Institute of Technology Madras, Madras,
India
P.K. Behera, A. Muhammad Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, D.K. Mishra, P.K. Netrakanti, L.M.
Pant, P. Shukla, P. Suggisetti Tata Institute of Fundamental Research-A,
Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, G.B. Mohanty, N. Sur, RavindraKumar Verma Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, Sa. Jain, S.
Karmakar, S. Kumar, G. Majumder, K. Mazumdar, N. Sahoo Indian Institute of
Science Education and Research (IISER), Pune, India
S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, A.
Rastogi, S. Sharma Institute for Research in Fundamental Sciences (IPM),
Tehran, Iran
S. Chenarani27, E. Eskandari Tadavani, S.M. Etesami27, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, F. Rezaei Hosseinabadi, B. Safarzadeh28, M.
Zeinali University College Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L. Cristellaa,b,
N. De Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, F. Erricoa,b, L. Fiorea,
A. Gelmia,b, G. Iasellia,c, M. Incea,b, S. Lezkia,b, G. Maggia,c, M. Maggia,
G. Minielloa,b, S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, R.
Radognaa, A. Ranieria, G. Selvaggia,b, A. Sharmaa, L. Silvestrisa, R.
Vendittia, P. Verwilligena INFN Sezione di Bologna a, Università di Bologna b,
Bologna, Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia,b, S. Braibant-
Giacomellia,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
S.S. Chhibraa,b, G. Codispotia,b, M. Cuffiania,b, G.M. Dallavallea, F.
Fabbria, A. Fanfania,b, E. Fontanesi, P. Giacomellia, C. Grandia, L.
Guiduccia,b, F. Iemmia,b, S. Lo Meoa,29, S. Marcellinia, G. Masettia, A.
Montanaria, F.L. Navarriaa,b, A. Perrottaa, F. Primaveraa,b, A.M. Rossia,b, T.
Rovellia,b, G.P. Sirolia,b, N. Tosia INFN Sezione di Catania a, Università di
Catania b, Catania, Italy
S. Albergoa,b, A. Di Mattiaa, R. Potenzaa,b, A. Tricomia,b, C. Tuvea,b INFN
Sezione di Firenze a, Università di Firenze b, Firenze, Italy
G. Barbaglia, K. Chatterjeea,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, G. Latino, P. Lenzia,b, M. Meschinia, S.
Paolettia, L. Russoa,30, G. Sguazzonia, D. Stroma, L. Viliania INFN Laboratori
Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, F. Fabbri, D. Piccolo INFN Sezione di Genova a,
Università di Genova b, Genova, Italy
F. Ferroa, R. Mulargiaa,b, E. Robuttia, S. Tosia,b INFN Sezione di Milano-
Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschib, F. Brivioa,b, V. Cirioloa,b,16, S. Di Guidaa,b,16,
M.E. Dinardoa,b, S. Fiorendia,b, S. Gennaia, A. Ghezzia,b, P. Govonia,b, M.
Malbertia,b, S. Malvezzia, D. Menascea, F. Monti, L. Moronia, M. Paganonia,b,
D. Pedrinia, S. Ragazzia,b, T. Tabarelli de Fatisa,b, D. Zuoloa,b INFN Sezione
di Napoli a, Università di Napoli ’Federico II’ b, Napoli, Italy, Università
della Basilicata c, Potenza, Italy, Università G. Marconi d, Roma, Italy
S. Buontempoa, N. Cavalloa,c, A. De Iorioa,b, A. Di Crescenzoa,b, F.
Fabozzia,c, F. Fiengaa, G. Galatia, A.O.M. Iorioa,b, L. Listaa, S.
Meolaa,d,16, P. Paoluccia,16, C. Sciaccaa,b, E. Voevodinaa,b INFN Sezione di
Padova a, Università di Padova b, Padova, Italy, Università di Trento c,
Trento, Italy
P. Azzia, N. Bacchettaa, D. Biselloa,b, A. Bolettia,b, A. Bragagnolo, R.
Carlina,b, P. Checchiaa, M. Dall’Ossoa,b, P. De Castro Manzanoa, T. Dorigoa,
U. Dossellia, F. Gasparinia,b, U. Gasparinia,b, A. Gozzelinoa, S.Y. Hoh, S.
Lacapraraa, P. Lujan, M. Margonia,b, A.T. Meneguzzoa,b, J. Pazzinia,b, M.
Presillab, P. Ronchesea,b, R. Rossina,b, F. Simonettoa,b, A. Tiko, E.
Torassaa, M. Tosia,b, M. Zanettia,b, P. Zottoa,b, G. Zumerlea,b INFN Sezione
di Pavia a, Università di Pavia b, Pavia, Italy
A. Braghieria, A. Magnania, P. Montagnaa,b, S.P. Rattia,b, V. Rea, M.
Ressegottia,b, C. Riccardia,b, P. Salvinia, I. Vaia,b, P. Vituloa,b INFN
Sezione di Perugia a, Università di Perugia b, Perugia, Italy
M. Biasinia,b, G.M. Bileia, C. Cecchia,b, D. Ciangottinia,b, L. Fanòa,b, P.
Laricciaa,b, R. Leonardia,b, E. Manonia, G. Mantovania,b, V. Mariania,b, M.
Menichellia, A. Rossia,b, A. Santocchiaa,b, D. Spigaa INFN Sezione di Pisa a,
Università di Pisa b, Scuola Normale Superiore di Pisa c, Pisa, Italy
K. Androsova, P. Azzurria, G. Bagliesia, L. Bianchinia, T. Boccalia, L.
Borrello, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, F. Fioria,c,
L. Gianninia,c, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c, G.
Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, G. Rolandi31, P.
Spagnoloa, R. Tenchinia, G. Tonellia,b, A. Venturia, P.G. Verdinia INFN
Sezione di Roma a, Sapienza Università di Roma b, Rome, Italy
L. Baronea,b, F. Cavallaria, M. Cipriania,b, D. Del Rea,b, E. Di Marcoa,b, M.
Diemoza, S. Gellia,b, E. Longoa,b, B. Marzocchia,b, P. Meridiania, G.
Organtinia,b, F. Pandolfia, R. Paramattia,b, F. Preiatoa,b, S. Rahatloua,b, C.
Rovellia, F. Santanastasioa,b INFN Sezione di Torino a, Università di Torino
b, Torino, Italy, Università del Piemonte Orientale c, Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, C. Biinoa, A. Cappatia,b, N. Cartigliaa, F. Cennaa,b, S.
Comettia, M. Costaa,b, R. Covarellia,b, N. Demariaa, B. Kiania,b, C.
Mariottia, S. Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M.
Montenoa, M.M. Obertinoa,b, L. Pachera,b, N. Pastronea, M. Pelliccionia, G.L.
Pinna Angionia,b, A. Romeroa,b, M. Ruspaa,c, R. Sacchia,b, R. Salvaticoa,b, K.
Shchelinaa,b, V. Solaa, A. Solanoa,b, D. Soldia,b, A. Staianoa INFN Sezione di
Trieste a, Università di Trieste b, Trieste, Italy
S. Belfortea, V. Candelisea,b, M. Casarsaa, F. Cossuttia, A. Da Rolda,b, G.
Della Riccaa,b, F. Vazzolera,b, A. Zanettia Kyungpook National University,
Daegu, Korea
D.H. Kim, G.N. Kim, M.S. Kim, J. Lee, S. Lee, S.W. Lee, C.S. Moon, Y.D. Oh,
S.I. Pak, S. Sekmen, D.C. Son, Y.C. Yang Chonnam National University,
Institute for Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon, G. Oh Hanyang University, Seoul, Korea
B. Francois, J. Goh32, T.J. Kim Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, Y. Jo, K. Lee, K.S. Lee, S.
Lee, J. Lim, S.K. Park, Y. Roh Sejong University, Seoul, Korea
H.S. Kim Seoul National University, Seoul, Korea
J. Almond, J. Kim, J.S. Kim, H. Lee, K. Lee, K. Nam, S.B. Oh, B.C. Radburn-
Smith, S.h. Seo, U.K. Yang, H.D. Yoo, G.B. Yu University of Seoul, Seoul,
Korea
D. Jeon, H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park Sungkyunkwan University,
Suwon, Korea
Y. Choi, C. Hwang, J. Lee, I. Yu Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, J. Vaitkus National Centre for Particle Physics,
Universiti Malaya, Kuala Lumpur, Malaysia
Z.A. Ibrahim, M.A.B. Md Ali33, F. Mohamad Idris34, W.A.T. Wan Abdullah, M.N.
Yusli, Z. Zolkapli Universidad de Sonora (UNISON), Hermosillo, Mexico
J.F. Benitez, A. Castaneda Hernandez, J.A. Murillo Quijada Centro de
Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
H. Castilla-Valdez, E. De La Cruz-Burelo, M.C. Duran-Osuna, I. Heredia-De La
Cruz35, R. Lopez-Fernandez, J. Mejia Guisao, R.I. Rabadan-Trejo, M. Ramirez-
Garcia, G. Ramirez-Sanchez, R. Reyes-Almanza, A. Sanchez-Hernandez Universidad
Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, F. Vazquez Valencia Benemerita
Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M. Ahmad, M.I. Asghar, Q. Hassan, H.R. Hoorani, W.A. Khan, M.A.
Shah, M. Shoaib, M. Waqas National Centre for Nuclear Research, Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. Górski, M. Kazana, M.
Szleper, P. Traczyk, P. Zalewski Institute of Experimental Physics, Faculty of
Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk36, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Misiura, M. Olszewski, A. Pyskir, M. Walczak Laboratório de
Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
M. Araujo, P. Bargassa, C. Beirão Da Cruz E Silva, A. Di Francesco, P.
Faccioli, B. Galinhas, M. Gallinaro, J. Hollar, N. Leonardo, J. Seixas, G.
Strong, O. Toldaiev, J. Varela Joint Institute for Nuclear Research, Dubna,
Russia
S. Afanasiev, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov, A. Kamenev,
V. Karjavine, A. Lanev, A. Malakhov, V. Matveev37,38, P. Moisenz, V. Palichik,
V. Perelygin, S. Shmatov, S. Shulha, N. Skatchkov, V. Smirnov, N. Voytishin,
A. Zarubin Petersburg Nuclear Physics Institute, Gatchina (St. Petersburg),
Russia
V. Golovtsov, Y. Ivanov, V. Kim39, E. Kuznetsova40, P. Levchenko, V. Murzin,
V. Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Vavilov, A.
Vorobyev Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, A. Shabanov, D. Tlisov, A. Toropin Institute for
Theoretical and Experimental Physics, Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, V. Popov, I. Pozdnyakov, G.
Safronov, A. Spiridonov, A. Stepennov, V. Stolin, M. Toms, E. Vlasov, A.
Zhokin Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
R. Chistov41, M. Danilov41, S. Polikarpov41, E. Tarkovskii P.N. Lebedev
Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin38, M. Kirakosyan, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia
A. Belyaev, E. Boos, V. Bunichev, M. Dubinin42, L. Dudko, A. Ershov, V.
Klyukhin, O. Kodolova, I. Lokhtin, S. Obraztsov, M. Perfilov, V. Savrin, A.
Snigirev Novosibirsk State University (NSU), Novosibirsk, Russia
A. Barnyakov43, V. Blinov43, T. Dimova43, L. Kardapoltsev43, Y. Skovpen43
Institute for High Energy Physics of National Research Centre ’Kurchatov
Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, V. Kachanov, A. Kalinin, D.
Konstantinov, P. Mandrik, V. Petrov, R. Ryutin, S. Slabospitskii, A. Sobol, S.
Troshin, N. Tyurin, A. Uzunian, A. Volkov National Research Tomsk Polytechnic
University, Tomsk, Russia
A. Babaev, S. Baidali, V. Okhotnikov University of Belgrade, Faculty of
Physics and Vinca Institute of Nuclear Sciences, Belgrade, Serbia
P. Adzic44, P. Cirkovic, D. Devetak, M. Dordevic, J. Milosevic Centro de
Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid,
Spain
J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M. Barrio Luna, J.A.
Brochero Cifuentes, M. Cerrada, N. Colino, B. De La Cruz, A. Delgado Peris, C.
Fernandez Bedoya, J.P. Fernández Ramos, J. Flix, M.C. Fouz, O. Gonzalez Lopez,
S. Goy Lopez, J.M. Hernandez, M.I. Josa, D. Moran, A. Pérez-Calero Yzquierdo,
J. Puerta Pelayo, I. Redondo, L. Romero, S. Sánchez Navas, M.S. Soares, A.
Triossi Universidad Autónoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz Universidad de Oviedo, Oviedo, Spain
J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras, I. Gonzalez
Caballero, J.R. González Fernández, E. Palencia Cortezon, V. Rodríguez Bouza,
S. Sanchez Cruz, J.M. Vizan Garcia Instituto de Física de Cantabria (IFCA),
CSIC-Universidad de Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, J. Duarte Campderros, M.
Fernandez, P.J. Fernández Manteca, A. García Alonso, J. Garcia-Ferrero, G.
Gomez, A. Lopez Virto, J. Marco, C. Martinez Rivero, P. Martinez Ruiz del
Arbol, F. Matorras, J. Piedra Gomez, C. Prieels, T. Rodrigo, A. Ruiz-Jimeno,
L. Scodellaro, N. Trevisani, I. Vila, R. Vilar Cortabitarte University of
Ruhuna, Department of Physics, Matara, Sri Lanka
N. Wickramage CERN, European Organization for Nuclear Research, Geneva,
Switzerland
D. Abbaneo, B. Akgun, E. Auffray, G. Auzinger, P. Baillon, A.H. Ball, D.
Barney, J. Bendavid, M. Bianco, A. Bocci, C. Botta, E. Brondolin, T.
Camporesi, M. Cepeda, G. Cerminara, E. Chapon, Y. Chen, G. Cucciati, D.
d’Enterria, A. Dabrowski, N. Daci, V. Daponte, A. David, A. De Roeck, N.
Deelen, M. Dobson, M. Dünser, N. Dupont, A. Elliott-Peisert, P. Everaerts, F.
Fallavollita45, D. Fasanella, G. Franzoni, J. Fulcher, W. Funk, D. Gigi, A.
Gilbert, K. Gill, F. Glege, M. Gruchala, M. Guilbaud, D. Gulhan, J. Hegeman,
C. Heidegger, V. Innocente, A. Jafari, P. Janot, O. Karacheban19, J. Kieseler,
A. Kornmayer, M. Krammer1, C. Lange, P. Lecoq, C. Lourenço, L. Malgeri, M.
Mannelli, A. Massironi, F. Meijers, J.A. Merlin, S. Mersi, E. Meschi, P.
Milenovic46, F. Moortgat, M. Mulders, J. Ngadiuba, S. Nourbakhsh, S.
Orfanelli, L. Orsini, F. Pantaleo16, L. Pape, E. Perez, M. Peruzzi, A.
Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, F.M. Pitters, D. Rabady, A.
Racz, T. Reis, M. Rovere, H. Sakulin, C. Schäfer, C. Schwick, M. Selvaggi, A.
Sharma, P. Silva, P. Sphicas47, A. Stakia, J. Steggemann, D. Treille, A.
Tsirou, A. Vartak, V. Veckalns48, M. Verzetti, W.D. Zeuner Paul Scherrer
Institut, Villigen, Switzerland
L. Caminada49, K. Deiters, W. Erdmann, R. Horisberger, Q. Ingram, H.C.
Kaestli, D. Kotlinski, U. Langenegger, T. Rohe, S.A. Wiederkehr ETH Zurich -
Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland
M. Backhaus, L. Bäni, P. Berger, N. Chernyavskaya, G. Dissertori, M. Dittmar,
M. Donegà, C. Dorfer, T.A. Gómez Espinosa, C. Grab, D. Hits, T. Klijnsma, W.
Lustermann, R.A. Manzoni, M. Marionneau, M.T. Meinhard, F. Micheli, P.
Musella, F. Nessi-Tedaldi, F. Pauss, G. Perrin, L. Perrozzi, S. Pigazzini, C.
Reissel, D. Ruini, D.A. Sanz Becerra, M. Schönenberger, L. Shchutska, V.R.
Tavolaro, K. Theofilatos, M.L. Vesterbacka Olsson, R. Wallny, D.H. Zhu
Universität Zürich, Zurich, Switzerland
T.K. Aarrestad, C. Amsler50, D. Brzhechko, M.F. Canelli, A. De Cosa, R. Del
Burgo, S. Donato, C. Galloni, T. Hreus, B. Kilminster, S. Leontsinis, I.
Neutelings, G. Rauco, P. Robmann, D. Salerno, K. Schweiger, C. Seitz, Y.
Takahashi, A. Zucchetta National Central University, Chung-Li, Taiwan
T.H. Doan, R. Khurana, C.M. Kuo, W. Lin, A. Pozdnyakov, S.S. Yu National
Taiwan University (NTU), Taipei, Taiwan
P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.F. Liu, R.-S. Lu, E.
Paganis, A. Psallidas, A. Steen Chulalongkorn University, Faculty of Science,
Department of Physics, Bangkok, Thailand
B. Asavapibhop, N. Srimanobhas, N. Suwonjandee Çukurova University, Physics
Department, Science and Art Faculty, Adana, Turkey
A. Bat, F. Boran, S. Cerci51, S. Damarseckin, Z.S. Demiroglu, F. Dolek, C.
Dozen, I. Dumanoglu, G. Gokbulut, Y. Guler, E. Gurpinar, I. Hos52, C. Isik,
E.E. Kangal53, O. Kara, A. Kayis Topaksu, U. Kiminsu, M. Oglakci, G. Onengut,
K. Ozdemir54, S. Ozturk55, D. Sunar Cerci51, B. Tali51, U.G. Tok, S.
Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle East Technical University,
Physics Department, Ankara, Turkey
B. Isildak56, G. Karapinar57, M. Yalvac, M. Zeyrek Bogazici University,
Istanbul, Turkey
I.O. Atakisi, E. Gülmez, M. Kaya58, O. Kaya59, S. Ozkorucuklu60, S. Tekten,
E.A. Yetkin61 Istanbul Technical University, Istanbul, Turkey
M.N. Agaras, A. Cakir, K. Cankocak, Y. Komurcu, S. Sen62 Institute for
Scintillation Materials of National Academy of Science of Ukraine, Kharkov,
Ukraine
B. Grynyov National Scientific Center, Kharkov Institute of Physics and
Technology, Kharkov, Ukraine
L. Levchuk University of Bristol, Bristol, United Kingdom
F. Ball, J.J. Brooke, D. Burns, E. Clement, D. Cussans, O. Davignon, H.
Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, D.M. Newbold63, S.
Paramesvaran, B. Penning, T. Sakuma, D. Smith, V.J. Smith, J. Taylor, A.
Titterton Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev64, C. Brew, R.M. Brown, D. Cieri, D.J.A. Cockerill, J.A.
Coughlan, K. Harder, S. Harper, J. Linacre, K. Manolopoulos, E. Olaiya, D.
Petyt, C.H. Shepherd-Themistocleous, A. Thea, I.R. Tomalin, T. Williams, W.J.
Womersley Imperial College, London, United Kingdom
R. Bainbridge, P. Bloch, J. Borg, S. Breeze, O. Buchmuller, A. Bundock, D.
Colling, P. Dauncey, G. Davies, M. Della Negra, R. Di Maria, G. Hall, G. Iles,
T. James, M. Komm, C. Laner, L. Lyons, A.-M. Magnan, S. Malik, A. Martelli, J.
Nash65, A. Nikitenko7, V. Palladino, M. Pesaresi, D.M. Raymond, A. Richards,
A. Rose, E. Scott, C. Seez, A. Shtipliyski, G. Singh, M. Stoye, T. Strebler,
S. Summers, A. Tapper, K. Uchida, T. Virdee16, N. Wardle, D. Winterbottom, J.
Wright, S.C. Zenz Brunel University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, C.K. Mackay, A. Morton, I.D. Reid,
L. Teodorescu, S. Zahid Baylor University, Waco, USA
K. Call, J. Dittmann, K. Hatakeyama, H. Liu, C. Madrid, B. McMaster, N.
Pastika, C. Smith Catholic University of America, Washington, DC, USA
R. Bartek, A. Dominguez The University of Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, C. Henderson, P. Rumerio, C. West Boston University,
Boston, USA
D. Arcaro, T. Bose, D. Gastler, S. Girgis, D. Pinna, C. Richardson, J. Rohlf,
L. Sulak, D. Zou Brown University, Providence, USA
G. Benelli, B. Burkle, X. Coubez, D. Cutts, M. Hadley, J. Hakala, U. Heintz,
J.M. Hogan66, K.H.M. Kwok, E. Laird, G. Landsberg, J. Lee, Z. Mao, M. Narain,
S. Sagir67, R. Syarif, E. Usai, D. Yu University of California, Davis, Davis,
USA
R. Band, C. Brainerd, R. Breedon, D. Burns, M. Calderon De La Barca Sanchez,
M. Chertok, J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk,
W. Ko, O. Kukral, R. Lander, M. Mulhearn, D. Pellett, J. Pilot, S. Shalhout,
M. Shi, D. Stolp, D. Taylor, K. Tos, M. Tripathi, Z. Wang, F. Zhang University
of California, Los Angeles, USA
M. Bachtis, C. Bravo, R. Cousins, A. Dasgupta, S. Erhan, A. Florent, J.
Hauser, M. Ignatenko, N. Mccoll, S. Regnard, D. Saltzberg, C. Schnaible, V.
Valuev University of California, Riverside, Riverside, USA
E. Bouvier, K. Burt, R. Clare, J.W. Gary, S.M.A. Ghiasi Shirazi, G. Hanson, G.
Karapostoli, E. Kennedy, F. Lacroix, O.R. Long, M. Olmedo Negrete, M.I.
Paneva, W. Si, L. Wang, H. Wei, S. Wimpenny, B.R. Yates University of
California, San Diego, La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, M. Derdzinski, R. Gerosa, D. Gilbert, B.
Hashemi, A. Holzner, D. Klein, G. Kole, V. Krutelyov, J. Letts, M.
Masciovecchio, S. May, D. Olivito, S. Padhi, M. Pieri, V. Sharma, M. Tadel, J.
Wood, F. Würthwein, A. Yagil, G. Zevi Della Porta University of California,
Santa Barbara - Department of Physics, Santa Barbara, USA
N. Amin, R. Bhandari, C. Campagnari, M. Citron, V. Dutta, M. Franco Sevilla,
L. Gouskos, R. Heller, J. Incandela, H. Mei, A. Ovcharova, H. Qu, J. Richman,
D. Stuart, I. Suarez, S. Wang, J. Yoo California Institute of Technology,
Pasadena, USA
D. Anderson, A. Bornheim, J.M. Lawhorn, N. Lu, H.B. Newman, T.Q. Nguyen, J.
Pata, M. Spiropulu, J.R. Vlimant, R. Wilkinson, S. Xie, Z. Zhang, R.Y. Zhu
Carnegie Mellon University, Pittsburgh, USA
M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, M. Sun, I. Vorobiev, M.
Weinberg University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, F. Jensen, A. Johnson, E. MacDonald, T. Mulholland,
R. Patel, A. Perloff, K. Stenson, K.A. Ulmer, S.R. Wagner Cornell University,
Ithaca, USA
J. Alexander, J. Chaves, Y. Cheng, J. Chu, A. Datta, K. Mcdermott, N. Mirman,
J.R. Patterson, D. Quach, A. Rinkevicius, A. Ryd, L. Skinnari, L. Soffi, S.M.
Tan, Z. Tao, J. Thom, J. Tucker, P. Wittich, M. Zientek Fermi National
Accelerator Laboratory, Batavia, USA
S. Abdullin, M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S.
Banerjee, L.A.T. Bauerdick, A. Beretvas, J. Berryhill, P.C. Bhat, K. Burkett,
J.N. Butler, A. Canepa, G.B. Cerati, H.W.K. Cheung, F. Chlebana, M. Cremonesi,
J. Duarte, V.D. Elvira, J. Freeman, Z. Gecse, E. Gottschalk, L. Gray, D.
Green, S. Grünendahl, O. Gutsche, J. Hanlon, R.M. Harris, S. Hasegawa, J.
Hirschauer, Z. Hu, B. Jayatilaka, S. Jindariani, M. Johnson, U. Joshi, B.
Klima, M.J. Kortelainen, B. Kreis, S. Lammel, D. Lincoln, R. Lipton, M. Liu,
T. Liu, J. Lykken, K. Maeshima, J.M. Marraffino, D. Mason, P. McBride, P.
Merkel, S. Mrenna, S. Nahn, V. O’Dell, K. Pedro, C. Pena, O. Prokofyev, G.
Rakness, F. Ravera, A. Reinsvold, L. Ristori, A. Savoy-Navarro68, B.
Schneider, E. Sexton-Kennedy, A. Soha, W.J. Spalding, L. Spiegel, S. Stoynev,
J. Strait, N. Strobbe, L. Taylor, S. Tkaczyk, N.V. Tran, L. Uplegger, E.W.
Vaandering, C. Vernieri, M. Verzocchi, R. Vidal, M. Wang, H.A. Weber
University of Florida, Gainesville, USA
D. Acosta, P. Avery, P. Bortignon, D. Bourilkov, A. Brinkerhoff, L. Cadamuro,
A. Carnes, D. Curry, R.D. Field, S.V. Gleyzer, B.M. Joshi, J. Konigsberg, A.
Korytov, K.H. Lo, P. Ma, K. Matchev, G. Mitselmakher, D. Rosenzweig, K. Shi,
D. Sperka, J. Wang, S. Wang, X. Zuo Florida International University, Miami,
USA
Y.R. Joshi, S. Linn Florida State University, Tallahassee, USA
A. Ackert, T. Adams, A. Askew, S. Hagopian, V. Hagopian, K.F. Johnson, T.
Kolberg, G. Martinez, T. Perry, H. Prosper, A. Saha, C. Schiber, R. Yohay
Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, V. Bhopatkar, S. Colafranceschi, M. Hohlmann, D. Noonan, M.
Rahmani, T. Roy, M. Saunders, F. Yumiceva University of Illinois at Chicago
(UIC), Chicago, USA
M.R. Adams, L. Apanasevich, D. Berry, R.R. Betts, R. Cavanaugh, X. Chen, S.
Dittmer, O. Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, K. Jung, J.
Kamin, C. Mills, M.B. Tonjes, N. Varelas, H. Wang, X. Wang, Z. Wu, J. Zhang
The University of Iowa, Iowa City, USA
M. Alhusseini, B. Bilki69, W. Clarida, K. Dilsiz70, S. Durgut, R.P.
Gandrajula, M. Haytmyradov, V. Khristenko, J.-P. Merlo, A. Mestvirishvili, A.
Moeller, J. Nachtman, H. Ogul71, Y. Onel, F. Ozok72, A. Penzo, C. Snyder, E.
Tiras, J. Wetzel Johns Hopkins University, Baltimore, USA
B. Blumenfeld, A. Cocoros, N. Eminizer, D. Fehling, L. Feng, A.V. Gritsan,
W.T. Hung, P. Maksimovic, J. Roskes, U. Sarica, M. Swartz, M. Xiao The
University of Kansas, Lawrence, USA
A. Al-bataineh, P. Baringer, A. Bean, S. Boren, J. Bowen, A. Bylinkin, J.
Castle, S. Khalil, A. Kropivnitskaya, D. Majumder, W. Mcbrayer, M. Murray, C.
Rogan, S. Sanders, E. Schmitz, J.D. Tapia Takaki, Q. Wang Kansas State
University, Manhattan, USA
S. Duric, A. Ivanov, K. Kaadze, D. Kim, Y. Maravin, D.R. Mendis, T. Mitchell,
A. Modak, A. Mohammadi Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, C. Ferraioli, N.J. Hadley,
S. Jabeen, G.Y. Jeng, R.G. Kellogg, J. Kunkle, A.C. Mignerey, S. Nabili, F.
Ricci-Tam, M. Seidel, Y.H. Shin, A. Skuja, S.C. Tonwar, K. Wong Massachusetts
Institute of Technology, Cambridge, USA
D. Abercrombie, B. Allen, V. Azzolini, A. Baty, R. Bi, S. Brandt, W. Busza,
I.A. Cali, M. D’Alfonso, Z. Demiragli, G. Gomez Ceballos, M. Goncharov, P.
Harris, D. Hsu, M. Hu, Y. Iiyama, G.M. Innocenti, M. Klute, D. Kovalskyi,
Y.-J. Lee, P.D. Luckey, B. Maier, A.C. Marini, C. Mcginn, C. Mironov, S.
Narayanan, X. Niu, C. Paus, D. Rankin, C. Roland, G. Roland, Z. Shi, G.S.F.
Stephans, K. Sumorok, K. Tatar, D. Velicanu, J. Wang, T.W. Wang, B. Wyslouch
University of Minnesota, Minneapolis, USA
A.C. Benvenuti${}^{\textrm{\textdagger}}$, R.M. Chatterjee, A. Evans, P.
Hansen, J. Hiltbrand, Sh. Jain, S. Kalafut, M. Krohn, Y. Kubota, Z. Lesko, J.
Mans, R. Rusack, M.A. Wadud University of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
E. Avdeeva, K. Bloom, D.R. Claes, C. Fangmeier, F. Golf, R. Gonzalez Suarez,
R. Kamalieddin, I. Kravchenko, J. Monroy, J.E. Siado, G.R. Snow, B. Stieger
State University of New York at Buffalo, Buffalo, USA
A. Godshalk, C. Harrington, I. Iashvili, A. Kharchilava, C. Mclean, D. Nguyen,
A. Parker, S. Rappoccio, B. Roozbahani Northeastern University, Boston, USA
G. Alverson, E. Barberis, C. Freer, Y. Haddad, A. Hortiangtham, G. Madigan,
D.M. Morse, T. Orimoto, A. Tishelman-charny, T. Wamorkar, B. Wang, A.
Wisecarver, D. Wood Northwestern University, Evanston, USA
S. Bhattacharya, J. Bueghly, O. Charaf, T. Gunter, K.A. Hahn, N. Odell, M.H.
Schmitt, K. Sung, M. Trovato, M. Velasco University of Notre Dame, Notre Dame,
USA
R. Bucci, N. Dev, R. Goldouzian, M. Hildreth, K. Hurtado Anampa, C. Jessop,
D.J. Karmgard, K. Lannon, W. Li, N. Loukas, N. Marinelli, F. Meng, C. Mueller,
Y. Musienko37, M. Planer, R. Ruchti, P. Siddireddy, G. Smith, S. Taroni, M.
Wayne, A. Wightman, M. Wolf, A. Woodard The Ohio State University, Columbus,
USA
J. Alimena, L. Antonelli, B. Bylsma, L.S. Durkin, S. Flowers, B. Francis, C.
Hill, W. Ji, T.Y. Ling, W. Luo, B.L. Winer Princeton University, Princeton,
USA
S. Cooperstein, P. Elmer, J. Hardenbrook, N. Haubrich, S. Higginbotham, A.
Kalogeropoulos, S. Kwan, D. Lange, M.T. Lucchini, J. Luo, D. Marlow, K. Mei,
I. Ojalvo, J. Olsen, C. Palmer, P. Piroué, J. Salfeld-Nebgen, D. Stickland, C.
Tully University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A. Barker, V.E. Barnes, S. Das, L. Gutay, M. Jones, A.W. Jung, A. Khatiwada,
B. Mahakud, D.H. Miller, N. Neumeister, C.C. Peng, S. Piperov, H. Qiu, J.F.
Schulte, J. Sun, F. Wang, R. Xiao, W. Xie Purdue University Northwest,
Hammond, USA
T. Cheng, J. Dolen, N. Parashar Rice University, Houston, USA
Z. Chen, K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Kilpatrick, Arun Kumar, W.
Li, B.P. Padley, R. Redjimi, J. Roberts, J. Rorie, W. Shi, Z. Tu, A. Zhang
University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, Y.t. Duh, J.L. Dulemba, C. Fallon, T.
Ferbel, M. Galanti, A. Garcia-Bellido, J. Han, O. Hindrichs, A.
Khukhunaishvili, E. Ranken, P. Tan, R. Taus Rutgers, The State University of
New Jersey, Piscataway, USA
B. Chiarito, J.P. Chou, Y. Gershtein, E. Halkiadakis, A. Hart, M. Heindl, E.
Hughes, S. Kaplan, R. Kunnawalkam Elayavalli, S. Kyriacou, I. Laflotte, A.
Lath, R. Montalvo, K. Nash, M. Osherson, H. Saka, S. Salur, S. Schnetzer, D.
Sheffield, S. Somalwar, R. Stone, S. Thomas, P. Thomassen University of
Tennessee, Knoxville, USA
H. Acharya, A.G. Delannoy, J. Heideman, G. Riley, S. Spanier Texas A&M
University, College Station, USA
O. Bouhali73, A. Celik, M. Dalchenko, M. De Mattia, A. Delgado, S. Dildick, R.
Eusebi, J. Gilmore, T. Huang, T. Kamon74, S. Luo, D. Marley, R. Mueller, D.
Overton, L. Perniè, D. Rathjens, A. Safonov Texas Tech University, Lubbock,
USA
N. Akchurin, J. Damgov, F. De Guio, P.R. Dudero, S. Kunori, K. Lamichhane,
S.W. Lee, T. Mengke, S. Muthumuni, T. Peltola, S. Undleeb, I. Volobouev, Z.
Wang, A. Whitbeck Vanderbilt University, Nashville, USA
S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H. Ni, K.
Padeken, F. Romeo, J.D. Ruiz Alvarez, P. Sheldon, S. Tuo, J. Velkovska, M.
Verweij, Q. Xu University of Virginia, Charlottesville, USA
M.W. Arenton, P. Barria, B. Cox, R. Hirosky, M. Joyce, A. Ledovskoy, H. Li, C.
Neu, T. Sinthuprasith, Y. Wang, E. Wolfe, F. Xia Wayne State University,
Detroit, USA
R. Harr, P.E. Karchin, N. Poudyal, J. Sturdy, P. Thapa, S. Zaleski University
of Wisconsin - Madison, Madison, WI, USA
J. Buchanan, C. Caillol, D. Carlsmith, S. Dasu, I. De Bruyn, L. Dodd, B.
Gomber75, M. Grothe, M. Herndon, A. Hervé, U. Hussain, P. Klabbers, A. Lanaro,
K. Long, R. Loveless, T. Ruggles, A. Savin, V. Sharma, N. Smith, W.H. Smith,
N. Woods †: Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
3: Also at Universidade Estadual de Campinas, Campinas, Brazil
4: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
5: Also at Université Libre de Bruxelles, Bruxelles, Belgium
6: Also at University of Chinese Academy of Sciences, Beijing, China
7: Also at Institute for Theoretical and Experimental Physics, Moscow, Russia
8: Also at Joint Institute for Nuclear Research, Dubna, Russia
9: Now at British University in Egypt, Cairo, Egypt
10: Now at Helwan University, Cairo, Egypt
11: Now at Fayoum University, El-Fayoum, Egypt
12: Also at Department of Physics, King Abdulaziz University, Jeddah, Saudi
Arabia
13: Also at Université de Haute Alsace, Mulhouse, France
14: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
15: Also at Tbilisi State University, Tbilisi, Georgia
16: Also at CERN, European Organization for Nuclear Research, Geneva,
Switzerland
17: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
18: Also at University of Hamburg, Hamburg, Germany
19: Also at Brandenburg University of Technology, Cottbus, Germany
20: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary
21: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
22: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
23: Also at Indian Institute of Technology Bhubaneswar, Bhubaneswar, India
24: Also at Institute of Physics, Bhubaneswar, India
25: Also at Shoolini University, Solan, India
26: Also at University of Visva-Bharati, Santiniketan, India
27: Also at Isfahan University of Technology, Isfahan, Iran
28: Also at Plasma Physics Research Center, Science and Research Branch,
Islamic Azad University, Tehran, Iran
29: Also at ITALIAN NATIONAL AGENCY FOR NEW TECHNOLOGIES, ENERGY AND
SUSTAINABLE ECONOMIC DEVELOPMENT, Bologna, Italy
30: Also at Università degli Studi di Siena, Siena, Italy
31: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy
32: Also at Kyunghee University, Seoul, Korea
33: Also at International Islamic University of Malaysia, Kuala Lumpur,
Malaysia
34: Also at Malaysian Nuclear Agency, MOSTI, Kajang, Malaysia
35: Also at Consejo Nacional de Ciencia y Tecnología, Mexico City, Mexico
36: Also at Warsaw University of Technology, Institute of Electronic Systems,
Warsaw, Poland
37: Also at Institute for Nuclear Research, Moscow, Russia
38: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
39: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
40: Also at University of Florida, Gainesville, USA
41: Also at P.N. Lebedev Physical Institute, Moscow, Russia
42: Also at California Institute of Technology, Pasadena, USA
43: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
44: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
45: Also at INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
46: Also at University of Belgrade, Faculty of Physics and Vinca Institute of
Nuclear Sciences, Belgrade, Serbia
47: Also at National and Kapodistrian University of Athens, Athens, Greece
48: Also at Riga Technical University, Riga, Latvia
49: Also at Universität Zürich, Zurich, Switzerland
50: Also at Stefan Meyer Institute for Subatomic Physics (SMI), Vienna,
Austria
51: Also at Adiyaman University, Adiyaman, Turkey
52: Also at Istanbul Aydin University, Istanbul, Turkey
53: Also at Mersin University, Mersin, Turkey
54: Also at Piri Reis University, Istanbul, Turkey
55: Also at Gaziosmanpasa University, Tokat, Turkey
56: Also at Ozyegin University, Istanbul, Turkey
57: Also at Izmir Institute of Technology, Izmir, Turkey
58: Also at Marmara University, Istanbul, Turkey
59: Also at Kafkas University, Kars, Turkey
60: Also at Istanbul University, Faculty of Science, Istanbul, Turkey
61: Also at Istanbul Bilgi University, Istanbul, Turkey
62: Also at Hacettepe University, Ankara, Turkey
63: Also at Rutherford Appleton Laboratory, Didcot, United Kingdom
64: Also at School of Physics and Astronomy, University of Southampton,
Southampton, United Kingdom
65: Also at Monash University, Faculty of Science, Clayton, Australia
66: Also at Bethel University, St. Paul, USA
67: Also at Karamanoğlu Mehmetbey University, Karaman, Turkey
68: Also at Purdue University, West Lafayette, USA
69: Also at Beykent University, Istanbul, Turkey
70: Also at Bingol University, Bingol, Turkey
71: Also at Sinop University, Sinop, Turkey
72: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey
73: Also at Texas A&M University at Qatar, Doha, Qatar
74: Also at Kyungpook National University, Daegu, Korea
75: Also at University of Hyderabad, Hyderabad, India