EXO-17-024

$HeadURL:svn+ssh://svn.cern.ch/reps/tdr2/papers/EXO-17-024/trunk/EXO-17-024.tex$
$Id:EXO-17-024.tex4837342018-12-0615:28:36Zwoodson$

EXO-17-024

# Search for low-mass resonances decaying into bottom quark-antiquark pairs in
proton-proton collisions at $\sqrt{s}=13\TeV$

###### Abstract

A search for narrow, low-mass, scalar and pseudoscalar resonances decaying to
bottom quark-antiquark pairs is presented. The search is based on events
recorded in $\sqrt{s}=13\TeV$ proton-proton collisions with the CMS detector
at the LHC, collected in 2016, and corresponding to an integrated luminosity
of 35.9. The search selects events in which the resonance would be produced
with high transverse momentum because of the presence of initial- or final-
state radiation. In such events, the decay products of the resonance would be
reconstructed as a single large-radius jet with high mass and two-prong
substructure. A potential signal would be identified as a narrow excess in the
jet invariant mass spectrum. No evidence for such a resonance is observed
within the mass range from 50 to 350, and upper limits at 95% confidence level
are set on the product of the cross section and branching fraction to a bottom
quark-antiquark pair. These constitute the first constraints from the LHC on
exotic bottom quark-antiquark resonances with masses below 325.

##  0.1 Introduction

Many models of physics beyond the standard model (SM) require new particles
that couple to quarks and gluons and can be observed as dijet resonances. One
example is a model in which dark matter particles ($\chi$) couple to SM
particles through a spin-0 scalar ($\Phi$) or pseudoscalar
($\cmsSymbolFace{A}$) mediator, which decays preferentially to a bottom quark-
antiquark ($\bbbar$) pair [1, 2, 3, 4, 5]. As the mass of such a mediator is
an unknown parameter of the model, it is important to search in as broad a
mass range as possible.

Because of the overwhelming background of events from jets produced through
the strong interaction, referred to as quantum chromodynamics (QCD) multijet
events, inclusive searches for dijet resonances at the CERN LHC have
historically been limited to dijet invariant masses greater than 1. Several
techniques have been explored to evade this limitation. Trigger-level
analyses, also known as “data scouting,” increase the number of events
collected at lower dijet invariant masses by recording a minimal subset of the
total event content. The ATLAS and CMS experiments have used this technique to
search for resonances with masses as low as 450 [6, 7, 8, 9]. The invariant
mass threshold can also be lowered by performing bottom quark tagging at the
trigger level, enabling masses as low as 325to be probed [10, 11]. The
analysis presented here uses a different technique, requiring that the dijet
resonances be produced with significant initial- or final-state radiation. The
technique has been employed in searches for low mass resonances decaying to
quark-antiquark pairs [12, 13, 14], which have provided the best sensitivity
to date for resonances with masses between 50 and 300. This technique has also
been used to search for SM Higgs bosons ($\PH$) produced through gluon fusion
and decaying to $\bbbar$ pairs [15], with an observed significance of 1.5
standard deviations.

This paper presents the first LHC search for new particles that decay to
$\bbbar$ resonances with masses as low as 50. Spin-0 scalar and pseudoscalar
resonances, which may mediate interactions between dark matter particles and
SM particles, are considered. Minimal flavor violation is assumed, to ensure
consistency with flavor constraints [1, 2, 3, 4, 5]. Under this assumption,
the $\Phi$ or $\cmsSymbolFace{A}$ particles decay only to fermion-antifermion
pairs of the same flavor. Further, the SM couplings are assumed to be
proportional to the SM Yukawa couplings with a single universal constant of
proportionality, $g_{\Pq\Phi}$ or $g_{\Pq\cmsSymbolFace{A}}$. The two
interaction Lagrangians are

$\displaystyle\mathcal{L}_{\Phi}$
$\displaystyle=g_{\chi\Phi}\Phi\overline{\chi}\chi+\frac{\Phi}{\sqrt{2}}\sum_{\cmsSymbolFace{f}}g_{\Pq\Phi}y_{\cmsSymbolFace{f}}\cmsSymbolFace{\overline{f}}\cmsSymbolFace{f},$
(1) $\displaystyle\mathcal{L}_{\cmsSymbolFace{A}}$
$\displaystyle=ig_{\chi\cmsSymbolFace{A}}\cmsSymbolFace{A}\overline{\chi}\gamma_{5}\chi+\frac{i\cmsSymbolFace{A}}{\sqrt{2}}\sum_{\cmsSymbolFace{f}}g_{\Pq\cmsSymbolFace{A}}y_{\cmsSymbolFace{f}}\cmsSymbolFace{\overline{f}}\gamma_{5}\cmsSymbolFace{f},$
(2)

where the sum is over all charged SM fermions, $g_{\chi\Phi}$ and
$g_{\chi\cmsSymbolFace{A}}$ are the couplings to the dark matter particle, the
Yukawa couplings of fermions $y_{\cmsSymbolFace{f}}$ are normalized to the
Higgs vacuum expectation value as
$y_{\cmsSymbolFace{f}}=\sqrt{2}m_{\cmsSymbolFace{f}}/v$ with $v=246\GeV$ and
$m_{\cmsSymbolFace{f}}$ the corresponding fermion mass. For resonance masses
below twice the dark matter particle mass ($m_{\chi}$), $\Phi$ and
$\cmsSymbolFace{A}$ couple preferentially to heavier quarks. Consequently, the
resonances are predominantly produced via a loop-induced coupling to gluons,
and, for resonance masses below twice the top quark mass ($m_{\cPqt}$), decay
mostly to $\bbbar$ pairs. This search is also sensitive to extensions of the
SM that include a new gauge boson that couples to the right-handed components
of the bottom and charm quarks [16].

This paper reports the results of a search for narrow $\bbbar$ resonances with
masses between 50 and 350in events collected in $\sqrt{s}=13\TeV$ proton-
proton ($\Pp\Pp$) collisions with the CMS detector at the LHC. The data sample
corresponds to an integrated luminosity of $35.9\fbinv$. We search for
resonances produced with high transverse momentum $\pt$ because of significant
initial- or final-state radiation (ISR or FSR). This ISR or FSR ensures the
events pass stringent trigger restrictions set by bandwidth limitations,
allowing resonance masses as low as 50to be probed. The resonance decay
products are merged into a single wide jet. Two wide-jet algorithms are
considered: the anti-algorithm [17] with distance parameter $R=0.8$ (AK8), and
the Cambridge–Aachen algorithm [18, 19] with distance parameter $R=1.5$
(CA15). The AK8 algorithm provides better sensitivity at signal masses below
175, while the CA15 algorithm provides better sensitivity at higher masses
because of the increased acceptance of decay products with wider angular
separation [20]. Jet substructure [21] techniques and dedicated $\cPqb$
tagging [22] algorithms are used to distinguish the signal from the QCD
background.

##  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. A silicon pixel and
strip tracker, a lead tungstate crystal electromagnetic calorimeter, and a
brass and scintillator hadron calorimeter, each composed of a barrel and two
endcap sections, reside within the solenoid. Forward calorimeters extend the
pseudorapidity ($\eta$) coverage provided by the barrel and endcap detectors.
Muons are detected in gas-ionization chambers embedded in the steel flux-
return yoke outside the solenoid.

Events of interest are selected using a two-tiered trigger system [23]. The
first level, composed of custom hardware processors, uses information from the
calorimeters and muon detectors to select events at a rate of around 100kHz
within a time interval of less than 4. The second level, known as the high-
level trigger, consists of a farm of processors running a version of the full
event reconstruction software optimized for fast processing, and reduces the
event rate to around 1kHz before data storage.

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. [24].

##  0.3 Simulated samples

Simulated samples of signal and background events are produced using various
Monte Carlo (MC) event generators, with the CMS detector response modeled by
[25]. The benchmark $\Phi$ and $\cmsSymbolFace{A}$ signal events, produced
primarily via gluon fusion, are simulated using the 2.4.2 generator [26] for
various mass hypotheses in the range 50–500. The events are generated with a
parton-level filter requiring total hadronic transverse energy $\HT>400\GeV$;
events failing this requirement fall outside the acceptance of the analysis
selection, discussed in the following section. Figure 1 shows representative
one-loop Feynman diagrams producing a boosted jet originating from a $\bbbar$
pair (double-$\cPqb$ jet).

Figure 1: One-loop Feynman diagrams of processes exchanging a scalar $\Phi$
(left) or pseudoscalar $\cmsSymbolFace{A}$ (right) mediator, leading to a
boosted double-$\cPqb$ jet signature.

In accordance with the recommendations of the ATLAS-CMS Dark Matter Forum [1]
and the LHC Dark Matter Working Group [5], the $\Phi$ and $\cmsSymbolFace{A}$
signal samples are normalized to their production cross sections at leading
order (LO) accuracy calculated with the 2.4.2 generator using the DMSimp
package [27]. The total cross sections, which are compared to the upper limits
obtained with this analysis, are calculated using the LO diagram with no
additional partons and no cuts applied to the final state kinematics. The
production cross section at next-to-leading order (NLO) accuracy including the
finite $m_{\cPqt}$ has only been calculated for a scalar with a mass of 125,
where it is approximately a factor of 2 greater [28]. This NLO correction is
not used in this analysis; applying it would not affect the sensitivity of the
search to the signal production cross section, but it would improve the
sensitivity to the couplings $g_{\Pq\Phi}$ or $g_{\Pq\cmsSymbolFace{A}}$ by a
factor of approximately $\sqrt{2}$.

The 2.3.3 [26] generator is used for the diboson, $\PW+$jets, $\cPZ+$jets, and
QCD multijet samples, at LO accuracy with matching [29] between jets from the
matrix element calculation and the parton shower description, while 2.0 [30,
31, 32] at NLO precision is used to model the $\ttbar$ and single top
processes. The Higgs boson signal samples are produced using the + MiNLO [31,
33] event generator with $m_{\PH}=125\GeV$. For the gluon fusion production
mode, the generated sample with up to one extra jet in matrix element
calculations is normalized to the inclusive cross section at next-to-next-to-
next-to-leading order (N3LO) accuracy [34, 35, 36, 37], with a $\pt$-dependent
correction to account for the effects of the finite $m_{\cPqt}$ and associated
higher-order QCD corrections [15].

For parton showering and hadronization, the and samples are interfaced with
8.212 [38]. The parameters for the underlying event description are set to the
CUETP8M1 tune [39]. The production cross sections for the diboson samples are
calculated to next-to-next-to-leading order (NNLO) accuracy with the mcfm 7.0
program [40]. The cross section for top quark pair production is computed with
Top++ 2.0 [41] at NNLO including soft-gluon resummation to next-to-next-to-
leading-log order. The cross sections for $\PW+$jets and $\cPZ+$jets samples
include higher-order QCD and electroweak (EW) corrections improving the
modeling of high-$\pt$ $\PW$ and $\PZ$ bosons events [42, 43, 44, 45]. The
parton distribution function set NNPDF3.0 [46] is used to produce all
simulated samples, with the accuracy (LO or NLO) corresponding to that of the
matrix elements used for generation.

##  0.4 Event reconstruction and selection

Event reconstruction is based on a particle-flow algorithm [47], which aims to
reconstruct and identify each individual particle with an optimized
combination of information from the various elements of the CMS detector. The
algorithm identifies each reconstructed particle as an electron, a muon, a
photon, or a charged or neutral hadron. The missing transverse momentum vector
is defined as the negative vector sum of the transverse momenta of all the
particles identified in the event, and its magnitude is referred to as
$\ptmiss$. Particles are clustered into AK8 [17] or CA15 [18] jets, depending
on the signal mass hypothesis. The clustering algorithms are implemented by
the FastJet package [48]. To mitigate the effect from the contributions of
extraneous $\Pp\Pp$ collisions (pileup), the pileup per particle
identification algorithm [49] assigns a weight to each particle prior to jet
clustering based on the likelihood of the particle to originate from the hard
scattering vertex. Further corrections are applied to the jet energy as a
function of jet $\eta$ and $\pt$ to bring the measured response of jets to
that of particle level jets on average [50].

A combination of several event selection criteria is used to trigger on
events, all imposing minimum thresholds either on $\HT$ or on the AK8 jet
$\pt$. In addition, a minimum threshold on the trimmed jet mass, where
remnants of soft radiation are removed before computing the mass [51], is
imposed to reduce the $\HT$ or $\pt$ thresholds and improve the signal
acceptance. The trigger selection is greater than 95% efficient at selecting
events with at least one AK8 jet with $\pt>450\GeV$, $\abs{\eta}<2.5$, and
mass greater than 40or events with at least one CA15 jet with $\pt>500\GeV$
and $\abs{\eta}<2.5$. We also define six (five) $\pt$ categories from 450
(500)to 1for AK8 (CA15) jets with variable width from 50 to 200. To reduce
backgrounds from SM EW processes, events containing isolated electrons [52] or
muons [53], or hadronically decaying $\tau$ leptons with $\pt>10$, 10, or
18and $\abs{\eta}<2.5$, 2.4, or 2.3, respectively, are vetoed. For electrons
or muons, the isolation criteria require that the pileup-corrected sum of the
$\pt$ of charged hadrons and neutral particles surrounding the lepton divided
by the lepton $\pt$ be less than approximately 15 or 25%, respectively,
depending on $\eta$ [52, 53]. Events with $\ptmiss>140\GeV$ are vetoed in
order to reduce the top quark background contamination. For each event, the
leading jet in $\pt$ is assumed to be the $\Phi(\bbbar)$ or
$\cmsSymbolFace{A}(\bbbar)$ candidate. The soft-drop algorithm [54] with
angular exponent $\beta=0$ is applied to the jet to remove soft and wide-angle
radiation with a soft radiation fraction $z$ less than 0.1. The parameter
$\beta$ controls the grooming profile as a function of subjet separation; when
$\beta=0$, the grooming threshold is independent of subjet separation, and the
algorithm is equivalent to the modified mass-drop tagger [55]. For background
QCD multijet events where large jet masses arise from soft gluon radiation,
the soft-drop jet mass $m_{\mathrm{SD}}$ is reduced relative to the ungroomed
jet mass. On the other hand, for signal events $m_{\mathrm{SD}}$ is primarily
determined by the $\Phi(\bbbar)$ decay kinematics, and the distribution peaks
at the mass of the $\Phi(\bbbar)$ signal.

Dedicated $m_{\mathrm{SD}}$ corrections are derived from a comparison of
simulated and measured samples in a region enriched with merged $\PW(\qqbar)$
decays from $\ttbar$ events [56]. The $m_{\mathrm{SD}}$ corrections remove a
residual dependence on the jet $\pt$, and match the simulated jet mass scale
and resolution to those observed in data. A lower $m_{\mathrm{SD}}$ bound of
40is applied in the search with AK8 jets to ensure that the trigger has
greater than 95% efficiency, while a lower $m_{\mathrm{SD}}$ bound of 82is
applied in the search with CA15 jets to ensure the background model described
in Section 0.5 is robust. The resulting $m_{\mathrm{SD}}$ distributions are
binned with a bin width of 7, corresponding to the $m_{\mathrm{SD}}$
resolution near the $\PW$ and $\PZ$ resonances.

The dimensionless mass scale variable for QCD multijet jets,
$\rho=\ln(m_{\mathrm{SD}}^{2}/\pt^{2})$ [55, 57], is used to characterize the
correlation between the jet $\cPqb$ tagging discriminator, jet mass, and jet
$\pt$. Its distribution is roughly invariant in different ranges of jet $\pt$.
Only events in the range $-6.0<\rho<-2.1$ ($-4.7<\rho<-1.0$) are considered
for AK8 (CA15) jets, effectively defining different $m_{\mathrm{SD}}$ ranges
depending on jet $\pt$. The upper bound is imposed to avoid instabilities at
the edges of the distribution due to finite cone limitations from the jet
clustering, while the lower bound avoids the nonperturbative regime of the
$m_{\mathrm{SD}}$ calculation. This requirement is about 98% efficient for the
$\Phi(\bbbar)$ signal at low masses (50–125) when reconstructed as an AK8 jet,
and 60–85% efficient at high masses (200–350) when reconstructed as a CA15
jet.

The $N_{2}^{1}$ variable [21] is used to determine how consistent a jet is
with having a two-prong substructure. It is based on a ratio of 2-point
(${}_{1}e_{2}$) and 3-point (${}_{2}e_{3}$) generalized energy correlation
functions [58],

${}_{1}e_{2}$ $\displaystyle=\sum_{1\leq i<j\leq n}z_{i}z_{j}\Delta R_{ij},$
(3) ${}_{2}e_{3}$ $\displaystyle=\sum_{1\leq i<j<k\leq
n}z_{i}z_{j}z_{k}\min\\{\Delta R_{ij}\Delta R_{ik},\Delta R_{ij}\Delta
R_{jk},\Delta R_{ik}\Delta R_{jk}\\},$ (4)

where $z_{i}$ represents the energy fraction of the constituent $i$ in the jet
and $\Delta R_{ij}$ is the angular separation between constituents $i$ and
$j$. These generalized energy correlation functions ${{}_{v}e_{n}}$ are
sensitive to correlations of $v$ pairwise angles among $n$-jet constituents
[21]. For a two-prong structure, signal jets have a stronger 2-point
correlation than a 3-point correlation. The discriminant variable $N_{2}^{1}$
is then constructed via the ratio:

$\quad N_{2}^{1}=\frac{{}_{2}e_{3}}{(_{1}e_{2})^{2}}.$ (5)

The calculation of $N_{2}^{1}$ is based on the jet constituents after
application of the soft-drop grooming algorithm to the jet. It provides
excellent discrimination between two-prong signal jets and QCD background
jets. However, imposing requirements on $N_{2}^{1}$, or other similar
variables, distorts the jet mass distributions differently depending on the
$\pt$ of the jet [59]. To minimize this distortion, a transformation is
applied to $N_{2}^{1}$ following the designed decorrelated tagger (DDT)
technique [57] to reduce its correlation with $\rho$ and $\pt$ in multijet
events. The transformed variable is defined as
$\mathrm{N}_{\mathrm{2}}^{\mathrm{1,DDT}}\equiv N_{2}^{1}-X_{(26\%)}$, where
$X_{(26\%)}$ is the 26th percentile of the $N_{2}^{1}$ distribution in
simulated QCD events as a function of $\rho$ and $\pt$. The transformation is
derived in bins of $\rho$ and $\pt$, separately for AK8 and CA15 jets. This
ensures that the selection $\mathrm{N}_{\mathrm{2}}^{\mathrm{1,DDT}}<0$ yields
a constant QCD background efficiency across the $\rho$ and $\pt$ range
considered in this search. The chosen background efficiency of 26% maximizes
the signal sensitivity, independent of the signal mass.

A dedicated double-$\cPqb$ tagger is used to select jets likely to originate
from two $\cPqb$ quarks [22]. Events where the selected wide jet is
double-$\cPqb$-tagged constitute the “passing,” or signal, region, while
events failing the double-$\cPqb$ tagger form the “failing” region, which is
used to estimate the QCD multijet background in the signal region. The
multivariate algorithm, based on a boosted decision tree, takes as inputs
several observables that characterize the distinct properties of $\cPqb$
hadrons and their flight directions in relation to the jet substructure. A
wide jet is considered double-$\cPqb$ tagged if its double-$\cPqb$ tagger
discriminator value exceeds a threshold corresponding to a 1%
misidentification rate for QCD jets and a 33% efficiency for $\Phi(\bbbar)$
candidates with a mass of 125reconstructed as AK8 jets.

For CA15 jets, because of the larger cone with radius parameter of 1.5, it is
often possible to resolve two subjets within the wide jet; hence additional
background discrimination can be obtained by incorporating the individual
subjet $\PQb$ tagging probabilities. The subjets are constructed using the
soft-drop algorithm, and assigned $\cPqb$ tagging scores using the combined
secondary vertex algorithm (CSVv2) [22] that combines information from
displaced tracks and vertices using a multilayer perceptron. The second
highest CSVv2 score is then used as an additional input to the boosted
decision tree of the double-$\cPqb$ tagger. For the chosen discriminator
threshold, the double-$\cPqb$ tagger algorithm has a misidentification rate of
about 4%, and a signal efficiency which decreases with mass, equalling 25
(13)% for a signal mass of 200(350).

The efficiency (in percent) of the cumulative selection criteria for the
scalar $\Phi(\bbbar)$ signal benchmark is shown in Table 0.4. The efficiencies
for the $\cmsSymbolFace{A}(\bbbar)$ signal are consistent within the
statistical uncertainties.

The selection efficiencies in percent for simulated $\Phi(\bbbar)$ signal
events with parton-level $\HT>400\GeV$, at different stages of the event
selection, shown for different mass hypotheses and for AK8 and CA15 jets. The
statistical uncertainties due to the simulated sample size are also shown.  c
D, ± 3.2 D, ± 3.2 D, ± 3.2 D, ± 3.2 D, ± 3.2 D, ± 3.2 D, ± 2.2 AK8 jets
$m_{\Phi}$ (GeV) $\pt$ $m_{\mathrm{SD}}$ Lepton veto $\ptmiss$
$\mathrm{N}_{\mathrm{2}}^{\mathrm{1,DDT}}<0$ $-6<\rho<2.1$ double-$\cPqb$ tag
$>450\GeV$ $>40\GeV$ $<140\GeV$
50 75.0 , 0.1 37.5 , 0.2 36.2 , 0.2 32.9 , 0.2 14.7 , 0.1 14.3 , 0.1 7.3 , 0.1
100 75.4 , 0.1 42.2 , 0.2 40.6 , 0.2 37.5 , 0.2 18.0 , 0.1 17.5 , 0.1 7.1 ,
0.1
125 75.5 , 0.2 42.3 , 0.2 40.6 , 0.2 37.5 , 0.2 18.1 , 0.1 17.5 , 0.1 6.1 ,
0.1
CA15 jets
$m_{\Phi}$ (GeV) $\pt$ $m_{\mathrm{SD}}$ Lepton veto $\ptmiss$
$\mathrm{N}_{\mathrm{2}}^{\mathrm{1,DDT}}<0$ $-4.7<\rho<-1.0$ double-$\cPqb$
tag
$>500\GeV$ $>82\GeV$ $<140\GeV$
200 61.0 , 0.1 35.6 , 0.1 33.9 , 0.1 31.1 , 0.1 13.9 , 0.1 13.0 , 0.1 3.3 ,
0.1
300 63.4 , 0.1 35.7 , 0.1 34.0 , 0.1 31.1 , 0.1 13.2 , 0.1 11.1 , 0.1 1.9 ,
0.1
350 64.3 , 0.1 35.8 , 0.1 33.9 , 0.1 31.1 , 0.1 13.0 , 0.1 8.6 , 0.1 1.1 , 0.1

##  0.5 Background estimation

The $\PW$, $\PZ$, and $\PH+$jets backgrounds are modeled using MC simulation.
Their overall contribution is less than 5% of the total SM background. The
normalization and shape of the simulated $\PW/\PZ+$jets backgrounds are
corrected for NLO QCD and EW effects. Other EW processes, such as diboson,
triboson, and $\ttbar+\PW/\PZ$, are estimated from simulation and found to be
negligible.

The contribution of $\ttbar$ production to the total SM background, estimated
to be less than 3%, is obtained from simulation corrected with scale factors
derived from a $\ttbar$-enriched control sample in which an isolated muon [53]
is required. Scale factors correct the overall $\ttbar$ normalization and the
double-$\cPqb$ mistag efficiency for jets originating from top quark decays.
The control sample is included in the global fit used to extract the signal,
with the scale factors treated as unconstrained parameters.

The main background in the passing region, QCD multijet production, has a
nontrivial jet mass shape that is difficult to model parametrically and
depends on jet $\pt$. Therefore, we constrain it using the background-enriched
failing region, i.e., events failing the double-$\cPqb$ tagger selection.
Since the double-$\cPqb$ tagger discriminator and the jet mass are largely
uncorrelated, the passing and failing regions have similar QCD jet mass
distributions, and their ratio, the “pass-fail ratio”
$R_{\mathrm{p}/\mathrm{f}}$, is expected to be nearly constant as a function
of jet mass and $\pt$. To account for the residual difference between the
shapes of passing and failing events, $R_{\mathrm{p}/\mathrm{f}}$ is
parametrized as a Bernstein polynomial in $\rho$ and $\pt$,

$\displaystyle R_{\mathrm{p}/\mathrm{f}}(\rho,\pt)$
$\displaystyle=\sum^{n_{\rho}}_{k=0}\sum^{n_{\pt}}_{\ell=0}a_{k,\ell}b_{k,n_{\rho}}(\rho)b_{\ell,n_{\pt}}(\pt),~{}$
(6)

where $n_{\rho}$ is the degree of the polynomial in $\rho$, $n_{\pt}$ is the
degree of the polynomial in $\pt$, $a_{k,\ell}$ is a Bernstein coefficient,
and

$\displaystyle b_{{\nu,n}}(x)$
$\displaystyle=\binom{n}{\nu}x^{{\nu}}\left(1-x\right)^{{n-\nu}}$ (7)

is a Bernstein basis polynomial of degree $n$.

The coefficients $a_{k,\ell}$ have no external constraints, but are determined
from a simultaneous binned fit to data in passing and failing regions across
the whole jet mass and $\pt$ range. The $\pt$ binning, varying from 50 to 200,
is chosen to provide enough data points to constrain the shape of
$R_{\mathrm{p}/\mathrm{f}}$. To determine the degree of polynomial necessary
to fit the data, a Fisher $F$-test [60] is performed. Based on its results, a
polynomial of second (fifth) degree in $\rho$ and first degree in $\pt$ is
selected for the AK8 (CA15) analysis category. The fitted pass-fail ratios
$R_{\mathrm{p}/\mathrm{f}}$ as functions of $\rho$ and $\pt$ under the
background-only hypothesis are shown in Fig. 2 for the AK8 and CA15
selections.

Figure 2: The fitted pass-fail ratio $R_{\mathrm{p}/\mathrm{f}}$ as a function
of $\pt$ and $\rho$ for the AK8 selection (left) and the CA15 selection
(right).

Figures 3 and 4 show the $m_{\mathrm{SD}}$ distributions in the full data set
for the passing and failing regions with fitted SM background for AK8 and CA15
selections, respectively. Note that the different $\rho$ boundaries define
different $m_{\mathrm{SD}}$ ranges for the AK8 and CA15 selections as well as
within each $\pt$ category, giving rise to the features at 166, 180, 201, 215,
and 250in Fig. 3 and at 285, 313, 341, 376, and 432in Fig. 4. The bottom
panels of Figs. 3–6 show the difference between the data and the prediction
from the nonresonant background, composed of the QCD multijet and $\ttbar$
processes, divided by the statistical uncertainty in the data. These highlight
the agreement between the data and the contributions from $\PW$ and $\PZ$
boson production, which are clearly visible in the failing and passing
regions, respectively. The remaining $\PW$ boson contribution in the passing
region is due to the misidentification of $\PW(\qqbar)$ decays. No significant
deviations from the background-only expectations are observed. In Figs. 5 and
6, the $m_{\mathrm{SD}}$ distributions are reported for each $\pt$ category
for AK8 and CA15 jets, respectively.

In order to validate the background estimation method and associated
systematic uncertainties, bias studies are performed on simulated samples and
on the background-only fits. Pseudo-experiment data sets are generated, with
and without the injection of signal events, and then fit with the signal plus
background model. No significant bias in the fitted signal strength is
observed; specifically, the means of the differences between the fitted and
injected signal strengths divided by the fitted uncertainty are found to be
less than 15%.

In addition, to validate the corrections and uncertainties related to the
$\PW(\qqbar)$ and $\PZ(\qqbar)$ resonances, we perform a consistency check by
directly measuring a combined signal strength for those contributions assuming
the SM background-only hypothesis. We find agreement with the SM expectation
within the measured uncertainties.

Figure 3: The observed and fitted background $m_{\mathrm{SD}}$ distributions
for the AK8 selection for the failing (left) and passing (right) regions,
combining all the $\pt$ categories. The background fit is performed under the
background-only hypothesis. A hypothetical $\Phi(\bbbar)$ signal at a mass of
140is also indicated. The features at 166, 180, 201, 215, and 250in the
$m_{\mathrm{SD}}$ distribution are due to the $\rho$ boundaries, which define
different $m_{\mathrm{SD}}$ ranges for each $\pt$ category. The shaded blue
band shows the systematic uncertainty in the total background prediction. The
bottom panel shows the difference between the data and the nonresonant
background prediction, divided by the statistical uncertainty in the data.

Figure 4: The observed and fitted background $m_{\mathrm{SD}}$ distributions
for the CA15 selection for the failing (left) and passing (right) regions,
combining all the $\pt$ categories. The background fit is performed under the
background-only hypothesis. A hypothetical $\cmsSymbolFace{A}(\bbbar)$ signal
at a mass of 260is also indicated. The features at 285, 313, 341, 376, and
432in the $m_{\mathrm{SD}}$ distribution are due to the $\rho$ boundaries,
which define different $m_{\mathrm{SD}}$ ranges for each $\pt$ category. The
shaded blue band shows the systematic uncertainty in the total background
prediction. The bottom panel shows the difference between the data and the
nonresonant background prediction, divided by the statistical uncertainty in
the data.

Figure 5: The observed and fitted background $m_{\mathrm{SD}}$ distributions
in each $\pt$ category for the AK8 selection in the passing regions. The fit
is performed under the background-only hypothesis. A hypothetical
$\Phi(\bbbar)$ signal at a mass of 140is also indicated. The shaded blue band
shows the systematic uncertainty in the total background prediction. The
bottom panel shows the difference between the data and the nonresonant
background prediction, divided by the statistical uncertainty in the data.

Figure 6: The observed and fitted background $m_{\mathrm{SD}}$ distributions
in each $\pt$ category for the CA15 selection in the passing regions. The fit
is performed under the background-only hypothesis. A hypothetical
$\cmsSymbolFace{A}(\bbbar)$ signal at a mass of 260is also indicated. The
shaded blue band shows the systematic uncertainty in the total background
prediction. The bottom panel shows the difference between the data and the
nonresonant background prediction, divided by the statistical uncertainty in
the data.

##  0.6 Systematic uncertainties

The systematic uncertainties associated with the jet mass scale, the jet mass
resolution, and the $\mathrm{N}_{\mathrm{2}}^{\mathrm{1,DDT}}$ selection
efficiency are correlated among the $\PW$, $\PZ$, $\PH(\bbbar)$, and
$\Phi(\bbbar)$ or $\cmsSymbolFace{A}(\bbbar)$ processes. These uncertainties
are estimated using an independent sample of merged $\PW$ jets in semileptonic
$\ttbar$ events in data.

To select a sample of merged $\PW$ jets from semileptonic $\ttbar$ production,
events are required to have an energetic muon with $\pt>100\GeV$,
$\ptmiss>80\GeV$, a high-$\pt$ AK8 or CA15 jet with $\pt>200\GeV$, and an
additional jet separated from the AK8 (CA15) jet by $\Delta R>0.8\,(1.5)$.
Using the same $\mathrm{N}_{\mathrm{2}}^{\mathrm{1,DDT}}$ requirements that
define the signal regions, we define samples with events that pass and fail
the selection for merged $\PW$ boson jets in data and simulation. A
simultaneous fit to the two samples is performed in order to extract the
selection efficiency of a merged $\PW$ jet in simulation and in data. This is
performed separately for AK8 and CA15 selections. We measure the data-to-
simulation scale factor for the $\mathrm{N}_{\mathrm{2}}^{\mathrm{1,DDT}}$
selection to be $0.99\pm 0.04$ for AK8 jets and $0.97\pm 0.06$ for CA15 jets.
The jet mass scales in data and simulation are found to be consistent within
1%. The jet mass resolution data-to-simulation scale factor is $1.08\pm 0.09$
for AK8 jets and $0.99\pm 0.08$ for CA15 jets. As the semileptonic $\ttbar$
sample does not contain a large population of jets with very high $\pt$, an
additional systematic uncertainty is included to account for the extrapolation
to very high $\pt$ jets. The jet mass scale uncertainty is allowed to vary in
the signal extraction differently depending on the jet $\pt$, and ranges from
2% at 450to 4% at 1.

The efficiency of the double-$\cPqb$ tagger is measured in data and simulation
in a sample enriched in $\bbbar$ pairs from gluon splitting [22]. Scale
factors relating data and simulation are then computed and applied to the
simulation. The measured double-$\cPqb$ tagger efficiency scale factor is
found to be $0.86\pm 0.07$ for CA15 jets and $0.91\pm 0.04$ for AK8 jets,
where the uncertainty accounts for various systematic effects including the
calibration of the jet probability tagger algorithm used in the method, the
modeling of the track reconstruction efficiency, the modeling of $\cPqb$ quark
fragmentation, and others [22].

The scale factors described above determine the initial distributions of the
jet mass for the $\PW(\qqbar)$, $\PZ(\qqbar)$, $\PH(\bbbar)$, and
$\Phi(\bbbar)$ or $\cmsSymbolFace{A}(\bbbar)$ processes and are further
constrained in the fit to data by the presence of the $\PW$ and $\PZ$
resonances in the jet mass distribution.

To account for potential $\pt$-dependent deviations due to missing higher-
order corrections, uncertainties are applied to the $\PW(\qqbar)$ and
$\PZ(\qqbar)$ yields that are $\pt$ dependent and correlated per $\pt$ bin
[61, 62, 63, 64, 65, 42, 43]. An additional systematic uncertainty is included
to account for potential differences between the $\PW$ and $\PZ$ higher-order
corrections (EW $\PW/\PZ$ decorrelation) [61].

Finally, additional systematic uncertainties are applied to the $\PW(\qqbar)$,
$\PZ(\qqbar)$, $\ttbar$, $\PH(\bbbar)$, and $\Phi(\bbbar)$ or
$\cmsSymbolFace{A}(\bbbar)$ yields to account for the uncertainties due to the
jet energy scale and resolution [66], variations in the amount of pileup, the
integrated luminosity determination [67], and the limited simulation sample
sizes. A quantitative summary of the systematic effects considered is shown in
Table 0.6.

Summary of the systematic uncertainties affecting the signal, $\PW$ and $\PZ+$
jets processes. Instances where the uncertainty does not apply are indicated
by a dash. The reported percentages reflect a one standard deviation effect on
the product of acceptance and efficiency of each process. For the
uncertainties related to the jet mass scale and resolution, which affect the
mass distribution shapes, the reported percentages reflect a one standard
deviation effect on the nominal jet mass.  lcccc Uncertainty source & Process
$\PW$ or $\PZ$ (AK8) $\PW$ or $\PZ$ (CA15) $\Phi$ or $\cmsSymbolFace{A}$ (AK8)
$\Phi$ or $\cmsSymbolFace{A}$ (CA15)
Integrated luminosity 2.5% 2.5% 2.5% 2.5%
Trigger efficiency 2% 2% 2% 2%
Pileup $<$1% $<$1% $<$1% $<$1%
$\mathrm{N}_{\mathrm{2}}^{\mathrm{1,DDT}}$ selection efficiency 4.3% 6% 4.3%
6%
Double-$\cPqb$ tag 4% () 8% () 4% 8%
Jet energy scale / resolution 5–15% 5–15% 5–15% 5–15%
Jet mass resolution 8% 8% 8% 8%
Jet mass scale $\left(\%\,/\,(\pt\,\mathrm{[GeV]}\,/\,100)\right)$ 0.4% 1%
0.4% 1%
Simulation sample size 2–25% 2–25% 4–20% 4–20%
NLO QCD corrections 10% 10% — —
NLO EW corrections 15–35% 15–35% — —
NLO EW $\PW/\PZ$ decorrelation 5–15% 5–15% — —

##  0.7 Results

The search results are interpreted in the context of the scalar and
pseudoscalar signal models described in Section 0.1. The signals are modeled
using MC simulation. For the search with AK8 (CA15) jets, a binned maximum
likelihood fit to the observed $m_{\mathrm{SD}}$ distributions in the range 40
to 201 (82 to 399)with a 7bin width is performed using the sum of the signal,
$\PH(\bbbar)$, $\PW$, $\PZ$, $\ttbar$, and QCD multijet contributions. The fit
is performed simultaneously in the passing and failing regions of the six
(five) $\pt$ categories within $450(500)<\pt<1000\GeV$ for AK8 (CA15) jets, as
well as in the passing and failing components of the $\ttbar$-enriched control
region.

The chosen test statistic, used to determine how signal- or background-like
the data are, is based on the profile likelihood ratio [68] using the
criterion [69, 70]. Systematic uncertainties are incorporated into the
analysis via nuisance parameters and treated according to the frequentist
paradigm. Upper limits at 95% confidence level (CL) are obtained using
asymptotic formulae [68, 71, 72].

The 95% CL upper limits on the $\Phi(\bbbar)$ and $\cmsSymbolFace{A}(\bbbar)$
production as a function of resonance mass are shown in Figs. 7 and 8,
respectively. Based on the expected sensitivity, the AK8 jet selection is used
for signal masses below 175, and the CA15 jet selection is used above. We
exclude $\Phi$ or $\cmsSymbolFace{A}$ production with a product of the cross
section and branching fraction ($\sigma\mathcal{B}(\bbbar)$) as low as 79 or
$86\unit{pb}$, respectively, at a resonance mass of 175. The exclusions are
converted to upper limits on the coupling $g_{\Pq\Phi}$ for the scalar model
and the coupling $g_{\Pq\cmsSymbolFace{A}}$ for the pseudoscalar model. The
abrupt loss in sensitivity to the coupling constants for resonance masses
greater than $2m_{\cPqt}$ is because the branching fraction to $\bbbar$ falls
steeply as the decay to $\ttbar$ becomes kinematically allowed. For a
resonance mass of 175, the exclusion corresponds to an upper limit on
$g_{\Pq\Phi}$ or $g_{\Pq\cmsSymbolFace{A}}$ of 3.9 or 2.5, respectively.

For the search with AK8 jets, the maximum local significance [73] corresponds
to 0.5 standard deviations from the background-only expectation at a
$\Phi(\bbbar)$ mass of 140. The hypothetical $\Phi(\bbbar)$ signal is
indicated in Figs. 3 and 5 with $g_{\Pq\Phi}=4.7$, which is equivalent to the
95% CL upper limit. Similarly, for the CA15 search, the maximum local
significance is 1.2 standard deviations at an $\cmsSymbolFace{A}(\bbbar)$ mass
of 260. The hypothetical $\cmsSymbolFace{A}(\bbbar)$ signal is indicated in
Figs. 4 and 6 with $g_{\Pq\cmsSymbolFace{A}}=4.6$, which is equivalent to the
95% CL upper limit. The largest downward fluctuation in the limits occurs at
an $\cmsSymbolFace{A}(\bbbar)$ mass of 175in the AK8 search, corresponding to
a local significance of $-2.9$ standard deviations and a global significance
[73], calculated over the probed mass range (50–350), of approximately $-1.7$
standard deviations. A corresponding deficit is not seen in CA15 search, as
the events used in the AK8 and CA15 searches are largely independent;
approximately 20 (37)% of events in the CA15 search are selected in the AK8
search, while conversely, approximately 37% of events in the AK8 search are
selected in the CA15 search.

Figure 7: Upper limits at 95% CL on the product of the $\Phi$ production cross
section and the branching fraction to $\bbbar$ (left) and on $g_{\Pq\Phi}$
(right), as a function of the resonance mass $m_{\Phi}$. The blue dash-dotted
line indicates the theoretical scalar production cross section assuming
$g_{\Pq\Phi}=1$ as a chosen benchmark [5]. The vertical line at 175corresponds
to the transition between the AK8 and CA15 jet selections.

Figure 8: Upper limits at 95% CL on the product of the $\cmsSymbolFace{A}$
production cross section and the branching fraction to $\bbbar$ (left) and on
$g_{\Pq\cmsSymbolFace{A}}$ (right), as a function of the resonance mass
$m_{\cmsSymbolFace{A}}$. The blue dash-dotted line indicates the theoretical
pseudoscalar production cross section assuming $g_{\Pq\cmsSymbolFace{A}}=1$ as
a chosen benchmark [5]. The vertical line at 175corresponds to the transition
between the AK8 and CA15 jet selections.

##  0.8 Summary

A search for a low-mass resonance decaying into a bottom quark-antiquark pair
and reconstructed as a single wide jet has been presented, using a data set of
proton-proton collisions at $\sqrt{s}=13\TeV$ corresponding to an integrated
luminosity of 35.9. Dedicated substructure and double-$\cPqb$ tagging
techniques were employed to identify jets containing a resonance candidate
over a smoothly falling soft-drop jet mass distribution in data. No
significant excess above the standard model prediction was observed for signal
masses between 50–350. Upper limits at 95% confidence level are set on the
product of the resonance production cross section and the branching fraction
to bottom quark-antiquark pairs, as well as on the coupling $g_{\Pq\Phi}$
($g_{\Pq\cmsSymbolFace{A}}$) of a scalar (pseudoscalar) boson decaying to
quarks. The search excludes the production through gluon fusion of a scalar
(pseudoscalar) decaying to $\bbbar$ with a product of the cross section and
branching fraction as low as 79 (86)pb at a resonance mass of 175,
corresponding to an upper limit on $g_{\Pq\Phi}$ ($g_{\Pq\cmsSymbolFace{A}}$)
of 3.9 (2.5). This constitutes the first LHC constraint on exotic bottom
quark-antiquark resonances below 325.

###### 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); MSIP and NRF (Republic of Korea); MES
(Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT,
LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC
(Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom,
RAS, RFBR, and NRC KI (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER
(Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST
(Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK
(Turkey); NASU and SFFR (Uxkraine); STFC (United Kingdom); DOE and NSF (USA).
Individuals have received support from the Marie-Curie program and the
European Research Council and Horizon 2020 Grant, contract 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 n. 30820817; the Ministry of
Education, Youth and Sports (MEYS) of the Czech Republic; the Lendület
(“Momentum”) Program and the János Bolyai Research Scholarship of the
Hungarian Academy of Sciences, the New National Excellence Program ÚNKP, the
NKFIA research grants 123842, 123959, 124845, 124850 and 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
2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and
2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406; the National Priorities
Research Program by Qatar National Research Fund; the Programa Estatal de
Fomento de la Investigación Científica y Técnica de Excelencia María de
Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa del Principado de
Asturias; the Thalis and Aristeia programs cofinanced by EU-ESF and the Greek
NSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn
University and the Chulalongkorn Academic into Its 2nd Century Project
Advancement Project (Thailand); the Welch Foundation, contract C-1845; and the
Weston Havens Foundation (USA).

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##  .9 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, A. Taurok, 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, 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, R. Goldouzian, 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, S. Wertz, J. Zobec Centro
Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
F.L. Alves, G.A. Alves, M. Correa Martins Junior, 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, F. Romeo, S.M. Shaheen6, A. Spiezia, J. Tao, Z. Wang,
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, 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
Y. Assran9,10, S. Elgammal10, A. Ellithi Kamel11 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, P. Pigard, 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
I. Bagaturia16 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. Stahl17 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. Borras18, 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. Gallo19, A. Geiser, J.M. Grados
Luyando, A. Grohsjean, M. Guthoff, M. Haranko, A. Harb, J. Hauk, H. Jung, M.
Kasemann, J. Keaveney, C. Kleinwort, J. Knolle, D. Krücker, W. Lange, A.
Lelek, T. Lenz, J. Leonard, K. Lipka, W. Lohmann20, 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, M. Savitskyi, P. Saxena, P.
Schütze, C. Schwanenberger, R. Shevchenko, A. Singh, H. Tholen, O. Turkot, A.
Vagnerini, 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, V. Blobel, 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, A. Vanhoefer, 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. Hartmann17, 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
G. Karathanasis, P. Kontaxakis, A. Panagiotou, I. Papavergou, N. Saoulidou, E.
Tziaferi, 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. Bartók21, 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. Horvath22, 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. Karancsi21, 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. Bahinipati24, C. Kar, P. Mal, K. Mandal, A. Nayak25, D.K. Sahoo24, 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, 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. Bhardwaj26, M. Bharti26, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep26,
D. Bhowmik, S. Dey, S. Dutt26, S. Dutta, S. Ghosh, K. Mondal, S. Nandan, A.
Purohit, P.K. Rout, A. Roy, S. Roy Chowdhury, G. Saha, S. Sarkar, M. Sharan,
B. Singh26, S. Thakur26 Indian Institute of Technology Madras, Madras, India
P.K. Behera Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, P.K. Netrakanti, L.M. Pant, P.
Shukla Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, G.B. Mohanty, N. Sur, B. Sutar, 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, M. Maity27, G. Majumder, K. Mazumdar, N. Sahoo, T.
Sarkar27 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. Chenarani28, E. Eskandari Tadavani, S.M. Etesami28, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, F. Rezaei Hosseinabadi, B. Safarzadeh29, 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, G. Zitoa 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, C. Cioccaa, 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, S. Marcellinia, G. Masettia,
A. Montanaria, F.L. Navarriaa,b, A. Perrottaa, F. Primaveraa,b,17, 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, F. Raveraa,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,17, S. Di Guidaa,d,17,
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, W.A. Khana, L. Listaa, S.
Meolaa,d,17, P. Paoluccia,17, 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, 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
I. Ahmed, 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, A. Saddique, 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, 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
M. Chadeeva41, P. Parygin, D. Philippov, S. Polikarpov41, E. Popova, V.
Rusinov 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. Baskakov, A. Belyaev, E. Boos, V. Bunichev, M. Dubinin42, L. Dudko, A.
Gribushin, V. Klyukhin, O. Kodolova, I. Lokhtin, I. Miagkov, S. Obraztsov, S.
Petrushanko, 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, D. Elumakhov, A. Godizov, 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, 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, P. Vischia, 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. Karacheban20, 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. Pantaleo17, 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, 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, J. Pata, F. Pauss, G. Perrin, L. Perrozzi, S.
Pigazzini, M. Quittnat, 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, Arun Kumar, 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
M.N. Bakirci51, A. Bat, F. Boran, S. Damarseckin, Z.S. Demiroglu, F. Dolek, C.
Dozen, E. Eskut, S. Girgis, G. Gokbulut, Y. Guler, E. Gurpinar, I. Hos52, C.
Isik, E.E. Kangal53, O. Kara, U. Kiminsu, M. Oglakci, G. Onengut, K.
Ozdemir54, S. Ozturk51, D. Sunar Cerci55, B. Tali55, U.G. Tok, H. Topakli51,
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. Virdee17, 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, D. Pinna, D. Rankin, C. Richardson, J. Rohlf,
L. Sulak, D. Zou Brown University, Providence, USA
G. Benelli, 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, 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, D. Olivito, S. Padhi, M. Pieri, M. Sani, V. Sharma, S. Simon,
M. Tadel, A. Vartak, S. Wasserbaech68, 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, 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, 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, L. Ristori, A. Savoy-Navarro69, 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, A. Whitbeck 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, H. Mei, 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, 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. Bilki70, W. Clarida, K. Dilsiz71, S. Durgut, R.P.
Gandrajula, M. Haytmyradov, V. Khristenko, J.-P. Merlo, A. Mestvirishvili, A.
Moeller, J. Nachtman, H. Ogul72, Y. Onel, F. Ozok73, 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, C. You 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, L.K. Saini 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, G. Bauer, 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, 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, N. Ruckstuhl, 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, D.M. Morse, T.
Orimoto, R. Teixeira De Lima, T. Wamorkar, B. Wang, A. Wisecarver, D. Wood
Northwestern University, Evanston, USA
S. Bhattacharya, J. Bueghly, O. Charaf, K.A. Hahn, N. Mucia, N. Odell, M.H.
Schmitt, K. Sung, M. Trovato, M. Velasco University of Notre Dame, Notre Dame,
USA
R. Bucci, N. Dev, M. Hildreth, K. Hurtado Anampa, C. Jessop, D.J. Karmgard, N.
Kellams, K. Lannon, W. Li, N. Loukas, N. Marinelli, F. Meng, C. Mueller, Y.
Musienko37, M. Planer, A. Reinsvold, 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, S. Higginbotham, A. Kalogeropoulos,
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, Z. Wang
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, 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
A. Agapitos, J.P. Chou, Y. Gershtein, E. Halkiadakis, A. Hart, M. Heindl, E.
Hughes, S. Kaplan, R. Kunnawalkam Elayavalli, S. Kyriacou, A. Lath, R.
Montalvo, K. Nash, M. Osherson, H. Saka, S. Salur, S. Schnetzer, D. Sheffield,
S. Somalwar, R. Stone, S. Thomas, P. Thomassen, M. Walker University of
Tennessee, Knoxville, USA
A.G. Delannoy, J. Heideman, G. Riley, S. Spanier Texas A&M University, College
Station, USA
O. Bouhali74, A. Celik, M. Dalchenko, M. De Mattia, A. Delgado, S. Dildick, R.
Eusebi, J. Gilmore, T. Huang, T. Kamon75, S. Luo, 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 Vanderbilt University, Nashville, USA
S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H. Ni, K.
Padeken, 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
M. Brodski, J. Buchanan, C. Caillol, D. Carlsmith, S. Dasu, I. De Bruyn, L.
Dodd, B. Gomber, 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: Also at Suez University, Suez, Egypt
10: Now at British University in Egypt, Cairo, Egypt
11: Now at Cairo University, Cairo, 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 Ilia State University, Tbilisi, Georgia
17: Also at CERN, European Organization for Nuclear Research, Geneva,
Switzerland
18: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
19: Also at University of Hamburg, Hamburg, Germany
20: Also at Brandenburg University of Technology, Cottbus, Germany
21: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary
22: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
23: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
24: Also at Indian Institute of Technology Bhubaneswar, Bhubaneswar, India
25: Also at Institute of Physics, Bhubaneswar, India
26: Also at Shoolini University, Solan, India
27: Also at University of Visva-Bharati, Santiniketan, India
28: Also at Isfahan University of Technology, Isfahan, Iran
29: Also at Plasma Physics Research Center, Science and Research Branch,
Islamic Azad University, Tehran, Iran
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 Gaziosmanpasa University, Tokat, 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 Adiyaman University, Adiyaman, 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 Utah Valley University, Orem, USA
69: Also at Purdue University, West Lafayette, USA
70: Also at Beykent University, Istanbul, Turkey
71: Also at Bingol University, Bingol, Turkey
72: Also at Sinop University, Sinop, Turkey
73: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey
74: Also at Texas A&M University at Qatar, Doha, Qatar
75: Also at Kyungpook National University, Daegu, Korea