EXO-18-012

$Heal$ $Id$

EXO-18-012

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

###### Abstract

A search for low mass narrow vector resonances decaying into quark-antiquark
pairs is presented. The analysis is based on data collected in 2017 with the
CMS detector at the LHC in proton-proton collisions at a center-of-mass energy
of 13, corresponding to an integrated luminosity of 41.1. The results of this
analysis are combined with those of an earlier analysis based on data
collected at the same collision energy in 2016, corresponding to 35.9. Signal
candidates will be recoiling against initial state radiation and are
identified as energetic, large-radius jets with two pronged substructure. The
invariant jet mass spectrum is probed for a potential narrow peaking signal
over a smoothly falling background. No evidence for such resonances is
observed within the mass range of 50–450. Upper limits at the 95% confidence
level are set on the coupling of narrow resonances to quarks, as a function of
the resonance mass. For masses between 50 and 300these are the most sensitive
limits to date. This analysis extends the earlier search to a mass range of
300–450, which is probed for the first time with jet substructure techniques.

##  0.1 Introduction

Many extensions of the standard model (SM), including models with extra
dimensions or with new gauge symmetries, amongst others, predict the existence
of leptophobic vector or axial-vector mediators that couple to SM quarks ()
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]. These particles would be observed
as resonances in the dijet mass distribution. At the CERN LHC, searches for
such particles have reached the TeV scale, placing limits on resonances with
masses between 1.0 and 7.6 [14, 15]. Below 1, the sensitivity of these
searches is limited by the large background rate from quantum chromodynamics
(QCD) multijet events that saturate the hardware selection algorithm (trigger)
bandwidth. Complementary techniques have been explored to overcome this
limitation. For masses between 450 and 1000, limits on resonances have been
set by trigger-level analyses that record only partial event information and
perform searches in the dijet mass spectrum with lower trigger thresholds [16,
17, 15, 18]. In order to extend searches to even lower resonance masses, this
study looks for dijet resonances that would be produced with significant
initial-state radiation (ISR). The presence of ISR ensures that the events
have enough energy to satisfy the trigger requirement, either by the ISR jet
or by the resonance itself. For low resonance masses, the decay products of
the resonance are expected to be collimated into a single, large-radius jet.
Previous searches have probed the mass regime between 10 and 300using this
event signature [19, 20, 21, 22]. An ATLAS search with events containing a
dijet and a high transverse momentum () photon in the final state, sets limits
above 225, probing the mass range between 225 and 450where the resonance decay
products start to fall outside the large-radius cone [23].

This paper focuses on a search for narrow leptophobic vector resonances with
masses below 450and a natural width small relative to the detector’s mass
resolution. We take a model [24] as a proxy for such states. We consider a
Lorentz-boosted event topology where the resonance recoils against significant
ISR from quark/gluon radiation, increasing the momenta of the decay daughters
and enabling more efficient triggering in the low resonance mass region. The
resonance is reconstructed as a single, large-radius jet and it is
distinguished from the dominant QCD background using jet substructure. We
extend previous searches to higher resonance masses by using a jet clustering
algorithm with a larger distance parameter. Using wider jets enhances the
acceptance at masses above 200where the resonance decay products tend to have
a larger angular separation. The data sample used in this paper was collected
with the CMS detector in 2017 at $\sqrt{s}=13\TeV$ and corresponds to an
integrated luminosity of 41.1. The reach of this search is further extended by
statistically combining the results with those from a similar analysis [20]
based on data collected by CMS at the same collision energy in 2016. The
resulting search for new dijet resonances in boosted topologies is based on a
total integrated luminosity of 77.0.

##  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, and a brass and scintillator hadron calorimeter,
each composed of a barrel and two endcap sections. Forward calorimeters extend
the pseudorapidity ($\eta$) coverage provided by the barrel and endcap
detectors. Muons are detected in gas-ionization chambers embedded in the steel
flux-return yoke outside the solenoid. A more detailed description of the CMS
detector, together with a definition of the coordinate system used and the
relevant kinematic variables, can be found in Ref. [25].

Events are selected using a two-tiered trigger system [26]. The first tier,
composed of custom hardware processors, uses information from the calorimeters
and muon detectors to select events of interest in a time interval of less
than 4. The second tier, 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 further reduces the event rate from around
100kHz to less than 1kHz before data storage.

##  0.3 Event simulation, reconstruction, and selection

Simulated samples of signal and background events are generated using various
Monte Carlo (MC) generators, and further processed through a [27] modeling of
the CMS detector. The +jet(s) signal events are generated at leading order
(LO) with the 2.4.3 generator [28], for various mass hypotheses in the range
50–450. The events are generated with one or two jets in the matrix element
calculations and a parton-level filter requires the scalar sum of transverse
energies of all the jets in the event () to satisfy the condition
$\HT>400\GeV$. These signal events generally satisfy the event topology with
the presence of large ISR. To keep consistency with the generated distribution
of the samples used in the analysis of 2016 data [20], signal events are
reweighted by comparing their distribution with those including up to 3 jets
in the matrix element calculations.

The generator is also used to simulate background processes, including
multijet, $\PZ$+jets, and $\PW$+jets events, at LO accuracy with the MLM
matching scheme [29] between jets from the matrix element calculations and the
parton shower description. The 2.0 [30, 31, 32] generator at next-to-leading
order (NLO) precision is used to model the and single top quark processes. The
generators used for signal and background processes are interfaced with 8.230
[33] to simulate parton showering and hadronization. The parameters for the
underlying event description are set with the CP5 tune as described in Ref.
[34]. The parton distribution function set NNPDF3.1 [35] is used to produce
all simulated samples.

The generation of $\PW$+jets and $\PZ$+jets processes at LO accuracy is purely
due to technical constraints, owing to the large number of simulated events
needed to accurately describe and processes. Their cross sections include
higher-order QCD and electroweak (EW) differential corrections, as a function
of the boson , to improve the modeling of high-and bosons events [36, 37, 38,
39, 40]. The NLO QCD and EW corrections to the cross sections for the boson
signal do not yet exist. The NLO QCD corrections to the boson cross section
are assumed to be valid for the boson, within the range of this analysis, and
are applied to the signal events. However, since the EW couplings of the could
differ from those of the boson, the NLO EW corrections are not applied to the
signal events.

Event reconstruction is based on a particle-flow (PF) algorithm [41], which
reconstructs and identifies individual particles with an optimized combination
of information from the various elements of the CMS detector. The algorithm
classifies each particle candidate as either an electron, muon, photon,
charged or neutral hadron. The missing transverse momentum vector is defined
as the negative vector sum of the of all the particles identified in the
event, and its magnitude is referred to as $\ptmiss$. The PF candidates are
clustered into jets using two wide-jet algorithms: the anti-algorithm [42, 43]
with a distance parameter ($R$) of 0.8 and the Cambridge–Aachen algorithm [44]
with $R=1.5$. These jets are referred to as AK8 and CA15 jets, respectively.

To mitigate the impact of particles arising from additional proton-proton
interactions within the same bunch crossings (referred to as pileup
particles), weights calculated with the pileup-per-particle identification
algorithm [45] are applied to each PF candidate prior to jet clustering, based
on the likelihood of the particle originating from the hard scattering vertex.
Further corrections are applied to simulated jet energies as a function of jet
$\eta$ and to match the observed detector response [46, 47]. The most
energetic jet in the event is assumed to correspond to the $\PZpr\to\PQq\PAQq$
system, and is reconstructed as a single AK8 or CA15 jet. The AK8 jets provide
better sensitivity for signal mass hypotheses below 175, while the CA15 jets
provide better sensitivity at mass hypotheses above 175. This is because a
heavier resonance with the same transverse momentum has a lower Lorentz boost
and a larger radius jet is required to contain the $\PZpr$ hadronization
products.

Signal jets are identified using the soft-drop (SD) algorithm [48, 49], the
$\pt-$invariant variable $\rho$ [48, 50], and a jet substructure variable,
$N_{2}^{1}$ [51] . The SD algorithm with angular exponent $\beta=0$ is applied
to the jet to remove soft and wide-angle radiation with a soft radiation
fraction $z_{\text{cut}}$ less than 0.1. The SD grooming algorithm has the
effect of reducing the mass of QCD background jets for which soft gluon
radiation tends to increase, while preserving the masses of merged
$\PZpr/\PZ\to\PQq\PAQq$ and $\PW\to\PQq^{\prime}\PAQq$ jets. This algorithm is
used for the offline analysis, while the jet-trimming algorithm [52] is used
at trigger level, as explained below. The jet-trimming algorithm reclusters
the jet constituents into $\kt-$subjets [53] with $R=0.2$, and discards any
subjet with $\pt/\pt^{\mathrm{jet}}<0.03$.

The jet mass ($m_{\text{SD}}$) is corrected by a factor derived in simulated
boson samples to ensure a $\pt-$ and $\eta$-independent jet mass distribution
centered on the nominal boson mass. The dimensionless variable $\rho$, defined
as $\rho\equiv\ln(m_{\text{SD}}^{2}/\pt^{2})$, is used to characterize the
correlation between the jet $N_{2}^{1}$, jet mass, and jet .

The observable $N_{2}^{1}$ is used to determine the consistency of a given jet
with a two pronged topology. It is constructed from the ratio of 3-point
(${}_{2}e_{3}$) and 2-point (${}_{1}e_{2}$) generalized energy correlation
functions ${{}_{v}e_{n}}$ that are based on the energies and $v$ pairwise
angles among $n$ particles within a jet, as described in Ref. [51]. Jets
originating from a two pronged decay have a larger 2-point correlation than a
3-point correlation, leading to a smaller value of $N_{2}^{1}$.

Since this search probes a wide range of jet mass and jet , we decorrelate the
$N_{2}^{1}$ variable from the jet mass and following the procedure described
in Refs. [50, 20, 19]. Without decorrelation, a selection based on
$N_{2}^{1}$, or a similar variable, would distort the jet mass distribution as
a function of the jet , making the search for a resonant peak difficult. The
transformed variable, denoted as a designed decorrelated tagger (DDT), is
defined as $N_{2}^{1,\text{DDT}}(\rho,\pt)$ $\equiv$
$N_{2}^{1}(\rho,\pt)-X_{(5\%)}(\rho,\pt)$. The distribution of $X_{(5\%)}$ is
the 5th percentile of $N_{2}^{1}$ in simulated QCD multijet events and
indicates the values of $N_{2}^{1}$ that divide the multijet events into
groups with 5 and 95% of background efficiency, for each $\rho$ and bin. This
ensures that the selection $N_{2}^{1,\text{DDT}}<0$, or equivalently
$N_{2}^{1}<X_{(5\%)}$, yields a constant 5% of simulated QCD multijet events,
irrespective of $\rho$ and . The 5% quantile choice maximizes the sensitivity
to a boson signal. The distributions of $X_{(5\%)}$ for the AK8 and CA15 jets
are shown in Fig. 6 of the Appendix .8.

In order to fully exploit the differential variation of $N_{2}^{1}$ between
adjacent bins of and $\rho$ and to reduce the dependence on the number of
available events from simulation, we use a Gaussian kernel estimate to build
the $X_{(5\%)}$ map. In contrast to the search performed using 2016 data [20],
which used an ad hoc k-nearest-neighbor (kNN) approach [54] to smooth the
$X_{(5\%)}$ distribution, this analysis is based on the detector resolutions
of the $N_{2}^{1}$ and $\rho$ distributions as a function of the jet . The
$X_{(5\%)}$ distribution is derived from distributions of the jet $N_{2}^{1}$
and $\rho$ at the generator level. These distributions are smeared to include
detector effects, taking into account correlations between these variables.
Each of these jet observables is multiplied by a random number drawn from a
Gaussian distribution, such that the smeared jet matches the resolution
obtained from fully simulated events. The advantage of this method over the
kNN approach is that it allows better control of the smoothness of the
transformation map while maintaining similar performance in terms of the
amount of jet mass decorrelation.

Events are triggered using a combination of online signatures requiring
minimum thresholds on or on the AK8 jet . We also make use of a jet
substructure trigger, which places a requirement on the trimmed jet mass [52],
in addition to a minimum required or . Trimming the jet removes soft radiation
remnants from the jet, which allows to lower and jet trigger thresholds while
maintaining a similar rate, and improves the signal acceptance.

The trigger efficiency with respect to the offline selection is measured as a
function of the soft-drop jet mass in an independent single muon data set. The
efficiency does not reach 100% smoothly since the trimmed jet mass triggers
were not available early in the 2017 data collection, corresponding to the
first 4.8of data recorded. This condition also motivates the use of a higher
threshold compared to that used for the 2016 data period ($\pt>500\GeV$). The
trigger selection is greater than 95% efficient for events with at least one
AK8 jet with $\pt>525\GeV$, or with at least one CA15 jet with $\pt>575\GeV$.
Following this selection, the trigger efficiency for both AK8 and CA15 jets is
shown in Fig. 1. At high jet masses, the trigger efficiency for the larger
CA15 jet decreases slightly. This decrease is due to events in which the jet
passes the CA15 jet selection but fails the trigger-level AK8 jet and trimmed
mass requirements.

Figure 1: High-level trigger efficiency as a function of the soft-drop jet
mass ($m_{\text{SD}}$) for AK8 jets with $\pt>525\GeV$ (blue squares) and CA15
jets with $\pt>575\GeV$ (red circles). The trigger selection is $>$95%
efficient for 2017 data for both cone sizes and is applied to AK8 jets with
masses between 50 and 275and CA15 jets with masses between 150 and 450. For
jet masses above 200, the trigger efficiency for the larger CA15 jet decreases
slightly. This is due to events for which a reconstructed jet passing the CA15
jet selection does not satisfy the AK8 jet selection at the trigger level.

Events are selected by requiring, with $\abs{\eta}<2.5$, at least one AK8 jet
with $\pt>525\GeV$ or at least one CA15 jet with $\pt>575\GeV$. To reduce SM
EW backgrounds, events are rejected if they contain isolated charged leptons
with $\pt>10\GeV$ and $\abs{\eta}<2.5$, $2.4$, or $2.3$, for electrons, muons
[55, 56], and tau leptons. For electrons or muons, the isolation criteria
require that the pileup-corrected sum of the of charged hadrons and neutral
particles surrounding the lepton divided by the lepton be less than
approximately 15 or 25%, respectively, depending on $\eta$ [55, 56]. Tau
leptons, reconstructed by combining information from charged hadrons and
$\pi^{0}$ candidates, are required to satisfy the loose working point of a
multivariate-based identification discriminant that combines information on
isolation and lifetime of the tau lepton [57].

For QCD events, the distribution of $\rho$ is approximately independent of jet
. To avoid departure from this invariance, only events with jets in the range
$-5.5<\rho<-2.0$ $(-4.7<\rho<-1.0)$ are considered for the AK8 (CA15) jets.
This results in the $m_{\text{SD}}$ range under study depending on the jet .
Nonperturbative effects are large at low masses and scale as
$1/m_{\text{SD}}$; this region is avoided by the lower bound on $\rho$. The
upper bound is imposed to avoid instabilities because the cone size of the
jets is insufficient to provide complete containment at high masses [20].

Finally, jets are required to have $N_{2}^{1,\text{DDT}}<0$. This selection
rejects 95% of the multijet background independently of the jet mass and .
Events failing this requirement, with $N_{2}^{1,\text{DDT}}>0$, are used in
the background estimate from data described in the next section.

##  0.4 Background estimate

The background is dominated by QCD multijet events with smaller contributions
from $\PW(\PQq^{\prime}\PAQq)$+jets, $\PZ(\PQq\PAQq)$+jets, and top quark
processes. Backgrounds from other EW processes are found to be negligible.

The contributions from top pair and single top quark production are obtained
from simulation. Scale factors correct the overall top quark background
normalization and the $N_{2}^{\text{1,DDT}}$ mistag efficiency for jets
originating from top quark decays. These are computed from a dedicated
-enriched control region in data, in which an isolated muon is required.

The $\PW$+jets and $\PZ$+jets backgrounds are modeled using simulation. Their
cross sections are corrected for NLO QCD and EW effects, following Refs. [36,
38, 39, 40].

The dominant QCD multijet background, estimated from data, has a jet mass
shape that depends on the jet . Because of the decorrelation of
$N_{2}^{\text{1,DDT}}$ from $\rho$ and , the QCD jet mass distributions for
events passing and failing the $N_{2}^{\text{1,DDT}}$ selection exhibit the
same smoothly falling shape. Thus, we can use the distribution of events
failing the selection to constrain the distribution of QCD events passing the
selection as:

$n^{\mathrm{QCD}}_{\mathrm{pass}}=R_{\mathrm{p/f}}~{}n^{\mathrm{QCD}}_{\mathrm{fail}}~{},$
(1)

where $n^{\mathrm{QCD}}_{\mathrm{pass}}$ and
$n^{\mathrm{QCD}}_{\mathrm{fail}}$ are the number of passing and failing
events in a given $m_{\text{SD}}$, bin, and $R_{\mathrm{p/f}}$ is the “pass-
to-fail ratio”.

The fraction of events, p, passing the $N_{2}^{\text{1,DDT}}$ selection in
simulated QCD multijet events is, by construction, 5% irrespective of $\rho$
and . Therefore, the correction $R_{\mathrm{p/f}}$ is flat at $\mathrm{p}=5\%$
and $\mathrm{f}=95\%$ in the QCD background simulation. To account for
residual differences between data and simulation, $R_{\mathrm{p/f}}$ is
allowed to deviate from a constant. This deviation is modeled by parametrizing
$R_{\mathrm{p/f}}$ as a function of $\rho$ and and expanding it in a Bernstein
polynomial basis of the form:

$R_{\mathrm{p/f}}(\rho,\pt)=\mathrm{p}/\mathrm{f}~{}\sum_{k=0}^{n_{\rho}}\sum_{\ell=0}^{n_{\pt}}a_{k\ell}b_{\ell,n_{\pt}}(\pt)b_{k,n_{\rho}}(\rho),$
(2)

where $a_{k\ell}$ are the polynomial coefficients, and

$b_{\nu,n}(x)=\binom{n}{\nu}x^{\nu}\left(1-x\right)^{n-\nu}$ (3)

is a polynomial of degree $n$ in the Bernstein basis.

The Bernstein basis is chosen over a standard polynomial because with the
variable $x$ bounded between 0 and 1 it is more stable numerically and the
function is nonnegative.

With the exception of $a_{00}$, which is fixed to unity by choice, the
coefficients $a_{k\ell}$ and p are unconstrained and determined together with
the signal yield from a simultaneous fit to the data events passing and
failing the $N_{2}^{\text{1,DDT}}$ selection. The minimum number of
coefficients needed to model the $R_{\mathrm{p/f}}$ shape is determined using
a Fisher $F$-test on data [58]. The test is performed by iteratively comparing
two parametrizations of the $R_{\mathrm{p/f}}$, one with higher polynomial
order than the other, and computing the expected change in the log likelihood,
i.e. using the goodness-of-fit as the $F$-statistic. To determine whether the
polynomial order is sufficient, we compare the $F$-statistic observed in data
to that computed from a set of simulated samples generated from the default
fit model and fit with the higher order polynomial using the background only
fit. If one provides a significantly better fit (p-value $<$5%), we choose
that as the new default. For the AK8 jets, the optimal parametrization is
found to be third order in $\pt$ and fifth order in $\rho$; for the CA15 jets,
it is second order in $\rho$ and fifth order in $\pt$. The result is a slow
variation of $R_{\mathrm{p/f}}$ over the $m_{\mathrm{SD}}$–plane, with
$\mathrm{p}$ bounded between 4.5–6.5%. This allows one to estimate the
background under a narrow signal resonance across the jet mass range under
investigation. As an example, the parametric shape of $R_{\mathrm{p/f}}$
derived from data for the AK8 jet analysis is given in Appendix .8 as Fig. 7.

In order to validate the robustness of the fit and its associated systematic
uncertainties, we perform a goodness-of-fit test and signal injection studies
on background-only fits that estimate the possible bias on the background
estimate due to the presence of a signal. We generate pseudo-experiments, with
and without the injection of simulated signal, and then fit with the signal
plus background model, for different values of the boson mass. No significant
bias in the fitted signal strength is observed. As a further test of the
$R_{\mathrm{p/f}}$ fit robustness, we split the subset of events failing the
$N_{2}^{\text{1,DDT}}$ selection into two smaller subsets mimicking the
passing and failing selection in the data fit. The mimicked passing-like
events also reject 95% of the QCD background events in the failing region. We
repeat our background estimation procedure on this selection and use the
coefficients $a_{k\ell}$ from this fit to generate pseudo-experiments. We then
fit the data with the signal plus background model and find the biases in the
fitted signal strength to be negligible.

##  0.5 Systematic uncertainties

The dominant uncertainty in this analysis is the uncertainty in the fit for
$R_{\mathrm{p/f}}$, as described in Eq. 2 (1–3%), arising from the parameters
$a_{k\ell}$, and the statistical uncertainty on the data in the
$N_{2}^{\text{1,DDT}}<0$ region.

The systematic uncertainties in the shapes and normalization of the and boson
backgrounds and the signal are correlated since they are affected by similar
systematic effects. The uncertainties in the jet mass scale and resolution,
and the $N_{2}^{\text{1,DDT}}$ selection efficiency, are estimated using an
independent sample of merged boson jets in semileptonic events in data. In
this region, we require events to have an energetic muon with $\pt>100\GeV$,
$\ptmiss>80\GeV$, a high-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). The efficiency
of the $N_{2}^{\text{1,DDT}}<0$ requirement is measured in simulation and data
by fitting the boson mass peak in the jet mass distribution for events passing
and failing this requirement in the control region. This efficiency is used to
correct overall yields for resonant backgrounds obtained from simulation in
the signal region and is measured to be $0.90\pm 0.09$ ($1.02\pm 0.06$) for
AK8 (CA15) jets. The jet mass resolution data-to-simulation scale factor is
measured to be $1.1\pm 0.1$ for both AK8 and CA15 jets. The jet mass scales in
data and simulation are found to be consistent within 1%. The variation of the
jet mass scale with jet is studied using large cone size jets. At high momenta
($\pt>350\GeV$) the decay products of the top quark are contained in a single
jet, and the $m_{\mathrm{SD}}$ distribution exhibits a top quark peak. By
performing simultaneous fits to data and simulation of this peak binned in , a
small (1%) variation in jet mass scale is observed and applied in the fit as
an additional $\pt-$dependent nuisance parameter. These scale factors
determine the initial shape and normalization of the jet mass distribution for
the , boson, and signal but they are further constrained in the fit to data
because of the presence of the and resonances in the jet mass distribution.

To account for potential deviations due to missing higher-order corrections,
uncertainties are applied to the and boson yields. These uncertainties
increase with the jet and are correlated per bin. An additional systematic
uncertainty is included to account for potential differences between the and
boson higher-order corrections (NLO EW /decorrelation). The uncertainties
associated with the modeling of the boson spectrum when considering extra jets
in the generation and similar NLO QCD corrections to the Z boson are
propagated to the overall normalization of the signal. Finally, uncertainties
associated with the jet energy resolution [46], trigger efficiency, variations
in the amount of pileup and the integrated luminosity determination [59] are
also applied to the , , and boson signal yields.

A quantitative summary of the systematic effects considered for signal and
/boson background processes is given in Table 0.5.

Summary of the systematic uncertainties for signal () and /boson background
processes, for AK8 and CA15 jet reconstruction. The reported ranges denote a
variation of the uncertainty across $\pt$ bins, from 525 to 1500(AK8 jets) and
from 575 to 1500(CA15 jets). The symbol △ denotes uncorrelated uncertainties
for each $\pt$ bin. For the uncertainties related to the jet mass scale and
resolution, the reported percentage reflects a one standard deviation effect
on the nominal jet mass shape. Three dots () indicates that the uncertainty
does not apply. lcccc Uncertainty source & Systematic Uncertainty

(AK8) /(AK8) (CA15) /(CA15)

NLO EW corrections△ 15–35% 15–35%

NLO QCD corrections 10% 10% 10% 10%

NLO EW /decorrelation△ 5–15% 5–15%

Simulation sample size 1–12% 1–12% 1–12% 1–12%

$N_{2}^{\text{1,DDT}}$ selection efficiency 10% 10% 7% 7%

Jet mass scale 1% 1% 1% 1%

Jet mass resolution 10% 10% 7% 7%

Jet mass scale (% / ( []/100)) △ 0.5–2% 0.5–2% 0.5–2% 0.5–2%

Jet energy resolution 1–7% 1–7% 1–7% 1–7%

Signal correction 5% 5%

Integrated luminosity 2.3% 2.3% 2.3% 2.3%

Trigger efficiency 2% 2% 2% 2%

Pileup 1–2% 1–2% 1–2% 1–2%

Lepton veto efficiency 0.5% 0.5% 0.5% 0.5%

##  0.6 Results

A binned maximum likelihood fit to the shape of the observed $m_{\text{SD}}$
distribution is performed using the sum of the signal, , , , and QCD
contributions. We search for a signal from a resonance in the mass range from
50 to 450. Signal shapes are taken directly from simulation. The fit is
performed simultaneously in the passing and failing regions of five (four)
categories for AK8 (CA15) jets, as well as in the passing and failing
components of the -enriched control region. The boundaries of the categories
are: 525, 575, 625, 700, 800, and 1500for the AK8 jets and 575, 625, 700, 800,
and 1500for the CA15 jets. The bin boundaries are chosen so that approximately
the same number of events are used to constrain $R_{\mathrm{p/f}}$ in each
bin.

The number of observed events is consistent with the predicted background from
SM processes. Figure 2 shows the $m_{\text{SD}}$ distribution for data and
measured background contributions for AK8 jets in each category of the fit for
a mass hypothesis of 110. Figure 3 shows the distributions for CA15 jets in
each category for a mass hypothesis of 210. For AK8 jets, the and boson
contributions are clearly visible as a merged peak in the data, while for CA15
jets, due to the $\rho$ selection and increased QCD background, the
$\PW$/$\PZ$ contributions are only visible in the lower categories.

Figure 2: Jet $m_{\text{SD}}$ distribution for AK8 jets for each category of
the fit. Data are shown by the black points. The multijet background
prediction, including uncertainties, is shown by the shaded bands.
Contributions from the and bosons, and top quark background processes are
shown as well. A hypothetical boson signal with a mass of 110is also
indicated. In the bottom panel, the ratio of the data to its statistical
uncertainty, after subtracting the non-resonant backgrounds, is shown.

Figure 3: Jet $m_{\text{SD}}$ distribution for CA15 jets for the different
ranges of the fit from 575 to 1500. Data are shown as black points. The
multijet background prediction, including uncertainties, is shown by the
shaded bands. Smaller contributions from the and bosons, and top quark
background processes are shown as well. A hypothetical boson signal with a
mass of 210is also indicated. In the bottom panel, the ratio of the data to
its statistical uncertainty, after subtracting the non-resonant backgrounds,
is shown.

The results of the fit are used to set 95% confidence level (CL) upper limits
of the boson coupling to quarks $g^{\prime}_{\mathrm{\PQq}}$, which is related
to the coupling convention of Ref. [24] by
$g^{\prime}_{\mathrm{\PQq}}=g_{\mathrm{B}}/6$. Upper limits are computed using
the modified frequentist approach for CL, taking the profile likelihood ratio
as the test statistic [60, 61] in the asymptotic approximation [62].
Systematic uncertainties are incorporated as nuisance parameters and profiled
over in the limit calculations, using log-normal priors for normalization
uncertainties and Gaussian constraints for shape uncertainties. The dominant
uncertainty on the $g^{\prime}_{\mathrm{\PQq}}$ limit arises from the fit
parameters of the $R_{\mathrm{p/f}}$ followed by the theoretical uncertainties
on the signal yield due to missing NLO QCD corrections.

Limits on $g^{\prime}_{\mathrm{\PQq}}$ as a function of the boson mass are
shown in Fig. 4, using only data collected in 2017. Based on the expected
sensitivity, the AK8 and CA15 jet selections are used for signal masses below
and above 175, respectively. Coupling values above the solid curves are
excluded at the 95% CL. The maximum local observed p-value corresponds to 2.9
standard deviations at a $\PZpr(\PQq\PAQq)$ mass of 200. The largest downward
fluctuation in the limits occurs at a $\PZpr(\PQq\PAQq)$ mass of 60,
corresponding to a local significance of $-3$ standard deviations. A loss of
sensitivity of 20%, relative to the results set by the previous search [20],
is observed, due to the higher threshold determined by the trigger turn-on for
the 2017 data set.

We summarize the results of this paper in the mass vs. coupling plane in Fig.
5. For masses between 50 and 220, the most restrictive limits for this search
are obtained from the statistical combination of the upper limits set by the
2016 and 2017 data sets using AK8 jets. For the mass range between 175 and
220, this combination is as sensitive as that obtained from the limits set by
the 2016 AK8 jet and 2017 CA15 jet searches. The limits correspond to a total
integrated luminosity of 77.0. For higher masses, between 220 and 450, the
most stringent limits come from the analysis of 2017 data using CA15 jets,
corresponding to an integrated luminosity of 41.1. For comparison, less
sensitive limits set by the AK8 jet analysis in the range from 220 to 300,
using the combined data sets recorded in 2016 and 2017, are presented in Fig.
8 of Appendix .8. The sensitivity is driven by the multijet background
uncertainty on the parametric fit of $R_{\mathrm{p/f}}$, which is modeled with
different polynomial orders for the 2016 and 2017 data sets. A local excess in
the observed limit over the expected limit, corresponding to 2.9 standard
deviations, was observed at a mass hypothesis near 115in the 2016 analysis
with 35.9of integrated luminosity. This excess is not confirmed by the 2017
analysis, where the local observed p-value for a boson mass of 115is 0.5 and
the data agrees with the prediction. The combined observed limit with the full
2016 and 2017 dataset at a mass hypothesis of 115in Fig. 5, corresponds to 2.2
standard deviations from the background-only expectation.

In the mass range between 50 and 300this analysis places the most sensitive
limits to date. Above 300the most sensitive limits are set by the searches for
dijet resonances in the resolved regime produced in association with a jet
[63] or with a photon [23]. The CA15 jet analysis sensitivity is lower due to
the lack of a dedicated CA15 jet trigger-level selection.

Figure 4: Upper limits at 95% CL on the coupling $g^{\prime}_{\mathrm{\PQq}}$
as a function of the resonance mass for a leptophobic boson that couples only
to quarks, based on the 2017 analysis. The observed limits (solid), expected
limits (dashed), and their variation at the 1 and 2 standard deviation levels
(shaded bands) are shown. The vertical line at 175corresponds to the
transition between the AK8 and CA15 jet selections. Figure 5: Upper limits at
95% CL on the coupling $g^{\prime}_{\mathrm{\PQq}}$ as a function of the
resonance mass for a leptophobic boson that couples only to quarks. The
observed limits (solid), expected limits (dashed), and their variation at the
1 and 2 standard deviation levels (shaded bands) are shown. For masses between
50 and 220the limits correspond to a boson reconstructed in AK8 jets using
77.0of statistically combined data from 2016 and 2017. For masses above 220 up
to 450, the results correspond to a resonance reconstructed in CA15 jets using
41.1of data collected in 2017.

##  0.7 Summary

A search for a narrow vector resonance ($\PZpr$) decaying into a quark-
antiquark pair and reconstructed as a single jet with a topology of a
resonance recoiling against initial state radiation has been presented. The
analysis uses a data set comprised of proton-proton collisions at
$\sqrt{s}=13\TeV$ collected in 2017 at the LHC, corresponding to an integrated
luminosity of 41.1. The results are statistically combined with those obtained
with data collected in 2016 to achieve more sensitive exclusion limits with a
total integrated luminosity of 77.0. Jet substructure techniques are employed
to identify a jet containing a $\PZpr$ boson candidate over a smoothly falling
jet mass distribution in data. No significant excess above the standard model
prediction is observed. Upper limits at 95% confidence level are set on the
$\PZpr$ boson coupling to quarks, $g^{\prime}_{\mathrm{\PQq}}$, as a function
of the $\PZpr$ boson mass. Coupling values of $g^{\prime}_{\mathrm{\PQq}}>0.4$
are excluded over the signal mass range from 50 to 450, with the most
stringent constraints set for masses below 250where coupling values of
$g^{\prime}_{\mathrm{\PQq}}>0.2$ are excluded. For masses between 50 and
300these are the most sensitive limits to date. The results obtained for
masses from 300 to 450represent the first direct limits to be published in
this range for a leptophobic signal reconstructed as a single large-radius
jet.

###### 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, PUT and ERDF (Estonia); Academy
of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG,
and HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM
(Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES
(Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT,
LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC
(Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom,
RAS, RFBR, and NRC KI (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER
(Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST
(Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK
(Turkey); NASU and SFFR (Ukraine); STFC (United Kingdom); DOE and NSF (USA).
Individuals have received support from the Marie-Curie program and the
European Research Council and Horizon 2020 Grant, contract Nos. 675440,
752730, and 765710 (European Union); the Leventis Foundation; the A.P. Sloan
Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science
Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et
dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door
Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium)
under the “Excellence of Science – EOS” – be.h project n. 30820817; the
Beijing Municipal Science & Technology Commission, No. Z181100004218003; 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, 125105, 128713, 128786,
and 129058 (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
Ministry of Science and Education, grant no. 3.2989.2017 (Russia); 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).

## References

  * [1] E. Eichten, I. Hinchliffe, K. D. Lane, and C. Quigg, “Super collider physics”,  Rev. Mod. Phys. 56 (1984) 579, 10.1103/RevModPhys.56.579.
  * [2] U. Baur, I. Hinchliffe, and D. Zeppenfeld, “Excited quark production at hadron colliders”,  Int. J. Mod. Phys. A 2 (1987) 1285, 10.1142/S0217751X87000661.
  * [3] P. H. Frampton and S. L. Glashow, “Chiral color: An alternative to the standard model”,  Phys. Lett. B 190 (1987) 157, 10.1016/0370-2693(87)90859-8.
  * [4] J. L. Hewett and T. G. Rizzo, “Low-energy phenomenology of superstring inspired E(6) models”,  Phys. Rept. 183 (1989) 193, 10.1016/0370-1573(89)90071-9.
  * [5] U. Baur, M. Spira, and P. M. Zerwas, “Excited quark and lepton production at hadron colliders”,  Phys. Rev. D 42 (1990) 815, 10.1103/PhysRevD.42.815.
  * [6] E. H. Simmons, “Coloron phenomenology”,  Phys. Rev. D 55 (1997) 1678, 10.1103/PhysRevD.55.1678, arXiv:hep-ph/9608269.
  * [7] L. Randall and R. Sundrum, “An alternative to compactification”,  Phys. Rev. Lett. 83 (1999) 4690, 10.1103/PhysRevLett.83.4690, arXiv:hep-th/9906064.
  * [8] S. Cullen, M. Perelstein, and M. E. Peskin, “TeV strings and collider probes of large extra dimensions”,  Phys. Rev. D 62 (2000) 055012, 10.1103/PhysRevD.62.055012, arXiv:hep-ph/0001166.
  * [9] L. A. Anchordoqui et al., “Dijet signals for low mass strings at the LHC”,  Phys. Rev. Lett. 101 (2008) 241803, 10.1103/PhysRevLett.101.241803, arXiv:0808.0497.
  * [10] T. Han, I. Lewis, and Z. Liu, “Colored resonant signals at the LHC: Largest rate and simplest topology”,  JHEP 12 (2010) 085, 10.1007/JHEP12(2010)085, arXiv:1010.4309.
  * [11] R. S. Chivukula, A. Farzinnia, E. H. Simmons, and R. Foadi, “Production of massive color-octet vector bosons at next-to-leading order”,  Phys. Rev. D 85 (2012) 054005, 10.1103/PhysRevD.85.054005, arXiv:1111.7261.
  * [12] D. Abercrombie et al., “Dark matter benchmark models for early LHC Run-2 searches: Report of the ATLAS/CMS Dark Matter Forum”, (2015). arXiv:1507.00966.
  * [13] G. Busoni et al., “Recommendations on presenting LHC searches for missing transverse energy signals using simplified $s$-channel models of dark matter”, (2016). arXiv:1603.04156.
  * [14] ATLAS Collaboration, “Search for new phenomena in dijet events using $37\fbinv$ of $pp$ collision data collected at $\sqrt{s}=13$ TeV with the ATLAS detector”,  Phys. Rev. D 96 (2017) 052004, 10.1103/PhysRevD.96.052004.
  * [15] CMS Collaboration, “Search for narrow and broad dijet resonances in proton-proton collisions at $\sqrt{s}=13$ TeV and constraints on dark matter mediators and other new particles”,  JHEP 08 (2018) 130, 10.1007/JHEP08(2018)130, arXiv:1806.00843.
  * [16] CMS Collaboration, “Search for narrow resonances in dijet final states at $\sqrt{s}=8\TeV$ with the novel CMS technique of data scouting”,  Phys. Rev. Lett. 117 (2016) 031802, 10.1103/PhysRevLett.117.031802, arXiv:1604.08907.
  * [17] CMS Collaboration, “Search for dijet resonances in proton-proton collisions at $\sqrt{s}=13\TeV$ and constraints on dark matter and other models”,  Phys. Lett. B 769 (2017) 520, 10.1016/j.physletb.2017.02.012, arXiv:1611.03568.
  * [18] ATLAS Collaboration, “Search for low-mass dijet resonances using trigger-level jets with the ATLAS detector in $pp$ collisions at $\sqrt{s}=13$ TeV”,  Phys. Rev. Lett. 121 (2018) 081801, 10.1103/PhysRevLett.121.081801, arXiv:1804.03496.
  * [19] CMS Collaboration, “Search for low mass vector resonances decaying to quark-antiquark pairs in proton-proton collisions at $\sqrt{s}=13\TeV$”,  Phys. Rev. Lett. 119 (2017) 111802, 10.1103/PhysRevLett.119.111802, arXiv:1705.10532.
  * [20] CMS Collaboration, “Search for low mass vector resonances decaying into quark-antiquark pairs in proton-proton collisions at $\sqrt{s}=13$ TeV”,  JHEP 01 (2018) 097, 10.1007/JHEP01(2018)097, arXiv:1710.00159.
  * [21] ATLAS Collaboration, “Search for light resonances decaying to boosted quark pairs and produced in association with a photon or a jet in proton-proton collisions at $\sqrt{s}=13$ TeV with the ATLAS detector”,  Phys. Lett. B 788 (2019) 316, 10.1016/j.physletb.2018.09.062, arXiv:1801.08769.
  * [22] CMS Collaboration, “Search for low-mass quark-antiquark resonances produced in association with a photon at $\sqrt{s}=$ 13 TeV”,  Phys. Rev. Lett. 123 (2019), no. 23, 231803, 10.1103/PhysRevLett.123.231803, arXiv:1905.10331.
  * [23] ATLAS Collaboration, “Search for low-mass resonances decaying into two jets and produced in association with a photon using $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector”,  Phys. Lett. 795 (2019) 56, 10.1016/j.physletb.2019.03.067, arXiv:1901.10917.
  * [24] B. A. Dobrescu and F. Yu, “Coupling-mass mapping of dijet peak searches”,  Phys. Rev. D 88 (2013) 035021, 10.1103/PhysRevD.88.035021, arXiv:1306.2629. [Erratum: 10.1103/PhysRevD.90.079901].
  * [25] CMS Collaboration, “The CMS experiment at the CERN LHC”,  JINST 3 (2008) S08004, 10.1088/1748-0221/3/08/S08004.
  * [26] CMS Collaboration, “The CMS trigger system”,  JINST 12 (2017) 1020, 10.1088/1748-0221/12/01/P01020, arXiv:1609.02366.
  * [27] GEANT4 Collaboration, “GEANT4—a simulation toolkit”,  Nucl. Instrum. Meth. A 506 (2003) 250, 10.1016/S0168-9002(03)01368-8.
  * [28] J. Alwall et al., “The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations”,  JHEP 07 (2014) 079, 10.1007/JHEP07(2014)079, arXiv:1405.0301.
  * [29] J. Alwall et al., “Comparative study of various algorithms for the merging of parton showers and matrix elements in hadronic collisions”,  Eur. Phys. J. C 53 (2008) 473, 10.1140/epjc/s10052-007-0490-5, arXiv:0706.2569.
  * [30] P. Nason, “A new method for combining NLO QCD with shower Monte Carlo algorithms”,  JHEP 11 (2004) 040, 10.1088/1126-6708/2004/11/040, arXiv:hep-ph/0409146.
  * [31] S. Frixione, P. Nason, and C. Oleari, “Matching NLO QCD computations with Parton Shower simulations: the POWHEG method”,  JHEP 11 (2007) 070, 10.1088/1126-6708/2007/11/070, arXiv:0709.2092.
  * [32] S. Alioli, P. Nason, C. Oleari, and E. Re, “A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX”,  JHEP 06 (2010) 043, 10.1007/JHEP06(2010)043, arXiv:1002.2581.
  * [33] T. Sjöstrand et al., “An introduction to PYTHIA 8.2”,  Comput. Phys. Commun. 191 (2015) 159, 10.1016/j.cpc.2015.01.024, arXiv:1410.3012.
  * [34] CMS Collaboration, “Extraction and validation of a new set of CMS PYTHIA8 tunes from underlying-event measurements”, (2019). arXiv:1903.12179. Submitted to Phys. Rev. D.
  * [35] NNPDF Collaboration, “Parton distributions from high-precision collider data”,  Eur. Phys. J. C 77 (2017) 663, 10.1140/epjc/s10052-017-5199-5, arXiv:1706.00428.
  * [36] CMS Collaboration, “Search for dark matter produced with an energetic jet or a hadronically decaying W or Z boson at $\sqrt{s}=13\TeV$”,  JHEP 07 (2017) 014, 10.1007/JHEP07(2017)014, arXiv:1703.01651.
  * [37] S. Kallweit et al., “NLO electroweak automation and precise predictions for W+multijet production at the LHC”,  JHEP 04 (2015) 012, 10.1007/JHEP04(2015)012, arXiv:1412.5157.
  * [38] S. Kallweit et al., “NLO QCD+EW predictions for V+jets including off-shell vector-boson decays and multijet merging”,  JHEP 04 (2016) 021, 10.1007/JHEP04(2016)021, arXiv:1511.08692.
  * [39] S. Kallweit et al., “NLO QCD+EW automation and precise predictions for V+multijet production”, in  Proceedings, 50th Rencontres de Moriond, QCD and high energy interactions, p. 121, ARISF.  ARISF, 2015.  arXiv:1505.05704.
  * [40] J. M. Lindert et al., “Precise predictions for $V~{}+$ jets dark matter backgrounds”,  Eur. Phys. J. C 77 (2017) 829, 10.1140/epjc/s10052-017-5389-1, arXiv:1705.04664.
  * [41] CMS Collaboration, “Particle-flow reconstruction and global event description with the CMS detector”,  JINST 12 (2017) P10003, 10.1088/1748-0221/12/10/P10003, arXiv:1706.04965.
  * [42] M. Cacciari, G. P. Salam, and G. Soyez, “The anti-jet clustering algorithm”,  JHEP 04 (2008) 063, 10.1088/1126-6708/2008/04/063, arXiv:0802.1189.
  * [43] M. Cacciari, G. P. Salam, and G. Soyez, “FastJet user manual”,  Eur. Phys. J. C 72 (2012) 1896, 10.1140/epjc/s10052-012-1896-2, arXiv:1111.6097.
  * [44] Y. L. Dokshitzer, G. D. Leder, S. Moretti, and B. R. Webber, “Better jet clustering algorithms”,  JHEP 08 (1997) 001, 10.1088/1126-6708/1997/08/001, arXiv:hep-ph/9707323.
  * [45] D. Bertolini, P. Harris, M. Low, and N. Tran, “Pileup per particle identification”,  JHEP 10 (2014) 059, 10.1007/JHEP10(2014)059, arXiv:1407.6013.
  * [46] CMS Collaboration, “Determination of jet energy calibration and transverse momentum resolution in CMS”,  JINST 6 (2011) 11002, 10.1088/1748-0221/6/11/P11002, arXiv:1107.4277.
  * [47] CMS Collaboration, “Jet energy scale and resolution in the CMS experiment in pp collisions at 8”,  JINST 12 (2017) 2014, 10.1088/1748-0221/12/02/P02014, arXiv:1607.03663.
  * [48] M. Dasgupta, A. Fregoso, S. Marzani, and G. P. Salam, “Towards an understanding of jet substructure”,  JHEP 09 (2013) 029, 10.1007/JHEP09(2013)029, arXiv:1307.0007.
  * [49] A. J. Larkoski, S. Marzani, G. Soyez, and J. Thaler, “Soft drop”,  JHEP 05 (2014) 146, 10.1007/JHEP05(2014)146, arXiv:1402.2657.
  * [50] J. Dolen et al., “Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure”,  JHEP 05 (2016) 156, 10.1007/JHEP05(2016)156, arXiv:1603.00027.
  * [51] I. Moult, L. Necib, and J. Thaler, “New angles on energy correlation functions”,  JHEP 12 (2016) 153, 10.1007/JHEP12(2016)153, arXiv:1609.07483.
  * [52] D. Krohn, J. Thaler, and L.-T. Wang, “Jet trimming”,  JHEP 02 (2010) 084, 10.1007/JHEP02(2010)084, arXiv:0912.1342.
  * [53] S. Catani, Y. Dokshitzer, M. Seymour, and B. Webber, “Longitudinally-invariant k-clustering algorithms for hadron-hadron collisions”,  Nucl. Phys. B 406 (1993) 187, 10.1016/0550-3213(93)90166-M.
  * [54] S. A. Dudani, “The distance-weighted k-nearest-neighbor rule”,  IEEE Trans. Syst. Man Cybern. B Cybern. SMC-6 (1976) 325, 10.1109/TSMC.1976.5408784.
  * [55] CMS Collaboration, “Performance of electron reconstruction and selection with the CMS detector in proton-proton collisions at $\sqrt{s}$ = 8 TeV”,  JINST 10 (2015) P06005, 10.1088/1748-0221/10/06/P06005, arXiv:1502.02701.
  * [56] CMS Collaboration, “Performance of CMS muon reconstruction in pp collision events at $\sqrt{s}=7$ TeV”,  JINST 7 (2012) P10002, 10.1088/1748-0221/7/10/P10002, arXiv:1206.4071.
  * [57] CMS Collaboration, “Performance of reconstruction and identification of $\tau$ leptons decaying to hadrons and $\nu_{\tau}$ in pp collisions at $\sqrt{s}=$ 13 TeV”,  JINST 13 (2018) P10005, 10.1088/1748-0221/13/10/P10005, arXiv:1809.02816.
  * [58] R. A. Fisher, “On the interpretation of $\chi^{2}$ from contingency tables, and the calculation of P”,  J. Roy. Statis. Soc. 85 (1922) 87, 10.2307/2340521.
  * [59] CMS Collaboration, “CMS luminosity measurement for the 2017 data-taking period at $\sqrt{s}=13~{}\mathrm{TeV}$”, CMS Physics Analysis Summary, 2018.
  * [60] T. Junk, “Confidence level computation for combining searches with small statistics”,  Nucl. Instrum. Meth. A 434 (1999) 435, 10.1016/S0168-9002(99)00498-2, arXiv:hep-ex/9902006.
  * [61] A. L. Read, “Presentation of search results: the $\rm CL_{s}$ technique”,  J. Phys. G 28 (2002) 2693, 10.1088/0954-3899/28/10/313.
  * [62] G. Cowan, K. Cranmer, E. Gross, and O. Vitells, “Asymptotic formulae for likelihood-based tests of new physics”,  Eur. Phys. J. C 71 (2011) 1554, 10.1140/epjc/s10052-011-1554-0, arXiv:1007.1727. [Erratum, EPJ C 73, 2501 (2013), 10.1140/epjc/s10052-013-2501-z].
  * [63] CMS Collaboration, “Search for dijet resonances using events with three jets in proton-proton collisions at $\sqrt{s}=$ 13 TeV”, (2019). arXiv:1911.03761.

##  .8 Additional analysis distributions

Figure 6: Distributions of $X_{(5\%)}$ used to define the
$N_{2}^{1,\text{DDT}}$ variable for AK8 jets (right) and CA15 jets (left),
corresponding to the 5% quantile of the $N_{2}^{1}$ distribution in simulated
multijet events. The distributions are shown as a function of the jet $\rho$
and $\pt$. The $N_{2}^{1}$ variable is mostly insensitive to the jet $\rho$
and $\pt$ in the kinematic phase space considered for this analysis:
$-5.5<\rho<-2.0$ (AK8 jets) and $-4.7<\rho<-1.0$ (CA15 jets). The
distributions of $X_{(5\%)}$ are used to take into account residual
correlations in simulation by applying a decorrelation procedure that yields
the $N_{2}^{1,\text{DDT}}$ variable. In order to ensure smoothness of the
transformation, we simulate particle-level QCD multijet events and smear them
using a parametric detector response derived for the $N_{2}^{1}$ variable as a
function of $\rho$ and $\pt$. This method overcomes the limitation from the
limited event count in simulated samples by generating $10^{4}$ the original
number of events available in the multijet simulation.  Figure 7:  Pass-to-
fail ratio, $R_{\mathrm{p/f}}(\rho(m_{\text{SD}},\pt))$, defined from the
events passing and failing the $N_{2}^{\text{1,DDT}}$ selection. The variable
$N_{2}^{\text{1,DDT}}$ is constructed so that, for simulated multijet events,
$R_{\mathrm{p/f}}$ is constant at $\mathrm{p}=5\%$ and $\mathrm{f}=95\%$
(blue). To account for residual differences between data and simulation,
$R_{\mathrm{p/f}}$ is extracted by performing a two-dimensional fit to data in
($\rho,\pt$) space (orange). The $R_{\mathrm{p/f}}$ shown is derived for AK8
jets using 41.1of data collected in 2017 and corresponds to a polynomial in
the Bernstein basis of third order in $\pt$ and fifth order in $\rho$.  Figure
8:  Upper limits at 95% CL on the coupling $g^{\prime}_{\mathrm{\PQq}}$ as a
function of the resonance mass for a leptophobic boson that couples only to
quarks. based on the statistical combination of the 2016 and 2017 analyses
using AK8 jets. The observed limits (solid), expected limits (dashed), and
their variation at the 1 and 2 standard deviation levels (shaded bands) are
shown.

##  .9 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan${}^{\textrm{\textdagger}}$, A. Tumasyan Institut für
Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, T. Bergauer, J. Brandstetter, M. Dragicevic, J. Erö, A.
Escalante Del Valle, M. Flechl, R. Frühwirth1, M. Jeitler1, N. Krammer, I.
Krätschmer, D. Liko, T. Madlener, I. Mikulec, N. Rad, J. Schieck1, R.
Schöfbeck, M. Spanring, D. Spitzbart, W. Waltenberger, C.-E. Wulz1, M. Zarucki
Institute for Nuclear Problems, Minsk, Belarus
V. Drugakov, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
M.R. Darwish, E.A. De Wolf, D. Di Croce, X. Janssen, A. Lelek, M. Pieters, H.
Rejeb Sfar, H. Van Haevermaet, P. Van Mechelen, S. Van Putte, N. Van Remortel
Vrije Universiteit Brussel, Brussel, Belgium
F. Blekman, E.S. Bols, S.S. Chhibra, J. D’Hondt, J. De Clercq, D. Lontkovskyi,
S. Lowette, I. Marchesini, S. Moortgat, Q. Python, K. Skovpen, S. Tavernier,
W. Van Doninck, P. Van Mulders Université Libre de Bruxelles, Bruxelles,
Belgium
D. Beghin, B. Bilin, H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, B.
Dorney, L. Favart, A. Grebenyuk, A.K. Kalsi, A. Popov, N. Postiau, E.
Starling, L. Thomas, C. Vander Velde, P. Vanlaer, D. Vannerom Ghent
University, Ghent, Belgium
T. Cornelis, D. Dobur, I. Khvastunov2, M. Niedziela, C. Roskas, D. Trocino, M.
Tytgat, W. Verbeke, B. Vermassen, M. Vit, N. Zaganidis Université Catholique
de Louvain, Louvain-la-Neuve, Belgium
O. Bondu, G. Bruno, C. Caputo, P. David, C. Delaere, M. Delcourt, A.
Giammanco, V. Lemaitre, A. Magitteri, J. Prisciandaro, A. Saggio, M. Vidal
Marono, P. Vischia, J. Zobec Centro Brasileiro de Pesquisas Fisicas, Rio de
Janeiro, Brazil
F.L. Alves, G.A. Alves, G. Correia Silva, C. Hensel, A. Moraes, 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, L.M. Huertas Guativa, H. Malbouisson, J. Martins5, D. Matos
Figueiredo, M. Medina Jaime6, 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
C.A. Bernardesa, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M. Gregoresb,
D.S. Lemos, P.G. Mercadanteb, S.F. Novaesa, SandraS. Padulaa Institute for
Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia,
Bulgaria
A. Aleksandrov, G. Antchev, R. Hadjiiska, P. Iaydjiev, M. Misheva, M. Rodozov,
M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
M. Bonchev, A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov Beihang
University, Beijing, China
W. Fang7, X. Gao7, L. Yuan Institute of High Energy Physics, Beijing, China
M. Ahmad, G.M. Chen, H.S. Chen, M. Chen, C.H. Jiang, D. Leggat, H. Liao, Z.
Liu, S.M. Shaheen8, A. Spiezia, J. Tao, E. Yazgan, H. Zhang, S. Zhang8, J.
Zhao State Key Laboratory of Nuclear Physics and Technology, Peking
University, Beijing, China
A. Agapitos, Y. Ban, G. Chen, A. Levin, J. Li, L. Li, Q. Li, Y. Mao, S.J.
Qian, D. Wang, Q. Wang Tsinghua University, Beijing, China
Z. Hu, Y. Wang Zhejiang University, Hangzhou, China
M. Xiao Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C. Florez, C.F. González Hernández, M.A. Segura Delgado
Universidad de Antioquia, Medellin, Colombia
J. Mejia Guisao, J.D. Ruiz Alvarez, C.A. Salazar González, N. Vanegas Arbelaez
University of Split, Faculty of Electrical Engineering, Mechanical Engineering
and Naval Architecture, Split, Croatia
D. Giljanović, 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, S. Ceci, D. Ferencek, K. Kadija, B. Mesic, M. Roguljic, A.
Starodumov9, T. Susa University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, E. Erodotou, A. Ioannou, M. Kolosova, S. Konstantinou,
G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H.
Rykaczewski, D. Tsiakkouri Charles University, Prague, Czech Republic
M. Finger10, M. Finger Jr.10, A. Kveton, J. Tomsa Escuela Politecnica
Nacional, Quito, Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
Y. Assran11,12, S. Elgammal12 National Institute of Chemical Physics and
Biophysics, Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, M. Raidal, C. Veelken Department of Physics, University of Helsinki,
Helsinki, Finland
P. Eerola, L. Forthomme, H. Kirschenmann, K. Osterberg, M. Voutilainen
Helsinki Institute of Physics, Helsinki, Finland
F. Garcia, J. Havukainen, J.K. Heikkilä, T. Järvinen, V. Karimäki, M.S. Kim,
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, B. Fabbro, J.L. Faure, F.
Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
C. Leloup, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin, A. Savoy-
Navarro13, M. Titov Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole
Polytechnique, Institut Polytechnique de Paris
S. Ahuja, C. Amendola, F. Beaudette, P. Busson, C. Charlot, B. Diab, G.
Falmagne, R. Granier de Cassagnac, I. Kucher, A. Lobanov, C. Martin Perez, M.
Nguyen, C. Ochando, P. Paganini, J. Rembser, R. Salerno, J.B. Sauvan, Y.
Sirois, A. Zabi, A. Zghiche Université de Strasbourg, CNRS, IPHC UMR 7178,
Strasbourg, France
J.-L. Agram14, J. Andrea, D. Bloch, G. Bourgatte, J.-M. Brom, E.C. Chabert, C.
Collard, E. Conte14, J.-C. Fontaine14, 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, C. Camen, A. Carle, N. Chanon, R.
Chierici, D. Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M.
Gouzevitch, B. Ille, Sa. Jain, F. Lagarde, I.B. Laktineh, H. Lattaud, A.
Lesauvage, M. Lethuillier, L. Mirabito, S. Perries, V. Sordini, L. Torterotot,
G. Touquet, M. Vander Donckt, S. Viret Georgian Technical University, Tbilisi,
Georgia
G. Adamov 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, D. Meuser, A.
Pauls, M. Preuten, M.P. Rauch, C. Schomakers, J. Schulz, M. Teroerde, B.
Wittmer RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
A. Albert, M. Erdmann, S. Erdweg, T. Esch, B. Fischer, S. Ghosh, T. Hebbeker,
K. Hoepfner, H. Keller, L. Mastrolorenzo, M. Merschmeyer, A. Meyer, P. Millet,
G. Mocellin, S. Mondal, S. Mukherjee, D. Noll, A. Novak, T. Pook, A.
Pozdnyakov, T. Quast, M. Radziej, Y. Rath, H. Reithler, M. Rieger, J. Roemer,
A. Schmidt, S.C. Schuler, A. Sharma, S. Wiedenbeck, S. Zaleski RWTH Aachen
University, III. Physikalisches Institut B, Aachen, Germany
G. Flügge, W. Haj Ahmad15, 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, P. Asmuss, I. Babounikau, H. Bakhshiansohi, K. Beernaert, O.
Behnke, U. Behrens, A. Bermúdez Martínez, D. Bertsche, A.A. Bin Anuar, K.
Borras17, V. Botta, A. Campbell, A. Cardini, P. Connor, S. Consuegra
Rodríguez, C. Contreras-Campana, V. Danilov, A. De Wit, M.M. Defranchis, C.
Diez Pardos, D. Domínguez Damiani, G. Eckerlin, D. Eckstein, T. Eichhorn, A.
Elwood, E. Eren, E. Gallo18, A. Geiser, J.M. Grados Luyando, A. Grohsjean, M.
Guthoff, M. Haranko, A. Harb, A. Jafari, N.Z. Jomhari, H. Jung, A. Kasem17, M.
Kasemann, H. Kaveh, J. Keaveney, C. Kleinwort, J. Knolle, D. Krücker, W.
Lange, T. Lenz, J. Leonard, J. Lidrych, K. Lipka, W. Lohmann19, R. Mankel,
I.-A. Melzer-Pellmann, A.B. Meyer, M. Meyer, M. Missiroli, G. Mittag, J.
Mnich, A. Mussgiller, V. Myronenko, D. Pérez Adán, S.K. Pflitsch, D. Pitzl, A.
Raspereza, A. Saibel, M. Savitskyi, V. Scheurer, 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, R. Zlebcik University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, V. Blobel, T. Dreyer, A.
Ebrahimi, A. Fröhlich, C. Garbers, 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, T. Lange, A. Malara, J. Multhaup,
C.E.N. Niemeyer, 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, T. Berger, E. Butz, R. Caspart, T.
Chwalek, W. De Boer, A. Dierlamm, K. El Morabit, N. Faltermann, M. Giffels, P.
Goldenzweig, A. Gottmann, M.A. Harrendorf, F. Hartmann16, U. Husemann, S.
Kudella, S. Mitra, M.U. Mozer, D. Müller, Th. Müller, M. Musich, A. Nürnberg,
G. Quast, K. Rabbertz, M. Schröder, I. Shvetsov, H.J. Simonis, R. Ulrich, M.
Wassmer, M. Weber, C. Wöhrmann, R. Wolf Institute of Nuclear and Particle
Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, P. Asenov, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas,
G. Paspalaki National and Kapodistrian University of Athens, Athens, Greece
M. Diamantopoulou, G. Karathanasis, P. Kontaxakis, A. Manousakis-katsikakis,
A. Panagiotou, I. Papavergou, N. Saoulidou, A. Stakia, K. Theofilatos, K.
Vellidis, E. Vourliotis National Technical University of Athens, Athens,
Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis University of
Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, K.
Manitara, N. Manthos, I. Papadopoulos, J. Strologas, F.A. Triantis, D.
Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
M. Bartók20, R. Chudasama, M. Csanad, P. Major, K. Mandal, A. Mehta, M.I.
Nagy, G. Pasztor, O. Surányi, G.I. Veres Wigner Research Centre for Physics,
Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath21, 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, D. Teyssier, Z.L. Trocsanyi, B. Ujvari Eszterhazy Karoly University,
Karoly Robert Campus, Gyongyos, Hungary
T. Csorgo, W.J. Metzger, F. Nemes, T. Novak 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, G. Kole, P. Mal, V.K. Muraleedharan Nair Bindhu, A.
Nayak24, 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, 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. Dutta, S. Ghosh, M. Maity26, K. Mondal, S. Nandan, A. Purohit,
P.K. Rout, G. Saha, S. Sarkar, T. Sarkar26, M. Sharan, B. Singh25, S. Thakur25
Indian Institute of Technology Madras, Madras, India
P.K. Behera, P. Kalbhor, A. Muhammad, P.R. Pujahari, A. Sharma, A.K. Sikdar
Bhabha Atomic Research Centre, Mumbai, India
D. Dutta, V. Jha, V. Kumar, D.K. Mishra, 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, RavindraKumar Verma Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, S. Karmakar,
S. Kumar, G. Majumder, K. Mazumdar, N. Sahoo, S. Sawant Indian Institute of
Science Education and Research (IISER), Pune, India
S. Chauhan, S. Dube, V. Hegde, B. Kansal, 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 University College
Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, R. Alya,b,28, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L.
Cristellaa,b, N. De Filippisa,c, M. De Palmaa,b, A. Di Florioa,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, 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,
C. Cioccaa, G. Codispotia,b, M. Cuffiania,b, G.M. Dallavallea, F. Fabbria, A.
Fanfania,b, E. Fontanesia,b, P. Giacomellia, C. Grandia, L. Guiduccia,b, F.
Iemmia,b, S. Lo Meoa,29, S. Marcellinia, G. Masettia, 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,30, S. Costaa,b, A. Di Mattiaa, R. Potenzaa,b, A. Tricomia,b,30,
C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b, Firenze, Italy
G. Barbaglia, A. Cassese, R. Ceccarelli, K. Chatterjeea,b, V. Ciullia,b, C.
Civininia, R. D’Alessandroa,b, E. Focardia,b, G. Latinoa,b, P. Lenzia,b, M.
Meschinia, S. Paolettia, G. Sguazzonia, L. Viliania INFN Laboratori Nazionali
di Frascati, Frascati, Italy
L. Benussi, S. Bianco, D. Piccolo INFN Sezione di Genova a, Università di
Genova b, Genova, Italy
M. Bozzoa,b, F. Ferroa, R. Mulargiaa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschia,b, F. Brivioa,b, V. Cirioloa,b,16, S. Di Guidaa,b,16,
M.E. Dinardoa,b, P. Dinia, S. Gennaia, A. Ghezzia,b, P. Govonia,b, L.
Guzzia,b, M. Malbertia, S. Malvezzia, D. Menascea, F. Montia,b, L. Moronia, G.
Ortonaa,b, 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,b, S.
Meolaa,d,16, P. Paoluccia,16, B. Rossia, C. Sciaccaa,b, E. Voevodinaa,b INFN
Sezione di Padova a, Università di Padova b, Padova, Italy, Università di
Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, D. Biselloa,b, A. Bolettia,b, A. Bragagnoloa,b, R.
Carlina,b, P. Checchiaa, P. De Castro Manzanoa, T. Dorigoa, U. Dossellia, F.
Gasparinia,b, U. Gasparinia,b, A. Gozzelinoa, S.Y. Hoha,b, P. Lujana, M.
Margonia,b, A.T. Meneguzzoa,b, J. Pazzinia,b, M. Presillab, P. Ronchesea,b, R.
Rossina,b, F. Simonettoa,b, A. Tikoa, 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, D. Fiorinaa,b, 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, D. Ciangottinia,b, L. Fanòa,b, P. Laricciaa,b, R.
Leonardia,b, 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, V. Bertacchia,c, L. Bianchinia, T.
Boccalia, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, 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, S. Roy
Chowdhury, A. Scribanoa, P. Spagnoloa, R. Tenchinia, G. Tonellia,b, N. Turini,
A. Venturia, P.G. Verdinia INFN Sezione di Roma a, Sapienza Università di Roma
b, Rome, Italy
F. Cavallaria, M. Cipriania,b, D. Del Rea,b, E. Di Marcoa,b, M. Diemoza, E.
Longoa,b, B. Marzocchia,b, P. Meridiania, G. Organtinia,b, F. Pandolfia, R.
Paramattia,b, C. Quarantaa,b, S. Rahatloua,b, C. Rovellia, F.
Santanastasioa,b, L. Soffia,b INFN Sezione di Torino a, Università di Torino
b, Torino, Italy, Università del Piemonte Orientale c, Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, A. Bellora, C. Biinoa, A. Cappatia,b, N. Cartigliaa, 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, 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
B. Kim, D.H. Kim, G.N. Kim, J. 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, T.J. Kim, J. Park Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, K. Lee, K.S. Lee, J. Lim, J.
Park, S.K. Park, Y. Roh, J. Yoo Kyung Hee University, Department of Physics
J. Goh Sejong University, Seoul, Korea
H.S. Kim Seoul National University, Seoul, Korea
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, H. Lee, K. Lee, S.
Lee, K. Nam, M. Oh, S.B. Oh, B.C. Radburn-Smith, U.K. Yang, H.D. Yoo, I. Yoon,
G.B. Yu University of Seoul, Seoul, Korea
D. Jeon, H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park, I. Watson Sungkyunkwan
University, Suwon, Korea
Y. Choi, C. Hwang, Y. Jeong, J. Lee, Y. Lee, I. Yu Riga Technical University,
Riga, Latvia
V. Veckalns32 Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, G. Tamulaitis, J. Vaitkus National Centre for
Particle Physics, Universiti Malaya, Kuala Lumpur, Malaysia
Z.A. Ibrahim, F. Mohamad Idris33, W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli
Universidad de Sonora (UNISON), Hermosillo, Mexico
J.F. Benitez, A. Castaneda Hernandez, J.A. Murillo Quijada, L. Valencia Palomo
Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz34, R. Lopez-
Fernandez, A. Sanchez-Hernandez Universidad Iberoamericana, Mexico City,
Mexico
S. Carrillo Moreno, C. Oropeza Barrera, M. Ramirez-Garcia, F. Vazquez Valencia
Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Montenegro, Podgorica, Montenegro
J. Mijuskovic, N. Raicevic University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M. Ahmad, Q. Hassan, H.R. Hoorani, W.A. Khan, M.A. Shah, M. Shoaib,
M. Waqas AGH University of Science and Technology Faculty of Computer Science,
Electronics and Telecommunications, Krakow, Poland
V. Avati, L. Grzanka, M. Malawski National Centre for Nuclear Research,
Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, M. Górski, M. Kazana, M. Szleper, P.
Zalewski Institute of Experimental Physics, Faculty of Physics, University of
Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk35, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Misiura, M. Olszewski, M. Walczak Laboratório de
Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
M. Araujo, P. Bargassa, D. Bastos, A. Di Francesco, P. Faccioli, B. Galinhas,
M. Gallinaro, J. Hollar, N. Leonardo, J. Seixas, K. Shchelina, G. Strong, O.
Toldaiev, J. Varela Joint Institute for Nuclear Research, Dubna, Russia
V. Alexakhin, P. Bunin, M. Gavrilenko, A. Golunov, I. Golutvin, I. Gorbunov,
A. Kamenev, V. Karjavine, V. Korenkov, A. Lanev, A. Malakhov, V. Matveev36,37,
P. Moisenz, V. Palichik, V. Perelygin, M. Savina, S. Shmatov, O. Teryaev, B.S.
Yuldashev38, A. Zarubin Petersburg Nuclear Physics Institute, Gatchina (St.
Petersburg), Russia
L. Chtchipounov, V. Golovtcov, Y. Ivanov, V. Kim39, E. Kuznetsova40, P.
Levchenko, V. Murzin, V. Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L.
Uvarov, 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 named by A.I. Alikhanov of NRC ‘Kurchatov Institute’,
Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, A. Nikitenko41, V. Popov, I.
Pozdnyakov, G. Safronov, A. Spiridonov, A. Stepennov, 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
P. Parygin, D. Philippov, E. Popova, V. Rusinov, E. Zhemchugov P.N. Lebedev
Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin, M. Kirakosyan, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia
A. Belyaev, E. Boos, M. Dubinin42, L. Dudko, A. Ershov, A. Gribushin, V.
Klyukhin, O. Kodolova, I. Lokhtin, 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, V. Kachanov, 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, A. Iuzhakov, V. Okhotnikov Tomsk State University, Tomsk, Russia
V. Borchsh, V. Ivanchenko, E. Tcherniaev University of Belgrade: Faculty of
Physics and VINCA Institute of Nuclear Sciences
P. Adzic44, P. Cirkovic, D. Devetak, M. Dordevic, P. Milenovic, J. Milosevic,
M. Stojanovic Centro de Investigaciones Energéticas Medioambientales y
Tecnológicas (CIEMAT), Madrid, Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M.
Barrio Luna, J.A. Brochero Cifuentes, C.A. Carrillo Montoya, M. Cepeda, 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, Á. Navarro Tobar, A. Pérez-Calero Yzquierdo,
J. Puerta Pelayo, I. Redondo, L. Romero, S. Sánchez Navas, M.S. Soares, A.
Triossi, C. Willmott Universidad Autónoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz, R. Reyes-Almanza Universidad de Oviedo,
Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias
(ICTEA), Oviedo, Spain
B. Alvarez Gonzalez, 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 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, G. Gomez, C. Martinez
Rivero, P. Martinez Ruiz del Arbol, F. Matorras, J. Piedra Gomez, C. Prieels,
T. Rodrigo, A. Ruiz-Jimeno, L. Russo45, L. Scodellaro, N. Trevisani, I. Vila,
J.M. Vizan Garcia University of Colombo, Colombo, Sri Lanka
K. Malagalage University of Ruhuna, Department of Physics, Matara, Sri Lanka
W.G.D. Dharmaratna, N. Wickramage CERN, European Organization for Nuclear
Research, Geneva, Switzerland
D. Abbaneo, B. Akgun, E. Auffray, G. Auzinger, J. Baechler, P. Baillon, A.H.
Ball, D. Barney, J. Bendavid, M. Bianco, A. Bocci, P. Bortignon, E. Bossini,
C. Botta, E. Brondolin, T. Camporesi, A. Caratelli, G. Cerminara, E. Chapon,
G. Cucciati, D. d’Enterria, A. Dabrowski, N. Daci, V. Daponte, A. David, O.
Davignon, A. De Roeck, N. Deelen, M. Deile, M. Dobson, M. Dünser, N. Dupont,
A. Elliott-Peisert, N. Emriskova, F. Fallavollita46, D. Fasanella, S.
Fiorendi, G. Franzoni, J. Fulcher, W. Funk, S. Giani, D. Gigi, A. Gilbert, K.
Gill, F. Glege, M. Gruchala, M. Guilbaud, D. Gulhan, J. Hegeman, C. Heidegger,
Y. Iiyama, V. Innocente, P. Janot, O. Karacheban19, J. Kaspar, J. Kieseler, M.
Krammer1, C. Lange, P. Lecoq, C. Lourenço, L. Malgeri, M. Mannelli, A.
Massironi, F. Meijers, J.A. Merlin, S. Mersi, E. Meschi, F. Moortgat, M.
Mulders, J. Ngadiuba, J. Niedziela, 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, M. Rovere, H. Sakulin,
C. Schäfer, C. Schwick, M. Selvaggi, A. Sharma, P. Silva, W. Snoeys, P.
Sphicas47, J. Steggemann, S. Summers, V.R. Tavolaro, D. Treille, A. Tsirou, A.
Vartak, M. Verzetti, W.D. Zeuner Paul Scherrer Institut, Villigen, Switzerland
L. Caminada48, 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, 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,
M.G. Ratti, M. Reichmann, C. Reissel, T. Reitenspiess, D. Ruini, D.A. Sanz
Becerra, M. Schönenberger, L. Shchutska, M.L. Vesterbacka Olsson, R. Wallny,
D.H. Zhu Universität Zürich, Zurich, Switzerland
T.K. Aarrestad, C. Amsler49, D. Brzhechko, M.F. Canelli, A. De Cosa, R. Del
Burgo, S. Donato, B. Kilminster, S. Leontsinis, V.M. Mikuni, I. Neutelings, G.
Rauco, P. Robmann, D. Salerno, K. Schweiger, C. Seitz, Y. Takahashi, S. Wertz,
A. Zucchetta National Central University, Chung-Li, Taiwan
T.H. Doan, C.M. Kuo, W. Lin, A. Roy, S.S. Yu National Taiwan University (NTU),
Taipei, Taiwan
P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.y. Li, R.-S. Lu, E.
Paganis, A. Psallidas, A. Steen Chulalongkorn University, Faculty of Science,
Department of Physics, Bangkok, Thailand
B. Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas, N. Suwonjandee Çukurova
University, Physics Department, Science and Art Faculty, Adana, Turkey
A. Bat, F. Boran, A. Celik50, S. Cerci51, S. Damarseckin52, Z.S. Demiroglu, F.
Dolek, C. Dozen, I. Dumanoglu, G. Gokbulut, EmineGurpinar Guler53, Y. Guler,
I. Hos54, C. Isik, E.E. Kangal55, O. Kara, A. Kayis Topaksu, U. Kiminsu, M.
Oglakci, G. Onengut, K. Ozdemir56, S. Ozturk57, A.E. Simsek, D. Sunar Cerci51,
U.G. Tok, S. Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle East Technical
University, Physics Department, Ankara, Turkey
B. Isildak58, G. Karapinar59, M. Yalvac Bogazici University, Istanbul, Turkey
I.O. Atakisi, E. Gülmez, M. Kaya60, O. Kaya61, B. Kaynak, Ö. Özçelik, S.
Tekten, E.A. Yetkin62 Istanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak, Y. Komurcu, S. Sen63 Istanbul University, Istanbul,
Turkey
S. Ozkorucuklu 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, E. Bhal, S. Bologna, J.J. Brooke, D. Burns64, E. Clement, D. Cussans,
H. Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, S. Paramesvaran,
B. Penning, T. Sakuma, S. Seif El Nasr-Storey, V.J. Smith, J. Taylor, A.
Titterton Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev65, C. Brew, R.M. Brown, D. Cieri, D.J.A. Cockerill, J.A.
Coughlan, K. Harder, S. Harper, J. Linacre, K. Manolopoulos, D.M. Newbold, E.
Olaiya, D. Petyt, T. Reis, T. Schuh, 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,
GurpreetSingh CHAHAL66, D. Colling, P. Dauncey, G. Davies, M. Della Negra, R.
Di Maria, P. Everaerts, G. Hall, G. Iles, T. James, M. Komm, C. Laner, L.
Lyons, A.-M. Magnan, S. Malik, A. Martelli, V. Milosevic, J. Nash67, V.
Palladino, M. Pesaresi, D.M. Raymond, A. Richards, A. Rose, E. Scott, C. Seez,
A. Shtipliyski, M. Stoye, T. Strebler, A. Tapper, K. Uchida, T. Virdee16, N.
Wardle, D. Winterbottom, J. Wright, A.G. Zecchinelli, 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, B. Caraway, J. Dittmann, K. Hatakeyama, C. Madrid, B. McMaster, N.
Pastika, C. Smith Catholic University of America, Washington, DC, USA
R. Bartek, A. Dominguez, R. Uniyal The University of Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, C. Henderson, P. Rumerio, C. West Boston University,
Boston, USA
D. Arcaro, Z. Demiragli, D. Gastler, S. Girgis, D. Pinna, C. Richardson, J.
Rohlf, D. Sperka, I. Suarez, L. Sulak, D. Zou Brown University, Providence,
USA
G. Benelli, B. Burkle, X. Coubez17, D. Cutts, Y.t. Duh, M. Hadley, J. Hakala,
U. Heintz, J.M. Hogan68, K.H.M. Kwok, E. Laird, G. Landsberg, J. Lee, Z. Mao,
M. Narain, S. Sagir69, R. Syarif, E. Usai, D. Yu, W. Zhang University of
California, Davis, Davis, USA
R. Band, C. Brainerd, R. Breedon, M. Calderon De La Barca Sanchez, M. Chertok,
J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk, F. Jensen, W.
Ko, O. Kukral, R. Lander, M. Mulhearn, D. Pellett, J. Pilot, M. Shi, 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, W.A. Nash, S. Regnard, D. Saltzberg, C. Schnaible, B.
Stone, V. Valuev University of California, Riverside, Riverside, USA
K. Burt, Y. Chen, R. Clare, J.W. Gary, S.M.A. Ghiasi Shirazi, G. Hanson, G.
Karapostoli, E. Kennedy, O.R. Long, M. Olmedo Negrete, M.I. Paneva, W. Si, L.
Wang, S. Wimpenny, B.R. Yates, Y. Zhang University of California, San Diego,
La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, M. Derdzinski, R. Gerosa, D. Gilbert, B.
Hashemi, D. Klein, V. Krutelyov, J. Letts, M. Masciovecchio, S. May, S. Padhi,
M. Pieri, V. Sharma, M. Tadel, 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, J. Incandela, B. Marsh, H. Mei, A. Ovcharova, H. Qu, J. Richman,
U. Sarica, D. Stuart, S. Wang California Institute of Technology, Pasadena,
USA
D. Anderson, A. Bornheim, O. Cerri, I. Dutta, J.M. Lawhorn, N. Lu, J. Mao,
H.B. Newman, T.Q. Nguyen, J. Pata, M. Spiropulu, J.R. Vlimant, 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, 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, A. Frankenthal, K.
Mcdermott, J.R. Patterson, D. Quach, A. Rinkevicius70, A. Ryd, S.M. Tan, Z.
Tao, J. Thom, 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, AllisonReinsvold Hall, J. Hanlon, R.M.
Harris, S. Hasegawa, R. Heller, J. Hirschauer, B. Jayatilaka, S. Jindariani,
M. Johnson, U. Joshi, B. Klima, M.J. Kortelainen, B. Kreis, S. Lammel, J.
Lewis, 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, V.
Papadimitriou, K. Pedro, C. Pena, G. Rakness, F. Ravera, L. Ristori, B.
Schneider, E. Sexton-Kennedy, N. Smith, 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, R. Vidal, M. Wang, H.A. Weber University of
Florida, Gainesville, USA
D. Acosta, P. Avery, D. Bourilkov, A. Brinkerhoff, L. Cadamuro, A. Carnes, V.
Cherepanov, D. Curry, F. Errico, R.D. Field, S.V. Gleyzer, B.M. Joshi, M. Kim,
J. Konigsberg, A. Korytov, K.H. Lo, P. Ma, K. Matchev, N. Menendez, G.
Mitselmakher, D. Rosenzweig, K. Shi, J. Wang, S. Wang, X. Zuo Florida
International University, Miami, USA
Y.R. Joshi Florida State University, Tallahassee, USA
T. Adams, A. Askew, S. Hagopian, V. Hagopian, K.F. Johnson, R. Khurana, T.
Kolberg, G. Martinez, T. Perry, H. Prosper, C. Schiber, R. Yohay, J. Zhang
Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, M. Hohlmann, D. Noonan, M. Rahmani, 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, C.
Mills, T. Roy, M.B. Tonjes, N. Varelas, J. Viinikainen, H. Wang, X. Wang, Z.
Wu The University of Iowa, Iowa City, USA
M. Alhusseini, B. Bilki53, W. Clarida, K. Dilsiz71, S. Durgut, R.P.
Gandrajula, M. Haytmyradov, V. Khristenko, O.K. Köseyan, J.-P. Merlo, A.
Mestvirishvili72, A. Moeller, J. Nachtman, H. Ogul73, Y. Onel, F. Ozok74, 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, C. Mantilla, J. Roskes, M. Swartz The University of
Kansas, Lawrence, USA
C. Baldenegro Barrera, P. Baringer, A. Bean, S. Boren, J. Bowen, A. Bylinkin,
T. Isidori, S. Khalil, J. King, G. Krintiras, A. Kropivnitskaya, C. Lindsey,
D. Majumder, W. Mcbrayer, N. Minafra, M. Murray, C. Rogan, C. Royon, S.
Sanders, E. Schmitz, J.D. Tapia Takaki, Q. Wang, J. Williams, G. Wilson 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, 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, A. Baty, R. Bi, S. Brandt, W. Busza, I.A. Cali, M.
D’Alfonso, G. Gomez Ceballos, M. Goncharov, P. Harris, D. Hsu, M. Hu, M.
Klute, D. Kovalskyi, J. Krupa, 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, S. Guts,
P. Hansen, J. Hiltbrand, 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
K. Bloom, D.R. Claes, C. Fangmeier, L. Finco, F. Golf, R. Gonzalez Suarez, R.
Kamalieddin, I. Kravchenko, J.E. Siado, G.R. Snow${}^{\textrm{\textdagger}}$,
B. Stieger, W. Tabb State University of New York at Buffalo, Buffalo, USA
G. Agarwal, C. Harrington, I. Iashvili, A. Kharchilava, C. McLean, D. Nguyen,
A. Parker, J. Pekkanen, 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, L. Skinnari, A. Tishelman-Charny, T. Wamorkar, B.
Wang, A. Wisecarver, D. Wood Northwestern University, Evanston, USA
S. Bhattacharya, J. Bueghly, 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, I. Mcalister, F.
Meng, C. Mueller, Y. Musienko36, 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, B. Bylsma, L.S. Durkin, S. Flowers, B. Francis, C. Hill, W. Ji, A.
Lefeld, T.Y. Ling, B.L. Winer Princeton University, Princeton, USA
S. Cooperstein, G. Dezoort, 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, 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, G. Negro, 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
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, A.G. Stahl Leiton, Z. Tu, A.
Zhang University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, J.L. Dulemba, C. Fallon, T. Ferbel, M.
Galanti, A. Garcia-Bellido, O. Hindrichs, A. Khukhunaishvili, E. Ranken, P.
Tan, R. Taus Rutgers, The State University of New Jersey, Piscataway, USA
B. Chiarito, J.P. Chou, A. Gandrakota, Y. Gershtein, E. Halkiadakis, A. Hart,
M. Heindl, E. Hughes, S. Kaplan, S. Kyriacou, I. Laflotte, A. Lath, R.
Montalvo, K. Nash, M. Osherson, H. Saka, S. Salur, S. Schnetzer, S. Somalwar,
R. Stone, S. Thomas University of Tennessee, Knoxville, USA
H. Acharya, A.G. Delannoy, G. Riley, S. Spanier Texas A&M University, College
Station, USA
O. Bouhali75, M. Dalchenko, M. De Mattia, A. Delgado, S. Dildick, R. Eusebi,
J. Gilmore, T. Huang, T. Kamon76, 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, 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, P. Sheldon, S. Tuo, J. Velkovska, M. Verweij University of
Virginia, Charlottesville, USA
M.W. Arenton, P. Barria, B. Cox, G. Cummings, R. Hirosky, M. Joyce, A.
Ledovskoy, C. Neu, B. Tannenwald, Y. Wang, E. Wolfe, F. Xia Wayne State
University, Detroit, USA
R. Harr, P.E. Karchin, N. Poudyal, J. Sturdy, P. Thapa University of Wisconsin
- Madison, Madison, WI, USA
T. Bose, J. Buchanan, C. Caillol, D. Carlsmith, S. Dasu, I. De Bruyn, L. Dodd,
F. Fiori, C. Galloni, B. Gomber77, H. He, M. Herndon, A. Hervé, U. Hussain, P.
Klabbers, A. Lanaro, A. Loeliger, K. Long, R. Loveless, J. Madhusudanan
Sreekala, T. Ruggles, A. Savin, V. Sharma, W.H. Smith, D. Teague, S. Trembath-
reichert, 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 UFMS, Nova Andradina, Brazil
6: Also at Universidade Federal de Pelotas, Pelotas, Brazil
7: Also at Université Libre de Bruxelles, Bruxelles, Belgium
8: Also at University of Chinese Academy of Sciences, Beijing, China
9: Also at Institute for Theoretical and Experimental Physics named by A.I.
Alikhanov of NRC ‘Kurchatov Institute’, Moscow, Russia
10: Also at Joint Institute for Nuclear Research, Dubna, Russia
11: Also at Suez University, Suez, Egypt
12: Now at British University in Egypt, Cairo, Egypt
13: Also at Purdue University, West Lafayette, USA
14: Also at Université de Haute Alsace, Mulhouse, France
15: Also at Erzincan Binali Yildirim University, Erzincan, Turkey
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,
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, Budapest, Hungary
23: Also at IIT Bhubaneswar, Bhubaneswar, India, 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: Now at INFN Sezione di Bari a, Università di Bari b, Politecnico di Bari
c, Bari, Italy
29: Also at Italian National Agency for New Technologies, Energy and
Sustainable Economic Development, Bologna, Italy
30: Also at Centro Siciliano di Fisica Nucleare e di Struttura Della Materia,
Catania, Italy
31: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy
32: Also at Riga Technical University, Riga, Latvia, Riga, Latvia
33: Also at Malaysian Nuclear Agency, MOSTI, Kajang, Malaysia
34: Also at Consejo Nacional de Ciencia y Tecnología, Mexico City, Mexico
35: Also at Warsaw University of Technology, Institute of Electronic Systems,
Warsaw, Poland
36: Also at Institute for Nuclear Research, Moscow, Russia
37: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
38: Also at Institute of Nuclear Physics of the Uzbekistan Academy of
Sciences, Tashkent, Uzbekistan
39: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
40: Also at University of Florida, Gainesville, USA
41: Also at Imperial College, London, United Kingdom
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 Università degli Studi di Siena, Siena, Italy
46: Also at INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy,
Pavia, Italy
47: Also at National and Kapodistrian University of Athens, Athens, Greece
48: Also at Universität Zürich, Zurich, Switzerland
49: Also at Stefan Meyer Institute for Subatomic Physics, Vienna, Austria,
Vienna, Austria
50: Also at Burdur Mehmet Akif Ersoy University, BURDUR, Turkey
51: Also at Adiyaman University, Adiyaman, Turkey
52: Also at Şırnak University, Sirnak, Turkey
53: Also at Beykent University, Istanbul, Turkey, Istanbul, Turkey
54: Also at Istanbul Aydin University, Istanbul, Turkey
55: Also at Mersin University, Mersin, Turkey
56: Also at Piri Reis University, Istanbul, Turkey
57: Also at Gaziosmanpasa University, Tokat, Turkey
58: Also at Ozyegin University, Istanbul, Turkey
59: Also at Izmir Institute of Technology, Izmir, Turkey
60: Also at Marmara University, Istanbul, Turkey
61: Also at Kafkas University, Kars, Turkey
62: Also at Istanbul Bilgi University, Istanbul, Turkey
63: Also at Hacettepe University, Ankara, Turkey
64: Also at Vrije Universiteit Brussel, Brussel, Belgium
65: Also at School of Physics and Astronomy, University of Southampton,
Southampton, United Kingdom
66: Also at IPPP Durham University, Durham, United Kingdom
67: Also at Monash University, Faculty of Science, Clayton, Australia
68: Also at Bethel University, St. Paul, Minneapolis, USA, St. Paul, USA
69: Also at Karamanoğlu Mehmetbey University, Karaman, Turkey
70: Also at Vilnius University, Vilnius, Lithuania
71: Also at Bingol University, Bingol, Turkey
72: Also at Georgian Technical University, Tbilisi, Georgia
73: Also at Sinop University, Sinop, Turkey
74: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey
75: Also at Texas A&M University at Qatar, Doha, Qatar
76: Also at Kyungpook National University, Daegu, Korea, Daegu, Korea
77: Also at University of Hyderabad, Hyderabad, India