HIG-17-017

$HeadURL:svn+ssh://svn.cern.ch/reps/tdr2/papers/HIG-17-017/trunk/HIG-17-017.tex$
$Id:HIG-17-017.tex4940022019-04-1817:58:11Zatiko$

HIG-17-017

# Search for nonresonant Higgs boson pair production in the $\bbbar\bbbar$
final state at $\sqrt{s}=13\TeV$

###### Abstract

Results of a search for nonresonant production of Higgs boson pairs, with each
Higgs boson decaying to a pair, are presented. This search uses data from
proton-proton collisions at a centre-of-mass energy of 13, corresponding to an
integrated luminosity of $35.9\fbinv$, collected by the CMS detector at the
LHC. No signal is observed, and a 95% confidence level upper limit of 847fbis
set on the cross section for standard model nonresonant Higgs boson pair
production times the squared branching fraction of the Higgs boson decay to a
pair. The same signature is studied, and upper limits are set, in the context
of models of physics beyond the standard model that predict modified couplings
of the Higgs boson.

##  0.1 Introduction

The detailed understanding of the properties of the Higgs boson () discovered
in 2012 by the CERN LHC experiments [1, 2, 3] remains an important subject in
fundamental physics. Current determinations of the properties of the new
particle by the ATLAS and CMS Collaborations are found to be in agreement with
standard model (SM) predictions [4, 5]. However, there are still many
measurements that could reveal unexpected deviations from the SM. A number of
models of physics beyond the SM (BSM) can be tested using their predictions of
the properties of the observed state, including the Higgs boson self-coupling
and couplings to bosons and fermions [6, 7, 8, 9, 10].

The production of Higgs boson pairs ($\PH\PH$) is the most direct way to
access the Higgs boson self-coupling [11] and to study in detail the SM Higgs
potential. The $\PH\PH$ production cross section predicted by the SM for
13proton-proton () collisions and $m_{\PH}=125.09\GeV$ [5, 12] is
$33.49^{+4.3\%}_{-6.0\%}(\text{scale})\pm 2.3\%(\alpS)\pm
2.1\%(\text{PDF})\unit{fb}$ [13, 14, 15, 16, 17], where the uncertainty is due
to the variation of the renormalization ($\mu_{\mathrm{R}}$) and factorization
($\mu_{\mathrm{F}}$) scales (scale), the strong coupling constant ()
uncertainties, and the uncertainty in parton distribution functions (PDF). The
predicted cross section results in a low expected event rate, and the
acceptance for $\PH\PH$ events in the detector is small. This means that the
SM $\PH\PH$ production process cannot be observed with the data collected so
far at the LHC: the expectation is that it will only be possible to set an
upper limit on the $\PH\PH$ production cross section, as discussed, in Refs.
[18, 19]. However, the cross section can be enhanced by anomalous couplings in
BSM models [20] and in some cases the enhancement is large enough that
$\PH\PH$ production could be observed with the current data.

The first searches for nonresonant $\PH\PH$ production were performed by LHC
experiments using collisions data at $\sqrt{s}=8\TeV$ [21, 22]. The data
collected in 2015 and 2016 at $\sqrt{s}=13\TeV$ were used for improved
analyses in the decay channels: $\bbbar\bbbar$ [19], $\bbbar\ell\PGn\ell\PGn$
[23], $\bbbar\ell\PGn\Pq\Pq$ [24], $\bbbar\PGt\PGt$ [18, 25], $\bbbar\PGg\PGg$
[26, 27], $\PGg\PGg\PW\PW^{*}$ [28], and $\PW\PW^{*}\PW\PW^{*}$ [29]. An
additional search in the $\bbbar\bbbar$ decay channel focused on the region of
phase space where one pair is highly Lorentz-boosted and is reconstructed as a
single large-area jet [30]. In the cases mentioned above, at least one of the
two Higgs bosons is required to decay to to exploit the large branching
fraction of this decay. Results were found to be compatible within
uncertainties to the expected SM background contribution. The measurement of
nonresonant $\PH\PH$ production at the LHC with the tightest expected upper
limit (15 times the SM rate) was made in the $\bbbar\PGt\PGt$ channel [25],
yielding an observed upper limit equivalent to 13 times the SM rate.

This article reports the results of a search for $\PH\PH$ production with both
Higgs bosons decaying into bottom quark pairs, resulting in four resolved
hadronic jets. The search is performed using 13 collisions data corresponding
to an integrated luminosity of 35.9, collected by the CMS detector in 2016.
The final state containing four quarks has the highest branching fraction of
all possible $\PH\PH$ final states, corresponding to ${\approx}0.339$ for an
SM Higgs boson with a mass of 125. It is one of the most sensitive signatures
for the investigation of $\PH\PH$ production, as confirmed by the results of a
similar search recently performed by the ATLAS Collaboration [19]. The main
challenge for this analysis is the large background from multijet final states
produced by quantum chromodynamics (QCD) processes, which collectively yield
rates exceeding that of the signal by several orders of magnitude. We address
this by fully exploiting the distinctive features of the signal: the presence
of four quarks and the kinematical properties of the decay process. In a
sample selected by requiring four quark jets, a multivariate event classifier
is trained to discriminate signal from background. This sample is studied by
comparing it to a model of all contributing background processes, which is
completely based on data. Because of the use of different data sets, triggers,
and offline selection requirements, this analysis is fully independent from
the CMS searches mentioned above [23, 18, 26, 30].

##  0.2 Beyond-the-standard-model extensions

In the SM, $\PH\PH$ production occurs predominantly by gluon-gluon fusion
($\cPg\cPg\text{F}$) via an internal fermion loop, where the top quark ()
contribution is dominant. In the absence of new light states, the
$\cPg\cPg\text{F}$ $\PH\PH$ production at the LHC can be generally described
(considering operators up to dimension 6) by five parameters controlling the
tree-level interactions of the Higgs boson. Without considering CP violating
effects, the relevant part of the Lagrangian then takes the form:

$\displaystyle{\cal
L}_{\PH}=\frac{1}{2}\partial_{\mu}\,\PH\,\partial^{\mu}\PH-\frac{1}{2}{m_{\PH}}^{2}\PH^{2}-\kappa_{\text{\Lam}}\,\Lam_{\text{SM}}v\,\PH^{3}-\frac{m_{\PQt}}{v}(v+{\kappa_{\cPqt}}\,\PH+\frac{c_{\mathrm{2}}}{v}\PH\PH)\,(\overline{\cmsSymbolFace{t}}_{\cmsSymbolFace}{L}\cmsSymbolFace{t}_{\cmsSymbolFace}{R}+\text{h.c.})$
(1) $\displaystyle+\frac{1}{4}\frac{\alpS}{3\pi
v}(c_{\cPg}\,\PH-\frac{c_{2\cPg}}{2v}\,\PH\PH)\,G^{\mu\nu}G_{\mu\nu}\,.$

This Lagrangian follows from extending the SM with operators of mass dimension
between four and six in the framework of an effective field theory [31],
encoding the effects of new heavy states currently beyond experimental reach.
The five parameters of the Lagrangian, named $\kappa_{\text{\Lam}}$,
$\kappa_{\cPqt}$, $c_{\cPg}$, $c_{2\cPg}$, and $c_{\mathrm{2}}$, are related
to the Higgs boson couplings. In particular, the multiplicative factors
$\kappa_{\text{\Lam}}=\Lam_{\PH\PH\PH}/\Lam_{\text{SM}}$ and
$\kappa_{\cPqt}=y_{\cPqt}/y_{\text{SM}}$ parametrize deviations from the SM
values of, respectively, the Higgs boson trilinear coupling and the top quark
Yukawa coupling. The former is given by $\Lam_{\text{SM}}=m_{\PH}^{2}/2v^{2}$,
with $v$ being the vacuum expectation value of the Higgs field. The absolute
couplings $c_{\cPg}$, $c_{2\cPg}$, and $c_{\mathrm{2}}$ parametrize contact
interactions not predicted by the SM, the coupling of the Higgs boson to
gluons and those of the two Higgs bosons to two gluons or to a top quark-
antiquark pair, which could arise through the mediation of very heavy new
states. In Eq. (1), $m_{\PQt}$ is the mass of the top quark, and
$\text{G}^{\mu\nu}$ the gluon field. We neglect possible modifications of the
bottom quark Yukawa coupling $\kappa_{\cPqb}$, which is already constrained by
LHC data [32]. The Feynman diagrams contributing to $\PH\PH$ production in
collisions at leading order (LO) are shown in Fig. 1.

Figure 1: Feynman diagrams that contribute to $\PH\PH$ production via gluon-
gluon fusion at LO. Diagrams (a) and (b) correspond to SM-like processes,
while diagrams (c), (d), and (e) correspond to pure BSM effects: (c) and (d)
describe contact interactions between the Higgs boson and gluons, and (e)
describes the contact interaction of two Higgs bosons with top quarks.

The translation of the above parametrization to the flavour-diagonal Higgs
basis (as discussed in Ref. [31]) is trivial; we use the notation of Eq. (1)
for simplicity.

The parameter space for the Higgs boson couplings in a BSM scenario has five
dimensions. Constraints on the ranges of the five parameters come from
measurements of single Higgs boson production already performed at the LHC, as
well as other theoretical considerations [33]. However, the allowed phase
space is still large and a precise scan is not feasible. The kinematical
properties of the pair-produced Higgs bosons depend strongly on the values of
those five couplings. A statistical approach has been developed in order to
identify regions of the parameter space that have similar final state
kinematical properties. The approach uses the generator-level distributions of
the invariant mass of the HH system ($m_{\PH\PH}^{\text{gen}}$) and the
modulus of the cosine of the polar angle of one Higgs boson with respect to
the beam axis ($\abs{\cos\theta^{*}_{\text{gen}}}$) to cluster the points in
the parameter space. In each region found, a representative benchmark model is
selected as the point having most similar kinematical properties to the other
points in the region. The procedure, described in Ref. [33], leads to twelve
benchmarks that taken together best represent, within a limited uncertainty,
the phenomenology of the whole five-dimensional space. An additional benchmark
(box), representative of the null Higgs boson self-coupling hypothesis, is
also considered. The parameter values of the benchmarks are listed in Table
0.2. The search for BSM signals presented here is focused on these benchmarks.

The values of the anomalous coupling parameters for the 13 benchmark models
studied [33]. For reference, the values of the parameters in the SM are also
included.  Benchmark point $\kappa_{\text{\Lam}}$ $\kappa_{\cPqt}$
$c_{\mathrm{2}}$ $c_{\cPg}$ $c_{2\cPg}$ 1 7.5 1.0 -1.0 0.0 0.0 2 1.0 1.0 0.5
-0.8 0.6 3 1.0 1.0 -1.5 0.0 -0.8 4 -3.5 1.5 -3.0 0.0 0.0 5 1.0 1.0 0.0 0.8
-1.0 6 2.4 1.0 0.0 0.2 -0.2 7 5.0 1.0 0.0 0.2 -0.2 8 15.0 1.0 0.0 -1.0 1.0 9
1.0 1.0 1.0 -0.6 0.6 10 10.0 1.5 -1.0 0.0 0.0 11 2.4 1.0 0.0 1.0 -1.0 12 15.0
1.0 1.0 0.0 0.0 Box 0.0 1.0 0.0 0.0 0.0 SM 1.0 1.0 0.0 0.0 0.0

##  0.3 The CMS detector

The CMS detector is a multipurpose apparatus designed to reconstruct the high-
energy interactions produced by the LHC. Its central feature is a
superconducting solenoid with an internal diameter of 6m. The solenoid
generates a magnetic field of 3.8T inside a volume occupied by four main sub-
detectors, each composed of a barrel and two endcap sections: silicon pixel
and strip tracker detectors, a lead tungstate crystal electromagnetic
calorimeter (ECAL), and a brass and scintillator hadron calorimeter (HCAL).
The pixel tracker provides an impact parameter resolution for charged tracks
of about 15, which allows for a precise reconstruction of secondary vertices,
crucially used to identify jets originating from the hadronization of quarks (
jets). Muons are measured in gas-ionization detectors embedded in a steel flux
return yoke outside the solenoid. Information from the calorimeters and muon
detectors is used by the first level of the CMS trigger [34], a system based
on custom hardware processors that provides the first online event selection.
The second level of the CMS trigger, also called high-level trigger and
consisting of a farm of processors running a version of the full event
reconstruction software optimized for fast processing, further selects events
using information from the whole detector before sending them downstream for
detailed processing and storage. Particles produced in the collisions are
detected in the pseudorapidity range $\abs{\eta}<5$. Pseudorapidity is defined
as $\abs{\eta}=-\ln\tan(\theta/2)$, where the polar angle $\theta$ is measured
from the z-axis, which points along the beam direction toward the Jura
mountains from LHC Point 5. A more detailed description of the CMS detector
can be found in [35].

##  0.4 Data sets

The online event selection for the data used in this analysis is designed to
select a sample of multijet events enriched with quark decays, reducing the
rate from the QCD multijet background with light quarks and gluons. The
combined secondary vertex (CSVv2) algorithm [36] is used to identify jets.
This algorithm exploits the relatively long lifetime of hadrons containing
quarks ($c\tau\sim 450\mum$), which results in a displaced decay point of the
produced hadrons. The reconstructed trajectories of charged decay products
from hadrons thus exhibit significant impact parameters with respect to the
quark production point. The CSVv2 algorithm uses the impact parameter
information together with information on other characteristics of the jets to
discriminate jets originating from quarks from those produced by the
hadronization of light quarks or gluons. Two trigger paths contribute to the
online selection. In the first trigger path jets are considered if their
momentum transverse to the beam direction, , is above 30and $\abs{\eta}<2.6$.
Selected events must contain at least four such jets of which at least three
are tagged as jets by the CSVv2 algorithm and at least two have $\pt>90\GeV$.
The second trigger path requires at least four jets with $\pt>45\GeV$ with at
least three tagged as jets by the CSVv2 algorithm. The logical or between
these two selections provides the data used in this analysis.

The production of nonresonant $\PH\PH$ in the SM is simulated following the
prescriptions of Ref. [37] at LO with 2.2.2 [38] used as the generator. Loop
factors are calculated on an event-by-event basis and applied to an effective
model, from Ref. [37]; the NNPDF30_lo_as_0130_nf_4 PDF set [39] is used. In
addition, for the study of BSM models involving anomalous Higgs boson
couplings, we generate for each of the parameter space points listed in Table
0.2 a set of 300 000 simulated events.

The 14 simulated signal samples are added together to obtain a larger signal
sample. We will refer to this ensemble of events as the Pangea sample. This
sample is then reweighted to reproduce the physics of any particular point in
the BSM phase space. The weights are obtained by looking at the matrix element
information for $m_{\PH\PH}^{\text{gen}}$ and $\cos\theta^{*}_{\text{gen}}$
from dedicated simulations, as described in Ref. [40]. The numbers of events
used to determine the weights at generator level are 3 000 000 for the SM
sample and 50 000 for each BSM benchmark. In the following, we always use the
Pangea sample instead of the 14 original samples to study signal properties in
each model considered.

Although our search employs an approach fully based on data to model
backgrounds, we make use of a simulation of QCD processes for several cross-
checks. This simulation consists of a collection of seven simulated data sets
of contiguous ranges in the $\HT^{\text{gen}}$ variable, which is defined as
the scalar sum of the of all partons that originate from the hard-scattering
process in a simulated event. The samples are generated by 2.2.2 at LO, using
the NNPDF30_lo_as_0130 set, and are then interfaced with 8.212 [41] for
fragmentation and parton showering, using the MLM matching [42]; their
equivalent integrated luminosity depends on the $\HT^{\text{gen}}$ range
considered and increases from 0.06 to 400as $\HT^{\text{gen}}$ varies between
200 and 2000. For additional studies of the sub-dominant background from top
quark pairs, a large next-to-leading order (NLO) 2.0 [43, 44, 45] sample of
inclusive [46] events is used. The behaviour of minor backgrounds is verified
and a study of their contamination of our selected sample is carried out using
2.0 NLO samples of single top quark $t$ channel [47], $\ttbar\PH$ [48], single
Higgs boson production [49], and associated $\PZ\PH$ production [50]. In
addition, we use single top quark $s$ channel, $\ttbar\ttbar$, $\ttbar\bbbar$,
and $\bbbar\PH$ samples generated with 2.2.2 at NLO. All of those samples are
interfaced with 8.212 for parton showering and fragmentation. The sample
utilises the generator tune CUETP8M2T4 [51] for the underlying event activity,
other samples interfaced with use the tune CUETP8M1 [52]. The , $\ttbar\PH$,
single Higgs boson, and $\PZ\PH$ samples are generated using the
NNPDF30_nlo_as_0118 PDF set. The single top quark, $\ttbar\bbbar$, and
$\bbbar\PH$ samples are generated with the NNPDF30_nlo_nf_4_pdfas set. The
NNPDF30_nlo_nf_5_pdfas set is used to generate the $\ttbar\ttbar$ sample. All
of the PDF sets are taken from the LHAPDF6 set [53]. The response of the CMS
detector is modelled using [54].

Finally, in order to study possible discrepancies between the efficiency of
the triggers used in our data selection and their modelling by the simulation,
we compare the effect of jet selection requirements on data collected by a
trigger requiring a single isolated muon of $\pt>18\GeV$ with its simulation,
using a mixture of events from /single-top described above and a 2.2.2 +jets
LO sample using the MLM matching, weighted appropriately, and a +jets sample
generated using the NNPDF30_lo_as_0130 PDF set.

##  0.5 Event reconstruction

Global event reconstruction is performed by the particle-flow (PF) algorithm
[55], which aims to reconstruct and identify each individual particle in an
event, with an optimized combination of information from the various elements
of the CMS detector. In this process, the identification of the particle type
(photon, electron, muon, charged hadron, neutral hadron) plays an important
role in the determination of the particle direction and energy. Electrons
(coming from photon conversions in the tracker material or from hadron
semileptonic decays) are identified as a primary charged particle track and
one or more ECAL energy clusters corresponding to this track extrapolation to
the ECAL and to possible bremsstrahlung photons emitted along the way through
the tracker material. Muons (from hadron semileptonic decays) are identified
as a track in the central tracker consistent with either a track or several
hits in the muon system, associated with an energy deficit in the
calorimeters. The objects primarily considered in this analysis are hadronic
jets, composed of particles produced by quark fragmentation and hadronization.
The energy of charged hadrons is determined from a combination of their
momentum measured in the tracker and the matching ECAL and HCAL energy
deposits, corrected for zero-suppression effects and for the response function
of the calorimeters to hadronic showers. The energy of neutral hadrons is
obtained from the corresponding corrected ECAL and HCAL energy. Jets are
reconstructed from PF candidates using the anti-clustering algorithm [56] with
a distance parameter of 0.4, as implemented in the package [57]. Jet energy
corrections are applied to both data and simulation to scale the energy and
correct for differences in the detector response in real and simulated
collisions [58]. Jet identification criteria are also applied in order to
reject fake jets from detector noise and jets originating from primary
vertices not associated with the hard interaction [59]. The combined
multivariate algorithm (cMVAv2) [36] is used in the offline analysis to
identify jets that originate from the hadronization of quarks. The cMVAv2
builds on the CSV algorithm by adding soft-lepton information to the combined
discriminant. The value of the multivariate discriminant used depends on the
required suppression of jets from light quarks and gluons. The medium working
point of the cMVAv2, defined such that the misidentification rate of light
quarks and gluons as jets is 1%, is used in this analysis. For jets produced
by the hadronization of quarks emitted in $\PH\PH$ production events, the
medium working point corresponds to a -tagging efficiency of about 65% for the
jets of interest of this analysis.

A weight is applied to each Monte Carlo (MC) event in order to match the
distribution of the number of primary interactions per event in data (pileup
correction), thus reproducing the effect on the selection efficiency of the
varying instantaneous luminosity conditions incurred during data taking. The
simulated events are also weighted to account for measured differences in the
tagging efficiency between data and simulation [36]. The trigger efficiency
for signal events is evaluated using a full simulation of the trigger [34].
The correction factor for the efficiency is found to be $0.96\pm 0.02$ based
on measurements performed in -tag multiplicity categories, using a top-pair
enriched sample collected with an inclusive muon trigger.

##  0.6 Analysis strategy

The focus of this search is the study of nonresonant production of $\PH\PH$ in
the $\bbbar\bbbar$ final state, as predicted by the SM and by several BSM
extensions. The analysis is optimized for sensitivity to the SM signal. We use
the same selection to extract limits on the $\PH\PH$ production cross section
for the BSM models.

The offline selection, performed on all data events passing one of the two
trigger paths described in Section 0.4, aims at increasing the fraction of
data events containing two Higgs boson candidates decaying into quark jet
pairs. This includes a preliminary selection of events where four or more jets
have been -tagged by the cMVAv2. Although this selection significantly reduces
the QCD multijet background rate, this background still dominates the selected
data, with contributions from events where light quark or gluon jets are
mistagged by the cMVAv2 and events containing heavy quarks.

After the selection of events with four or more tags, each reconstructed Higgs
boson candidate is composed of a pair of jets, referred to in the following as
“a dijet system” or simply “dijet”. A boosted decision tree (BDT) classifier
[60] is then trained to exploit the observable differences between the SM
signal and the background. Finally, a search for a signal contribution to the
selected data and an extraction of an upper limit in the number of selected
signal events is performed by means of a binned fit to the distribution of the
BDT classifier output. The limit on the number of events is converted to a
limit on the $\PH\PH$ production cross section times the square of the
branching fraction of the Higgs boson into a pair, using the corresponding
integrated luminosity and the computed signal efficiency.

Both the optimization of the BDT classifier and the extraction of upper limits
on $\PH\PH$ production require an accurate modelling of the multijet
background. Unfortunately, the precise simulation of QCD processes yielding a
large number of final-state partons is notoriously hard, as MC simulations are
not complete to beyond LO; in addition, the very large production cross
section for those processes makes it wholly impractical to produce simulated
data sets corresponding to an integrated luminosity comparable to that of
collision data. To address these issues, a dedicated method, fully based on
data, was developed to produce a precise model of the kinematical behaviour of
background events. This is described in detail in Section 0.8.

##  0.7 Event selection

The events of interest are identified by a jet-based selection applied to data
collected by the triggers described in Section 0.4, as well as to all
simulated samples. Jets are required to have ${\pt}_{\mathrm{j}}>30\GeV$ and
$\abs{\eta_{\mathrm{j}}}<2.4$. We require at least four such jets
($N_{\text{j}}\geq 4$) and these need to be defined as -tagged jets by the
medium working point of the cMVAv2 ($N_{\text{b}}\geq 4$). These criteria
strongly reduce the QCD multijet background and select $\PH\PH$ production
events where the final state can be fully reconstructed. The number of
selected events in the data set studied is 184 879.

The efficiencies for the SM signal are listed in Table 0.7. The efficiency for
the 13 BSM points varies from -40% to +10% compared to the SM values. The
average number of jets per selected event is ${\approx}5$. The four jets with
the highest cMVAv2 discriminant values are considered as the decay products of
two Higgs boson candidates. The pairing of the four jets into Higgs boson
candidates is performed by considering the invariant mass of the two dijet
candidates calculable for the three possible pairings, and computing the
absolute mass differences
$\Delta\cmsSymbolFace{M}_{klmn}=\abs{\cmsSymbolFace{M}_{kl}-\cmsSymbolFace{M}_{mn}}$,
where the $klmn$ indices run on the three permutations of the four jets. The
combination resulting in the smallest mass difference between the two dijet
systems is chosen as the one best describing the decay topology. This
procedure results in a correct pairing of the quarks to Higgs bosons in 54% of
the cases, as tested on the Pangea MC signal sample. The two selected dijets
are then labelled as “leading” and “trailing” according to their invariant
mass value. This procedure, which does not explicitly use the known mass of
the Higgs boson, allows the dijet masses for the selected combinations to
retain the power to discriminate the $\PH\PH$ production signal from the
background.

Cut-flow efficiency for the SM signal $\Pp\Pp\to\PH\PH\to\bbbar\bbbar$; the
efficiency and the relative reduction of each successive selection step is
shown. The number of expected SM signal events for an integrated luminosity of
1is also reported.  Produced Trigger $\geq$4  tags N events / fb 11.4 3.9 0.22
Relative eff. 34% 5.6% Efficiency 34% 1.9%

A multivariate technique is used in order to improve the sensitivity of the
analysis. A BDT discriminator is trained to distinguish the SM signal from
backgrounds (as described in Section 0.8), using the XGBoost library [60]. All
13 BSM models use the same BDT as the SM to distinguish signal from
background. We supply the BDT algorithm with a set of variables describing the
kinematical properties of the event. The list of variables is pruned to
discard those not contributing to the overall discrimination power of the
algorithm. In Table 0.7 we list the variables chosen to build the classifier.
In order to characterise the $\PH\PH$ system we use as mass variables the
invariant mass of the $\PH\PH$ system ($M_{\PH\PH}$), an estimator of the
combined mass of the $\PH\PH$ system, $M_{\mathrm{X}}$ (defined by Eq. 2
below), and the invariant masses of the reconstructed Higgs boson candidates
($M_{\PH_{1}}$, $M_{\PH_{2}}$). The of the $\PH\PH$ system and of each Higgs
boson candidate ($\pt^{\PH_{1}\PH_{2}}$, $\pt^{\PH_{1}}$ and $\pt^{\PH_{2}}$)
are used as well as the
$\DR=\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}$ and $\Delta\phi$
angles between the jets that form each reconstructed Higgs boson ($\Delta
R_{\mathrm{jj}}^{\PH_{1}}$, $\Delta R_{\mathrm{jj}}^{\PH_{2}}$,
$\Delta\phi_{\mathrm{jj}}^{\PH_{1}}$ and
$\Delta\phi_{\mathrm{jj}}^{\PH_{2}}$). Additionally, we use the $\theta^{*}$
angles between the $\PH\PH$ system and the leading Higgs boson candidate,
$\cos\theta^{*}_{\PH_{1}\PH_{2}-\PH_{1}}$, and between the leading Higgs boson
candidate and the leading jet,
$\cos\theta^{*}_{\PH_{1}\text{--}\mathrm{j}_{1}}$. We further use the
following jet-related variables: the ${\pt}_{\mathrm{j}}^{i}$ and
$\eta^{i}_{\mathrm{j}}$ ($i=1\text{--}4$) of the four jets with the highest
values of the cMVAv2 discriminant, the scalar sum of the jets in the event (),
and of the jets that are not part of the reconstructed $\PH\PH$ system (i.e.
the rest of the jets, $\HT^{\text{rest}}$). The cMVAv2 values of the third and
fourth jets sorted by cMVAv2 value ($CMVA_{3}$ and $CMVA_{4}$) are also used.
The estimator, $M_{\mathrm{X}}$, of the mass of the system of two Higgs bosons
is constructed as:

$M_{\mathrm{X}}=m_{4\mathrm{j}}-\Big{(}M_{\PH_{1}}-m_{\PH}\Big{)}-\Big{(}M_{\PH_{2}}-m_{\PH}\Big{)},$
(2)

where $m_{\PH}=125\GeV$. Even though $M_{\mathrm{X}}$ is strongly correlated
with other variables used in the BDT, its use improves the discrimination
power.

The invariant masses of the reconstructed Higgs boson candidates are the
variables with the largest discrimination power, but all the variables used
make significant contributions to the classifier.

List of BDT input variables.  $\PH\PH$ system candidates Jet variables
$M_{\mathrm{X}}$, $M_{\PH\PH}$, $M_{\PH_{1}}$, $M_{\PH_{2}}$
${\pt}_{\mathrm{j}}^{(i=1\text{--}4)}$, $\eta^{(i=1\text{--}4)}_{\text{j}}$,
$\pt^{\PH_{1}\PH_{2}}$ $\pt^{\PH_{1}}$, $\pt^{\PH_{2}}$ $\HT^{\text{rest}}$,
$\cos\theta^{*}_{\PH_{1}\PH_{2}-\PH_{1}}$
$\cos\theta^{*}_{\PH_{1}\text{--}\mathrm{j}_{1}}$ $CMVA_{3}$, $CMVA_{4}$
$\Delta R_{\mathrm{jj}}^{\PH_{1}}$, $\Delta R_{\mathrm{jj}}^{\PH_{2}}$,
$\Delta\phi_{\mathrm{jj}}^{\PH_{1}}$, $\Delta\phi_{\mathrm{jj}}^{\PH_{2}}$

We use 60% of the Pangea sample for training the classifier. The remaining 40%
of the Pangea sample is employed for the validation (20%) and application
(20%) steps; this splitting has been found to produce maximal sensitivity to a
possible $\PH\PH$ signal. As a background sample, an artificial data set
constructed with a custom mixing procedure, as described in Section 0.8, is
employed.

##  0.8 The background model

A method exploiting collision data only, based on hemisphere mixing, has been
developed [61] to perform two separate tasks: first, to provide input to the
training of the BDT classifier; and second, to reproduce the expected shape of
the BDT output in background-only events. The method does not require the
presence of signal-depleted sidebands in order to extract a background
estimation; in fact, it aims at creating an artificial background data set
using the whole original data set as the input. Thus, rather than a model of a
single distribution, a full model of the original data is produced.

###  0.8.1 The hemisphere mixing technique

The basic concept at the heart of the method is to divide each data event in
two hemispheres. The collection of hemispheres can then be used to create new
events by recombining them in pairs. To create a good background model, the
kinematical properties of the new events must be as similar as possible to the
ones of the original data but also insensitive to the possible presence of
signal. In order to define the hemispheres, we use the transverse thrust axis.
This is defined as the axis on which the sum of the absolute values of the
projections of the of the jets is maximal, and correspondingly, transverse
thrust ($T$) is the value of this sum. Once the transverse thrust axis is
identified, the event is divided into two halves by cutting perpendicular to
the transverse thrust axis. One such half is called a hemisphere ($h$). In a
preliminary step, each event in the original $N$-event data set is split into
two hemispheres that are collected in a library of $2N$ elements. Once the
library is created, each event is used as a basis for creating artificial
events. These are constructed by picking two hemispheres from the library that
are similar, according to a measure defined below, to the two hemispheres that
make up the original event. An illustration of the procedure can be found in
Fig. 2.

Figure 2: An illustration of the hemisphere mixing procedure. The transverse
thrust axis is defined as the axis on which the sum of the absolute values of
the projections of the of the jets is maximal. Once the thrust axis is
identified, the event is divided into two halves by cutting along the axis
perpendicular to the transverse thrust axis. One such half is called a
hemisphere ($h$). In a preliminary step, each event in the original $N$-event
data set is split into two hemispheres that are collected in a library of $2N$
hemispheres. Once the library is created, each event is used as a basis for
creating artificial events. These are constructed by picking two hemispheres
from the library that are similar to the two hemispheres that make up the
original event.

The number of jets $N_{\text{j}}^{h}$ and number of -tagged jets
$N_{\text{b}}^{h}$ in each hemisphere, together with four jet-related
variables, are used to define a hemisphere similarity criterion. The four
variables are the combined invariant mass of all jets contained in the
hemisphere $M_{\text{tot}}^{h}$, transverse thrust of the hemisphere $T^{h}$,
the scalar sum of the projections of the of all the jets onto the axis
orthogonal to the thrust axis on the transverse plane, $T_{a}^{h}$, the
projection of the vectorial sum of the momenta of the jets along the beam
axis, $\Sigma p_{z}^{h}$. If we label the original hemisphere $o$, and $q$ the
one in the library that is compared to $o$, the number of jets in $o$ and $q$
is required to be equal, $N_{\text{j}}^{o}=N_{\text{j}}^{q}$, and also the
number of -tagged jets are required to be equal,
$N_{\text{b}}^{o}=N_{\text{b}}^{q}$. These two requirements are used to
maintain the topology of the original events and to avoid introducing events
that would not pass the selection described in Section 0.7 (by combining a
hemisphere with 2 jets with a hemisphere with 1 jet, resulting in an event
with 3 jets). The requirement for equal numbers of jets is waived for the
infrequently occurring pairs of hemispheres that both have at least four jets
and at least four -tagged jets. For each hemisphere $q$ in the library
fulfilling the above criteria, a multidimensional distance from hemisphere $o$
is computed using the four jet-related variables, as follows:

${D(o,q)}^{2}=\frac{(M_{\text{tot}}^{o}-M_{\text{tot}}^{q})^{2}}{V(M_{\text{tot}})}+\frac{(T^{o}-T^{q})^{2}}{V(T)}+\frac{(T_{a}^{o}-T_{a}^{q})^{2}}{V(T_{a})}+\frac{(\abs{\Sigma
p_{z}^{o}}-\abs{\Sigma p_{z}^{q}})^{2}}{V(\Sigma p_{z})}.$ (3)

In the equation above, $V(x)$ represents the variance for the variable $x$,
within the subset of events of given $N_{\text{b}}$ and $N_{\text{j}}$
characterizing the hemisphere in question. Once all ${D(o,q)}$ are computed,
the $k$th nearest-neighbour hemisphere in the library, with $k\geq 1$ (the one
such that $0={D(o,0)}<\ldots<{D(q,k)}$) can be chosen to model the
corresponding hemisphere of the original event; the nearest hemisphere,
corresponding to $k=0$, is by construction the original one. We match the
$\Sigma p_{z}$ variables by considering only their absolute value (assuming
forward-backward symmetric detector acceptance to jets, as is safe to do in
the case of the CMS detector) and invert the sign of jet $\Sigma p_{z}$
components in one of the two matched hemispheres ($q_{1}$ and $q_{2}$) if
$\sgn(\Sigma p_{z}^{o_{1}}\Sigma p_{z}^{o_{2}})\neq\sgn(\Sigma
p_{z}^{q_{1}}\Sigma p_{z}^{q_{2}})$, where indices $o_{1}$ and $o_{2}$ are the
two hemispheres of the original event. Finally, the four-vectors of the jets
contained in the two hemispheres are rotated along the $\phi$ coordinate to
match the original transverse thrust axis of the modelled event. To keep track
of the distance criterion used to choose each hemisphere, the artificial event
may be labelled as $(k_{1},k_{2})$ using the neighbour indices, indicating
that one hemisphere of the original event was replaced by its $k_{1}$th
neighbour and the other hemisphere of the original event by its $k_{2}$th
neighbour and these were used to form the artificial event. By applying this
procedure to the whole set of events of the data to be modelled, and by
choosing a limiting value $K$ for $k$, we obtain a total of $K^{2}$ data sets,
each equal in size to the original one, and each featuring very similar
characteristics to the original one, despite being made up entirely of
artificial events.

The procedure described above is successful at modelling multijet events
because it exploits the fact that their production can be idealized at LO as a
$2\to 2$ process, which is made complex by a number of sub-leading effects
(QCD radiation, pileup, multiple interactions). The reconstruction of the
transverse thrust axis, and the decomposition of events into hemispheres using
that axis as a seed, uses the independent fragmentation of the two final state
partons as a working hypothesis to create artificial replicas of the original
events. The method destroys any correlation in the jet distribution between
the two hemispheres, so that any physical effect, such as the decay of a heavy
object into jet pairs, is washed out in the artificial samples. Because of
this, the resulting artificial data sets are unaffected by the presence of a
small signal contamination in the original data. This has been verified by
signal injection tests. We started with an original data set composed of
simulated QCD multijet events to which is added an additional component of
signal corresponding to a cross section 100 times larger than the one expected
by the SM. After hemisphere mixing, the kinematical properties of the
resulting artificial samples are found to resemble closely those from the QCD
multijet part of the original data set, which is its dominant component, and
unaffected by the minority component (the signal contamination). Naively this
can be understood if we note that, if the signal fraction in the original
sample amounts to 0.1%, the probability that a signal event is modelled using
two different hemispheres both originally belonging to signal events is of the
order of 0.0001%. Event-based variables such as the two Higgs boson candidate
masses, which are obtained by the minimum $\Delta M$ criterion described in
Section 0.7 and are thus sensitive to the characteristics of both hemispheres
together, do not retain their distribution in events where only one hemisphere
is taken from an $\PH\PH$ decay event.

We apply the hemisphere mixing technique to data events selected with the
$N_{\text{j}}\geq 4$, $N_{\text{b}}\geq 4$ criteria, using $K=10$ neighbour
hemispheres to each hemisphere of the original event, which were found to
still provide good modelling. The resulting artificial samples are used to
provide a background model in the training of the BDT classifier (training
sample), as well as an independent set for the BDT validation and optimization
(validation sample), and a third data set used to extract the predicted shape
of the optimized BDT (application sample). Not all of the data sets are fully
independent so only a subset can be safely employed for further studies. We
use the following collections of artificial events in the measurement: for the
training sample, we use all artificial events of types $(1,1)$, $(1,2)$,
$(2,1)$, and $(2,2)$; for the validation sample, all artificial events of
types $(3,4)$, $(5,6)$, $(7,8)$, and $(9,10)$; and for the application sample,
all artificial events of types $(4,3)$, $(6,5)$, $(8,7)$, and $(10,9)$. This
split guarantees that the three samples have equal number of events, and that
the validation and application samples are independent of each other, being
constituted of artificial events made up of different hemispheres. For the
training sample the partial use of the same hemispheres in modelling different
artificial events might at most slightly degrade the discrimination power but
does not have a detrimental effect on the subsequent steps of the analysis. A
study is performed by switching the validation and application samples and we
find that this does not change the results. The fraction of data events that
are totally replicated in the background template is completely negligible. A
comparison between the distributions obtained through the procedure described
above and by using MC simulation for QCD multijet processes can be seen in
Fig. 3 for a number of variables. The compatibility is good, although the
statistical uncertainties in the model from MC simulation are large.

Figure 3: Comparison between the background model obtained with the hemisphere
mixing technique and MC simulation of QCD multijet processes for
${\pt}_{\mathrm{j}}^{1}$ (upper left), $\eta^{1}_{\text{j}}$ (upper right),
$\pt^{\PH_{1}}$ (lower left), and $M_{\PH\PH}$ (lower right). Bias correction
for the background model, described in Section 0.8.2, is applied by rescaling
the weight of each event using the event yield ratio between corrected and
uncorrected BDT distributions. Only statistical uncertainties are shown as the
uncertainties related to the bias correction can not be propagated from the
BDT classifier to a different variable.

###  0.8.2 The background template validation

We perform a number of stringent checks to verify that the background is well
modelled by the hemisphere mixing procedure. For this purpose, we define two
control regions (CRs): the first one, called the $m_{\PH}$ CR, is obtained by
removing from the data events where the leading and trailing dijet masses are
in the region $90<M_{\PH_{1}}<150\GeV$, $80<M_{\PH_{2}}<140\GeV$. This avoids
using events belonging to the signal-enriched region. In the second region,
the tag CR, fully orthogonal to the default selection, we select events with
at least four -tagged jets as defined by the loose working point of the
cMVAv2, while vetoing events with any jets that are defined as -tagged jets
according to the medium working point of the cMVAv2. The loose working point
of the cMVAv2 has a misidentification rate of ${\approx}10\%$ and a -tagging
efficiency of ${\approx}85\%$ for jets produced by the hadronization of quarks
emitted in $\PH\PH$ production events. The distributions of all individual
event variables for the artificial data sets are compared to those from the
original data set in these two CRs and are found to be in agreement. This is
illustrated for a number of variables in Figs. 4 and 5. However, the power of
the technique rests in its ability to provide fully multidimensional
modelling. To verify this, a first cross-check consists of comparing the full
BDT shape for data and the artificial model in the $m_{\PH}$ CR. We observe an
agreement in the shape of the BDT discriminator with a slight excess of
background events in the lower range of the BDT output (as can be seen in Fig.
6, left). A similar trend is seen in the tag CR.

Figure 4: Comparison between the background model obtained with the hemisphere
mixing technique and data in the $m_{\PH}$ CR for the variables
${\pt}_{\mathrm{j}}^{1}$ (upper left), $\eta^{1}_{\text{j}}$ (upper right),
$\cos\theta^{*}_{\PH_{1}\text{--}\mathrm{j}_{1}}$ (lower left), and $CMVA_{4}$
(lower right). Bias correction for the background model, described in Section
0.8.2, is applied by rescaling the weight of each event using the event yield
ratio between corrected and uncorrected BDT distributions in this CR. Only
statistical uncertainties are shown as the uncertainties related to the bias
correction can not be propagated from the BDT classifier to a different
variable.

Figure 5: Comparison between the background model obtained with the hemisphere
mixing technique and data in the tag CR for the variables
${\pt}_{\mathrm{j}}^{1}$ (upper left), $\eta^{1}_{\text{j}}$ (upper right),
$M_{\PH_{1}}$ (lower left), and $M_{\PH_{2}}$ (lower right). Bias correction
for the background model, described in Section 0.8.2, is applied by rescaling
the weight of each event using the event yield ratio between corrected and
uncorrected BDT distributions in this CR. Only statistical uncertainties are
shown as the uncertainties related to the bias correction can not be
propagated from the BDT classifier to a different variable.

A high-precision study is required to investigate the need for a correction to
the background shape of the BDT discriminator and a corresponding systematic
uncertainty. For this purpose, all the possible combinations of neighbouring
hemispheres in the range $1$ to $10$, except the ones used for training
($(1,1),(1,2),(2,1),(2,2)$), are merged into a unique sample $M$. We re-sample
200 new replicas with the same number of events as the original data set
without replacement from $M$, each time starting from the full sample $M$.
Each of the replicas is then used as a new original data set, and artificial
samples are created from it using the hemisphere mixing procedure. The output
distribution of a previously trained BDT for the large sample $M$ is then
compared to that for its artificial counterpart, obtaining a distribution of
differences between actual and predicted data in each of the 80 BDT bins. A
schematic of the procedure and the results are available in Appendix .12. A
systematic bias is detected and the background template is corrected for the
value obtained from this comparison. The variance related to the background
bias extraction, together with expected statistical uncertainty, are estimated
and accounted for as a systematic uncertainty in the final fit described in
Section 0.10. The validity of this background bias extraction procedure has
been checked by applying it to the data in the two CRs previously mentioned.
The means of the per bin expectation values minus the observed values are
compatible with zero after the bias correction in both control regions, the
root-mean-square of the pulls is compatible with one after the bias correction
in the tag CR, but not in the $m_{\PH}$ CR, as shown on Fig. 6 (upper right).
To account for this, we increase the uncertainty in the background such that
the value of standard deviation (s.d.) becomes 1.0 in the $m_{\PH}$ CR after
the bias correction is applied (Fig. 6, lower right).

Figure 6: Left: comparison of the distribution of BDT output for data (left)
selected in a region of the leading versus trailing Higgs boson candidate mass
plane that excludes a 60--wide box around the most probable values of the
dijet masses of signal events, with the corresponding output on an artificial
sample obtained from the same data set by hemisphere mixing. Right: bin-by-bin
differences between data and model, in s.d.units before (upper right) and
after (lower right) bias correction; pull distribution for the differences,
fit to a Gaussian distribution. The bias correction uncertainty is increased
to take the s.d.of the residuals to 1.0.

##  0.9 Systematic uncertainties

The sources of systematic uncertainties found to be relevant to this analysis
are listed in Table 0.9. The systematic uncertainty in the shape of the
background model is accounted for by assigning an uncertainty to each BDT
output bin that includes the statistical uncertainty and the systematic
uncertainty related to the bias extraction discussed in the previous section.
The background normalization is left freely floating in the BDT distribution
fit. The uncertainty due to the tagging efficiencies is estimated by varying
them within their uncertainties. The uncertainty due to the pileup modelling
is computed by considering a ${\pm}4.6\%$ variation in the total inelastic
cross section value at 13 [62]. The effect of jet energy resolution is
evaluated by smearing jet energies according to their estimated uncertainty.
The jet energy scale is varied within one s.d. as a function of jet and
$\abs{\eta}$, and the efficiency of the selection criteria is recomputed. The
trigger efficiency correction factor discussed in Section 0.5 is affected by a
2% uncertainty that is taken as a systematic uncertainty in the related
source. In the mentioned sources of systematic uncertainty, both shape and
normalization shifts are considered in the model. The signal yield for a given
production cross section is affected by a systematic uncertainty in the
measured integrated luminosity of 2.5% [63]. The effect of variation of the
$\mu_{\mathrm{R}}$ and $\mu_{\mathrm{F}}$ scales on the signal acceptance is
estimated by taking the maximum and the minimum difference with respect to the
nominal acceptance when varying $\mu_{\mathrm{F}}$ and $\mu_{\mathrm{R}}$ each
individually as well as both together up and down by a factor of two. Lastly,
to estimate the signal acceptance uncertainty due to PDF uncertainties, the
PDF4LHC [64] recommendation is followed, using as the uncertainty the s.d.in
the acceptance for a set of 100 MC replicas of the NNPDF 3.0 set [39].

Systematic uncertainties considered in the analysis and relative impact on the
expected limit for the SM $\PH\PH$ production. The relative impact is obtained
by fixing the nuisance parameters corresponding to each source and
recalculating the expected limit.  Source Affects Exp. limit variation Bkg.
shape bkg. 30% Bkg. norm. bkg. 8.6% tagging eff. sig 2.8% Pileup sig
${<}0.01\%$ Jet energy res. sig ${<}0.01\%$ Jet energy scale sig ${<}0.01\%$
Int. luminosity sig ${<}0.01\%$ Trigger eff. sig ${<}0.01\%$
$\mu_{\mathrm{F}}$ and $\mu_{\mathrm{R}}$ scales sig ${<}0.01\%$ PDF sig
${<}0.01\%$

##  0.10 Results

We search for the presence of $\PH\PH$ events in CMS data collected in the
2016 run of the LHC using the BDT discriminant trained on the SM signal
simulation and artificial background data. Two-component likelihood fits to
the binned BDT output distributions are performed, using the BDT distribution
for the background resulting from the artificial data set described in Section
0.8 and the signal simulations corresponding to the SM and each of the BSM
benchmark points. The validation samples were used to study the dependence of
both the expected limit and the compatibility of the data and background
distributions on the value of the BDT discriminator used for the selection.
Selecting BDT discriminator values ${>}0.2$ results in a small loss of
sensitivity (${\approx}1.5\%$) with improved data-background compatibility. As
a result, the 64 bins with BDT ${>}0.2$ are used to extract the limits. The
fit to the SM signal is shown in Fig. 7 and the postfit distributions of
reconstructed Higgs boson masses are shown in Fig. 8. Minor background
contamination arising from $\ttbar\PH$, $\PZ\PH$, $\bbbar\PH$, and single
Higgs boson production processes do not show a signal-like BDT distribution
and their effect is found to be negligible in the selected data at our level
of sensitivity.

Figure 7: Results of the fit to the BDT distribution for the SM $\PH\PH$
production signal. In the bottom panel a comparison is shown between the best
fit signal and best fit background subtracted from measured data. The band,
centred at zero, shows the total uncertainty.

Figure 8: Post-fit distribution of $M_{\PH_{1}}$ (left) and $M_{\PH_{2}}$
(right). Bias correction for the background model is applied by rescaling the
weight of each event using the event yield ratio between corrected and
uncorrected BDT distributions.

The observed and expected 95% confidence level (CL) upper limits on the cross
section for $\Pp\Pp\to\PH\PH\to\bbbar\bbbar$ nonresonant production, are
computed using the asymptotic approximation [65] of the criterion [66, 67,
68], using a test statistic based on the profile likelihood ratio (the LHC
test statistic) [65]. The systematic uncertainties are treated as nuisance
parameters and are profiled in the minimization. The limits are shown in Table
0.10 together with the 1s.d.and 2s.d.CL intervals around the expected limits.
For the SM process, the expected limit is 419fb, which corresponds to
${\approx}37$ times the SM $\PH\PH$ production cross section times the square
of the branching fraction for the $\PH\to\bbbar$ decay. The observed upper
limit obtained is 847fb, which is ${\approx}2\unit{s.d.}$ above the expected
upper limit. This corresponds to an observed limit of 2496fbfor
$\sigma{(\Pp\Pp\to\PH\PH)_{\text{SM}}}$.

The observed and expected upper limits on
$\sigma{(\Pp\Pp\to\PH\PH\to\bbbar\bbbar)}$ in the SM at 95% CL in units of fb.
Category Observed Expected -2s.d. -1s.d. +1s.d. +2s.d. SM
$\PH\PH\to\bbbar\bbbar$ 847 419 221 297 601 834

We perform the procedure described above in turn on the 13 BSM benchmark
models considered. The results are shown in Fig. 9 and reported in Table 0.10.
The difference between observed and expected limits is similar for SM and all
the benchmark models. This is explained by the fact that the benchmark points
use the same BDT as SM, resulting in the same background shape as an input to
the fit. The background shape has a deficit of events compared to data in the
last bins of the BDT distribution, as seen in Fig. 7. We also search for
$\PH\PH$ production with values of $\kappa_{\text{\Lam}}$ in the range [-20,
20], assuming $\kappa_{\cPqt}=1$, and the results are shown in Fig. 10. The
kinematic properties vary significantly across the points in this range. We do
not exclude any values of $\kappa_{\text{\Lam}}$, assuming $\kappa_{\cPqt}=1$.

The observed and expected upper limits on the
$\sigma{(\Pp\Pp\to\PH\PH\to\bbbar\bbbar)}$ cross section for the 13 BSM
benchmark models at 95% CL in units of fb.  Benchmark point Observed Expected
-2s.d. -1s.d. +1s.d. +2s.d. 1 602 295 155 209 424 592 2 554 269 141 190 389
548 3 705 346 182 245 497 691 4 939 461 244 327 662 920 5 508 248 131 176 357
501 6 937 457 240 323 657 916 7 3510 1710 905 1210 2440 3390 8 686 336 177 238
483 674 9 529 259 136 183 373 520 10 2090 1000 527 709 1440 2010 11 1080 525
277 372 755 1050 12 1744 859 455 611 1230 1710 Box 1090 542 286 384 775 1080

Figure 9: The observed and expected upper limits at 95% CL on the
$\sigma{(\Pp\Pp\to\PH\PH\to\bbbar\bbbar)}$ cross section for the 13 BSM models
investigated. See Table 0.2 for their respective parameter values. Figure 10:
95% CL cross section limits on $\sigma{(\Pp\Pp\to\PH\PH\to\bbbar\bbbar)}$ for
values of $\kappa_{\text{\Lam}}$ in the [-20,20] range, assuming
$\kappa_{\cPqt}=1$; the theoretical prediction with $\kappa_{\cPqt}=1$ is also
shown.

##  0.11 Summary

This paper presents a search for nonresonant Higgs boson pair ($\PH\PH$)
production with both Higgs bosons decaying into pairs. The standard model (SM)
production has been studied along with 13 beyond the SM (BSM) benchmark
models, using a data set of $\sqrt{s}=13\TeV$ proton-proton collision events,
corresponding to an integrated luminosity of 35.9collected by the CMS detector
during the 2016 LHC run. The analysis of events acquired by a hadronic
multijet trigger includes the selection of events with 4 -tagged jets and a
classification using boosted decision trees, optimized for discovery of the SM
$\PH\PH$ signal. Limits at 95% confidence level on the $\PH\PH$ production
cross section times the square of the branching fraction for the Higgs boson
decay to quark pairs are extracted for the SM and each BSM model considered,
using binned likelihood fits of the shape of the boosted decision tree
classifier output. The background model is derived from a novel technique
based on data that provides a multidimensional representation of the dominant
quantum chromodynamics multijet background and also models well the overall
background distribution. The expected upper limit on
$\sigma{(\Pp\Pp\to\PH\PH\to\bbbar\bbbar)}$ is 419fb, corresponding to 37 times
the expected value for the SM process. The observed upper limit is 847fb.
Anomalous couplings of the Higgs boson are also investigated. The upper limits
extracted for the $\PH\PH$ production cross section in the 13 BSM benchmark
models range from 508 to 3513fb.

###### Acknowledgements.

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

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##  .12 Supplemental material

Figure 11: Diagram describing the procedure used to estimate the background
bias correction. All possible combinations of mixed hemispheres except those
used for training are added together to create a large sample $M$ of $96N$
events from which we repeatedly subsample without replacement 200 replicas
$M_{i}$ of $N$ events. The hemisphere mixing procedure is then carried out
again for each of this replicas to produce a set of re-mixed data replicas
$R_{i}$. The trained multivariate classifier trained is then evaluated over
all the events of $M$ and each $R_{i}$. and the histograms of the classifier
output are compared to obtain a the differences for each of the replicas. The
median difference is taken as bias correction. Figure 12: Bias estimation by
resampling, in relative units of the statistical uncertainty of the predicted
background, used to correct the background estimation. The median (red line)
and the upper and lower one s.d.quantiles (green lines) have been computed
from 200 subsamples of the re-mixed data comparing the predicted background
$n^{p}_{b}$ with the observed $n^{o}_{b}$. The variability due to the limited
number of subsamples is estimated by bootstrap and it is shown for each
estimation using a coloured shadow around the quantile estimation. The light
yellow shadow represents the uncertainty due to the limited statistics of the
reference observed sample. The separation between the one s.d.quantiles is
compatible with the expected variance if the estimation was Poisson or
Gaussian distributed.

##  .13 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan, A. Tumasyan Institut für Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, E. Asilar, T. Bergauer, J. Brandstetter, M. Dragicevic,
J. Erö, A. Escalante Del Valle, M. Flechl, R. Frühwirth1, V.M. Ghete, J.
Hrubec, M. Jeitler1, N. Krammer, I. Krätschmer, D. Liko, T. Madlener, I.
Mikulec, N. Rad, H. Rohringer, J. Schieck1, R. Schöfbeck, M. Spanring, D.
Spitzbart, A. Taurok, W. Waltenberger, J. Wittmann, C.-E. Wulz1, M. Zarucki
Institute for Nuclear Problems, Minsk, Belarus
V. Chekhovsky, V. Mossolov, J. Suarez Gonzalez Universiteit Antwerpen,
Antwerpen, Belgium
E.A. De Wolf, D. Di Croce, X. Janssen, J. Lauwers, M. Pieters, H. Van
Haevermaet, P. Van Mechelen, N. Van Remortel Vrije Universiteit Brussel,
Brussel, Belgium
S. Abu Zeid, F. Blekman, J. D’Hondt, J. De Clercq, K. Deroover, G. Flouris, D.
Lontkovskyi, S. Lowette, I. Marchesini, S. Moortgat, L. Moreels, Q. Python, K.
Skovpen, S. Tavernier, W. Van Doninck, P. Van Mulders, I. Van Parijs
Université Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, H. Brun, B. Clerbaux, G. De Lentdecker, H. Delannoy, B.
Dorney, G. Fasanella, L. Favart, R. Goldouzian, A. Grebenyuk, A.K. Kalsi, T.
Lenzi, J. Luetic, N. Postiau, E. Starling, L. Thomas, C. Vander Velde, P.
Vanlaer, D. Vannerom, Q. Wang Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, A. Fagot, M. Gul, I. Khvastunov2, D. Poyraz, C. Roskas,
D. Trocino, M. Tytgat, W. Verbeke, B. Vermassen, M. Vit, N. Zaganidis
Université Catholique de Louvain, Louvain-la-Neuve, Belgium
H. Bakhshiansohi, O. Bondu, S. Brochet, G. Bruno, C. Caputo, P. David, C.
Delaere, M. Delcourt, A. Giammanco, G. Krintiras, V. Lemaitre, A. Magitteri,
K. Piotrzkowski, A. Saggio, M. Vidal Marono, S. Wertz, J. Zobec Centro
Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
F.L. Alves, G.A. Alves, M. Correa Martins Junior, G. Correia Silva, C. Hensel,
A. Moraes, M.E. Pol, P. Rebello Teles Universidade do Estado do Rio de
Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, C. De Oliveira Martins, S.
Fonseca De Souza, H. Malbouisson, D. Matos Figueiredo, M. Melo De Almeida, C.
Mora Herrera, L. Mundim, H. Nogima, W.L. Prado Da Silva, L.J. Sanchez Rosas,
A. Santoro, A. Sznajder, M. Thiel, E.J. Tonelli Manganote3, F. Torres Da Silva
De Araujo, A. Vilela Pereira Universidade Estadual Paulista a, Universidade
Federal do ABC b, São Paulo, Brazil
S. Ahujaa, C.A. Bernardesa, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M.
Gregoresb, P.G. Mercadanteb, S.F. Novaesa, SandraS. Padulaa Institute for
Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia,
Bulgaria
A. Aleksandrov, R. Hadjiiska, P. Iaydjiev, A. Marinov, M. Misheva, M. Rodozov,
M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
A. Dimitrov, L. Litov, B. Pavlov, P. Petkov Beihang University, Beijing, China
W. Fang5, X. Gao5, L. Yuan Institute of High Energy Physics, Beijing, China
M. Ahmad, J.G. Bian, G.M. Chen, H.S. Chen, M. Chen, Y. Chen, C.H. Jiang, D.
Leggat, H. Liao, Z. Liu, F. Romeo, S.M. Shaheen6, A. Spiezia, J. Tao, Z. Wang,
E. Yazgan, H. Zhang, S. Zhang6, J. Zhao State Key Laboratory of Nuclear
Physics and Technology, Peking University, Beijing, China
Y. Ban, G. Chen, A. Levin, J. Li, L. Li, Q. Li, Y. Mao, S.J. Qian, D. Wang
Tsinghua University, Beijing, China
Y. Wang Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C.A. Carrillo Montoya, L.F. Chaparro Sierra, C. Florez,
C.F. González Hernández, M.A. Segura Delgado University of Split, Faculty of
Electrical Engineering, Mechanical Engineering and Naval Architecture, Split,
Croatia
B. Courbon, N. Godinovic, D. Lelas, I. Puljak, T. Sculac University of Split,
Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, K. Kadija, B. Mesic, A. Starodumov7, T. Susa
University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, M. Kolosova, G. Mavromanolakis, J. Mousa, C. Nicolaou,
F. Ptochos, P.A. Razis, H. Rykaczewski Charles University, Prague, Czech
Republic
M. Finger8, M. Finger Jr.8 Escuela Politecnica Nacional, Quito, Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
H. Abdalla9, A.A. Abdelalim10,11, A. Mohamed11 National Institute of Chemical
Physics and Biophysics, Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, M. Raidal, C. Veelken Department of Physics, University of Helsinki,
Helsinki, Finland
P. Eerola, H. Kirschenmann, J. Pekkanen, M. Voutilainen Helsinki Institute of
Physics, Helsinki, Finland
J. Havukainen, J.K. Heikkilä, T. Järvinen, V. Karimäki, R. Kinnunen, T.
Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, P. Luukka, T.
Mäenpää, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta University of
Technology, Lappeenranta, Finland
T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F. Ferri, S.
Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry, C. Leloup,
E. Locci, J. Malcles, G. Negro, J. Rander, A. Rosowsky, M.Ö. Sahin, M. Titov
Laboratoire Leprince-Ringuet, Ecole polytechnique, CNRS/IN2P3, Université
Paris-Saclay, Palaiseau, France
A. Abdulsalam12, C. Amendola, I. Antropov, F. Beaudette, P. Busson, C.
Charlot, R. Granier de Cassagnac, I. Kucher, A. Lobanov, J. Martin Blanco, C.
Martin Perez, M. Nguyen, C. Ochando, G. Ortona, P. Paganini, P. Pigard, J.
Rembser, R. Salerno, J.B. Sauvan, Y. Sirois, A.G. Stahl Leiton, A. Zabi, A.
Zghiche Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France
J.-L. Agram13, J. Andrea, D. Bloch, J.-M. Brom, E.C. Chabert, V. Cherepanov,
C. Collard, E. Conte13, J.-C. Fontaine13, D. Gelé, U. Goerlach, M. Jansová,
A.-C. Le Bihan, N. Tonon, P. Van Hove Centre de Calcul de l’Institut National
de Physique Nucleaire et de Physique des Particules, CNRS/IN2P3, Villeurbanne,
France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, N. Chanon, R. Chierici, D. Contardo, P.
Depasse, H. El Mamouni, J. Fay, L. Finco, S. Gascon, M. Gouzevitch, G.
Grenier, B. Ille, F. Lagarde, I.B. Laktineh, H. Lattaud, M. Lethuillier, L.
Mirabito, S. Perries, A. Popov14, V. Sordini, G. Touquet, M. Vander Donckt, S.
Viret Georgian Technical University, Tbilisi, Georgia
A. Khvedelidze8 Tbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze8 RWTH Aachen University, I. Physikalisches Institut, Aachen,
Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, M. Preuten, M.P.
Rauch, C. Schomakers, J. Schulz, M. Teroerde, B. Wittmer RWTH Aachen
University, III. Physikalisches Institut A, Aachen, Germany
A. Albert, D. Duchardt, M. Erdmann, S. Erdweg, T. Esch, R. Fischer, S. Ghosh,
A. Güth, T. Hebbeker, C. Heidemann, K. Hoepfner, H. Keller, L. Mastrolorenzo,
M. Merschmeyer, A. Meyer, P. Millet, S. Mukherjee, T. Pook, M. Radziej, H.
Reithler, M. Rieger, A. Schmidt, D. Teyssier, S. Thüer RWTH Aachen University,
III. Physikalisches Institut B, Aachen, Germany
G. Flügge, O. Hlushchenko, T. Kress, T. Müller, A. Nehrkorn, A. Nowack, C.
Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl15 Deutsches Elektronen-
Synchrotron, Hamburg, Germany
M. Aldaya Martin, T. Arndt, C. Asawatangtrakuldee, I. Babounikau, K.
Beernaert, O. Behnke, U. Behrens, A. Bermúdez Martínez, D. Bertsche, A.A. Bin
Anuar, K. Borras16, V. Botta, A. Campbell, P. Connor, C. Contreras-Campana, V.
Danilov, A. De Wit, M.M. Defranchis, C. Diez Pardos, D. Domínguez Damiani, G.
Eckerlin, T. Eichhorn, A. Elwood, E. Eren, E. Gallo17, A. Geiser, J.M. Grados
Luyando, A. Grohsjean, M. Guthoff, M. Haranko, A. Harb, J. Hauk, H. Jung, M.
Kasemann, J. Keaveney, C. Kleinwort, J. Knolle, D. Krücker, W. Lange, A.
Lelek, T. Lenz, J. Leonard, K. Lipka, W. Lohmann18, R. Mankel, I.-A. Melzer-
Pellmann, A.B. Meyer, M. Meyer, M. Missiroli, G. Mittag, J. Mnich, V.
Myronenko, S.K. Pflitsch, D. Pitzl, A. Raspereza, M. Savitskyi, P. Saxena, P.
Schütze, C. Schwanenberger, R. Shevchenko, A. Singh, H. Tholen, O. Turkot, A.
Vagnerini, G.P. Van Onsem, R. Walsh, Y. Wen, K. Wichmann, C. Wissing, O.
Zenaiev University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, V. Blobel, T. Dreyer, A.
Ebrahimi, E. Garutti, D. Gonzalez, P. Gunnellini, J. Haller, A. Hinzmann, A.
Karavdina, G. Kasieczka, R. Klanner, R. Kogler, N. Kovalchuk, S. Kurz, V.
Kutzner, J. Lange, D. Marconi, J. Multhaup, M. Niedziela, C.E.N. Niemeyer, D.
Nowatschin, A. Perieanu, A. Reimers, O. Rieger, C. Scharf, P. Schleper, S.
Schumann, J. Schwandt, J. Sonneveld, H. Stadie, G. Steinbrück, F.M. Stober, M.
Stöver, A. Vanhoefer, B. Vormwald, I. Zoi Karlsruher Institut fuer
Technologie, Karlsruhe, Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, E. Butz, R. Caspart, T. Chwalek, F.
Colombo, W. De Boer, A. Dierlamm, K. El Morabit, N. Faltermann, B. Freund, M.
Giffels, M.A. Harrendorf, F. Hartmann15, S.M. Heindl, U. Husemann, I.
Katkov14, S. Kudella, S. Mitra, M.U. Mozer, Th. Müller, M. Musich, M. Plagge,
G. Quast, K. Rabbertz, M. Schröder, I. Shvetsov, H.J. Simonis, R. Ulrich, S.
Wayand, M. Weber, T. Weiler, C. Wöhrmann, R. Wolf Institute of Nuclear and
Particle Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas, G. Paspalaki
National and Kapodistrian University of Athens, Athens, Greece
G. Karathanasis, P. Kontaxakis, A. Panagiotou, I. Papavergou, N. Saoulidou, E.
Tziaferi, K. Vellidis National Technical University of Athens, Athens, Greece
K. Kousouris, I. Papakrivopoulos, G. Tsipolitis University of Ioánnina,
Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, N.
Manthos, I. Papadopoulos, E. Paradas, J. Strologas, F.A. Triantis, D.
Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
M. Bartók19, M. Csanad, N. Filipovic, P. Major, M.I. Nagy, G. Pasztor, O.
Surányi, G.I. Veres Wigner Research Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath20, Á. Hunyadi, F. Sikler, T.Á. Vámi, V.
Veszpremi, G. Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear
Research ATOMKI, Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi19, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
P. Raics, Z.L. Trocsanyi, B. Ujvari Indian Institute of Science (IISc),
Bangalore, India
S. Choudhury, J.R. Komaragiri, P.C. Tiwari National Institute of Science
Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati22, C. Kar, P. Mal, K. Mandal, A. Nayak23, D.K. Sahoo22, S.K.
Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, R. Chawla, N. Dhingra, R.
Gupta, A. Kaur, M. Kaur, S. Kaur, P. Kumari, M. Lohan, A. Mehta, K. Sandeep,
S. Sharma, J.B. Singh, A.K. Virdi, G. Walia University of Delhi, Delhi, India
A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, Ashok Kumar, S.
Malhotra, M. Naimuddin, P. Priyanka, K. Ranjan, Aashaq Shah, R. Sharma Saha
Institute of Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj24, M. Bharti24, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep24,
D. Bhowmik, S. Dey, S. Dutt24, S. Dutta, S. Ghosh, K. Mondal, S. Nandan, A.
Purohit, P.K. Rout, A. Roy, S. Roy Chowdhury, G. Saha, S. Sarkar, M. Sharan,
B. Singh24, S. Thakur24 Indian Institute of Technology Madras, Madras, India
P.K. Behera Bhabha Atomic Research Centre, Mumbai, India
R. Chudasama, D. Dutta, V. Jha, V. Kumar, P.K. Netrakanti, L.M. Pant, P.
Shukla Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, G.B. Mohanty, N. Sur, B. Sutar, RavindraKumar
Verma Tata Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, Sa. Jain, S.
Karmakar, S. Kumar, M. Maity25, G. Majumder, K. Mazumdar, N. Sahoo, T.
Sarkar25 Indian Institute of Science Education and Research (IISER), Pune,
India
S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, A.
Rastogi, S. Sharma Institute for Research in Fundamental Sciences (IPM),
Tehran, Iran
S. Chenarani26, E. Eskandari Tadavani, S.M. Etesami26, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, F. Rezaei Hosseinabadi, B. Safarzadeh27, M.
Zeinali University College Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L. Cristellaa,b,
N. De Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, F. Erricoa,b, L. Fiorea,
A. Gelmia,b, G. Iasellia,c, M. Incea,b, S. Lezkia,b, G. Maggia,c, M. Maggia,
G. Minielloa,b, S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, R.
Radognaa, A. Ranieria, G. Selvaggia,b, A. Sharmaa, L. Silvestrisa, R.
Vendittia, P. Verwilligena, G. Zitoa INFN Sezione di Bologna a, Università di
Bologna b, Bologna, Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia,b, S. Braibant-
Giacomellia,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
S.S. Chhibraa,b, C. Cioccaa, G. Codispotia,b, M. Cuffiania,b, G.M.
Dallavallea, F. Fabbria, A. Fanfania,b, E. Fontanesi, P. Giacomellia, C.
Grandia, L. Guiduccia,b, S. Lo Meoa, S. Marcellinia, G. Masettia, A.
Montanaria, F.L. Navarriaa,b, A. Perrottaa, F. Primaveraa,b,15, A.M. Rossia,b,
T. Rovellia,b, G.P. Sirolia,b, N. Tosia INFN Sezione di Catania a, Università
di Catania b, Catania, Italy
S. Albergoa,b, A. Di Mattiaa, R. Potenzaa,b, A. Tricomia,b, C. Tuvea,b INFN
Sezione di Firenze a, Università di Firenze b, Firenze, Italy
G. Barbaglia, K. Chatterjeea,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, G. Latino, P. Lenzia,b, M. Meschinia, S.
Paolettia, L. Russoa,28, G. Sguazzonia, D. Stroma, L. Viliania INFN Laboratori
Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, F. Fabbri, D. Piccolo INFN Sezione di Genova a,
Università di Genova b, Genova, Italy
F. Ferroa, R. Mulargiaa,b, F. Raveraa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschib, F. Brivioa,b, V. Cirioloa,b,15, S. Di Guidaa,d,15,
M.E. Dinardoa,b, S. Fiorendia,b, S. Gennaia, A. Ghezzia,b, P. Govonia,b, M.
Malbertia,b, S. Malvezzia, A. Massironia,b, D. Menascea, F. Monti, L. Moronia,
M. Paganonia,b, D. Pedrinia, S. Ragazzia,b, T. Tabarelli de Fatisa,b, D.
Zuoloa,b INFN Sezione di Napoli a, Università di Napoli ’Federico II’ b,
Napoli, Italy, Università della Basilicata c, Potenza, Italy, Università G.
Marconi d, Roma, Italy
S. Buontempoa, N. Cavalloa,c, A. De Iorioa,b, A. Di Crescenzoa,b, F.
Fabozzia,c, F. Fiengaa, G. Galatia, A.O.M. Iorioa,b, W.A. Khana, L. Listaa, S.
Meolaa,d,15, P. Paoluccia,15, C. Sciaccaa,b, E. Voevodinaa,b INFN Sezione di
Padova a, Università di Padova b, Padova, Italy, Università di Trento c,
Trento, Italy
P. Azzia, N. Bacchettaa, D. Biselloa,b, A. Bolettia,b, A. Bragagnolo, R.
Carlina,b, P. Checchiaa, M. Dall’Ossoa,b, P. De Castro Manzanoa, T. Dorigoa,
F. Gasparinia,b, U. Gasparinia,b, A. Gozzelinoa, S.Y. Hoh, S. Lacapraraa, P.
Lujan, M. Margonia,b, A.T. Meneguzzoa,b, J. Pazzinia,b, N. Pozzobona,b, P.
Ronchesea,b, R. Rossina,b, F. Simonettoa,b, A. Tiko, E. Torassaa, M. Tosia,b,
M. Zanettia,b, P. Zottoa,b, G. Zumerlea,b INFN Sezione di Pavia a, Università
di Pavia b, Pavia, Italy
A. Braghieria, A. Magnania, P. Montagnaa,b, S.P. Rattia,b, V. Rea, M.
Ressegottia,b, C. Riccardia,b, P. Salvinia, I. Vaia,b, P. Vituloa,b INFN
Sezione di Perugia a, Università di Perugia b, Perugia, Italy
M. Biasinia,b, G.M. Bileia, C. Cecchia,b, D. Ciangottinia,b, L. Fanòa,b, P.
Laricciaa,b, R. Leonardia,b, E. Manonia, G. Mantovania,b, V. Mariania,b, M.
Menichellia, A. Rossia,b, A. Santocchiaa,b, D. Spigaa INFN Sezione di Pisa a,
Università di Pisa b, Scuola Normale Superiore di Pisa c, Pisa, Italy
K. Androsova, P. Azzurria, G. Bagliesia, L. Bianchinia, T. Boccalia, L.
Borrello, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, F. Fioria,c,
L. Gianninia,c, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c, G.
Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, G. Rolandi29, P.
Spagnoloa, R. Tenchinia, G. Tonellia,b, A. Venturia, P.G. Verdinia INFN
Sezione di Roma a, Sapienza Università di Roma b, Rome, Italy
L. Baronea,b, F. Cavallaria, M. Cipriania,b, D. Del Rea,b, E. Di Marcoa,b, M.
Diemoza, S. Gellia,b, E. Longoa,b, B. Marzocchia,b, P. Meridiania, G.
Organtinia,b, F. Pandolfia, R. Paramattia,b, F. Preiatoa,b, S. Rahatloua,b, C.
Rovellia, F. Santanastasioa,b INFN Sezione di Torino a, Università di Torino
b, Torino, Italy, Università del Piemonte Orientale c, Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, C. Biinoa, A. Cappatia,b, N. Cartigliaa, F. Cennaa,b, S.
Comettia, M. Costaa,b, R. Covarellia,b, N. Demariaa, B. Kiania,b, C.
Mariottia, S. Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M.
Montenoa, M.M. Obertinoa,b, L. Pachera,b, N. Pastronea, M. Pelliccionia, G.L.
Pinna Angionia,b, A. Romeroa,b, M. Ruspaa,c, R. Sacchia,b, R. Salvaticoa,b, K.
Shchelinaa,b, V. Solaa, A. Solanoa,b, D. Soldia,b, A. Staianoa INFN Sezione di
Trieste a, Università di Trieste b, Trieste, Italy
S. Belfortea, V. Candelisea,b, M. Casarsaa, F. Cossuttia, A. Da Rolda,b, G.
Della Riccaa,b, F. Vazzolera,b, A. Zanettia Kyungpook National University,
Daegu, Korea
D.H. Kim, G.N. Kim, M.S. Kim, J. Lee, S. Lee, S.W. Lee, C.S. Moon, Y.D. Oh,
S.I. Pak, S. Sekmen, D.C. Son, Y.C. Yang Chonnam National University,
Institute for Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon, G. Oh Hanyang University, Seoul, Korea
B. Francois, J. Goh30, T.J. Kim Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, Y. Jo, K. Lee, K.S. Lee, S.
Lee, J. Lim, S.K. Park, Y. Roh Sejong University, Seoul, Korea
H.S. Kim Seoul National University, Seoul, Korea
J. Almond, J. Kim, J.S. Kim, H. Lee, K. Lee, K. Nam, S.B. Oh, B.C. Radburn-
Smith, S.h. Seo, U.K. Yang, H.D. Yoo, G.B. Yu University of Seoul, Seoul,
Korea
D. Jeon, H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park Sungkyunkwan University,
Suwon, Korea
Y. Choi, C. Hwang, J. Lee, I. Yu Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, J. Vaitkus National Centre for Particle Physics,
Universiti Malaya, Kuala Lumpur, Malaysia
I. Ahmed, Z.A. Ibrahim, M.A.B. Md Ali31, F. Mohamad Idris32, W.A.T. Wan
Abdullah, M.N. Yusli, Z. Zolkapli Universidad de Sonora (UNISON), Hermosillo,
Mexico
J.F. Benitez, A. Castaneda Hernandez, J.A. Murillo Quijada Centro de
Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
H. Castilla-Valdez, E. De La Cruz-Burelo, M.C. Duran-Osuna, I. Heredia-De La
Cruz33, R. Lopez-Fernandez, J. Mejia Guisao, R.I. Rabadan-Trejo, M. Ramirez-
Garcia, G. Ramirez-Sanchez, R. Reyes-Almanza, A. Sanchez-Hernandez Universidad
Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, C. Oropeza Barrera, F. Vazquez Valencia Benemerita
Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M. Ahmad, M.I. Asghar, Q. Hassan, H.R. Hoorani, A. Saddique, M.A.
Shah, M. Shoaib, M. Waqas National Centre for Nuclear Research, Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. Górski, M. Kazana, M.
Szleper, P. Traczyk, P. Zalewski Institute of Experimental Physics, Faculty of
Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk34, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Misiura, M. Olszewski, A. Pyskir, M. Walczak Laboratório de
Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
M. Araujo, P. Bargassa, C. Beirão Da Cruz E Silva, A. Di Francesco, P.
Faccioli, B. Galinhas, M. Gallinaro, J. Hollar, N. Leonardo, J. Seixas, G.
Strong, O. Toldaiev, J. Varela Joint Institute for Nuclear Research, Dubna,
Russia
S. Afanasiev, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov, A. Kamenev,
V. Karjavine, A. Lanev, A. Malakhov, V. Matveev35,36, P. Moisenz, V. Palichik,
V. Perelygin, S. Shmatov, S. Shulha, N. Skatchkov, V. Smirnov, N. Voytishin,
A. Zarubin Petersburg Nuclear Physics Institute, Gatchina (St. Petersburg),
Russia
V. Golovtsov, Y. Ivanov, V. Kim37, E. Kuznetsova38, P. Levchenko, V. Murzin,
V. Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Vavilov, A.
Vorobyev Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, D. Tlisov, A. Toropin Institute for Theoretical
and Experimental Physics, Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, V. Popov, I. Pozdnyakov, G.
Safronov, A. Spiridonov, A. Stepennov, V. Stolin, M. Toms, E. Vlasov, A.
Zhokin Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
R. Chistov39, M. Danilov39, P. Parygin, D. Philippov, S. Polikarpov39, E.
Tarkovskii P.N. Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin36, M. Kirakosyan, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia
A. Baskakov, A. Belyaev, E. Boos, V. Bunichev, M. Dubinin40, L. Dudko, A.
Gribushin, V. Klyukhin, O. Kodolova, I. Lokhtin, I. Miagkov, S. Obraztsov, S.
Petrushanko, V. Savrin, A. Snigirev Novosibirsk State University (NSU),
Novosibirsk, Russia
A. Barnyakov41, V. Blinov41, T. Dimova41, L. Kardapoltsev41, Y. Skovpen41
Institute for High Energy Physics of National Research Centre ’Kurchatov
Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, D. Elumakhov, A. Godizov, V. Kachanov,
A. Kalinin, D. Konstantinov, P. Mandrik, V. Petrov, R. Ryutin, S.
Slabospitskii, A. Sobol, S. Troshin, N. Tyurin, A. Uzunian, A. Volkov National
Research Tomsk Polytechnic University, Tomsk, Russia
A. Babaev, S. Baidali, V. Okhotnikov University of Belgrade, Faculty of
Physics and Vinca Institute of Nuclear Sciences, Belgrade, Serbia
P. Adzic42, P. Cirkovic, D. Devetak, M. Dordevic, J. Milosevic Centro de
Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid,
Spain
J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M. Barrio Luna, J.A.
Brochero Cifuentes, M. Cerrada, N. Colino, B. De La Cruz, A. Delgado Peris, C.
Fernandez Bedoya, J.P. Fernández Ramos, J. Flix, M.C. Fouz, O. Gonzalez Lopez,
S. Goy Lopez, J.M. Hernandez, M.I. Josa, D. Moran, A. Pérez-Calero Yzquierdo,
J. Puerta Pelayo, I. Redondo, L. Romero, M.S. Soares, A. Triossi Universidad
Autónoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz Universidad de Oviedo, Oviedo, Spain
J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras, I. Gonzalez
Caballero, J.R. González Fernández, E. Palencia Cortezon, V. Rodríguez Bouza,
S. Sanchez Cruz, P. Vischia, J.M. Vizan Garcia Instituto de Física de
Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, J. Duarte Campderros, M.
Fernandez, P.J. Fernández Manteca, A. García Alonso, J. Garcia-Ferrero, G.
Gomez, A. Lopez Virto, J. Marco, C. Martinez Rivero, P. Martinez Ruiz del
Arbol, F. Matorras, J. Piedra Gomez, C. Prieels, T. Rodrigo, A. Ruiz-Jimeno,
L. Scodellaro, N. Trevisani, I. Vila, R. Vilar Cortabitarte University of
Ruhuna, Department of Physics, Matara, Sri Lanka
N. Wickramage CERN, European Organization for Nuclear Research, Geneva,
Switzerland
D. Abbaneo, B. Akgun, E. Auffray, G. Auzinger, P. Baillon, A.H. Ball, D.
Barney, J. Bendavid, M. Bianco, A. Bocci, C. Botta, E. Brondolin, T.
Camporesi, M. Cepeda, G. Cerminara, E. Chapon, Y. Chen, G. Cucciati, D.
d’Enterria, A. Dabrowski, N. Daci, V. Daponte, A. David, A. De Roeck, N.
Deelen, M. Dobson, M. Dünser, N. Dupont, A. Elliott-Peisert, P. Everaerts, F.
Fallavollita43, D. Fasanella, G. Franzoni, J. Fulcher, W. Funk, D. Gigi, A.
Gilbert, K. Gill, F. Glege, M. Gruchala, M. Guilbaud, D. Gulhan, J. Hegeman,
C. Heidegger, V. Innocente, A. Jafari, P. Janot, O. Karacheban18, J. Kieseler,
A. Kornmayer, M. Krammer1, C. Lange, P. Lecoq, C. Lourenço, L. Malgeri, M.
Mannelli, F. Meijers, J.A. Merlin, S. Mersi, E. Meschi, P. Milenovic44, F.
Moortgat, M. Mulders, J. Ngadiuba, S. Nourbakhsh, S. Orfanelli, L. Orsini, F.
Pantaleo15, L. Pape, E. Perez, M. Peruzzi, A. Petrilli, G. Petrucciani, A.
Pfeiffer, M. Pierini, F.M. Pitters, D. Rabady, A. Racz, T. Reis, M. Rovere, H.
Sakulin, C. Schäfer, C. Schwick, M. Seidel, M. Selvaggi, A. Sharma, P. Silva,
P. Sphicas45, A. Stakia, J. Steggemann, D. Treille, A. Tsirou, V. Veckalns46,
M. Verzetti, W.D. Zeuner Paul Scherrer Institut, Villigen, Switzerland
L. Caminada47, K. Deiters, W. Erdmann, R. Horisberger, Q. Ingram, H.C.
Kaestli, D. Kotlinski, U. Langenegger, T. Rohe, S.A. Wiederkehr ETH Zurich -
Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland
M. Backhaus, L. Bäni, P. Berger, N. Chernyavskaya, G. Dissertori, M. Dittmar,
M. Donegà, C. Dorfer, T.A. Gómez Espinosa, C. Grab, D. Hits, T. Klijnsma, W.
Lustermann, R.A. Manzoni, M. Marionneau, M.T. Meinhard, F. Micheli, P.
Musella, F. Nessi-Tedaldi, J. Pata, F. Pauss, G. Perrin, L. Perrozzi, S.
Pigazzini, M. Quittnat, C. Reissel, D. Ruini, D.A. Sanz Becerra, M.
Schönenberger, L. Shchutska, V.R. Tavolaro, K. Theofilatos, M.L. Vesterbacka
Olsson, R. Wallny, D.H. Zhu Universität Zürich, Zurich, Switzerland
T.K. Aarrestad, C. Amsler48, D. Brzhechko, M.F. Canelli, A. De Cosa, R. Del
Burgo, S. Donato, C. Galloni, T. Hreus, B. Kilminster, S. Leontsinis, I.
Neutelings, G. Rauco, P. Robmann, D. Salerno, K. Schweiger, C. Seitz, Y.
Takahashi, A. Zucchetta National Central University, Chung-Li, Taiwan
T.H. Doan, R. Khurana, C.M. Kuo, W. Lin, A. Pozdnyakov, S.S. Yu National
Taiwan University (NTU), Taipei, Taiwan
P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Arun Kumar, Y.F. Liu,
R.-S. Lu, E. Paganis, A. Psallidas, A. Steen Chulalongkorn University, Faculty
of Science, Department of Physics, Bangkok, Thailand
B. Asavapibhop, N. Srimanobhas, N. Suwonjandee Çukurova University, Physics
Department, Science and Art Faculty, Adana, Turkey
M.N. Bakirci49, A. Bat, F. Boran, S. Cerci50, S. Damarseckin, Z.S. Demiroglu,
F. Dolek, C. Dozen, I. Dumanoglu, E. Eskut, S. Girgis, G. Gokbulut, Y. Guler,
E. Gurpinar, I. Hos51, C. Isik, E.E. Kangal52, O. Kara, A. Kayis Topaksu, U.
Kiminsu, M. Oglakci, G. Onengut, K. Ozdemir53, A. Polatoz, U.G. Tok, S.
Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle East Technical University,
Physics Department, Ankara, Turkey
B. Isildak54, G. Karapinar55, M. Yalvac, M. Zeyrek Bogazici University,
Istanbul, Turkey
I.O. Atakisi, E. Gülmez, M. Kaya56, O. Kaya57, S. Ozkorucuklu58, S. Tekten,
E.A. Yetkin59 Istanbul Technical University, Istanbul, Turkey
M.N. Agaras, A. Cakir, K. Cankocak, Y. Komurcu, S. Sen60 Institute for
Scintillation Materials of National Academy of Science of Ukraine, Kharkov,
Ukraine
B. Grynyov National Scientific Center, Kharkov Institute of Physics and
Technology, Kharkov, Ukraine
L. Levchuk University of Bristol, Bristol, United Kingdom
F. Ball, J.J. Brooke, D. Burns, E. Clement, D. Cussans, O. Davignon, H.
Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, D.M. Newbold61, S.
Paramesvaran, B. Penning, T. Sakuma, D. Smith, V.J. Smith, J. Taylor, A.
Titterton Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev62, C. Brew, R.M. Brown, D. Cieri, D.J.A. Cockerill, J.A.
Coughlan, K. Harder, S. Harper, J. Linacre, E. Olaiya, D. Petyt, C.H.
Shepherd-Themistocleous, A. Thea, I.R. Tomalin, T. Williams, W.J. Womersley
Imperial College, London, United Kingdom
R. Bainbridge, P. Bloch, J. Borg, S. Breeze, O. Buchmuller, A. Bundock, D.
Colling, P. Dauncey, G. Davies, M. Della Negra, R. Di Maria, G. Hall, G. Iles,
T. James, M. Komm, C. Laner, L. Lyons, A.-M. Magnan, S. Malik, A. Martelli, J.
Nash63, A. Nikitenko7, V. Palladino, M. Pesaresi, D.M. Raymond, A. Richards,
A. Rose, E. Scott, C. Seez, A. Shtipliyski, G. Singh, M. Stoye, T. Strebler,
S. Summers, A. Tapper, K. Uchida, T. Virdee15, N. Wardle, D. Winterbottom, J.
Wright, S.C. Zenz Brunel University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, C.K. Mackay, A. Morton, I.D. Reid,
L. Teodorescu, S. Zahid Baylor University, Waco, USA
K. Call, J. Dittmann, K. Hatakeyama, H. Liu, C. Madrid, B. McMaster, N.
Pastika, C. Smith Catholic University of America, Washington DC, USA
R. Bartek, A. Dominguez The University of Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, C. Henderson, P. Rumerio, C. West Boston University,
Boston, USA
D. Arcaro, T. Bose, D. Gastler, D. Pinna, D. Rankin, C. Richardson, J. Rohlf,
L. Sulak, D. Zou Brown University, Providence, USA
G. Benelli, X. Coubez, D. Cutts, M. Hadley, J. Hakala, U. Heintz, J.M.
Hogan64, K.H.M. Kwok, E. Laird, G. Landsberg, J. Lee, Z. Mao, M. Narain, S.
Sagir65, R. Syarif, E. Usai, D. Yu University of California, Davis, Davis, USA
R. Band, C. Brainerd, R. Breedon, D. Burns, M. Calderon De La Barca Sanchez,
M. Chertok, J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk,
W. Ko, O. Kukral, R. Lander, M. Mulhearn, D. Pellett, J. Pilot, S. Shalhout,
M. Shi, D. Stolp, D. Taylor, K. Tos, M. Tripathi, Z. Wang, F. Zhang University
of California, Los Angeles, USA
M. Bachtis, C. Bravo, R. Cousins, A. Dasgupta, A. Florent, J. Hauser, M.
Ignatenko, N. Mccoll, S. Regnard, D. Saltzberg, C. Schnaible, V. Valuev
University of California, Riverside, Riverside, USA
E. Bouvier, K. Burt, R. Clare, J.W. Gary, S.M.A. Ghiasi Shirazi, G. Hanson, G.
Karapostoli, E. Kennedy, F. Lacroix, O.R. Long, M. Olmedo Negrete, M.I.
Paneva, W. Si, L. Wang, H. Wei, S. Wimpenny, B.R. Yates University of
California, San Diego, La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, M. Derdzinski, R. Gerosa, D. Gilbert, B.
Hashemi, A. Holzner, D. Klein, G. Kole, V. Krutelyov, J. Letts, M.
Masciovecchio, D. Olivito, S. Padhi, M. Pieri, M. Sani, V. Sharma, S. Simon,
M. Tadel, A. Vartak, S. Wasserbaech66, J. Wood, F. Würthwein, A. Yagil, G.
Zevi Della Porta University of California, Santa Barbara - Department of
Physics, Santa Barbara, USA
N. Amin, R. Bhandari, C. Campagnari, M. Citron, V. Dutta, M. Franco Sevilla,
L. Gouskos, R. Heller, J. Incandela, A. Ovcharova, H. Qu, J. Richman, D.
Stuart, I. Suarez, S. Wang, J. Yoo California Institute of Technology,
Pasadena, USA
D. Anderson, A. Bornheim, J.M. Lawhorn, N. Lu, H.B. Newman, T.Q. Nguyen, M.
Spiropulu, J.R. Vlimant, R. Wilkinson, S. Xie, Z. Zhang, R.Y. Zhu Carnegie
Mellon University, Pittsburgh, USA
M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, M. Sun, I. Vorobiev, M.
Weinberg University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, F. Jensen, A. Johnson, E. MacDonald, T. Mulholland,
R. Patel, A. Perloff, K. Stenson, K.A. Ulmer, S.R. Wagner Cornell University,
Ithaca, USA
J. Alexander, J. Chaves, Y. Cheng, J. Chu, A. Datta, K. Mcdermott, N. Mirman,
J.R. Patterson, D. Quach, A. Rinkevicius, A. Ryd, L. Skinnari, L. Soffi, S.M.
Tan, Z. Tao, J. Thom, J. Tucker, P. Wittich, M. Zientek Fermi National
Accelerator Laboratory, Batavia, USA
S. Abdullin, M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S.
Banerjee, L.A.T. Bauerdick, A. Beretvas, J. Berryhill, P.C. Bhat, K. Burkett,
J.N. Butler, A. Canepa, G.B. Cerati, H.W.K. Cheung, F. Chlebana, M. Cremonesi,
J. Duarte, V.D. Elvira, J. Freeman, Z. Gecse, E. Gottschalk, L. Gray, D.
Green, S. Grünendahl, O. Gutsche, J. Hanlon, R.M. Harris, S. Hasegawa, J.
Hirschauer, Z. Hu, B. Jayatilaka, S. Jindariani, M. Johnson, U. Joshi, B.
Klima, M.J. Kortelainen, B. Kreis, S. Lammel, D. Lincoln, R. Lipton, M. Liu,
T. Liu, J. Lykken, K. Maeshima, J.M. Marraffino, D. Mason, P. McBride, P.
Merkel, S. Mrenna, S. Nahn, V. O’Dell, K. Pedro, C. Pena, O. Prokofyev, G.
Rakness, L. Ristori, A. Savoy-Navarro67, B. Schneider, E. Sexton-Kennedy, A.
Soha, W.J. Spalding, L. Spiegel, S. Stoynev, J. Strait, N. Strobbe, L. Taylor,
S. Tkaczyk, N.V. Tran, L. Uplegger, E.W. Vaandering, C. Vernieri, M.
Verzocchi, R. Vidal, M. Wang, H.A. Weber, A. Whitbeck University of Florida,
Gainesville, USA
D. Acosta, P. Avery, P. Bortignon, D. Bourilkov, A. Brinkerhoff, L. Cadamuro,
A. Carnes, D. Curry, R.D. Field, S.V. Gleyzer, B.M. Joshi, J. Konigsberg, A.
Korytov, K.H. Lo, P. Ma, K. Matchev, H. Mei, G. Mitselmakher, D. Rosenzweig,
K. Shi, D. Sperka, J. Wang, S. Wang, X. Zuo Florida International University,
Miami, USA
Y.R. Joshi, S. Linn Florida State University, Tallahassee, USA
A. Ackert, T. Adams, A. Askew, S. Hagopian, V. Hagopian, K.F. Johnson, T.
Kolberg, G. Martinez, T. Perry, H. Prosper, A. Saha, C. Schiber, R. Yohay
Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, V. Bhopatkar, S. Colafranceschi, M. Hohlmann, D. Noonan, M.
Rahmani, T. Roy, F. Yumiceva University of Illinois at Chicago (UIC), Chicago,
USA
M.R. Adams, L. Apanasevich, D. Berry, R.R. Betts, R. Cavanaugh, X. Chen, S.
Dittmer, O. Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, K. Jung, J.
Kamin, C. Mills, M.B. Tonjes, N. Varelas, H. Wang, X. Wang, Z. Wu, J. Zhang
The University of Iowa, Iowa City, USA
M. Alhusseini, B. Bilki68, W. Clarida, K. Dilsiz69, S. Durgut, R.P.
Gandrajula, M. Haytmyradov, V. Khristenko, J.-P. Merlo, A. Mestvirishvili, A.
Moeller, J. Nachtman, H. Ogul70, Y. Onel, F. Ozok71, A. Penzo, C. Snyder, E.
Tiras, J. Wetzel Johns Hopkins University, Baltimore, USA
B. Blumenfeld, A. Cocoros, N. Eminizer, D. Fehling, L. Feng, A.V. Gritsan,
W.T. Hung, P. Maksimovic, J. Roskes, U. Sarica, M. Swartz, M. Xiao, C. You The
University of Kansas, Lawrence, USA
A. Al-bataineh, P. Baringer, A. Bean, S. Boren, J. Bowen, A. Bylinkin, J.
Castle, S. Khalil, A. Kropivnitskaya, D. Majumder, W. Mcbrayer, M. Murray, C.
Rogan, S. Sanders, E. Schmitz, J.D. Tapia Takaki, Q. Wang Kansas State
University, Manhattan, USA
S. Duric, A. Ivanov, K. Kaadze, D. Kim, Y. Maravin, D.R. Mendis, T. Mitchell,
A. Modak, A. Mohammadi, L.K. Saini Lawrence Livermore National Laboratory,
Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, C. Ferraioli, N.J. Hadley,
S. Jabeen, G.Y. Jeng, R.G. Kellogg, J. Kunkle, A.C. Mignerey, S. Nabili, F.
Ricci-Tam, Y.H. Shin, A. Skuja, S.C. Tonwar, K. Wong Massachusetts Institute
of Technology, Cambridge, USA
D. Abercrombie, B. Allen, V. Azzolini, A. Baty, G. Bauer, R. Bi, S. Brandt, W.
Busza, I.A. Cali, M. D’Alfonso, Z. Demiragli, G. Gomez Ceballos, M. Goncharov,
P. Harris, D. Hsu, M. Hu, Y. Iiyama, G.M. Innocenti, M. Klute, D. Kovalskyi,
Y.-J. Lee, P.D. Luckey, B. Maier, A.C. Marini, C. Mcginn, C. Mironov, S.
Narayanan, X. Niu, C. Paus, C. Roland, G. Roland, Z. Shi, G.S.F. Stephans, K.
Sumorok, K. Tatar, D. Velicanu, J. Wang, T.W. Wang, B. Wyslouch University of
Minnesota, Minneapolis, USA
A.C. Benvenuti${}^{\textrm{\textdagger}}$, R.M. Chatterjee, A. Evans, P.
Hansen, J. Hiltbrand, Sh. Jain, S. Kalafut, M. Krohn, Y. Kubota, Z. Lesko, J.
Mans, N. Ruckstuhl, R. Rusack, M.A. Wadud University of Mississippi, Oxford,
USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
E. Avdeeva, K. Bloom, D.R. Claes, C. Fangmeier, F. Golf, R. Gonzalez Suarez,
R. Kamalieddin, I. Kravchenko, J. Monroy, J.E. Siado, G.R. Snow, B. Stieger
State University of New York at Buffalo, Buffalo, USA
A. Godshalk, C. Harrington, I. Iashvili, A. Kharchilava, C. Mclean, D. Nguyen,
A. Parker, S. Rappoccio, B. Roozbahani Northeastern University, Boston, USA
G. Alverson, E. Barberis, C. Freer, Y. Haddad, A. Hortiangtham, D.M. Morse, T.
Orimoto, R. Teixeira De Lima, T. Wamorkar, B. Wang, A. Wisecarver, D. Wood
Northwestern University, Evanston, USA
S. Bhattacharya, J. Bueghly, O. Charaf, K.A. Hahn, N. Mucia, N. Odell, M.H.
Schmitt, K. Sung, M. Trovato, M. Velasco University of Notre Dame, Notre Dame,
USA
R. Bucci, N. Dev, M. Hildreth, K. Hurtado Anampa, C. Jessop, D.J. Karmgard, N.
Kellams, K. Lannon, W. Li, N. Loukas, N. Marinelli, F. Meng, C. Mueller, Y.
Musienko35, M. Planer, A. Reinsvold, R. Ruchti, P. Siddireddy, G. Smith, S.
Taroni, M. Wayne, A. Wightman, M. Wolf, A. Woodard The Ohio State University,
Columbus, USA
J. Alimena, L. Antonelli, B. Bylsma, L.S. Durkin, S. Flowers, B. Francis, C.
Hill, W. Ji, T.Y. Ling, W. Luo, B.L. Winer Princeton University, Princeton,
USA
S. Cooperstein, P. Elmer, J. Hardenbrook, S. Higginbotham, A. Kalogeropoulos,
D. Lange, M.T. Lucchini, J. Luo, D. Marlow, K. Mei, I. Ojalvo, J. Olsen, C.
Palmer, P. Piroué, J. Salfeld-Nebgen, D. Stickland, C. Tully, Z. Wang
University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A. Barker, V.E. Barnes, S. Das, L. Gutay, M. Jones, A.W. Jung, A. Khatiwada,
B. Mahakud, D.H. Miller, N. Neumeister, C.C. Peng, S. Piperov, H. Qiu, J.F.
Schulte, J. Sun, F. Wang, R. Xiao, W. Xie Purdue University Northwest,
Hammond, USA
T. Cheng, J. Dolen, N. Parashar Rice University, Houston, USA
Z. Chen, K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Kilpatrick, W. Li, B.P.
Padley, R. Redjimi, J. Roberts, J. Rorie, W. Shi, Z. Tu, A. Zhang University
of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, Y.t. Duh, J.L. Dulemba, C. Fallon, T.
Ferbel, M. Galanti, A. Garcia-Bellido, J. Han, O. Hindrichs, A.
Khukhunaishvili, E. Ranken, P. Tan, R. Taus Rutgers, The State University of
New Jersey, Piscataway, USA
A. Agapitos, J.P. Chou, Y. Gershtein, E. Halkiadakis, A. Hart, M. Heindl, E.
Hughes, S. Kaplan, R. Kunnawalkam Elayavalli, S. Kyriacou, A. Lath, R.
Montalvo, K. Nash, M. Osherson, H. Saka, S. Salur, S. Schnetzer, D. Sheffield,
S. Somalwar, R. Stone, S. Thomas, P. Thomassen, M. Walker University of
Tennessee, Knoxville, USA
A.G. Delannoy, J. Heideman, G. Riley, S. Spanier Texas A&M University, College
Station, USA
O. Bouhali72, A. Celik, M. Dalchenko, M. De Mattia, A. Delgado, S. Dildick, R.
Eusebi, J. Gilmore, T. Huang, T. Kamon73, S. Luo, R. Mueller, D. Overton, L.
Perniè, D. Rathjens, A. Safonov Texas Tech University, Lubbock, USA
N. Akchurin, J. Damgov, F. De Guio, P.R. Dudero, S. Kunori, K. Lamichhane,
S.W. Lee, T. Mengke, S. Muthumuni, T. Peltola, S. Undleeb, I. Volobouev, Z.
Wang Vanderbilt University, Nashville, USA
S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H. Ni, K.
Padeken, J.D. Ruiz Alvarez, P. Sheldon, S. Tuo, J. Velkovska, M. Verweij, Q.
Xu University of Virginia, Charlottesville, USA
M.W. Arenton, P. Barria, B. Cox, R. Hirosky, M. Joyce, A. Ledovskoy, H. Li, C.
Neu, T. Sinthuprasith, Y. Wang, E. Wolfe, F. Xia Wayne State University,
Detroit, USA
R. Harr, P.E. Karchin, N. Poudyal, J. Sturdy, P. Thapa, S. Zaleski University
of Wisconsin - Madison, Madison, WI, USA
M. Brodski, J. Buchanan, C. Caillol, D. Carlsmith, S. Dasu, I. De Bruyn, L.
Dodd, B. Gomber, M. Grothe, M. Herndon, A. Hervé, U. Hussain, P. Klabbers, A.
Lanaro, K. Long, R. Loveless, T. Ruggles, A. Savin, V. Sharma, N. Smith, W.H.
Smith, N. Woods †: Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
3: Also at Universidade Estadual de Campinas, Campinas, Brazil
4: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
5: Also at Université Libre de Bruxelles, Bruxelles, Belgium
6: Also at University of Chinese Academy of Sciences, Beijing, China
7: Also at Institute for Theoretical and Experimental Physics, Moscow, Russia
8: Also at Joint Institute for Nuclear Research, Dubna, Russia
9: Also at Cairo University, Cairo, Egypt
10: Also at Helwan University, Cairo, Egypt
11: Now at Zewail City of Science and Technology, Zewail, Egypt
12: Also at Department of Physics, King Abdulaziz University, Jeddah, Saudi
Arabia
13: Also at Université de Haute Alsace, Mulhouse, France
14: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
15: Also at CERN, European Organization for Nuclear Research, Geneva,
Switzerland
16: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
17: Also at University of Hamburg, Hamburg, Germany
18: Also at Brandenburg University of Technology, Cottbus, Germany
19: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary
20: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
21: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
22: Also at Indian Institute of Technology Bhubaneswar, Bhubaneswar, India
23: Also at Institute of Physics, Bhubaneswar, India
24: Also at Shoolini University, Solan, India
25: Also at University of Visva-Bharati, Santiniketan, India
26: Also at Isfahan University of Technology, Isfahan, Iran
27: Also at Plasma Physics Research Center, Science and Research Branch,
Islamic Azad University, Tehran, Iran
28: Also at Università degli Studi di Siena, Siena, Italy
29: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy
30: Also at Kyunghee University, Seoul, Korea
31: Also at International Islamic University of Malaysia, Kuala Lumpur,
Malaysia
32: Also at Malaysian Nuclear Agency, MOSTI, Kajang, Malaysia
33: Also at Consejo Nacional de Ciencia y Tecnología, Mexico city, Mexico
34: Also at Warsaw University of Technology, Institute of Electronic Systems,
Warsaw, Poland
35: Also at Institute for Nuclear Research, Moscow, Russia
36: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
37: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
38: Also at University of Florida, Gainesville, USA
39: Also at P.N. Lebedev Physical Institute, Moscow, Russia
40: Also at California Institute of Technology, Pasadena, USA
41: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
42: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
43: Also at INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
44: Also at University of Belgrade, Faculty of Physics and Vinca Institute of
Nuclear Sciences, Belgrade, Serbia
45: Also at National and Kapodistrian University of Athens, Athens, Greece
46: Also at Riga Technical University, Riga, Latvia
47: Also at Universität Zürich, Zurich, Switzerland
48: Also at Stefan Meyer Institute for Subatomic Physics (SMI), Vienna,
Austria
49: Also at Gaziosmanpasa University, Tokat, Turkey
50: Also at Adiyaman University, Adiyaman, Turkey
51: Also at Istanbul Aydin University, Istanbul, Turkey
52: Also at Mersin University, Mersin, Turkey
53: Also at Piri Reis University, Istanbul, Turkey
54: Also at Ozyegin University, Istanbul, Turkey
55: Also at Izmir Institute of Technology, Izmir, Turkey
56: Also at Marmara University, Istanbul, Turkey
57: Also at Kafkas University, Kars, Turkey
58: Also at Istanbul University, Faculty of Science, Istanbul, Turkey
59: Also at Istanbul Bilgi University, Istanbul, Turkey
60: Also at Hacettepe University, Ankara, Turkey
61: Also at Rutherford Appleton Laboratory, Didcot, United Kingdom
62: Also at School of Physics and Astronomy, University of Southampton,
Southampton, United Kingdom
63: Also at Monash University, Faculty of Science, Clayton, Australia
64: Also at Bethel University, St. Paul, USA
65: Also at Karamanoğlu Mehmetbey University, Karaman, Turkey
66: Also at Utah Valley University, Orem, USA
67: Also at Purdue University, West Lafayette, USA
68: Also at Beykent University, Istanbul, Turkey
69: Also at Bingol University, Bingol, Turkey
70: Also at Sinop University, Sinop, Turkey
71: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey
72: Also at Texas A&M University at Qatar, Doha, Qatar
73: Also at Kyungpook National University, Daegu, Korea