EXO-16-004

$HeadURL:svn+ssh://svn.cern.ch/reps/tdr2/papers/EXO-16-004/trunk/EXO-16-004.tex$
$Id:EXO-16-004.tex4562772018-04-1910:08:39Zjalimena$ EXO-16-004

# Search for decays of stopped exotic long-lived particles produced in proton-
proton collisions at $\sqrt{s}=13\TeV$

###### Abstract

A search is presented for the decays of heavy exotic long-lived particles
(LLPs) that are produced in proton-proton collisions at a center-of-mass
energy of 13at the CERN LHC and come to rest in the CMS detector. Their decays
would be visible during periods of time well separated from proton-proton
collisions. Two decay scenarios of stopped LLPs are explored: a hadronic decay
detected in the calorimeter and a decay into muons detected in the muon
system. The calorimeter (muon) search covers a period of sensitivity totaling
721 (744) hours in 38.6 (39.0)of data collected by the CMS detector in 2015
and 2016. The results are interpreted in several scenarios that predict LLPs.
Production cross section limits are set as a function of the mean proper
lifetime and the mass of the LLPs, for lifetimes between 100ns and 10 days.
These are the most stringent limits to date on the mass of hadronically
decaying stopped LLPs, and this is the first search at the LHC for stopped
LLPs that decay to muons.

##  0.1 Introduction

Heavy long-lived particles (LLPs) on the order of 100are not present in the
standard model (SM). Therefore, any sign of them would be an indication of new
physics. Many extensions of the SM predict the existence of LLPs [1, 2, 3, 4,
5, 6, 7, 8]. At the CERN LHC, the LLPs will stop inside the detector material
if they lose all of their kinetic energy while traversing the detector, which
will typically occur for particles with initial velocities less than about
$0.5c$ [9]. This energy loss can occur via nuclear interactions if they are
strongly interacting and/or through ionization if they are charged. The
observation of a stopped particle decay signature would not only indicate new
physics but also help measure the lifetime of LLPs, giving insights into
various beyond the standard model (BSM) theories.

If these stopped LLPs have lifetimes longer than tens of nanoseconds, most of
their decays would be reconstructed as separate events unrelated to their
production [10]. Owing to the difficulty of differentiating between the LLP
decay products and SM particles from LHC proton-proton ($\Pp\Pp$) collisions,
these subsequent decays are most easily identified when there are no proton
bunches in the detector. The detector is quiet during these out-of-collision
time periods with the exception of rare noncollision backgrounds, such as
cosmic rays, beam halo particles, and detector noise. If LLPs come to a stop
in the detector, they are most likely to do so in the densest detector
materials, which in the CMS detector are the electromagnetic calorimeter
(ECAL), the hadron calorimeter (HCAL), and the steel yoke in the muon system.
If the stopped LLPs decay in the calorimeters, relatively large energy
deposits occurring in the intervals between collisions could be observed.
Furthermore, if the stopped LLPs decay into muons, displaced muon tracks out
of time with the collisions could be detected.

In this paper we present two searches for stopped LLPs that decay out of time
with respect to the presence of proton bunches in the detector. One search
targets hadronic decays detected in the calorimeters, and the other looks for
decays to muon pairs in the muon system. These two search channels are
analyzed independently using data collected by the CMS experiment in 2015 and
2016 with separate dedicated triggers. The calorimeter (muon) search uses
$\sqrt{s}=13\TeV$ data corresponding to an integrated luminosity of 38.6
(39.0)collected with LHC $\Pp\Pp$ collisions separated by 25ns during a search
interval totaling 721 (744) hours. The size of the search sample is further
reduced by applying a series of offline selection criteria to decrease the
number of events that most likely come from the primary sources of background.

The calorimeter search presented here improves upon previous searches
performed by the CMS Collaboration, the most recent of which used
$\sqrt{s}=8\TeV$ $\Pp\Pp$ collision data corresponding to an integrated
luminosity of 18.6collected in 2012 [11]. This search excluded long-lived
gluinos ($\PSg$) with masses below 880and long-lived top squarks ($\PSQt$)
with masses below 470, for lifetimes between $10\mus$ and 1000s. The results
of earlier, similar searches have been reported by the D0 Collaboration at the
Tevatron [12] and by the CMS [13, 14] and ATLAS Collaborations [15, 16]. The
displaced muon search is newly added to investigate different models with
leptonic decays of stopped LLPs, such as those of gluinos [9] and multiply
charged massive particles (MCHAMPs) [17, 18, 19, 20]. Searches for decays of
stopped LLPs are complementary to searches for heavy stable charged particles
(HSCPs) that pass through the detector and can be identified by their energy
loss and time-of-flight (TOF) information [21, 22, 23, 24, 25, 26, 27, 28, 29,
30, 31, 32, 33, 34]. The searches presented here would allow the study of the
decay of such heavy particles, whereas dedicated HSCP searches typically look
for the particle itself, before it decays. However, both the searches for
decays of stopped LLPs and for HSCPs are sensitive to a similar range of
lifetimes.

##  0.2 The CMS detector

The central feature of the CMS apparatus is a superconducting solenoid of 6m
internal diameter, providing a magnetic field of 3.8T. Within the solenoid
volume are a silicon pixel and strip tracker, a lead tungstate crystal ECAL,
and a brass and scintillator HCAL, each composed of a barrel and two endcap
sections. Forward calorimeters extend the pseudorapidity $\eta$ coverage
provided by the barrel and endcap detectors. In the region $\abs{\eta}<1.74$,
the HCAL cells have widths of 0.087 in $\eta$ and 0.087 radians in azimuth
($\phi$). In the $\eta$-$\phi$ plane, and for $\abs{\eta}<1.48$, the HCAL
cells map on to $5\times 5$ arrays of ECAL crystals to form calorimeter towers
projecting radially outwards from close to the nominal $\Pp\Pp$ collision
interaction point (IP). For $\abs{\eta}>1.74$, the coverage of the towers
increases progressively to a maximum of 0.174 in $\Delta\eta$ and
$\Delta\phi$. Within each tower, the energy deposits in ECAL and HCAL cells
are summed to define the calorimeter tower energies, which are subsequently
used to provide the energies and directions of hadronic jets. In the HCAL
barrel (HB) and endcap, scintillation light is detected by hybrid photodiodes
(HPDs), and each HPD collects signals from 18 different HCAL channels. Signals
from four HPDs are then digitized by analog-to-digital converters within a
single readout box (RBX).

Muons are measured in gas-ionization chambers embedded in the steel flux-
return yoke outside the solenoid. Muons are measured in the range
$\abs{\eta}<2.4$, with detection planes made using three technologies: drift
tubes (DTs) in the barrel, cathode strip chambers (CSCs) in the endcaps, and
resistive plate chambers (RPCs) in both the barrel and the endcaps. All these
technologies provide both position and timing information. Hits within each DT
or CSC chamber are matched to form a reconstructed DT or CSC segment.

The first level (L1) of the CMS trigger system, composed of custom hardware
processors, uses information from the calorimeters and muon detectors to
select the most interesting events in a fixed time interval of less than 4.
The high-level trigger processor farm further decreases the event rate from
around 100kHz to less than 1kHz, before data storage.

A more detailed description of the CMS detector, together with a definition of
the coordinate system used and the relevant kinematic variables, can be found
in Ref. [35].

##  0.3 Data and Monte Carlo simulation

###  0.3.1 Data samples

The LHC accelerates two proton beams in opposite directions such that the
protons collide at several points along the LHC ring, including one at the CMS
detector. Each LHC beam consists of a number of proton bunches arranged into
an irregular pattern of “trains” [36]. Within a train, the proton bunches are
nominally spaced 25ns apart, with a larger spacing between trains to account
for the needs of the injection process. In an LHC orbit there are 3564 bunch
slots (BXs), which are 25ns long. Each BX could be filled with proton bunches,
which usually occupy the first 2.5ns of the BX, or could be empty. The trains
may be spaced such that there could be multiple empty BXs between filled BXs.
To search for LLP decays during these empty BXs, dedicated triggers select
events at least two BXs away from any proton bunches. Thus these triggers are
live only during these specific time windows. This distance of two BXs is
chosen so that we maximize the search time window while suppressing most of
the events from secondary pp interactions and from “beam halo”, which are
mostly muons traveling outside the LHC beam that are produced by LHC
beam–collimator scattering.

The search is performed with $\sqrt{s}=13\TeV$ $\Pp\Pp$ collision run data
collected by the CMS experiment in 2015 and 2016. The 2015 calorimeter (muon)
search sample, taken between August and November 2015, corresponds to an
integrated luminosity of 2.7 (2.8)and spans a trigger livetime, which is the
amount of time the triggers are live in between collisions, of 135 (155)
hours. The 2016 calorimeter (muon) search sample was taken between May and
October 2016, during which a data sample corresponding to an integrated
luminosity of 35.9 (36.2)was recorded, spanning a trigger livetime of 586
(589) hours. We do not consider the possibility of LLPs that were produced in
2015 but decayed in 2016. In both the 2015 and 2016 searches, we use cosmic
run data collected by dedicated triggers as a control sample. These dedicated
cosmic run data were recorded during LHC machine technical stops, several days
after collision runs. A negligible amount of long-lived signal produced during
collisions could have decayed during these cosmic runs for the lifetimes
considered in this analysis. The instrumental noise background estimate is
extrapolated from the instrumental noise measured in these control samples.
Most of the other sources of background are estimated from sideband regions of
the main data sample, except for the cosmic ray muon background in the
calorimeter search, which is estimated from MC simulation.

###  0.3.2 Benchmark models

Several simplified models are considered in this search, and samples are
generated for each using Monte Carlo (MC) simulation.

In the calorimeter search, we interpret the results in the context of two-body
($\PSg\to\cPg\PSGcz$) and three-body ($\PSg\to\cPq\cPaq\PSGcz$) decays of a
gluino into the lightest supersymmetric (SUSY) particle (LSP), the neutralino
($\PSGcz$). Long-lived gluinos are predicted by “split SUSY” [37, 38], in
which gauginos have relatively small masses with respect to sfermions, which
could be massive, since SUSY is broken at a scale much higher than the weak
scale. This large mass splitting causes the long lifetime of the gluinos,
since gluinos can only decay via a virtual squark. We also consider the decay
of a long-lived top squark ($\PSQt\rightarrow\cPqt\PSGcz$) that can be the
next-to-LSP particle (NLSP) in various dark matter scenarios [39, 40, 41].
Here the LSP should be loosely interpreted as any new, neutral, non-
interacting fermion, and not necessarily as a SUSY neutralino.

In the muon search, we consider a different model for a three-body decay of
the gluino ($\PSg\to\cPq\cPaq\PSGczDt,\PSGczDt\to\MM\PSGcz$), which is
complementary to the calorimeter search. In this model, the mass of the LSP
neutralino ($\PSGcz$) is chosen to be 0.25 times the gluino mass, and the mass
of the NLSP neutralino ($\PSGczDt$) is chosen to be 2.5 times the LSP
neutralino mass. A second simplified model used in the muon search predicts
exotic particles called MCHAMPs, whose charges are multiples of the elementary
charge $e$ and which are predicted by several BSM theories [20]. We assume an
MCHAMP with charge $\abs{Q}=2e$ decays into two same-sign muons
($\text{MCHAMP}\to\mu^{\pm}\mu^{\pm}$).

###  0.3.3 Signal generation

The signal generation process is divided into three major stages. In Stage 1,
the LLPs for each signal process are generated from $\Pp\Pp$ collisions with
[42, 43] and propagated through the detector with v9.2 [44, 45]. For the
MCHAMP signal, v6.4 is used, while for the gluino and top squark signals,
v8.205 is used. If the LLPs are strongly interacting, as in the case of the
gluinos and top squarks, they hadronize into R-hadrons [46, 47, 48] upon
production, whose interaction with the CMS detector in the simulation is
described by the cloud model [49, 50]. In this model, R-hadrons are treated as
SUSY particles surrounded by a cloud of loosely bound quarks and gluons. The
fraction of produced R-hadrons that contain a gluino and a valence gluon is
set to 10%, a convention used in previous analyses [11, 21]. However, because
the R-hadrons interact an average of ten times in the calorimeter, their
flavor is effectively randomized. Some fraction of these R-hadrons are
sufficiently slow moving to come to a stop in the detector material. Because
they are doubly charged, MCHAMPs ionize heavily and thus a significant number
also stop in the detector.

In Stage 2, the parent LLP or R-hadron is constrained to decay at the stopping
position defined in Stage 1. The LLP decay is simulated by a second step, and
the decay products are propagated through the detector.

Finally, in Stage 3, a pseudo-experiment MC simulation is conducted to
estimate the probability for stopped particle decays to occur in the time
window between collisions when data is being collected. The Stage 3 MC
simulation determines an effective integrated luminosity by using the good
data-taking periods and the LHC filling scheme to calculate the fraction of
stopped particle decays that occur when the trigger is live. For a given
particle lifetime, the effective integrated luminosity is defined as the total
integrated luminosity multiplied by the probability that the particle decays
at a time when the trigger is live in between collisions. In other words,
Stages 1 and 2 determine how the signal will look in the detector, and Stage 3
determines when it will occur. More details on the signal generation process
are given in Refs. [11, 13, 14].

##  0.4 Event selection

The calorimeter search and the muon search employ different search strategies
and thus different selection criteria, which are described in turn below.

###  0.4.1 Calorimeter search

In the calorimeter search, we look for hadronic decays of LLPs in the
calorimeter that produce energy deposits that could be reconstructed as at
least one high-energy jet. We trigger on calorimeter jets with energy greater
than 50and $\abs{\eta}<3$ that are at least two BXs away from $\Pp\Pp$
collisions.

The major background sources are cosmic rays, beam halo, and HCAL noise.
Cosmic ray and beam halo muons can emit a shower of photons via
bremsstrahlung, which could be reconstructed as a jet and mistaken for signal.
HCAL noise [51] can give rise to spurious signals, which in the barrel could
appear in one or several HPDs within a single RBX, and thus be incorrectly
reconstructed as a jet. We observe that the rate of each of these background
sources drops exponentially as a function of the jet energy. We thus require
the events to have a leading (highest energy) calorimeter-based jet with
energy greater than 70. The calorimeter-based jets are reconstructed using an
anti-clustering algorithm [52, 53] with a distance parameter of 0.4. To
increase the sensitivity of the search, we require that the leading jet in
each event is located within $\abs{\eta}<1.0$, where R-hadrons are more likely
to stop and where there is relatively less background from beam halo.

Secondary background sources include out-of-time collisions from remnant
protons between bunches, and beam-gas interactions in the detector. The rate
of these secondary background events becomes negligible after we require that
there are no reconstructed collision vertices in the events.

Cosmic ray muon events usually feature a large number of reconstructed DT
segments and RPC hits, whereas signal events in the calorimeter search would
not. We exploit this difference to distinguish signal events from cosmic ray
muons. While it is possible for the hadronic shower of an R-hadron decay to
pass through the first layers of the iron yoke and induce reconstructed DT
segments, these DT segments are located only in the inner layers of the muon
chambers ($r<560\cm$, where $r$ is the transverse distance to the IP) and
cluster near the leading jet. On the other hand, cosmic ray muons are equally
likely to leave DT segments in all layers in both the upper and lower
hemispheres of the muon system, and the angle between the jet and DT segments
in $\phi$ is more evenly distributed. As a result, we are able to
substantially reduce the cosmic ray muon background contamination in the
signal region by rejecting events that have at least two DT segments in the
outermost barrel layer of the muon system, events that have any DT segments in
the second outermost barrel layer, events that have two DT segments with a
large separation in $\phi$ ($\abs{\Delta\phi}>\pi/2$), events that have DT
segments in the three innermost layers that are separated in $\phi$ from the
leading jet by at least 1.0 radian, and events that have close-by RPC hits in
different layers
($\DR=\sqrt{\smash[b]{(\Delta\phi)^{2}+(\Delta\eta)^{2}}}<0.2$ and $\Delta
r>0.5\unit{m}$). We make looser DT segment requirements in the outermost than
in the second outermost layer because signals are very likely to coincide with
standalone DT segments that are not from cosmic ray muons but particles from
the $\Pp\Pp$ collision. Most of these standalone DT segments from the $\Pp\Pp$
collision are located in the outermost muon barrel layer. With these selection
criteria, we are able to avoid incorrectly rejecting signal events, thus
increasing the signal efficiency, while still rejecting most of the cosmic ray
muon events.

Beam halo muons travel closely along the beam pipe, typically traversing both
sides of the muon endcap systems and resulting in a few reconstructed CSC
segments. Therefore, we veto events with any CSC segments having at least five
reconstructed hits. As will be discussed in Section 0.5, since signal events
may include some CSC segments, requiring a minimum number of CSC hits in the
veto avoids a loss of signal efficiency.

Random electronic noise in the HCAL gives rise to events in which the time
response of the HCAL readout is very different from the well-defined response
from particles showering in the calorimeter. This HCAL noise creates spurious
clustered energy deposits that can be reconstructed as a jet, which would
contaminate the signal region and therefore should be removed. Analog signal
pulses produced by the HCAL electronics are read out over ten BXs centered
around the pulse maximum. The pulse shape from showering particles consists of
a peak at the collision BX and an exponential decay over the subsequent BXs.
Particle showers create clustered energy deposits spread over several
neighboring calorimeter towers in $z$ and $\phi$, while noise produces
deposits in just one or two towers, or several towers in a single HPD or RBX.
In addition to the standard HCAL noise filter [51], we use a series of offline
selection criteria that exploit these timing and topological characteristics
to remove the HCAL noise events. These criteria are described in detail in
Ref. [14].

###  0.4.2 Muon search

In the muon search, we look for LLPs where the decay products include two
muons. We expect the signal to look like a pair of muons originating anywhere
in the detector material, but displaced from the IP. The muons would be back-
to-back in the two-body MCHAMP decay, but not for the three-body gluino decay.

The primary background sources in the muon search include cosmic ray muons,
beam halo, and muon detector noise. The latter two background sources are
negligible after we apply the full selection.

The trigger used in the muon search selects events at least two BXs away from
the $\Pp\Pp$ collision time with at least one muon reconstructed in the muon
system, whose transverse momentum $\pt$ is at least 40. As in the calorimeter
search, we select events offline that have no reconstructed collision
vertices.

Tracks that are reconstructed using only hits in the muon system are called
standalone muon tracks [54]. However, the standard standalone track
reconstruction assumes that muons originate from the IP, which is
inappropriate for displaced muon searches. As a result, a new muon
reconstruction algorithm was developed for this analysis, which produces
displaced standalone (DSA) muon tracks [55]. The DSA tracks are reconstructed
using only hits in the muon detector, and they have no constraints to the IP.
Thus, DSA tracks are truly using only the muon system.

We require events to have exactly one good DSA track in the upper hemisphere
of the detector and exactly one good DSA track in the lower hemisphere. Both
DSA tracks must have $\pt>50\GeV$, at least three DT chambers with valid hits,
and at least three valid RPC hits. To reduce the background from beam halo,
the DSA tracks must also have zero valid CSC hits.

Timing information in the DTs and RPCs, indicating whether the muon is
incoming toward the detector center or outgoing away from the detector center,
is used to distinguish muons from a signal event from the cosmic ray muon
background. Cosmic ray muons are predominantly incoming when traversing the
upper hemisphere and outgoing when traversing the lower hemisphere, as they
come in from above the detector and continue to move downwards. Muons from a
signal event, on the other hand, would be outgoing in both hemispheres.

We place selection criteria on both the upper and lower hemisphere DSA tracks
in order to obtain a good time measurement. We require at least eight
independent time measurements for the TOF computation. We require that the
uncertainty in the time measured at the IP for DSA tracks, assuming the muon
is outgoing, is less than 5.0ns.

Next, we ask for the time measurement to be signal-like. We require that the
direction of the lower hemisphere DSA track, as determined by a least-squares
fit to the timing in each DT layer where the fit is not constrained to the IP,
is consistent with being in the downward direction. We define $t_{\text{DT}}$
as the time at the point of closest approach to the IP as measured by the DTs,
assuming the muon is outgoing. Since cosmic ray muons are incoming in the
upper hemisphere and outgoing in the lower hemisphere, the $t_{\text{DT}}$ of
the upper hemisphere track is expected to be 40 to 50ns earlier than that of
the lower hemisphere track. As for the signal, since both muons are outgoing,
they are reconstructed to have similar times as measured at the IP. Thus, we
require that $\Delta t_{\text{DT}}$, which is defined as $\Delta
t_{\text{DT}}=t_{\text{DT}}(\text{upper})-t_{\text{DT}}(\text{lower})$, is
greater than $-20$ns, which greatly reduces the cosmic ray muon background.

In addition to these DT timing variables, we use a timing measurement from the
RPCs that assigns a BX to each hit. For each of the six layers of the RPCs,
the hit is given a BX assignment. A typical prompt muon created at the IP has
a BX assignment of 0 for each of its RPC hits. The BX assignments of cosmic
ray muons are especially useful in the lower hemisphere of the detector, as
the incoming cosmic ray muons will typically trigger the event and thus be
assigned BX values of 0 in each RPC layer, but the outgoing cosmic ray muons
are often assigned positive BX values. For example, a lower hemisphere cosmic
ray muon typically has a BX assignment of 2 for each of its good RPC hits. For
the signal, each RPC BX assignment for each muon is typically 0.

Given the BX assignments in each RPC layer for a muon, we can compute the
average RPC hit BX assignment multiplied by 25ns as the RPC time for a track
($t_{\text{RPC}}$) and use this as a discriminating variable. A typical muon
from the benchmark decays has a $t_{\text{RPC}}$ of 0ns for both upper and
lower hemisphere DSA muon tracks. On the other hand, the $t_{\text{RPC}}$ of a
cosmic ray muon is typically 25 or 50ns in the lower hemisphere and 0ns in the
upper hemisphere. We define $\Delta
t_{\text{RPC}}=t_{\text{RPC}}(\text{upper})-t_{\text{RPC}}(\text{lower})$, and
we require $\Delta t_{\text{RPC}}>-7.5$ns to further select signal-like
events.

Figure 1 shows $\Delta t_{\text{DT}}$ (left) and $\Delta t_{\text{RPC}}$
(right) for data and MC simulation. The events shown here contain good-quality
DSA muon tracks, but they are dominated by the cosmic muon background; they
are selected with a subset of the criteria described above. This selection is
defined by the same trigger and reconstructed vertices requirements as above.
Additionally, exactly one DSA track in the upper hemisphere and exactly one
DSA track in the lower hemisphere are required. Looser requirements than in
the full selection are placed on the DSA track ($>$10), the number of DT
chambers with valid hits (greater than one), and the number of valid RPC hits
(greater than one). We require the same number of DT hits with good timing
measurements per DSA track and number of valid CSC hits as above for this
selection. None of the remaining criteria from the main selection criteria
described above are used to select the events in Fig. 1. As can be seen in
Fig. 1, the number of cosmic ray muon background events is greatly reduced
when the full selection is applied, as we require $\Delta t_{\text{DT}}>-20$ns
and $\Delta t_{\text{RPC}}>-7.5$ns. Since $\Delta t_{\text{DT}}$ and $\Delta
t_{\text{RPC}}$ correspond to independent measurements of essentially the same
quantity, a mismeasured cosmic ray muon is much less likely to pass both
selections than just one; adding the second requirement improves the rejection
of simulated cosmic ray muons by a factor of approximately 350.

Figure 1:  The $\Delta t_{\text{DT}}$ (left) and $\Delta t_{\text{RPC}}$
(right) distributions for 2016 data, MC simulated cosmic ray muon, 1000gluino
signal, and 600MCHAMP signal events, for the muon search. The events plotted
pass a subset of the full analysis selection that is designed to select good-
quality DSA muon tracks but does not reject the cosmic ray muon background.
The number of cosmic ray muon background events is greatly reduced when the
full selection is applied, as we require $\Delta t_{\text{DT}}>-20$ns and
$\Delta t_{\text{RPC}}>-7.5$ns. The gray bands indicate the statistical
uncertainty in the simulation. The histograms are normalized to unit area.

##  0.5 Signal efficiency

In this section, we describe the calculation of the signal efficiency
$\varepsilon_{\text{signal}}$, which is the product of several efficiencies.
In the calorimeter search, the stopping efficiency
$\varepsilon_{\text{stopping}}$ is the probability that the R-hadron stops in
the HB or ECAL barrel (EB), while in the muon search,
$\varepsilon_{\text{stopping}}$ is the probability of each LLP to stop in any
region of the detector. The Stage 1 simulation determines
$\varepsilon_{\text{stopping}}$. The reconstruction efficiency
$\varepsilon_{\text{reco}}$ is the efficiency of an event to pass all of the
selection criteria, including the trigger, and it is computed independently of
$\varepsilon_{\text{stopping}}$. In addition, $\varepsilon_{\text{reco}}$ is
calculated assuming that the LLP decay occurs when the trigger is live in
between collisions, and assuming a branching fraction ($\mathcal{B}$) of
$100\%$ to the decays in the signal models described above. The Stage 2
simulation determines $\varepsilon_{\text{reco}}$. The efficiency
$\varepsilon_{\text{signal}}$ is defined as the product of
$\varepsilon_{\text{stopping}}$ and $\varepsilon_{\text{reco}}$ for the muon
search. For the calorimeter search, $\varepsilon_{\text{signal}}$ is the
product of $\varepsilon_{\text{stopping}}$, $\varepsilon_{\text{reco}}$, and
two additional factors, $\varepsilon_{\text{CSCveto}}$ and
$\varepsilon_{\text{DTveto}}$, which are defined in the next subsection.

###  0.5.1 Calorimeter search

For the calorimeter search, $\varepsilon_{\text{stopping}}$ is constant at
about 0.054 for gluinos and 0.045 for top squarks, for the range of masses
considered. The $\varepsilon_{\text{stopping}}$ value is larger for gluinos
than for top squarks of the same mass because gluinos are more likely to
produce doubly charged R-hadrons.

The value of $\varepsilon_{\text{reco}}$ depends primarily on the energy of
the visible daughter particle(s) of the R-hadron decay, denoted by $E_{\cPg}$
($E_{\cPqt}$) if the daughter is a gluon (top quark). When $E_{\cPg}>130\GeV$
($E_{\cPqt}>170\GeV$), $\varepsilon_{\text{reco}}$ becomes approximately
constant, as shown in Fig. 2. For the three-body gluino decay,
$\varepsilon_{\text{reco}}$ depends approximately on the mass difference
between $\PSg$ and $\PSGcz$, becoming constant when
$m_{\PSg}-m_{\PSGcz}\gtrsim 160\GeV$.

Some physical effects that are not modeled in simulation can cause
reconstructed CSC or DT segments that are out of time with respect to a
collision. For example, thermal neutrons can take up to a tenth of a second
after being produced in $\Pp\Pp$ collisions before they arrive at the muon
detectors and induce a signal in the CSCs or DTs. Since these segments can
occur when the trigger is live, it is possible that some of the events in the
search sample could contain such segments. These events would be rejected by
the selection criteria, thus decreasing the probability for a signal to be
observed. The terms $\varepsilon_{\text{CSCveto}}$ and
$\varepsilon_{\text{DTveto}}$ measure this decrease in efficiency due to these
sources.

Summary of the values of $\varepsilon_{\text{stopping}}$,
$\varepsilon_{\text{CSCveto}}$, $\varepsilon_{\text{DTveto}}$, and the plateau
value of $\varepsilon_{\text{reco}}$ for different signals, for the
calorimeter search. The efficiency $\varepsilon_{\text{stopping}}$ is constant
for the range of signal masses considered. The efficiency
$\varepsilon_{\text{reco}}$ is given on the $E_{\cPg}$ or $E_{\cPqt}$ plateau
for each signal.  $\PSg\to\cPg\PSGcz$ $\PSg\to\cPq\cPaq\PSGcz$
$\PSQt\to\cPqt\PSGcz$ $\varepsilon_{\text{stopping}}$ 0.054 0.054 0.045
$\varepsilon_{\text{reco}}$ 0.533 0.566 0.399 $\varepsilon_{\text{CSCveto}}$
0.944 0.944 0.944 $\varepsilon_{\text{DTveto}}$ 0.877 0.877 0.877
$\varepsilon_{\text{signal}}$ 0.023 0.025 0.014

We define $\varepsilon_{\text{CSCveto}}$ ($\varepsilon_{\text{DTveto}}$) as
the conditional probability that a signal passes the beam halo (cosmic ray
muon) rejection criteria assuming the potential occurrence of coincident CSC
(DT) segments, given that the signal itself passes the full selection
criteria. HCAL noise events that are collected by the trigger are used to
estimate these two efficiencies from data, since this noise is independent of
any muon detector activities and should pass both beam halo rejection and
cosmic ray muon rejection criteria. These events are selected by inverting
some of the noise rejection criteria. Then $\varepsilon_{\text{CSCveto}}$
($\varepsilon_{\text{DTveto}}$) is simply the percentage of noise events that
survive the beam halo (cosmic ray muon) vetoes among all selected noise
events.

Table 0.5.1 summarizes the values of $\varepsilon_{\text{stopping}}$,
$\varepsilon_{\text{CSCveto}}$, $\varepsilon_{\text{DTveto}}$, and the plateau
value of $\varepsilon_{\text{reco}}$.

Figure 2: The $\varepsilon_{\text{reco}}$ values as a function of $E_{\cPg}$
or $E_{\cPqt}$ (left), and $m_{\PSg}-m_{\PSGcz}$ (right), for $\PSg$ and
$\PSQt$ R-hadrons that stop in the EB or HB, in the MC simulation, for the
calorimeter search. The $\varepsilon_{\text{reco}}$ values are plotted for the
two-body gluino and top squark decays (left) and for the three-body gluino
decay (right). The shaded bands correspond to the systematic uncertainties,
which are described in Section 0.7.

###  0.5.2 Muon search

Tables 0.5.2 and 0.5.2 show $\varepsilon_{\text{stopping}}$ and
$\varepsilon_{\text{reco}}$ for each assumed signal mass in the muon search.
The $\varepsilon_{\text{signal}}$ value is the product of these two
efficiencies. The $\varepsilon_{\text{stopping}}$ value is larger for MCHAMPs
than for gluinos because the MCHAMPs considered have $\abs{Q}=2e$ and the
gluinos sometimes produce singly charged R-hadrons. We lose signal efficiency
because the L1 muon trigger is designed to identify muons coming from the IP,
although the muons from the signal can be very displaced. A further loss in
signal efficiency is due to the very strict requirements on the quality of the
DSA muon track. Similarly, the requirement to have exactly one DSA track
traversing the upper hemisphere and exactly one DSA track traversing the lower
hemisphere further reduces the geometrical acceptance, particularly for the
gluino decay, which does not produce back-to-back muons, unlike the MCHAMP
decay. The numbers in Tables 0.5.2 and 0.5.2 represent the maximum number of
signal events that can be measured before applying the different search
windows depending on the lifetime of the stopped particle.

Gluino $\varepsilon_{\text{stopping}}$ and $\varepsilon_{\text{reco}}$, as
well as the number of expected gluino events with lifetimes between 10and
1000s, assuming
$\mathcal{B}(\PSg\to\cPq\cPaq\PSGczDt)\mathcal{B}(\PSGczDt\to\MM\PSGcz)=100\%$,
for each mass point considered for the 2016 muon search. The efficiencies are
constant for this range of lifetimes.  $m_{\PSg}$ []
$\varepsilon_{\text{stopping}}$ $\varepsilon_{\text{reco}}$ Expected events
400 0.19 0.0015 400 600 0.17 0.0024 50 800 0.17 0.0037 10 1000 0.17 0.0029 2
1200 0.18 0.0025 0.5 1400 0.20 0.0031 0.2 1600 0.21 0.0029 0.1

MCHAMP $\varepsilon_{\text{stopping}}$ and $\varepsilon_{\text{reco}}$, as
well as the number of expected MCHAMP events with lifetimes between 10and
1000s, assuming $\mathcal{B}(\text{MCHAMP}\to\mu^{\pm}\mu^{\pm})=100\%$, for
each mass point considered for the 2016 muon search. The efficiencies are
constant for this range of lifetimes.  $m_{\text{MCHAMP}}$ []
$\varepsilon_{\text{stopping}}$ $\varepsilon_{\text{reco}}$ Expected events
100 0.33 0.0059 100 200 0.29 0.041 50 400 0.28 0.045 4 600 0.25 0.042 0.5 800
0.30 0.038 0.1

##  0.6 Background estimation

Since the background sources in both the calorimeter and the muon searches are
not well modeled in simulation, we use control samples in data to estimate
their contributions after the full event selection is applied.

###  0.6.1 Calorimeter search

After applying the selection criteria in the calorimeter search, some
background sources from cosmic ray muons, beam halo, and calorimeter noise
remain in the data. We quantify the probability of background events escaping
the background vetoes and thus being observed by this search. These
inefficiencies are calculated as follows.

We generate a sample of cosmic ray muon events to estimate the rate of such
events escaping the cosmic ray muon rejection criteria. The events are
generated using cmscgen [56], a generator based on the air shower program
corsika [57] and validated in a CMS analysis [58]. We require that the events
pass the preselection criteria, namely that they are required to have
substantial energy deposits in the calorimeter and no CSC segments in the muon
endcap system. The cosmic ray muon veto inefficiency is defined as the
fraction of preselected simulated cosmic ray muon events that are not rejected
by the cosmic ray muon rejection criteria. It is found to be $1\times
10^{-3}$. To account for the small difference in occupancy between the cosmic
ray muon events in data and MC simulation, we first bin the simulated events
in the number of DT and outer barrel RPC hits and calculate the inefficiency
bin by bin. Then, we apply the halo veto and the noise veto to a sample of
events in data, and bin these data events in the same way as the simulated
events. For each bin, we multiply the inefficiency by the number of events in
data, giving the binned cosmic ray muon prediction. The nominal cosmic ray
muon background prediction is then the sum of the events in each bin.

The uncertainty in the cosmic ray muon background is due to the uncertainty in
the estimate of muons that escape detection by passing through uninstrumented
regions of the CMS detector, which is necessarily estimated from simulation.
Since data in the uninstrumented regions are ipso facto not available to
compare to simulation, we define equivalent fiducial volumes of instrumented
regions of the muon system. Using these as a proxy for the uninstrumented
regions, we assess the reliability of the simulation by comparing data and
simulation. We find the average discrepancy between cosmic ray muon data and
simulation in the number of detected muons traveling through various fiducial
regions in the detector to be about 32%, and we assign this to be the
systematic uncertainty in the cosmic ray muon background estimate. Thus, we
estimate the cosmic ray muon background to be $2.6\pm 0.9$ ($8.8\pm 3.1$)
events in 2015 (2016) data.

Because there was a high rate of beam halo production in 2015 and 2016 data,
and because it is possible for halo muons to escape the acceptance of the
endcap muon system, the halo background is nonnegligible. We estimate the halo
veto inefficiency using a tag-and-probe method [59] that analyzes a high-
purity sample of halo events by selecting events having one calorimeter jet
with $\abs{\eta}<1.0$ and CSC segments in at least two endcap layers of the
muon system. Since the rates of beam halo in each beam are not the same, the
events are first classified according to whether they originated in the
clockwise ($-z$ direction) or the counterclockwise ($+z$ direction) beam. Then
for each class, depending on whether these events have CSC segments in only
one endcap or both endcaps of the muon system, they are categorized into
events that have only the incoming portion of a halo muon track, events that
have only the outgoing portion, and events that have both portions. The number
of events that escape detection is
$N_{\text{IncomingOnly}}N_{\text{OutgoingOnly}}/N_{\text{Both}}$. We define
$N_{\text{IncomingOnly}}$ ($N_{\text{OutgoingOnly}}$) as the number of events
that have only an incoming (outgoing) portion of a halo muon track. The number
of events that have both an incoming and an outgoing halo muon track is
$N_{\text{Both}}$. After binning halo events in their $x$ and $y$ coordinates
and performing the classification and calculation discussed above, we estimate
the halo veto inefficiency to be $1\times 10^{-4}$. We then multiply this
inefficiency by the number of halo events vetoed in the search region.

To account for the possibility that the $x$-$y$ binning does not reproduce the
actual shape of the inactive or uninstrumented regions of the detector, thus
biasing the estimate, we repeat the calculation above, but binning events in
$\phi$ and $r$ instead. The systematic uncertainty is then defined as the
difference between the results from the two binning schemes. We find a halo
background estimate of $1.1\pm 0.1$ ($2.6\pm 0.2$) events in 2015 (2016) data.

Finally, the background estimation of instrumental noise is performed using
control data in dedicated cosmic runs with no beams in the LHC, which include
only cosmic ray muon and noise events. We select cosmic runs taken several
days after $\Pp\Pp$ collision runs so that there would be little chance for
the signal to appear. After applying all selection criteria on the control
data, we observe 2 events in each of the 2015 and 2016 control data. We then
subtract the expected cosmic ray muon background from the total event yield,
obtaining a noise background estimate of $0.3^{+2.4}_{-0.3}$
($0.0^{+2.2}_{-0.0}$) events in 2015 (2016) control data. Based on the number
of noise events in the control sample, we expect the noise veto inefficiency
to be $\leq 1\times 10^{-4}$. These noise estimates are then scaled to the
search data, assuming that the noise veto inefficiency remains the same. The
resulting noise background estimate is $0.4^{+2.9}_{-0.4}$
($0.0^{+9.8}_{-0.0}$) events in 2015 (2016). The uncertainty in the 2016
prediction is large because the trigger livetime of the cosmic runs in 2016
was about 60% shorter than that of the collision runs, and also because the
2016 trigger livetime in collision runs is larger than the 2015 trigger
livetime. Therefore, the uncertainty is scaled by a larger factor.

The total background estimate for the calorimeter search is
$4.1^{+3.0}_{-1.0}$ ($11.4^{+10.3}_{-3.1}$) events in 2015 (2016), as
summarized in Table 0.6.1.

The background prediction for the calorimeter search. The total background
median value is listed in parentheses; this value corresponds directly to the
median expected limits shown below.  LHC Trigger HCAL Cosmic ray Beam Total
period livetime [hrs] noise muons halo background 2015 135 $0.4^{+2.9}_{-0.4}$
$2.6\pm 0.9$ $1.1\pm 0.1$ $4.1^{+3.0}_{-1.0}$ (6.2) 2016 586
$0.0^{+9.8}_{-0.0}$ $8.8\pm 3.1$ $2.6\pm 0.2$ $11.4^{+10.3}_{-3.1}$ (17.4)

###  0.6.2 Muon search

In the muon search, a small number of cosmic ray muon background events
remains after applying the full event selection to the data. The cosmic ray
muon background is estimated by extrapolating the data from a background-
dominated region into the signal region. We apply the full event selection to
the data except the $\Delta t_{\text{DT}}$ criterion and invert the $\Delta
t_{\text{RPC}}$ criterion. We then fit the $\Delta t_{\text{DT}}$ distribution
with the sum of two Gaussian distributions and a Crystal Ball function [60],
since $\Delta t_{\text{DT}}$ is relatively Gaussian with a long asymmetrical
tail. Next, we compute the integral of the fit function, for $\Delta
t_{\text{DT}}>-20$ns. Then, we compute the same integral after having
tightened the selection criteria on $\Delta t_{\text{RPC}}$ to $-50<\Delta
t_{\text{RPC}}<-7.5$ns, then $-45<\Delta t_{\text{RPC}}<-7.5$ns, etc. in steps
of 5ns up to $-10<\Delta t_{\text{RPC}}<-7.5$ns. Finally, we plot each
integral as a function of the lower selection on $\Delta t_{\text{RPC}}$, and
fit this with an error function to extrapolate to the $\Delta
t_{\text{RPC}}>-7.5$ns region (see Fig. 3). We use an error function fit in
order to make a conservative background estimate. Given this extrapolation, we
predict 0.04 background events in 2015 data, with a negligible statistical
uncertainty, and $0.50\pm 0.02$ background events in 2016 data, where the
uncertainty given is statistical only. The statistical uncertainty in the
background prediction derives from the uncertainty in the error function fit
parameters. We checked the background prediction method by repeating the
procedure with nonoverlapping $\Delta t_{\text{RPC}}$ regions and found that
the numbers of background events predicted are consistent with the nominal
values.

Figure 3:  The background extrapolation for the muon search. The integral of
the fit function to $\Delta t_{\text{DT}}$ with the sum of two Gaussian
distributions and a Crystal Ball function, for $\Delta t_{\text{DT}}>-20$ns,
is plotted as a function of the lower $\Delta t_{\text{RPC}}$ selection, for
2015 (red squares) and 2016 (black circles) data. The points are fitted with
an error function and used to extrapolate to the signal region, which is
defined as $\Delta t_{\text{RPC}}>-7.5$ns.

The systematic uncertainty in the background prediction is evaluated by
repeating the steps above, except changing the fit of the $\Delta
t_{\text{DT}}$ distribution to the sum of two Gaussian distributions and a
Landau function [61]. Using the error function fits to extrapolate to $\Delta
t_{\text{RPC}}>-7.5$ns gives a prediction of $0.07\pm 0.06$ ($0.10\pm 0.01$)
background events in 2015 (2016), where the uncertainty given is statistical
only. Thus, the background prediction is: $0.04\pm 0.03$background events in
2015 data, with a negligible statistical uncertainty, and $0.50\pm
0.02\stat\pm 0.40\syst$ background events in 2016 data.

Despite the fact that we require exactly one upper hemisphere DSA track and
exactly one lower hemisphere DSA track, there could still be some background
from two coincident cosmic ray muons. This background from two coincident
cosmic ray muons could occur if the upper hemisphere DSA track of one cosmic
ray muon is reconstructed and if the lower hemisphere DSA track of the other
is also reconstructed. We estimate this contribution from data by finding the
rate of events with exactly one reconstructed DSA track in one hemisphere
satisfying all of the selection criteria except for the $\Delta t_{\text{DT}}$
and $\Delta t_{\text{RPC}}$ criteria, and no tracks in the other hemisphere.
Then, making simple assumptions about when the two coincident cosmic ray muons
could occur and about the DSA track reconstruction efficiency as a function of
BX, we calculate the number of accidentally coincident cosmic ray muons and
find it to be negligible.

##  0.7 Systematic uncertainties in the signal efficiency

While the simulation used to derive the stopping probability accurately models
both the electromagnetic and nuclear interaction energy loss mechanisms, the
relative contributions of these energy loss mechanisms to the stopping
probability depend significantly on unknown R-hadron spectroscopy. We do not
consider this dependence to be a source of uncertainty for either the
calorimeter or the muon search, however, since for any given model the
resultant uncertainty in the stopping probability is small. Nevertheless,
there are several sources of uncertainty in the signal efficiency measurement.

###  0.7.1 Calorimeter search

In the calorimeter search, the systematic uncertainty due to the trigger
efficiency is negligible since the offline jet energy criterion ensures the
data analyzed are well above the turn-on region, so
$\varepsilon_{\text{reco}}$ is constant. We consider possible systematic
uncertainties in $\varepsilon_{\text{CSCveto}}$ and
$\varepsilon_{\text{DTveto}}$ by varying the criteria used to select HCAL
noise events that were described in Section 0.5.1. We compare the efficiency
of data events to pass these new HCAL noise criteria with that of the nominal
HCAL noise selection criteria, and we find that the relative change in the
efficiencies is less than 0.2% for both $\varepsilon_{\text{CSCveto}}$ and
$\varepsilon_{\text{DTveto}}$, and therefore negligible. The uncertainty in
the integrated luminosity is estimated as 2.3 (2.5)% for 2015 (2016) data [62,
63]. The relative uncertainty in $\varepsilon_{\text{reco}}$ is estimated to
be 7.7 (5.2)% for $\PSg$ ($\PSQt$) in the 2015 analysis, and 7.5 (5.2)% for
$\PSg$ ($\PSQt$) in the 2016 analysis. This uncertainty, which is shown by the
shaded bands in Fig. 2, is determined by computing the maximal relative
difference among points on the plateau.

Jets in this analysis are not formed by particles originating from the center
of the detector, so the standard uncertainty in the jet energy scale does not
apply. Instead, we refer to a study performed on the HCAL during cosmic data
taking in 2008 [64]. This study compares the energy of the reconstructed jets
in simulated cosmic ray muon events and cosmic ray muon events in data,
concluding that the uncertainty in the jet energy in the simulation is about
2%. Moreover, a study conducted with 2012 data [65] compares the data and
simulation for dijets originating from the interaction point. The comparison
leads to an estimate of $<$2% for jets striking the HCAL barrel with angles of
incidence from 0 to $\pi/3$. After rescaling the jet energy by 2%, the signal
efficiency varies by 2%. This estimate is conservative since only the yield of
signals with jet energy near the offline threshold is affected by the
variation of the jet energy, and as a result the uncertainty decreases rapidly
as $E_{\cPg}$ ($E_{\cPqt}$) increases.

We have also considered the uncertainty associated with the jet energy
resolution. Studies have shown that the signal yield is insensitive to
variations in this uncertainty, and thus that the systematic uncertainty
associated with the jet energy resolution is negligible.

The total systematic uncertainty in the signal yield is 8.3 (8.2)% in the 2015
(2016) search. The systematic uncertainties are summarized in Table 0.7.1.

Systematic uncertainties in the signal efficiency in the 2015 and 2016
calorimeter searches.  Systematic uncertainty 2015 2016 Reconstruction
efficiency 7.7% 7.5% Integrated luminosity 2.3% 2.5% Jet energy scale 2.0%
2.0%

###  0.7.2 Muon search

The muon search also has several sources of systematic uncertainties. We
consider the systematic uncertainty associated with the MC simulation modeling
of the charge divided by the ($Q/\pt$) resolution by comparing this resolution
in cosmic ray muon data and cosmic ray muon MC simulation. The resolution
compares $Q/\pt$ of the upper and lower hemisphere tracks:

$R(Q/\pt)=\frac{(Q/\pt)^{\text{upper}}-(Q/\pt)^{\text{lower}}}{\sqrt{2}(Q/\pt)^{\text{lower}}}.$

We plot the standard deviation of Gaussian fits of the resolution, as a
function of the lower hemisphere track , for both cosmic ray muon data and MC
simulation. A fit of the ratio between data and MC simulation in this plot for
muon tracks in the lower hemisphere with $\pt>50\GeV$ gives a difference
between cosmic ray muon data and simulation of 9.0 (5.3)% in the 2015 (2016)
analysis. We propagate this resolution uncertainty to an uncertainty in the
signal efficiency by smearing the momentum distribution of muons in the signal
and observing the corresponding variation in the signal yields. We take the
largest variation in the signal yield, namely, 13 (7.0)% in the 2015 (2016)
analysis, as the systematic uncertainty in the modeling of the $Q/\pt$
resolution.

There is also a systematic uncertainty associated with the trigger acceptance.
Since the largest difference between data and MC simulation in the plateau of
the trigger turn-on curves is 13 (2.8)% in the 2015 (2016) analysis, we take
these values as the systematic uncertainty in the trigger acceptance.

The total systematic uncertainty in the signal yield is 19 (7.9)% in the 2015
(2016) search. The systematic uncertainties are summarized in Table 0.7.2.

Systematic uncertainties in the signal efficiency for the 2015 and 2016 muon
searches.  Systematic uncertainty 2015 2016 $Q/\pt$ resolution mismodeling 13%
7.0% Trigger acceptance 13% 2.8% Integrated luminosity 2.3% 2.5%

##  0.8 Results

In the calorimeter search, we predict $4.1^{+3.0}_{-1.0}$
($11.4^{+10.3}_{-3.1}$) background events in the 2015 (2016) data. Four events
that pass all of the selection criteria are observed in 2015 data, while 13
events are observed in 2016 data. Both observed numbers of events are
consistent with the predicted backgrounds. The observed events are most likely
cosmic ray muon or beam halo events, as they each consist of a single
reconstructed jet.

In the muon search, we predict $0.04\pm 0.03$ ($0.50\pm 0.40$) background
events in 2015 (2016). There are zero observed events in both 2015 and 2016
data that pass all of the selection criteria.

In both the calorimeter and muon searches, we count the number of observed
events in equally spaced $\log_{10}\text{(time)}$ bins of signal lifetime
hypotheses from $10^{-7}$ to $10^{6}$s. For lifetime hypotheses shorter than
one LHC orbit of $89\mus$, we search within a sensitivity-optimized time
window of 1.3 times the stopped particle’s lifetime, where the window starts
after each $\Pp\Pp$ collision, to avoid the addition of backgrounds for time
intervals during which a signal with a given lifetime has a large probability
to have already decayed. We assume that the cosmic ray muon background (and
noise background in the calorimeter search) is uniformly distributed in time.
In the calorimeter search, we estimate the halo background for each lifetime
hypothesis by finding the ratio of halo events in the search time window to
the total number of halo events, then multiplying this ratio by the halo
background estimate for the full trigger livetime. We select the halo events
by requiring events to pass all of the selection criteria except the CSC
segment veto described above, and then requiring the events to have at least
one CSC segment. Then, we determine if these halo events are within the search
window by observing how long after the most recent filled BX they occurred.

For lifetimes longer than one orbit, the trigger livetime, the expected
background, and the number of observed events are independent of the lifetime.
The effective integrated luminosity decreases with lifetime for lifetimes
longer than one LHC orbit, and the analysis sensitivity degrades with
lifetimes longer than one LHC fill because any signal that decays between
fills will have few chances to be observed.

For lifetime hypotheses shorter than one orbit, both the number of observed
events and the expected background depend on the time window considered, which
is a fraction of the total trigger livetime. Similarly, the effective
integrated luminosity is reduced for short lifetimes. As we gradually increase
the lifetime in the hypothesis from the minimal value, we include more
observed events in the search window. When the lifetime is shorter than one
orbit, to explicitly show the discontinuous changes of the upper limits
whenever the expanding search window covers a new observed event, we test two
lifetime hypotheses in addition to the equally spaced $\log_{10}\text{(time)}$
ones, for each observed event in these counting experiments. These two
additional lifetime hypotheses are the largest lifetime hypothesis for which
the event lies outside the time window, and the smallest lifetime hypothesis
for which the event is contained within the time window.

Tables 0.8 and 0.8 show the results of the counting experiment for the 2016
data. The data show no excess over background, and we set upper limits on the
signal production cross section ($\sigma$) using a hybrid method with the
$\mathrm{CL_{s}}$ criterion [66, 67] to incorporate the systematic
uncertainties [68], in both the calorimeter and muon searches. By combining
the likelihoods of the search results from the 2015 and 2016 analyses, we set
combined upper limits on $\mathcal{B}\sigma$ for the benchmark signal models.

Counting experiment results for different lifetimes in the calorimeter search
with 2016 data.  Lifetime [s] Effective integrated Trigger Expected Observed
luminosity [$\text{fb}^{-1}$] livetime [hrs] background events $5\ten{-8}$
0.27 17 $0.4^{+0.3}_{-0.1}$ 0 $8\ten{-8}$ 0.65 34 $0.8^{+0.6}_{-0.2}$ 0
$10^{-7}$ 1.27 67 $1.4^{+1.2}_{-0.4}$ 0 $10^{-6}$ 9.98 417 $8.4^{+7.5}_{-2.3}$
8 $10^{-5}$ 13.37 583 $11.3^{+10.2}_{-3.1}$ 13 $10^{-4}$ 13.70 583
$11.4^{+10.3}_{-3.1}$ 13 $10^{3}$ 13.57 583 $11.4^{+10.3}_{-3.1}$ 13 $10^{4}$
11.78 583 $11.4^{+10.3}_{-3.1}$ 13 $10^{5}$ 8.27 583 $11.4^{+10.3}_{-3.1}$ 13
$10^{6}$ 5.61 583 $11.4^{+10.3}_{-3.1}$ 13

Counting experiment results for different lifetimes in the muon search with
2016 data.  Lifetime [s] Effective integrated Trigger Expected Observed
luminosity [$\text{fb}^{-1}$] livetime [hrs] background events $5\ten{-8}$
0.27 11 $0.01\pm 0.01$ 0 $8\ten{-8}$ 0.64 34 $0.03\pm 0.02$ 0 $10^{-7}$ 1.27
68 $0.06\pm 0.05$ 0 $10^{-6}$ 9.95 422 $0.36\pm 0.29$ 0 $10^{-5}$ 13.34 581
$0.49\pm 0.39$ 0 $10^{-4}$ 13.67 589 $0.50\pm 0.40$ 0 1 13.67 589 $0.50\pm
0.40$ 0 $10^{3}$ 13.55 589 $0.50\pm 0.40$ 0 $10^{4}$ 11.75 589 $0.50\pm 0.40$
0 $10^{5}$ 8.26 589 $0.50\pm 0.40$ 0 $10^{6}$ 5.61 589 $0.50\pm 0.40$ 0

In the calorimeter search, the 95% confidence level (CL) upper limits on
$\mathcal{B}\sigma$ for $\PSg$ ($\PSQt$) pair production for combined 2015 and
2016 data as a function of the particle’s lifetime $\tau$ are shown in Fig. 4,
assuming $E_{\cPg}>130\GeV$ ($m_{\PSg}-m_{\PSGcz}\gtrapprox 160\GeV$ or
$E_{\cPqt}>170\GeV$). In Fig. 5, the gluino and top squark mass limits are
shown, assuming
$\mathcal{B}(\PSg\to\cPg\PSGcz)=\mathcal{B}(\PSg\to\cPq\cPaq\PSGcz)=\mathcal{B}(\PSQt\to\cPqt\PSGcz)=100\%$.
We exclude gluinos with $m_{\PSg}<1385$ (1393)that decay via
$\PSg\to\cPg\PSGcz$ ($\PSg\to\cPq\cPaq\PSGcz$) and top squarks with
$m_{\PSQt}<744\GeV$ at 95% CL for $10\mus<\tau<1000$s.

Figure 4:  The 95% CL upper limits on $\mathcal{B}\sigma$ for gluino and top
squark pair production, using the cloud model of R-hadron interactions, as a
function of lifetime, for combined 2015 and 2016 data for the calorimeter
search. We show gluinos that undergo a two-body decay (upper left), top
squarks that undergo a two-body decay (upper right), and gluinos that undergo
a three-body decay (lower). The discontinuous structure observed between
$10^{-7}$ and $10^{-5}$ s is due to the increase of the number of observed
events in the search window as the lifetime increases. The theory lines assume
$\mathcal{B}=100\%$.

Figure 5:  The 95% CL upper limits on the gluino and top squark mass, using
the cloud model of R-hadron interactions, as a function of lifetime, for
combined 2015 and 2016 data for the calorimeter search. We show gluinos and
top squarks that undergo a two-body decay (left) and gluinos that undergo a
three-body decay (right). The discontinuous structure observed between
$10^{-7}$ and $10^{-5}$ s is due to the increase of the number of observed
events in the search window as the lifetime increases.

Figure 6 shows the regions of the gluino (top squark) mass vs. neutralino mass
plane excluded by the calorimeter search, for lifetimes between 10and 1000s.
The borders of the regions are determined by the edge of the plateau in Fig. 2
and the gluino (top squark) mass limits.

Figure 6: The 95% CL upper limits in the neutralino mass vs. gluino (top
squark) mass plane, for lifetimes between 10and 1000s, for combined 2015 and
2016 data for the calorimeter search. The color map indicates the 95% CL upper
limits on $\mathcal{B}\sigma$. The mostly triangular region defined by the
black solid (dashed) line shows the excluded observed (expected) region. We
show gluinos that undergo a two-body decay (upper left), top squarks that
undergo a two-body decay (upper right), and gluinos that undergo a three-body
decay (lower).

For the muon search, the 95% CL upper limits on $\mathcal{B}\sigma$ as a
function of lifetime for 1000gluinos and 400MCHAMPs are shown in Fig. 7 for
combined 2015 and 2016 data. The combined 2015 and 2016 95% CL upper limits on
$\mathcal{B}\sigma$ of gluino and MCHAMP pair production as a function of mass
are shown in Fig. 8, for lifetimes between 10and 1000s. Gluinos with masses
between 400 and 980are excluded for lifetimes between 10and 1000s, assuming
$\mathcal{B}(\PSg\to\cPq\cPaq\PSGczDt)\mathcal{B}(\PSGczDt\to\MM\PSGcz)=100\%$,
$m_{\PSGcz}=0.25m_{\PSg}$ and $m_{\PSGczDt}=2.5m_{\PSGcz}$. MCHAMPs with
masses between 100 and 440and $\abs{Q}=2e$ are excluded for lifetimes between
10and 1000s, assuming $\mathcal{B}(\text{MCHAMP}\to\mu^{\pm}\mu^{\pm})=100\%$.

Figure 7: The 95% CL upper limits on $\mathcal{B}\sigma$ for 1000gluino (left)
and 400MCHAMP (right) pair production as a function of lifetime, for combined
2015 and 2016 data for the muon search. The theory lines assume
$\mathcal{B}=100\%$.

Figure 8: 95% CL upper limits on $\mathcal{B}\sigma$ for gluino (left) and
MCHAMP (right) pair production as a function of mass, for lifetimes between
10and 1000s, for combined 2015 and 2016 data for the muon search. The theory
curves assume $\mathcal{B}=100\%$.

##  0.9 Summary

A search has been presented for long-lived particles that stopped in the CMS
detector after being produced in proton-proton collisions at a center-of-mass
energy of 13at the CERN LHC. The subsequent decays of these particles to
produce calorimeter deposits or muon pairs were looked for during gaps between
proton bunches in the LHC beams. In the calorimeter (muon) search, with
collision data corresponding to an integrated luminosity of 2.7 (2.8)in a
period of sensitivity corresponding to 135 (155) hours of trigger livetime in
2015 and to an integrated luminosity of 35.9 (36.2)in a period of sensitivity
of 586 (589) hours of trigger livetime in 2016, no excess above the estimated
background has been observed. Cross section ($\sigma$) and mass limits have
been presented at 95% confidence level (CL) on gluino (), top squark (), and
multiply charged massive particle (MCHAMP) production over 13 orders of
magnitude in the mean proper lifetime of the stopped particle.

In the calorimeter search, combining the results from the 2015 and 2016
analyses and assuming a branching fraction ($\mathcal{B}$) of 100% for
$\PSg\to\cPg\PSGcz$ $(\PSg\to\cPq\cPaq\PSGcz)$, where $\PSGcz$ is the lightest
neutralino, gluinos with lifetimes from $10\mus$ to 1000s and $m_{\PSg}<1385$
(1393)have been excluded, for a cloud model of R-hadron interactions and for
the daughter gluon energy $E_{\cPg}>130\GeV$ ($m_{\PSg}-m_{\PSGcz}\gtrapprox
160\GeV$). Under similar assumptions, for the daughter top quark energy
$E_{\cPqt}>170\GeV$ and $\mathcal{B}(\PSQt\to\cPqt\PSGcz)=100\%$, long-lived
top squarks with lifetimes from 10to 1000s and $m_{\PSQt}<744\GeV$ have been
excluded. These are the first limits on stopped long-lived particles at 13and
the strongest limits to date.

In the muon search, 95% CL upper limits on $\mathcal{B}\sigma$ were set for
combined 2015 and 2016 data. For lifetimes between 10$\mus$ and 1000s, limits
were set between 1 and 0.01pb for gluinos with masses between 400 and 1600and
for MCHAMPs with masses between 100 and 800and charge $\abs{Q}=2e$. For
lifetimes between 10$\mus$ and 1000s, gluinos with masses between 400 and
980have been excluded, assuming
$\mathcal{B}(\PSg\to\cPq\cPaq\PSGczDt)\mathcal{B}(\PSGczDt\to\MM\PSGcz)=100\%$,
$m_{\PSGcz}=0.25m_{\PSg}$, and $m_{\PSGczDt}=2.5m_{\PSGcz}$, where $\PSGczDt$
is the next-to-lightest neutralino. Under the same lifetime hypothesis,
MCHAMPs with masses between 100 and 440and $\abs{Q}=2e$ have been excluded,
assuming $\mathcal{B}(\text{MCHAMP}\to\mu^{\pm}\mu^{\pm})=100\%$. These are
the first limits obtained at the LHC for stopped particles that decay to
muons.

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing centers and personnel of the Worldwide LHC Computing Grid for
delivering so effectively the computing infrastructure essential to our
analyses. Finally, we acknowledge the enduring support for the construction
and operation of the LHC and the CMS detector provided by the following
funding agencies: BMWFW and FWF (Austria); FNRS and FWO (Belgium); CNPq,
CAPES, FAPERJ, 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); OTKA and NIH (Hungary); DAE and DST (India); IPM
(Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); LAS
(Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and
UASLP-FAI (Mexico); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC
(Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS, RFBR and RAEP
(Russia); MESTD (Serbia); SEIDI, CPAN, PCTI and FEDER (Spain); Swiss Funding
Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR, and NSTDA
(Thailand); TUBITAK and TAEK (Turkey); NASU and SFFR (Ukraine); STFC (United
Kingdom); DOE and NSF (USA). Individuals have received support from the Marie-
Curie program and the European Research Council and Horizon 2020 Grant,
contract 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 Ministry of Education, Youth and
Sports (MEYS) of the Czech Republic; the Council of Science and Industrial
Research, India; the HOMING PLUS program of the Foundation for Polish Science,
cofinanced from European Union, Regional Development Fund, the Mobility Plus
program of the Ministry of Science and Higher Education, the National Science
Center (Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus
2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis
2012/07/E/ST2/01406; the National Priorities Research Program by Qatar
National Research Fund; the Programa Severo Ochoa del Principado de Asturias;
the Thalis and Aristeia programs cofinanced by EU-ESF and the Greek NSRF; the
Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University
and the Chulalongkorn Academic into Its 2nd Century Project Advancement
Project (Thailand); the Welch Foundation, contract C-1845; and the Weston
Havens Foundation (USA).

## References

  * [1] S. Dimopoulos, M. Dine, S. Raby, and S. D. Thomas, “Experimental signatures of low-energy gauge mediated supersymmetry breaking”,  Phys. Rev. Lett. 76 (1996) 3494, 10.1103/PhysRevLett.76.3494, arXiv:hep-ph/9601367.
  * [2] H. Baer, K.-M. Cheung, and J. F. Gunion, “A heavy gluino as the lightest supersymmetric particle”,  Phys. Rev. D 59 (1999) 075002, 10.1103/PhysRevD.59.075002, arXiv:hep-ph/9806361.
  * [3] T. Jittoh, J. Sato, T. Shimomura, and M. Yamanaka, “Long life stau in the minimal supersymmetric standard model”,  Phys. Rev. D 73 (2006) 055009, 10.1103/PhysRevD.73.055009, arXiv:hep-ph/0512197. [Erratum: 10.1103/PhysRevD.87.019901].
  * [4] M. Fairbairn et al., “Stable massive particles at colliders”,  Phys. Rept. 438 (2007) 1, 10.1016/j.physrep.2006.10.002, arXiv:hep-ph/0611040.
  * [5] M. J. Strassler and K. M. Zurek, “Echoes of a hidden valley at hadron colliders”,  Phys. Lett. B 651 (2007) 374, 10.1016/j.physletb.2007.06.055, arXiv:hep-ph/0604261.
  * [6] A. Arvanitaki et al., “Astrophysical probes of unification”,  Phys. Rev. D 79 (2009) 105022, 10.1103/PhysRevD.79.105022, arXiv:0812.2075.
  * [7] N. Arkani-Hamed and S. Dimopoulos, “Supersymmetric unification without low energy supersymmetry and signatures for fine-tuning at the LHC”,  JHEP 06 (2005) 073, 10.1088/1126-6708/2005/06/073, arXiv:hep-th/0405159.
  * [8] A. Arvanitaki, N. Craig, S. Dimopoulos, and G. Villadoro, “Mini-split”,  JHEP 02 (2013) 126, 10.1007/JHEP02(2013)126, arXiv:1210.0555.
  * [9] A. Arvanitaki et al., “Stopping gluinos”,  Phys. Rev. D 76 (2007) 055007, 10.1103/PhysRevD.76.055007, arXiv:hep-ph/0506242.
  * [10] P. W. Graham, K. Howe, S. Rajendran, and D. Stolarski, “New measurements with stopped particles at the LHC”,  Phys. Rev. D 86 (2012) 034020, 10.1103/PhysRevD.86.034020, arXiv:1111.4176.
  * [11] CMS Collaboration, “Search for decays of stopped long-lived particles produced in proton-proton collisions at $\sqrt{s}=8$ TeV”,  Eur. Phys. J. C 75 (2015) 151, 10.1140/epjc/s10052-015-3367-z, arXiv:1501.05603.
  * [12] D0 Collaboration, “Search for stopped gluinos from $p\bar{p}$ collisions at $\sqrt{s}=1.96$ TeV”,  Phys. Rev. Lett. 99 (2007) 131801, 10.1103/PhysRevLett.99.131801, arXiv:0705.0306.
  * [13] CMS Collaboration, “Search for stopped long-lived particles produced in pp collisions at $\sqrt{s}=7$ TeV”,  JHEP 08 (2012) 026, 10.1007/JHEP08(2012)026, arXiv:1207.0106.
  * [14] CMS Collaboration, “Search for stopped gluinos in pp collisions at $\sqrt{s}=7$ TeV”,  Phys. Rev. Lett. 106 (2011) 011801, 10.1103/PhysRevLett.106.011801, arXiv:1011.5861.
  * [15] ATLAS Collaboration, “Search for decays of stopped, long-lived particles from $7$ TeV pp collisions with the ATLAS detector”,  Eur. Phys. J. C 72 (2012) 1, 10.1140/epjc/s10052-012-1965-6, arXiv:1201.5595.
  * [16] ATLAS Collaboration, “Search for long-lived stopped $R$-hadrons decaying out of time with $pp$ collisions using the ATLAS detector”,  Phys. Rev. D 88 (2013) 112003, 10.1103/PhysRevD.88.112003, arXiv:1310.6584.
  * [17] M. Khlopov, C. A. Stephan, and D. Fargion, “Dark matter with invisible light from heavy double charged leptons of almost-commutative geometry?”,  Class. Quant. Grav. 23 (2006) 10.1088/0264-9381/23/24/008, arXiv:astro-ph/0511789.
  * [18] A. D. Rújula, S. L. Glashow, and U. Sarid, “Charged dark matter”,  Nucl. Phys. B 333 (1990) 173, 10.1016/0550-3213(90)90227-5.
  * [19] S. Dimopoulos, D. Eichler, R. Esmailzadeh, and G. D. Starkman, “Getting a charge out of dark matter”,  Phys. Rev. D 41 (1990) 2388, 10.1103/PhysRevD.41.2388.
  * [20] P. Langacker and G. Steigman, “Requiem for a fractionally charged, massive particle”,  Phys. Rev. D 84 (2011) 065040, 10.1103/PhysRevD.84.065040, arXiv:1107.3131.
  * [21] ATLAS Collaboration, “Search for heavy long-lived charged particles with the ATLAS detector in $pp$ collisions at $\sqrt{s}=7\TeV$”,  Phys. Lett. B 703 (2011) 428, 10.1016/j.physletb.2011.08.042, arXiv:1106.4495.
  * [22] ATLAS Collaboration, “Search for stable hadronising squarks and gluinos with the ATLAS experiment at the LHC”,  Phys. Lett. B 701 (2011) 1, 10.1016/j.physletb.2011.05.010, arXiv:1103.1984.
  * [23] ATLAS Collaboration, “Search for massive long-lived highly ionising particles with the ATLAS detector at the LHC”,  Phys. Lett. B 698 (2011) 353, 10.1016/j.physletb.2011.03.033, arXiv:1102.0459.
  * [24] ATLAS Collaboration, “Searches for heavy long-lived sleptons and R-hadrons with the ATLAS detector in pp collisions at $\sqrt{s}=7$ TeV”,  Phys. Lett. B 720 (2013) 277, 10.1016/j.physletb.2013.02.015, arXiv:1211.1597.
  * [25] ATLAS Collaboration, “Search for long-lived, multi-charged particles in pp collisions at $\sqrt{s}=7$ TeV using the ATLAS detector”,  Phys. Lett. B 722 (2013) 305, 10.1016/j.physletb.2013.04.036, arXiv:1301.5272.
  * [26] ATLAS Collaboration, “Search for heavy long-lived multi-charged particles in pp collisions at $\sqrt{s}=8$ TeV using the ATLAS detector”,  Eur. Phys. J. C 75 (2015) 362, 10.1140/epjc/s10052-015-3534-2, arXiv:1504.04188.
  * [27] ATLAS Collaboration, “Search for metastable heavy charged particles with large ionisation energy loss in pp collisions at $\sqrt{s}=8$ TeV using the ATLAS experiment”,  Eur. Phys. J. C 75 (2015) 407, 10.1140/epjc/s10052-015-3609-0, arXiv:1506.05332.
  * [28] CMS Collaboration, “Searches for long-lived charged particles in pp collisions at $\sqrt{s}=7$ and 8”,  J. High Energy Phys. 07 (2013) 122, 10.1007/JHEP07(2013)122, arXiv:1305.0491.
  * [29] CMS Collaboration, “Search for heavy stable charged particles in pp collisions at $\sqrt{s}=7$ TeV”,  JHEP 03 (2011) 24, 10.1007/JHEP03(2011)024, arXiv:1101.1645.
  * [30] CMS Collaboration, “Search for fractionally charged particles in pp collisions at $\sqrt{s}=7$ TeV”,  Phys. Rev. D 87 (2013) 092008, 10.1103/PhysRevD.87.092008, arXiv:1210.2311.
  * [31] CMS Collaboration, “Search for heavy long-lived charged particles in pp collisions at $\sqrt{s}=7$ TeV”,  Phys. Lett. B 713 (2012) 408, 10.1016/j.physletb.2012.06.023, arXiv:1205.0272.
  * [32] CMS Collaboration, “Constraints on the pMSSM, AMSB model and on other models from the search for long-lived charged particles in proton–proton collisions at $\sqrt{s}=8$ TeV”,  Eur. Phys. J. C 75 (2015) 325, 10.1140/epjc/s10052-015-3533-3, arXiv:1502.02522.
  * [33] LHCb Collaboration, “Search for long-lived heavy charged particles using a ring imaging Cherenkov technique at LHCb”,  Eur. Phys. J. C 75 (2015) 595, 10.1140/epjc/s10052-015-3809-7, arXiv:1506.09173.
  * [34] CMS Collaboration, “Search for long-lived charged particles in proton-proton collisions at $\sqrt{s}=13$”,  Phys. Rev. D 94 (2016) 112004, 10.1103/PhysRevD.94.112004, arXiv:1609.08382.
  * [35] CMS Collaboration, “The CMS experiment at the CERN LHC”,  JINST 3 (2008) S08004, 10.1088/1748-0221/3/08/S08004.
  * [36] R. Bailey and P. Collier, “Standard filling schemes for various LHC operation modes”, Technical Report LHC-PROJECT-NOTE-323, CERN, 2003.
  * [37] G. F. Giudice and A. Romanino, “Split supersymmetry”,  Nucl. Phys. B 699 (2004) 65, 10.1016/j.nuclphysb.2004.11.048, arXiv:hep-ph/0406088.
  * [38] N. Arkani-Hamed, S. Dimopoulos, G. F. Giudice, and A. Romanino, “Aspects of split supersymmetry”,  Nucl. Phys. B 709 (2005) 3, 10.1016/j.nuclphysb.2004.12.026, arXiv:hep-ph/0409232.
  * [39] D. Abercrombie et al., “Dark matter benchmark models for early LHC run-2 searches: report of the ATLAS/CMS dark matter forum”, (2015). arXiv:1507.00966.
  * [40] L. M. Carpenter, R. Colburn, J. Goodman, and T. Linden, “Indirect detection constraints on s and t channel simplified models of dark matter”,  Phys. Rev. D 94 (2016) 055027, 10.1103/PhysRevD.94.055027, arXiv:1606.04138.
  * [41] L. Covi and F. Dradi, “Long-lived stop at the LHC with or without R-parity”,  JCAP 10 (2014) 039, 10.1088/1475-7516/2014/10/039, arXiv:1403.4923.
  * [42] T. Sjöstrand, S. Mrenna, and P. Skands, “PYTHIA 6.4 physics and manual”,  JHEP 05 (2006) 026, 10.1088/1126-6708/2006/05/026, arXiv:hep-ph/0603175.
  * [43] T. Sjöstrand, S. Mrenna, and P. Skands, “A brief introduction to PYTHIA 8.1”,  Comput. Phys. Comm. 178 (2008) 852, 10.1016/j.cpc.2008.01.036, arXiv:0710.3820.
  * [44] GEANT4 Collaboration, “GEANT4—a simulation toolkit”,  Nucl. Instrum. Meth. A 506 (2003) 250, 10.1016/S0168-9002(03)01368-8.
  * [45] J. Allison et al., “Geant4 developments and applications”,  IEEE Trans. Nucl. Sci. 53 (2006) 270, 10.1109/TNS.2006.869826.
  * [46] P. Fayet, “Spontaneously broken supersymmetric theories of weak, electromagnetic and strong interactions”,  Phys. Lett. B 69 (1977) 489, 10.1016/0370-2693(77)90852-8.
  * [47] P. Fayet, “Massive gluinos”,  Phys. Lett. B 78 (1978) 417, 10.1016/0370-2693(78)90474-4.
  * [48] G. R. Farrar and P. Fayet, “Phenomenology of the production, decay, and detection of new hadronic states associated with supersymmetry”,  Phys. Lett. B 76 (1978) 575, 10.1016/0370-2693(78)90858-4.
  * [49] A. C. Kraan, “Interactions of heavy stable hadronizing particles”,  Eur. Phys. J. C 37 (2004) 91, 10.1140/epjc/s2004-01946-6, arXiv:hep-ex/0404001.
  * [50] R. Mackeprang and A. Rizzi, “Interactions of coloured heavy stable particles in matter”,  Eur. Phys. J. C 50 (2007) 353, 10.1140/epjc/s10052-007-0252-4, arXiv:hep-ph/0612161.
  * [51] CMS Collaboration, “Identification and filtering of uncharacteristic noise in the CMS hadron calorimeter”,  JINST 5 (2010) T03014, 10.1088/1748-0221/5/03/T03014, arXiv:0911.4881.
  * [52] M. Cacciari, G. P. Salam, and G. Soyez, “The anti-$k_{t}$ jet clustering algorithm”,  JHEP 04 (2008) 063, 10.1088/1126-6708/2008/04/063, arXiv:0802.1189.
  * [53] M. Cacciari, G. P. Salam, and G. Soyez, “FastJet user manual”,  Eur. Phys. J. C 72 (2012) 1896, 10.1140/epjc/s10052-012-1896-2, arXiv:1111.6097.
  * [54] CMS Collaboration, “Performance of CMS muon reconstruction in pp collision events at $\sqrt{s}=7$”,  JINST 7 (2012) P10002, 10.1088/1748-0221/7/10/P10002, arXiv:1206.4071.
  * [55] CMS Collaboration, “The CMS muon system in Run2: preparation, status and first results”, in  Proceedings, 2015 European Physical Society Conference on High Energy Physics (EPS-HEP 2015): Vienna, Austria, July 22-29, 2015, p. 237.  2015\.  arXiv:1510.05424.  [PoS(EPS-HEP2015)237].
  * [56] P. Biallass and T. Hebbeker, “Parametrization of the cosmic muon flux for the generator CMSCGEN”, (2009). arXiv:0907.5514.
  * [57] D. Heck et al., “CORSIKA: A Monte Carlo code to simulate extensive air showers”, Technical Report FZKA-6019, 1998.
  * [58] CMS Collaboration, “Measurement of the charge ratio of atmospheric muons with the CMS detector”,  Phys. Lett. B 692 (2010) 83, 10.1016/j.physletb.2010.07.033, arXiv:1005.5332.
  * [59] CMS Collaboration, “Measurements of inclusive W and Z cross sections in pp collisions at $\sqrt{s}=7$ TeV”,  JHEP 01 (2011) 080, 10.1007/JHEP01(2011)080, arXiv:1012.2466.
  * [60] M. J. Oreglia, “A study of the reactions $\psi^{\prime}\to\gamma\gamma\psi$”.  PhD thesis, Stanford University, 1980.  SLAC Report SLAC-236.
  * [61] L. Landau, “On the energy loss of fast particles by ionization”,  J. Phys. (USSR) 8 (1944) 201\.
  * [62] CMS Collaboration, “CMS luminosity measurement for the 2015 data taking period”, CMS Physics Analysis Summary CMS-PAS-LUM-15-001, 2016.
  * [63] CMS Collaboration, “CMS luminosity measurements for the 2016 data taking period”, CMS Physics Analysis Summary CMS-PAS-LUM-17-001, 2017.
  * [64] CMS Collaboration, “Performance of the CMS hadron calorimeter with cosmic ray muons and LHC beam data”,  JINST 5 (2010) T03012, 10.1088/1748-0221/5/03/T03012, arXiv:0911.4991.
  * [65] CMS Collaboration, “Performance of jets at CMS”, in  Proceedings, 16th Int. Conf. on Calorimetry in HEP (CALOR 2014), volume 587, p. 012004.  Giessen, Germany, April, 2015.  [J. Phys. Conf. Ser. 587 (2015) 012004]. 10.1088/1742-6596/587/1/012004.
  * [66] T. Junk, “Confidence level computation for combining searches with small statistics”,  Nucl. Instrum. Meth. A 434 (1999) 435, 10.1016/S0168-9002(99)00498-2, arXiv:hep-ex/9902006.
  * [67] A. L. Read, “Presentation of search results: the $CL_{s}$ technique”, in  Durham IPPP Workshop: Advanced Statistical Techniques in Particle Physics, p. 2693.  Durham, UK, March, 2002.  [J. Phys. G 28 (2002) 2693]. 10.1088/0954-3899/28/10/313.
  * [68] R. D. Cousins and V. L. Highland, “Incorporating systematic uncertainties into an upper limit”,  Nucl. Instrum. Meth. A 320 (1992) 331, 10.1016/0168-9002(92)90794-5.

##  .10 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan, A. Tumasyan Institut für Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, E. Asilar, T. Bergauer, J. Brandstetter, E. Brondolin, M.
Dragicevic, J. Erö, A. Escalante Del Valle, M. Flechl, M. Friedl, R.
Frühwirth1, V.M. Ghete, J. Grossmann, J. Hrubec, M. Jeitler1, A. König, N.
Krammer, I. Krätschmer, D. Liko, T. Madlener, I. Mikulec, E. Pree, 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, M. Van De
Klundert, H. Van Haevermaet, P. Van Mechelen, N. Van Remortel Vrije
Universiteit Brussel, Brussel, Belgium
S. Abu Zeid, F. Blekman, J. D’Hondt, I. De Bruyn, 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, T. Maerschalk, T. Seva, E. Starling, C. Vander Velde, P.
Vanlaer, D. Vannerom, R. Yonamine, F. Zenoni Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, A. Fagot, M. Gul, I. Khvastunov2, D. Poyraz, C. Roskas,
D. Trocino, M. Tytgat, W. Verbeke, M. Vit, N. Zaganidis Université Catholique
de Louvain, Louvain-la-Neuve, Belgium
H. Bakhshiansohi, O. Bondu, S. Brochet, G. Bruno, C. Caputo, A. Caudron, P.
David, S. De Visscher, C. Delaere, M. Delcourt, B. Francois, A. Giammanco, G.
Krintiras, V. Lemaitre, A. Magitteri, A. Mertens, M. Musich, K. Piotrzkowski,
L. Quertenmont, A. Saggio, M. Vidal Marono, S. Wertz, J. Zobec Centro
Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
W.L. Aldá Júnior, F.L. Alves, G.A. Alves, L. Brito, 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, S. Fonseca De Souza, L.M.
Huertas Guativa, H. Malbouisson, M. Melo De Almeida, C. Mora Herrera, L.
Mundim, H. Nogima, 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, T.R. Fernandez Perez Tomeia, E.M. Gregoresb, P.G.
Mercadanteb, S.F. Novaesa, Sandra S. Padulaa, D. Romero Abadb, J.C. Ruiz
Vargasa 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. Shaheen, A. Spiezia, J. Tao, C. Wang,
Z. Wang, E. Yazgan, H. Zhang, J. Zhao State Key Laboratory of Nuclear Physics
and Technology, Peking University, Beijing, China
Y. Ban, G. Chen, J. Li, Q. Li, S. Liu, Y. Mao, S.J. Qian, D. Wang, Z. Xu
Tsinghua University, Beijing, China
Y. Wang Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, L.F. Chaparro Sierra, C. Florez, C.F. González
Hernández, J.D. Ruiz Alvarez, 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, P.M. Ribeiro Cipriano, 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. Starodumov6, T. Susa
University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos,
P.A. Razis, H. Rykaczewski Charles University, Prague, Czech Republic
M. Finger7, M. Finger Jr.7 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
A.A. Abdelalim8,9, Y. Assran10,11, S. Elgammal11 National Institute of
Chemical Physics and Biophysics, Tallinn, Estonia
S. Bhowmik, R.K. Dewanjee, M. Kadastik, L. Perrini, 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, S. Ghosh, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
C. Leloup, E. Locci, M. Machet, 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, S. Baffioni, F. Beaudette, P.
Busson, L. Cadamuro, C. Charlot, R. Granier de Cassagnac, M. Jo, I. Kucher, S.
Lisniak, A. Lobanov, J. Martin Blanco, M. Nguyen, C. Ochando, G. Ortona, P.
Paganini, P. Pigard, R. Salerno, J.B. Sauvan, Y. Sirois, A.G. Stahl Leiton, Y.
Yilmaz, A. Zabi, A. Zghiche Université de Strasbourg, CNRS, IPHC UMR 7178,
F-67000 Strasbourg, France
J.-L. Agram13, J. Andrea, D. Bloch, J.-M. Brom, M. Buttignol, E.C. Chabert, C.
Collard, E. Conte13, X. Coubez, F. Drouhin13, J.-C. Fontaine13, D. Gelé, U.
Goerlach, M. Jansová, P. Juillot, 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, M. Lethuillier, L. Mirabito, A.L.
Pequegnot, S. Perries, A. Popov14, V. Sordini, M. Vander Donckt, S. Viret, S.
Zhang Georgian Technical University, Tbilisi, Georgia
A. Khvedelidze7 Tbilisi State University, Tbilisi, Georgia
D. Lomidze RWTH Aachen University, I. Physikalisches Institut, Aachen, Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, M. Preuten, C.
Schomakers, J. Schulz, M. Teroerde, B. Wittmer, V. Zhukov14 RWTH Aachen
University, III. Physikalisches Institut A, Aachen, Germany
A. Albert, D. Duchardt, M. Endres, M. Erdmann, S. Erdweg, T. Esch, R. Fischer,
A. Güth, T. Hebbeker, C. Heidemann, K. Hoepfner, S. Knutzen, M. Merschmeyer,
A. Meyer, P. Millet, S. Mukherjee, T. Pook, M. Radziej, H. Reithler, M.
Rieger, F. Scheuch, D. Teyssier, S. Thüer RWTH Aachen University, III.
Physikalisches Institut B, Aachen, Germany
G. Flügge, B. Kargoll, T. Kress, A. Künsken, T. Müller, A. Nehrkorn, A.
Nowack, C. Pistone, O. Pooth, A. Stahl15 Deutsches Elektronen-Synchrotron,
Hamburg, Germany
M. Aldaya Martin, T. Arndt, C. Asawatangtrakuldee, K. Beernaert, O. Behnke, U.
Behrens, A. Bermúdez Martínez, A.A. Bin Anuar, K. Borras16, V. Botta, A.
Campbell, P. Connor, C. Contreras-Campana, F. Costanza, C. Diez Pardos, G.
Eckerlin, D. Eckstein, T. Eichhorn, E. Eren, E. Gallo17, J. Garay Garcia, A.
Geiser, J.M. Grados Luyando, A. Grohsjean, P. Gunnellini, M. Guthoff, A. Harb,
J. Hauk, M. Hempel18, H. Jung, M. Kasemann, J. Keaveney, C. Kleinwort, I.
Korol, D. Krücker, W. Lange, A. Lelek, T. Lenz, K. Lipka, W. Lohmann18, R.
Mankel, I.-A. Melzer-Pellmann, A.B. Meyer, M. Missiroli, G. Mittag, J. Mnich,
A. Mussgiller, D. Pitzl, A. Raspereza, M. Savitskyi, P. Saxena, R. Shevchenko,
N. Stefaniuk, H. Tholen, G.P. Van Onsem, R. Walsh, Y. Wen, K. Wichmann, C.
Wissing, O. Zenaiev University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, V. Blobel, M. Centis Vignali, T. Dreyer, E. Garutti, D.
Gonzalez, J. Haller, A. Hinzmann, M. Hoffmann, A. Karavdina, G. Kasieczka, R.
Klanner, R. Kogler, N. Kovalchuk, S. Kurz, D. Marconi, M. Meyer, M. Niedziela,
D. Nowatschin, T. Peiffer, A. Perieanu, C. Scharf, P. Schleper, A. Schmidt, S.
Schumann, J. Schwandt, J. Sonneveld, H. Stadie, G. Steinbrück, F.M. Stober, M.
Stöver, D. Troendle, E. Usai, A. Vanhoefer, B. Vormwald Institut für
Experimentelle Kernphysik, Karlsruhe, Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, E. Butz, R. Caspart, T. Chwalek, F.
Colombo, W. De Boer, A. Dierlamm, N. Faltermann, B. Freund, R. Friese, M.
Giffels, M.A. Harrendorf, F. Hartmann15, S.M. Heindl, U. Husemann, F.
Kassel15, S. Kudella, H. Mildner, M.U. Mozer, Th. Müller, M. Plagge, G. Quast,
K. Rabbertz, M. Schröder, I. Shvetsov, G. Sieber, H.J. Simonis, R. Ulrich, S.
Wayand, M. Weber, T. Weiler, S. Williamson, 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, I. Topsis-
Giotis National and Kapodistrian University of Athens, Athens, Greece
G. Karathanasis, S. Kesisoglou, A. Panagiotou, N. Saoulidou, E. Tziaferi
National Technical University of Athens, Athens, Greece
K. Kousouris 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. Csanad, N. Filipovic, G. Pasztor, O. Surányi, G.I. Veres19 Wigner Research
Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath20, Á. Hunyadi, F. Sikler, V. Veszpremi, G.
Vesztergombi19 Institute of Nuclear Research ATOMKI, Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi21, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
M. Bartók19, P. Raics, Z.L. Trocsanyi, B. Ujvari Indian Institute of Science
(IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri National Institute of Science Education and
Research, Bhubaneswar, India
S. Bahinipati22, P. Mal, K. Mandal, A. Nayak23, D.K. Sahoo22, N. Sahoo, S.K.
Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, R. Chawla, N. Dhingra, A. Kaur, M. Kaur,
S. Kaur, R. Kumar, P. Kumari, A. Mehta, J.B. Singh, G. Walia University of
Delhi, Delhi, India
Ashok Kumar, Aashaq Shah, A. Bhardwaj, S. Chauhan, B.C. Choudhary, R.B. Garg,
S. Keshri, A. Kumar, S. Malhotra, M. Naimuddin, K. Ranjan, R. Sharma Saha
Institute of Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj24, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep24, D. Bhowmik,
S. Dey, S. Dutt24, S. Dutta, S. Ghosh, N. Majumdar, A. Modak, K. Mondal, S.
Mukhopadhyay, S. Nandan, A. Purohit, P.K. Rout, A. Roy, S. Roy Chowdhury, S.
Sarkar, M. Sharan, B. Singh, 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, A.K. Mohanty15, P.K. Netrakanti,
L.M. Pant, P. Shukla, A. Topkar Tata Institute of Fundamental Research-A,
Mumbai, India
T. Aziz, S. Dugad, B. Mahakud, S. Mitra, G.B. Mohanty, N. Sur, B. Sutar Tata
Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, Sa. Jain, S.
Kumar, M. Maity25, G. Majumder, K. Mazumdar, T. Sarkar25, N. Wickramage26
Indian Institute of Science Education and Research (IISER), Pune, India
S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, S.
Sharma Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
S. Chenarani27, E. Eskandari Tadavani, S.M. Etesami27, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, S. Paktinat Mehdiabadi28, F. Rezaei
Hosseinabadi, B. Safarzadeh29, M. Zeinali University College Dublin, Dublin,
Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L. Cristellaa,b,
N. De Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, F. Erricoa,b, L. Fiorea,
G. Iasellia,c, S. Lezkia,b, G. Maggia,c, M. Maggia, B. Marangellia,b, 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,15, 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, G. Codispotia,b, M. Cuffiania,b, G.M. Dallavallea, F.
Fabbria, A. Fanfania,b, D. Fasanellaa,b, P. Giacomellia, C. Grandia, L.
Guiduccia,b, F. Iemmi, S. Marcellinia, G. Masettia, A. Montanaria, F.L.
Navarriaa,b, A. Perrottaa, 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, S. Costaa,b, A. Di Mattiaa, F. Giordanoa,b, R. Potenzaa,b, A.
Tricomia,b, C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b,
Firenze, Italy
G. Barbaglia, K. Chatterjeea,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, E. Focardia,b, G. Latino, P. Lenzia,b, M. Meschinia, S.
Paolettia, L. Russoa,30, G. Sguazzonia, D. Stroma, L. Viliania INFN Laboratori
Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, F. Fabbri, D. Piccolo, F. Primavera15 INFN Sezione di
Genova a, Università di Genova b, Genova, Italy
V. Calvellia,b, F. Ferroa, 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, L. Brianzaa,b, F. Brivioa,b, V. Cirioloa,b,15, M.E.
Dinardoa,b, S. Fiorendia,b, S. Gennaia, A. Ghezzia,b, P. Govonia,b, M.
Malbertia,b, S. Malvezzia, R.A. Manzonia,b, D. Menascea, L. Moronia, M.
Paganonia,b, K. Pauwelsa,b, D. Pedrinia, S. Pigazzinia,b,31, S. Ragazzia,b, T.
Tabarelli de Fatisa,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, S. Di Guidaa,d,15, F. Fabozzia,c, F. Fiengaa,b,
A.O.M. Iorioa,b, W.A. Khana, L. Listaa, S. Meolaa,d,15, P. Paoluccia,15, C.
Sciaccaa,b, F. Thyssena INFN Sezione di Padova a, Università di Padova b,
Padova, Italy, Università di Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, L. Benatoa,b, D. Biselloa,b, A. Bolettia,b, R.
Carlina,b, A. Carvalho Antunes De Oliveiraa,b, P. Checchiaa, M. Dall’Ossoa,b,
P. De Castro Manzanoa, T. Dorigoa, U. Dossellia, U. Gasparinia,b, A.
Gozzelinoa, S. Lacapraraa, P. Lujan, M. Margonia,b, A.T. Meneguzzoa,b, N.
Pozzobona,b, P. Ronchesea,b, R. Rossina,b, F. Simonettoa,b, A. Tiko, E.
Torassaa, 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
L. Alunni Solestizia,b, 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,15, G. Bagliesia, L. Bianchinia, T. Boccalia, L.
Borrello, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, L.
Gianninia,c, A. Giassia, M.T. Grippoa,30, F. Ligabuea,c, T. Lomtadzea, E.
Mancaa,c, G. Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, 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, N. Dacia, D. Del Rea,b, E. Di
Marcoa,b, M. Diemoza, S. Gellia,b, E. Longoa,b, F. Margarolia,b, B.
Marzocchia,b, P. Meridiania, G. Organtinia,b, 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, N. Cartigliaa, R. Castelloa,b, F. Cennaa,b, M.
Costaa,b, R. Covarellia,b, A. Deganoa,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, K. Shchelinaa,b, V.
Solaa, A. Solanoa,b, A. Staianoa, P. Traczyka,b INFN Sezione di Trieste a,
Università di Trieste b, Trieste, Italy
S. Belfortea, M. Casarsaa, F. Cossuttia, G. Della Riccaa,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.
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
J.A. Brochero Cifuentes, J. Goh, T.J. Kim Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, Y. Jo, Y. Kim, K. Lee, K.S.
Lee, S. Lee, J. Lim, S.K. Park, Y. Roh 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
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 Ali32, F. Mohamad Idris33, W.A.T. Wan
Abdullah, M.N. Yusli, Z. Zolkapli Centro de Investigacion y de Estudios
Avanzados del IPN, Mexico City, Mexico
Reyes-Almanza, R, Ramirez-Sanchez, G., Duran-Osuna, M. C., H. Castilla-Valdez,
E. De La Cruz-Burelo, I. Heredia-De La Cruz34, Rabadan-Trejo, R. I., R. Lopez-
Fernandez, J. Mejia Guisao, 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
P.H. Butler National Centre for Physics, Quaid-I-Azam University, Islamabad,
Pakistan
A. Ahmad, M. Ahmad, 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, K.
Nawrocki, M. Szleper, P. Zalewski Institute of Experimental Physics, Faculty
of Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk35, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Misiura, M. Olszewski, A. Pyskir, M. Walczak Laboratório de
Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
P. Bargassa, C. Beirão Da Cruz E Silva, A. Di Francesco, P. Faccioli, B.
Galinhas, M. Gallinaro, J. Hollar, N. Leonardo, L. Lloret Iglesias, M.V.
Nemallapudi, J. Seixas, G. Strong, O. Toldaiev, D. Vadruccio, J. Varela Joint
Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov, A. Kamenev,
V. Karjavin, A. Lanev, A. Malakhov, V. Matveev36,37, 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
Y. Ivanov, V. Kim38, E. Kuznetsova39, 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, A. Bylinkin37 National Research Nuclear University ’Moscow
Engineering Physics Institute’ (MEPhI), Moscow, Russia
R. Chistov40, M. Danilov40, P. Parygin, D. Philippov, S. Polikarpov, E.
Tarkovskii P.N. Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin37, I. Dremin37, M. Kirakosyan37, S.V. Rusakov, A.
Terkulov Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
A. Baskakov, A. Belyaev, E. Boos, M. Dubinin41, L. Dudko, A. Ershov, A.
Gribushin, V. Klyukhin, O. Kodolova, I. Lokhtin, I. Miagkov, S. Obraztsov, S.
Petrushanko, V. Savrin, A. Snigirev Novosibirsk State University (NSU),
Novosibirsk, Russia
V. Blinov42, D. Shtol42, Y. Skovpen42 State Research Center of Russian
Federation, Institute for High Energy Physics of NRC "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, A. Sobol, S.
Troshin, N. Tyurin, A. Uzunian, A. Volkov National Research Tomsk Polytechnic
University, Tomsk, Russia
A. Babaev University of Belgrade, Faculty of Physics and Vinca Institute of
Nuclear Sciences, Belgrade, Serbia
P. Adzic43, P. Cirkovic, D. Devetak, M. Dordevic, J. Milosevic Centro de
Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid,
Spain
J. Alcaraz Maestre, I. Bachiller, M. Barrio Luna, 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, A. Álvarez Fernández 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, 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, J. Garcia-Ferrero, A. García Alonso, 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 CERN, European
Organization for Nuclear Research, Geneva, Switzerland
D. Abbaneo, B. Akgun, E. Auffray, P. Baillon, A.H. Ball, D. Barney, J.
Bendavid, M. Bianco, A. Bocci, C. Botta, T. Camporesi, M. Cepeda, G.
Cerminara, E. Chapon, Y. Chen, D. d’Enterria, A. Dabrowski, V. Daponte, A.
David, M. De Gruttola, A. De Roeck, N. Deelen, M. Dobson, T. du Pree, M.
Dünser, N. Dupont, A. Elliott-Peisert, P. Everaerts, F. Fallavollita44, G.
Franzoni, J. Fulcher, W. Funk, D. Gigi, A. Gilbert, K. Gill, F. Glege, D.
Gulhan, J. Hegeman, V. Innocente, A. Jafari, P. Janot, O. Karacheban18, J.
Kieseler, V. Knünz, A. Kornmayer, M.J. Kortelainen, M. Krammer1, C. Lange, P.
Lecoq, C. Lourenço, M.T. Lucchini, L. Malgeri, M. Mannelli, A. Martelli, F.
Meijers, J.A. Merlin, S. Mersi, E. Meschi, P. Milenovic45, F. Moortgat, M.
Mulders, H. Neugebauer, J. Ngadiuba, 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, G. Rolandi46, M. Rovere,
H. Sakulin, C. Schäfer, C. Schwick, M. Seidel, M. Selvaggi, A. Sharma, P.
Silva, P. Sphicas47, A. Stakia, J. Steggemann, M. Stoye, M. Tosi, D. Treille,
A. Tsirou, V. Veckalns48, M. Verweij, W.D. Zeuner Paul Scherrer Institut,
Villigen, Switzerland
W. Bertl${}^{\textrm{\textdagger}}$, L. Caminada49, K. Deiters, W. Erdmann, R.
Horisberger, Q. Ingram, H.C. Kaestli, D. Kotlinski, U. Langenegger, T. Rohe,
S.A. Wiederkehr ETH Zurich - Institute for Particle Physics and Astrophysics
(IPA), Zurich, Switzerland
M. Backhaus, L. Bäni, P. Berger, B. Casal, G. Dissertori, M. Dittmar, M.
Donegà, C. Dorfer, C. Grab, C. Heidegger, D. Hits, J. Hoss, T. Klijnsma, W.
Lustermann, B. Mangano, M. Marionneau, M.T. Meinhard, D. Meister, F. Micheli,
P. Musella, F. Nessi-Tedaldi, F. Pandolfi, J. Pata, F. Pauss, G. Perrin, L.
Perrozzi, M. Quittnat, M. Reichmann, D.A. Sanz Becerra, M. Schönenberger, L.
Shchutska, V.R. Tavolaro, K. Theofilatos, M.L. Vesterbacka Olsson, R. Wallny,
D.H. Zhu Universität Zürich, Zurich, Switzerland
T.K. Aarrestad, C. Amsler50, D. Brzhechko, M.F. Canelli, A. De Cosa, R. Del
Burgo, S. Donato, C. Galloni, T. Hreus, B. Kilminster, I. Neutelings, D.
Pinna, G. Rauco, P. Robmann, D. Salerno, K. Schweiger, C. Seitz, Y. Takahashi,
A. Zucchetta National Central University, Chung-Li, Taiwan
V. Candelise, Y.H. Chang, K.y. Cheng, T.H. Doan, Sh. Jain, R. Khurana, C.M.
Kuo, W. Lin, A. Pozdnyakov, S.S. Yu National Taiwan University (NTU), Taipei,
Taiwan
Arun Kumar, P. Chang, Y. Chao, K.F. Chen, P.H. Chen, F. Fiori, W.-S. Hou, Y.
Hsiung, Y.F. Liu, R.-S. Lu, E. Paganis, A. Psallidas, A. Steen, J.f. Tsai
Chulalongkorn University, Faculty of Science, Department of Physics, Bangkok,
Thailand
B. Asavapibhop, K. Kovitanggoon, G. Singh, N. Srimanobhas Çukurova University,
Physics Department, Science and Art Faculty, Adana, Turkey
A. Bat, F. Boran, S. Cerci51, S. Damarseckin, Z.S. Demiroglu, C. Dozen, I.
Dumanoglu, S. Girgis, G. Gokbulut, Y. Guler, I. Hos52, E.E. Kangal53, O. Kara,
A. Kayis Topaksu, U. Kiminsu, M. Oglakci, G. Onengut, K. Ozdemir54, D. Sunar
Cerci51, B. Tali51, U.G. Tok, S. Turkcapar, I.S. Zorbakir, C. Zorbilmez Middle
East Technical University, Physics Department, Ankara, Turkey
G. Karapinar55, K. Ocalan56, M. Yalvac, M. Zeyrek Bogazici University,
Istanbul, Turkey
E. Gülmez, M. Kaya57, O. Kaya58, S. Tekten, E.A. Yetkin59 Istanbul Technical
University, Istanbul, Turkey
M.N. Agaras, S. Atay, A. Cakir, K. Cankocak, Y. Komurcu 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, L. Beck, 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. Newbold60,
S. Paramesvaran, T. Sakuma, S. Seif El Nasr-storey, D. Smith, V.J. Smith
Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev61, C. Brew, R.M. Brown, L. Calligaris, 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
G. Auzinger, R. Bainbridge, P. Bloch, J. Borg, S. Breeze, O. Buchmuller, A.
Bundock, S. Casasso, D. Colling, L. Corpe, P. Dauncey, G. Davies, M. Della
Negra, R. Di Maria, Y. Haddad, G. Hall, G. Iles, T. James, M. Komm, R. Lane,
C. Laner, L. Lyons, A.-M. Magnan, S. Malik, L. Mastrolorenzo, T. Matsushita,
J. Nash62, A. Nikitenko6, V. Palladino, M. Pesaresi, A. Richards, A. Rose, E.
Scott, C. Seez, A. Shtipliyski, T. Strebler, S. Summers, A. Tapper, K. Uchida,
M. Vazquez Acosta63, 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, A. Morton, I.D. Reid, L.
Teodorescu, S. Zahid Baylor University, Waco, USA
A. Borzou, K. Call, J. Dittmann, K. Hatakeyama, H. Liu, 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, A. Avetisyan, T. Bose, D. Gastler, D. Rankin, C. Richardson, J.
Rohlf, L. Sulak, D. Zou Brown University, Providence, USA
G. Benelli, 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, J. Pazzini, S.
Piperov, S. Sagir, R. Syarif, 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, R. Lander, C. Mclean, M. Mulhearn, D. Pellett, J. Pilot, S. Shalhout,
M. Shi, J. Smith, 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. Ellison, 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, 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. Wasserbaech65, 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, J. Bradmiller-Feld, C. Campagnari, M. Citron, A. Dishaw,
V. Dutta, M. Franco Sevilla, L. Gouskos, R. Heller, J. Incandela, A.
Ovcharova, H. Qu, J. Richman, D. Stuart, I. Suarez, J. Yoo California
Institute of Technology, Pasadena, USA
D. Anderson, A. Bornheim, J. Bunn, J.M. Lawhorn, H.B. Newman, T. Q. Nguyen, C.
Pena, 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, J. Russ, M. Sun, H.
Vogel, I. Vorobiev, M. Weinberg University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, F. Jensen, A. Johnson, M. Krohn, S. Leontsinis, E.
Macdonald, T. Mulholland, K. Stenson, K.A. Ulmer, S.R. Wagner Cornell
University, Ithaca, USA
J. Alexander, J. Chaves, Y. Cheng, J. Chu, A. Datta, S. Dittmer, 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, G.
Bolla${}^{\textrm{\textdagger}}$, 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, B. Kreis, S.
Lammel, D. Lincoln, R. Lipton, M. Liu, T. Liu, R. Lopes De Sá, J. Lykken, K.
Maeshima, N. Magini, J.M. Marraffino, D. Mason, P. McBride, P. Merkel, S.
Mrenna, S. Nahn, V. O’Dell, K. Pedro, O. Prokofyev, G. Rakness, L. Ristori, A.
Savoy-Navarro66, 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, W. Wu University of Florida, Gainesville, USA
D. Acosta, P. Avery, P. Bortignon, D. Bourilkov, A. Brinkerhoff, A. Carnes, M.
Carver, D. Curry, R.D. Field, I.K. Furic, S.V. Gleyzer, B.M. Joshi, J.
Konigsberg, A. Korytov, K. Kotov, P. Ma, K. Matchev, H. Mei, G. Mitselmakher,
K. Shi, D. Sperka, N. Terentyev, L. Thomas, J. Wang, S. Wang, J. Yelton
Florida International University, Miami, USA
Y.R. Joshi, S. Linn, P. Markowitz, J.L. Rodriguez 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, A. Santra, V. Sharma, R.
Yohay Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, V. Bhopatkar, S. Colafranceschi, M. Hohlmann, D. Noonan, 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, O.
Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, K. Jung, J. Kamin, I.D.
Sandoval Gonzalez, M.B. Tonjes, N. Varelas, H. Wang, Z. Wu, J. Zhang The
University of Iowa, Iowa City, USA
B. Bilki67, W. Clarida, K. Dilsiz68, S. Durgut, R.P. Gandrajula, M.
Haytmyradov, V. Khristenko, J.-P. Merlo, H. Mermerkaya69, A. Mestvirishvili,
A. Moeller, J. Nachtman, H. Ogul70, Y. Onel, F. Ozok71, A. Penzo, C. Snyder,
E. Tiras, J. Wetzel, K. Yi Johns Hopkins University, Baltimore, USA
B. Blumenfeld, A. Cocoros, N. Eminizer, D. Fehling, L. Feng, A.V. Gritsan, 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, J. Castle, S.
Khalil, A. Kropivnitskaya, D. Majumder, W. Mcbrayer, M. Murray, C. Rogan, C.
Royon, S. Sanders, E. Schmitz, J.D. Tapia Takaki, Q. Wang Kansas State
University, Manhattan, USA
A. Ivanov, K. Kaadze, Y. Maravin, A. Mohammadi, L.K. Saini, N. Skhirtladze
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, F. Ricci-Tam,
Y.H. Shin, A. Skuja, S.C. Tonwar Massachusetts Institute of Technology,
Cambridge, USA
D. Abercrombie, B. Allen, V. Azzolini, R. Barbieri, 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, A. Levin, P.D. Luckey, B. Maier, A.C. Marini, C.
Mcginn, C. Mironov, S. Narayanan, X. Niu, C. Paus, C. Roland, G. Roland, J.
Salfeld-Nebgen, 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, R.M. Chatterjee, A. Evans, P. Hansen, S. Kalafut, Y. Kubota,
Z. Lesko, J. Mans, S. Nourbakhsh, N. Ruckstuhl, R. Rusack, J. Turkewitz, 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
J. Dolen, A. Godshalk, C. Harrington, I. Iashvili, D. Nguyen, A. Parker, S.
Rappoccio, B. Roozbahani Northeastern University, Boston, USA
G. Alverson, E. Barberis, C. Freer, A. Hortiangtham, A. Massironi, D.M. Morse,
T. Orimoto, R. Teixeira De Lima, T. Wamorkar, B. Wang, A. Wisecarver, D. Wood
Northwestern University, Evanston, USA
S. Bhattacharya, 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.
Musienko36, 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, A.
Hart, C. Hill, W. Ji, T.Y. Ling, W. Luo, B.L. Winer, H.W. Wulsin Princeton
University, Princeton, USA
S. Cooperstein, O. Driga, P. Elmer, J. Hardenbrook, P. Hebda, S. Higginbotham,
A. Kalogeropoulos, D. Lange, J. Luo, D. Marlow, K. Mei, I. Ojalvo, J. Olsen,
C. Palmer, P. Piroué, D. Stickland, C. Tully University of Puerto Rico,
Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A. Barker, V.E. Barnes, S. Das, L. Gutay, M. Jones, A.W. Jung, A. Khatiwada,
D.H. Miller, N. Neumeister, C.C. Peng, H. Qiu, J.F. Schulte, J. Sun, F. Wang,
R. Xiao, W. Xie Purdue University Northwest, Hammond, USA
T. Cheng, N. Parashar Rice University, Houston, USA
Z. Chen, K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Guilbaud, M. Kilpatrick, W.
Li, B. Michlin, B.P. Padley, J. Roberts, J. Rorie, W. Shi, Z. Tu, J. Zabel, A.
Zhang University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, Y.t. Duh, T. Ferbel, M. Galanti, A.
Garcia-Bellido, J. Han, O. Hindrichs, A. Khukhunaishvili, K.H. Lo, P. Tan, M.
Verzetti The Rockefeller University, New York, USA
R. Ciesielski, K. Goulianos, C. Mesropian Rutgers, The State University of New
Jersey, Piscataway, USA
A. Agapitos, J.P. Chou, Y. Gershtein, T.A. Gómez Espinosa, E. Halkiadakis, 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, K. Rose, S. Spanier, K. Thapa Texas A&M
University, College Station, USA
O. Bouhali72, A. Castaneda Hernandez72, A. Celik, M. Dalchenko, M. De Mattia,
A. Delgado, S. Dildick, R. Eusebi, J. Gilmore, T. Huang, T. Kamon73, R.
Mueller, Y. Pakhotin, R. Patel, A. Perloff, L. Perniè, D. Rathjens, A.
Safonov, A. Tatarinov Texas Tech University, Lubbock, USA
N. Akchurin, J. Damgov, F. De Guio, P.R. Dudero, J. Faulkner, E. Gurpinar, 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, P. Sheldon, S. Tuo, J. Velkovska, 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, L. Dodd, S. Duric,
B. Gomber, M. Grothe, M. Herndon, A. Hervé, U. Hussain, P. Klabbers, A.
Lanaro, A. Levine, K. Long, R. Loveless, V. Rekovic, T. Ruggles, A. Savin, 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 Institute for Theoretical and Experimental Physics, Moscow, Russia
7: Also at Joint Institute for Nuclear Research, Dubna, Russia
8: Also at Helwan University, Cairo, Egypt
9: Now at Zewail City of Science and Technology, Zewail, Egypt
10: Also at Suez University, Suez, Egypt
11: Now at British University in Egypt, Cairo, Egypt
12: Also at Department of Physics, King Abdulaziz University, Jeddah, Saudi
Arabia
13: Also at Université de Haute Alsace, Mulhouse, France
14: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
15: Also at 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 MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
20: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
21: Also at Institute of Physics, University of Debrecen, Debrecen, 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 University of Ruhuna, Matara, Sri Lanka
27: Also at Isfahan University of Technology, Isfahan, Iran
28: Also at Yazd University, Yazd, Iran
29: Also at Plasma Physics Research Center, Science and Research Branch,
Islamic Azad University, Tehran, Iran
30: Also at Università degli Studi di Siena, Siena, Italy
31: Also at INFN Sezione di Milano-Bicocca; Università di Milano-Bicocca,
Milano, Italy
32: Also at International Islamic University of Malaysia, Kuala Lumpur,
Malaysia
33: Also at Malaysian Nuclear Agency, MOSTI, Kajang, Malaysia
34: Also at Consejo Nacional de Ciencia y Tecnología, Mexico city, Mexico
35: Also at Warsaw University of Technology, Institute of Electronic Systems,
Warsaw, Poland
36: Also at Institute for Nuclear Research, Moscow, Russia
37: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
38: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
39: Also at University of Florida, Gainesville, USA
40: Also at P.N. Lebedev Physical Institute, Moscow, Russia
41: Also at California Institute of Technology, Pasadena, USA
42: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
43: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
44: Also at INFN Sezione di Pavia; Università di Pavia, Pavia, Italy
45: Also at University of Belgrade, Faculty of Physics and Vinca Institute of
Nuclear Sciences, Belgrade, Serbia
46: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy
47: Also at National and Kapodistrian University of Athens, Athens, Greece
48: Also at Riga Technical University, Riga, Latvia
49: Also at Universität Zürich, Zurich, Switzerland
50: Also at Stefan Meyer Institute for Subatomic Physics (SMI), Vienna,
Austria
51: Also at Adiyaman University, Adiyaman, Turkey
52: Also at Istanbul Aydin University, Istanbul, Turkey
53: Also at Mersin University, Mersin, Turkey
54: Also at Piri Reis University, Istanbul, Turkey
55: Also at Izmir Institute of Technology, Izmir, Turkey
56: Also at Necmettin Erbakan University, Konya, Turkey
57: Also at Marmara University, Istanbul, Turkey
58: Also at Kafkas University, Kars, Turkey
59: Also at Istanbul Bilgi University, Istanbul, Turkey
60: Also at Rutherford Appleton Laboratory, Didcot, United Kingdom
61: Also at School of Physics and Astronomy, University of Southampton,
Southampton, United Kingdom
62: Also at Monash University, Faculty of Science, Clayton, Australia
63: Also at Instituto de Astrofísica de Canarias, La Laguna, Spain
64: Also at Bethel University, ST. PAUL, USA
65: Also at Utah Valley University, Orem, USA
66: Also at Purdue University, West Lafayette, USA
67: Also at Beykent University, Istanbul, Turkey
68: Also at Bingol University, Bingol, Turkey
69: Also at Erzincan University, Erzincan, 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