PRF-18-001

$HeadURL:svn+ssh://svn.cern.ch/reps/tdr2/papers/PRF-18-001/trunk/PRF-18-001.tex$
$Id:PRF-18-001.tex4935322019-04-0620:54:22Zbanerjee$

PRF-18-001

# Calibration of the CMS hadron calorimeters using proton-proton collision
data at $\sqrt{s}=13\TeV$

###### Abstract

Methods are presented for calibrating the hadron calorimeter system of the CMS
detector at the LHC. The hadron calorimeters of the CMS experiment are
sampling calorimeters of brass and scintillator, and are in the form of one
central detector and two endcaps. These calorimeters cover pseudorapidities
$\abs{\eta}<3$ and are positioned inside the solenoidal magnet. An outer
calorimeter, outside the magnet coil, covers $\abs{\eta}<1.26$, and a steel
and quartz-fiber Cherenkov forward calorimeter extends the coverage to
$\abs{\eta}<5.19$. The initial calibration of the calorimeters was based on
results from test beams, augmented with the use of radioactive sources and
lasers. The calibration was improved substantially using proton-proton
collision data collected at $\sqrt{s}=7$, 8, and 13, as well as cosmic ray
muon data collected during the periods when the LHC beams were not present.
The present calibration is performed using the 13data collected during 2016
corresponding to an integrated luminosity of 35.9. The intercalibration of
channels exploits the approximate uniformity of energy collection over the
azimuthal angle. The absolute energy scale of the central and endcap
calorimeters is set using isolated charged hadrons. The energy scale for the
electromagnetic portion of the forward calorimeters is set using
$\PZ\to\Pe\Pe$ data. The energy scale of the outer calorimeters has been
determined with test beam data and is confirmed through data with high
transverse momentum jets. In this paper, we present the details of the
calibration methods and accuracy.

##  0.1 Introduction

Most precision studies of the standard model (SM) utilizing proton-proton
($\Pp\Pp$) collisions depend on reliable, precise measurements of jets and
missing transverse momentum. The uncertainties in these are often related to
the energy scale and the resolution of the measurement of hadron energy. For
example, a precision measurement of the mass of the top quark requires a
detailed understanding of the energy scale and resolution of jets; in a recent
measurement of the top quark mass by CMS [1, 2], the single largest source of
experimental uncertainty in two of the channels used was the jet energy scale.
Searches for physics beyond the SM (, searches for dark matter based on
measurements of jets produced through initial-state radiation [3]) often rely
on measurements of hadron properties.

The methods used to calibrate hadron calorimeters have been studied and
improved by several experiments. Previous publications from the CMS and ATLAS
Collaborations on calorimeter calibration are reported in Refs. [4, 5, 6, 7,
8, 9]. The hadron calorimeter (HCAL) [10, 11] is composed of four major
subdetectors: the hadron barrel (HB) [12], the hadron endcap (HE) [10], the
hadron forward (HF) [13], and the hadron outer (HO) calorimeters [7] (as shown
in Fig. 1). The CMS Collaboration has developed several techniques for
calibrating its HCAL. The initial calibration makes use of results from
several test beam exposures and from signals injected by dedicated calibration
systems based on lasers and radioactive sources. Details of the test beam
analyses and the calibration using radioactive sources can be found in Refs.
[6, 8, 14]. The calibration of the detector is improved through the analysis
of data from cosmic ray muons, taken when the CERN LHC was not operating, and
data containing energy deposits from secondary particles produced during LHC
beam tuning [15] and traversing the detector longitudinally.

The final calibration, described in this paper, uses information from
collision data to further improve the precision of the calibration, and to
establish a hadronic energy scale, which is stable over the course of the data
taking. Because of the complex structure of the HCAL, its large angular
coverage, nonuniformities in the amount of material in front of the
calorimeters, and the limited acceptance of the CMS tracking system, these
goals can only be achieved through the use of several techniques and data
samples. In addition, the calibration needs to take into account
nonlinearities in the HCAL energy response [8]. The calibration methods
reported in this paper were first used with data collected during 2010–2015 at
$\sqrt{s}=7$, 8, and 13. Here, we report the calibration performed using data
collected during 2016 at $\sqrt{s}=13\TeV$, corresponding to an integrated
luminosity up to 35.9.

Detector calibration is needed to reduce the uncertainty in the energy
measurement to less than 3%. Sources of changes in response larger than this
target goal include aging of the photocathodes of the photodetectors,
scintillator and fiber optic aging, and changes to the configuration of the
hardware during shutdown, e.g., the readout of the HF calorimeter was split
into two separate optical paths during a shutdown at the end of 2015.

Because of the nonlinear energy response of the HCAL, it is not possible to
set an absolute energy scale that is valid for all incident hadron momenta.
Although the response is closer to being linear at high momenta, there are
relatively few events available. Therefore, the calibration is performed with
a sample of isolated hadrons of moderate momentum. The calibration of the HB
and HE described in this paper yields a unit value for the relative energy
scale factor of isolated charged pions with momenta of 50, which do not
interact hadronically in the CMS electromagnetic calorimeter (ECAL). The
energy of a reconstructed particle [16] is corrected for the calorimeter
nonlinearity using a parametrization of the response as a function of
transverse momentum (), pseudorapidity ($\eta$), and the fractions of the
particle energy deposited in the different subdetectors, as determined from a
-based [17] CMS detector simulation. Residual nonlinearities for jets are
removed during the calibration of the jet energy scale [18]. The low-level
calibration, reported in this paper, followed by the corrections and high-
level calibration [16, 18], lead to the final jet energy calibration of the
CMS calorimeter system.

Unlike the HB and HE, the HF does not have a strong nonlinear energy response.
The measured e/$\PGp$ response decreases from 1.14 to 1.01 when the energy of
the incident particles increases from 30 to 150 [13]. The energy scale for the
HF is set to have a unit value of the relative energy scale factor for 100
electrons and $\PGpm$ mesons using test beam data [13].

The HO is used to measure the energy deposits from high- particles whose
showers are not fully contained in the ECAL and HB. The scale for the HO is
set to give the best possible energy resolution for 300$\PGpm$ mesons [7].

The calibration includes the following steps:

  * (i)

the responses of different channels at the same $\eta$ are equalized in the
HB, HE, and HF, exploiting the approximate uniformity of collected energies
over the azimuthal angle ($\phi$);

  * (ii)

for the HB and the part of HE within the acceptance of the CMS tracking system
($\abs{\eta}<2.5$), isolated charged hadrons are used to equalize the $\eta$
response and to set the absolute energy scale;

  * (iii)

the calibration for the electromagnetic portion of the HF is validated using
electrons from decays of bosons; and

  * (iv)

the responses of the HO channels are equalized across $\phi$ using muons from
collision data, and the energy scale factor is validated using dijet events.

The scale factors obtained from the calibration process are updated if they
differ from the current set of scale factors by more than 3%. During 2016,
only two updates were necessary for the final processing of the data.

The paper is organized as follows. Section 0.2 provides a brief description of
the HCAL. Section 0.3 describes the reconstruction of HCAL energies and of the
physical objects used in the analysis. Section 0.4 gives information about the
simulated event samples used in the design and testing of the calibration
methods. The analysis of the symmetry in the azimuthal angle is in Section
0.5, and the calibration using isolated tracks is discussed in Section 0.6.
The HF calorimeter calibration using $\PZ\to\Pe\Pe$ events appears in Section
0.7, and the intercalibration of the HO calorimeter and the validation of its
energy scale factors are described in Section 0.8. A brief summary of the
results is given in Section 0.9.

##  0.2 The CMS detector and its hadron calorimeter

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 the HB and HE calorimeters. Muons are detected in gas-ionization chambers
embedded in the steel flux-return yoke outside the solenoid. In the barrel
section of ECAL, an energy resolution of about 1% is achieved for unconverted
or late-converting photons which have energies in the range of tens of . For
the remaining barrel photons the resolution is about 1.3% up to
$\abs{\eta}=1$, deteriorating to about 2.5% at $\abs{\eta}=1.4$. In the
endcaps, the resolution for unconverted or late-converting photons is about
2.5%, whereas the resolution for the remaining endcap photons is between 3 and
4% [19]. For isolated particles of $1<\pt<10\GeV$ and $\abs{\eta}<1.4$, the
track resolutions are typically 1.5% in $\pt$ and 25–90 (45–150)in the
transverse (longitudinal) impact parameter [20]. When measurements from the
tracker and the calorimeters are used, the jet energy resolution amounts
typically to 15% at 10, 8% at 100, and 4% at 1, to be compared to about 40,
12, and 5% obtained when only the ECAL and the HCAL calorimeters are used
[16].

Events of interest are selected using a two-tiered trigger system [21]. The
first level (L1), composed of custom hardware processors, uses information
from the calorimeters and muon detectors to select events at a rate of around
100kHz within a time interval of less than 4. The second level, known as the
high-level trigger (HLT), consists of an array of processors running a version
of the full event reconstruction software optimized for fast processing, and
reduces the event rate to around 1kHz before data storage.

A more detailed description of the CMS detector, together with a definition of
the coordinate system used and the relevant kinematic variables, is reported
in Section 5 of Ref. [11].

Figure 1 shows a schematic view of the layout of the CMS HCAL during the 2016
LHC operation at $\sqrt{s}=13\TeV$. The HB is located between radii of 1775
and 2876.5and covers $\abs{\eta}<1.39$. The HB is divided into two half-
barrels in the direction along the beam ($z$), each assembled from 18 wedges.
Each wedge subtends $20^{\circ}$ in $\phi$ and extends to 4330from the CMS
detector mid-plane. A wedge contains absorber plates made of brass (an alloy
with 70% copper and 30% zinc) that are bolted together. The inner and outer
plates are made out of stainless steel. There are 17 slots per wedge that
house the plastic scintillator tiles. The inner and outer slots are 14thick
while the remaining ones are 9.5thick. The HB has about $40\,000$ scintillator
tiles. In order to limit the number of individual physical elements, the tiles
at the same $\phi$ and depth are grouped into a single scintillator unit,
referred to as a megatile. The megatiles in the first and last layers are of
9thickness, while the remaining layers have 3.7thick megatiles. Each megatile
covers roughly $5^{\circ}$ in $\phi$. Of the four $\phi$ segments within a
barrel wedge, the two segments at a larger radius are staggered with respect
to the inner two. There is 61of stainless steel between layers 0 and 1. There
are 50.5thick brass plates between adjacent layers 1–9, and the 56.5thick
brass plates up to layer 15. The back plate, which is in front of the last HB
calorimeter layer, is made of 75thick stainless steel. The megatiles are
divided into 16 sections along the $z$ axis, denoted by $\abs{i\eta}=1$ to 16,
so that each tile corresponds to $\Delta\eta$ of 0.087. The set of
scintillators corresponding to the same value of $i\eta$ and $i\phi$ (denoting
the $\phi$ segment) in different layers are grouped together and referred to
as a “tower”. All 17 layers are grouped into a single readout channel until
$\abs{i\eta}=14$, beyond which there are two depth sections, as shown in Fig.
1.

Figure 1: A schematic view of one quarter of the CMS HCAL during 2016 LHC
operation, showing the positions of its four major components: the hadron
barrel (HB), the hadron endcap (HE), the hadron outer (HO), and the hadron
forward (HF) calorimeters. The layers marked in blue are grouped together as
$\text{depth}=1$, while the ones in yellow, green, and magenta are combined as
depths 2, 3, and 4, respectively.

The HE calorimeter is also made of brass absorber plates with sampling layers
of plastic scintillator. The innermost surface of HE is located 4006.5from the
interaction point and covers $1.30<\abs{\eta}<3.00$. Each endcap has an
18-fold symmetry in $\phi$ and has 34.5thick sector plates each covering
$20^{\circ}$ in $\phi$. The sector layers are separated by 9thick brass
spacers to allow space for the scintillator inserts. Each scintillator insert
covers $10^{\circ}$ in $\phi$. The top edge of the front part of the endcap
module has a slope of $53^{\circ}$ corresponding to the gap angle between the
HB and HE calorimeters. It also has a nose-like structure, with an additional
layer of absorber and scintillator plate for $\abs{i\eta}$ of 18, to increase
the total interaction length for that tower. The absorber thickness between
successive layers amounts to 78of brass and scintillator, corresponding to the
thickness of two sector plates and one spacer. The endcap on either side is
divided into 14 parts along $\abs{\eta}$, and the 18 layers are combined into
1, 2, or 3 depth sections, as shown in Fig. 1.

The HO calorimeter consists of one or two layers of scintillator outside the
magnet coil. The entire assembly is divided into 5 rings, each having 12
sectors in $\phi$. Six trays of scintillators are assembled on a honeycomb
structure, which is then mounted in each of these sectors. The central ring
(ring 0) has two layers of 10thick scintillator on either side of a stainless
steel block at radial distances of 3850 and 4097, respectively. All other
rings have a single layer at a radial distance of 4097. The $\eta$-$\phi$
segmentation of the HO calorimeter matches closely that of the HB calorimeter.
The HO calorimeter covers $\abs{\eta}<1.26$.

For the HB and HE calorimeters, clear fibers carry the light to hybrid
photodiodes (HPDs) [22], and each HPD signal is digitized in 25ns time
intervals by a charge integrator and encoder (QIE) [23]. For the HO
calorimeter, light is carried to silicon photomultipliers (SiPMs) and the SiPM
signals are digitized by the QIEs.

The front faces of the HF calorimeters are located 11150away from the
interaction point on either side of the CMS detector and cover
$2.85<\abs{\eta}<5.19$. The detectors covering positive and negative $\eta$
ranges are referred to as HF$+$ and HF$-$. The inner and outer radii of the HF
calorimeter are 125 and 1570, respectively. Each HF module is composed of 18
wedges made of steel with quartz fibers embedded along its length. The
detection technique utilizes emission of Cherenkov light by secondary charged
particles going through the quartz fibers. Long (1649) and short (1426) quartz
fibers are placed alternately with a separation of 5. The long fibers reach
the front face of the calorimeter, while the short fibers start 12.5 radiation
lengths into the calorimeter. These fibers are bundled at the back and led to
a photodetector, and are grouped into 13 divisions in $\eta$ on either side of
the CMS detector and 36 divisions in $\phi$, except the two largest
$\abs{\eta}$ sections, which contain 18 divisions in $\phi$. While the HB and
HE calorimeters work in conjunction with the ECAL for particle measurement,
the difference in the energy deposits in the long and short fibers of the HF
calorimeter functions as a separator between electromagnetic and hadronic
showers. The light collected from an HF calorimeter fiber is converted to
charge by a photomultiplier tube and digitized by the QIE.

##  0.3 Event reconstruction

In recorded events, signals from the HB, HE, and HO calorimeters are stored as
10 consecutive QIE measurements (“time samples”, where each sample spans 25
ns). To reduce data volume and readout latency, a “zero suppression” mechanism
is introduced that passes to the data acquisition system only those channels
with at least one set of two consecutive samples above a threshold. The timing
of the readout system is adjusted so that the triggered bunch crossing is in
the fifth time sample. The data recorded from the HF channels are stored as
four consecutive time samples, with the triggered bunch crossing in the third
time sample. Energies are calculated as sums of time samples, after
subtraction of the electronic pedestal and application of the scale factor.
For the HB and HE, and for the analysis described in Section 0.5.2, the energy
is calculated by applying a scale factor to the sum of energies in the fifth-
and sixth-time samples, with a correction factor to account for the pulse
extending beyond 50ns. For the HF, since the Cherenkov pulse is shorter than
25ns, only one time sample is used.

A sophisticated reconstruction method is used for the HB and HE subsystems
because there is a large contribution from pileup, and the HPDs used for their
readout have larger noise than the SiPMs used in the HO. For the HB and HE the
energy deposited in the triggered bunch crossing, after subtracting the
electronic pedestal, is obtained by fitting the time samples with up to three
pulse shape templates shifted in time relative to each other. The
reconstruction algorithm also corrects for the residual contributions caused
by additional interactions in the triggered beam crossing as well as the
preceding and subsequent beam crossings (out-of-time pileup). This procedure
is employed for the standard reconstruction of HB and HE in the physics
analyses for all the results in this paper, except for the process described
in Section 0.5.2, which uses a simple sum of energies over the two time
slices.

The reduction of the light output of the scintillator caused by radiation
damage [24] and the decrease in the quantum efficiency of the HPD
photocathodes due to the ion feedback damage [25] are monitored using a laser
calibration system, and corrections are applied to the reconstructed hit
energies based on the monitored results.

Because the HO subsystem is outside the solenoid, the effect of pileup is
relatively small, and a simpler procedure is used. For HO signal pulses longer
than 100ns, a correction is applied to account for the signal beyond the
eighth time sample.

There are several sources of noise in the hadron calorimeter, such as noise in
the readout system, as well as noise from physical sources like particles
other than optical photons interacting directly with the readout system [26].

Noise caused by spurious electronic signals is detected and subtracted during
the calibration procedure. Although the overall rate is low, this noise would
result in large values of mismeasured missing transverse momentum so dedicated
filters are used to eliminate either the specific calorimeter channel, or the
entire event [27].

The reconstruction of physics objects produced in $\Pp\Pp$ collisions starts
with the reconstruction of particles. The CMS global event description is
based on the particle-flow (PF) algorithm [16], which reconstructs and
identifies each individual particle in an event with an optimized combination
of information from the various elements of the CMS detector. In this process,
the identification of the particle type (photon, electron, muon, charged or
neutral hadron) plays an important role in the determination of the particle
direction and energy. Photons, , coming from decays or from electron
bremsstrahlung, are identified as ECAL energy clusters not linked to any
charged-particle trajectory extrapolated to the ECAL. Electrons, , coming from
photon conversions in the tracker material or from hadron semileptonic decays,
are identified as primary charged-particle tracks and potentially many ECAL
energy clusters corresponding to this track extrapolation to the ECAL and to
possible bremsstrahlung photons emitted along the path through the tracker
material. Muons, , from hadron semileptonic decays, are identified as tracks
in the central tracker consistent with either a track or several hits in the
muon system that are associated with very low energy deposits in the
calorimeters. Charged hadrons are identified as charged-particle tracks that
are not identified as electrons or muons. The energy of a charged hadron is
determined from the combination of the measurements from the tracker and the
calorimeters. Finally, neutral hadrons are identified as HCAL energy clusters
not linked to any charged-hadron trajectory, or as an excess in the combined
ECAL and HCAL energy with respect to the expected charged-hadron energy
deposit. The reconstructed particles are referred to as PF candidates.

For each event, hadronic jets (PF jets) are clustered from the PF candidates
using the infrared and collinear safe anti-algorithm [28, 29] with a distance
parameter of 0.4. The jet momentum is determined as the vectorial sum of all
particle momenta in the jet, and is found from simulation to be, on average,
within 5 to 10% of its true momentum over the entire spectrum and detector
acceptance. The PF candidates associated with a jet are referred to as jet
constituents. The PF missing transverse energy is calculated from the jet
momenta as an imbalance of the energy flow in the transverse plane of the
detector. Because PF makes optimal use of the detector information,
contributions to the jet and missing transverse energy resolutions from
charged hadrons are dominated by the precision of the momentum measurement
from the tracker. The ECAL determines the contribution to the energy
resolution from photons. However, for the highest energy charged hadrons,
which can be produced in jets originating from decays of high-mass new
particles, for neutral hadrons, and for all particles at large rapidities, the
hadron calorimeter dominates the momentum measurement. Accurate measurement of
HCAL energies is also important in isolation calculations, which are used in
particle identification.

During the 2016 data taking, the mean number of $\Pp\Pp$ interactions per
bunch crossing was approximately 23. The reconstructed vertex with the largest
value of summed physics-object $\pt^{2}$ is identified as the primary $\Pp\Pp$
interaction vertex. The physics objects in this case are jets, clustered using
the jet finding algorithm [28, 29] with the tracks assigned to the vertex as
inputs, and the associated missing transverse momentum, taken as the negative
vector sum of those jets. Other reconstructed vertices are referred to as
pileup vertices.

##  0.4 Simulated event samples

The methodologies used for the calibration are tested using simulated samples
of $\Pp\Pp$ interactions. Simulation of SM processes, unless otherwise stated,
is performed with 8.206 [30] or 2.2.2 [31] event generators at leading order
in the strong coupling, which is set to 0.130 at the boson mass scale. The
event generators employ the NNPDF3.0 [32] parton distribution functions.
Parton shower development and hadronization are simulated with using the
underlying event tune CUETP8M1 [33]. Simulated samples consisting of single
high- particles are also produced. Samples that do not contain a collision in
the nominal bunch crossing are used to simulate noise (see Section 0.5.2). The
detector response is simulated using a detailed description of the CMS
detector implemented with the package [17]. The simulated events are
reconstructed with the same algorithms used for the data. The simulated
samples include pileup with the distribution matching that observed in the
data.

##  0.5 Calibration using azimuthal symmetry

The first step in the calibration of the HB, HE, and HF with collision data is
to equalize the response in $\phi$ for each $i\eta$ ring and depth section.
The procedure takes advantage of the $\phi$-symmetric particle energies and
the corresponding $\phi$-symmetric collected energy from minimum bias (MB)
events (events selected with triggers designed to collect inelastic collisions
with maximum efficiency while suppressing noncollision events). The layouts of
the barrel and the endcap detectors have some $\phi$ dependence because of the
absorber structure; the scintillator layers are staggered, and the absorber
layers are also used as a part of the support structure. For the forward
calorimeter, a radial shift in the beam spot position may also introduce
asymmetry in the $i\eta$ rings close to the beam pipe, which can change with
time. The relative contributions to the $\phi$ asymmetries from materials,
inhomogeneous magnetic field, beam spot shift, and miscalibration could, in
principle, be understood using simulation. However, the material description
in the simulation and the modeling of the beam spot position is not exact, and
the difference between the actual detector and its Monte Carlo description can
increase with time because of stresses from the magnetic field, gravity,

Therefore, the intercalibration is performed by comparing the average
collected energy in a calorimeter channel to the average collected energy in
the entire $i\eta$ ring. Two different calibration procedures are adopted.

Iterative method:

A set of multiplicative correction factors (scale factors) for the
uncalibrated energies are determined iteratively by equalizing the mean of the
measured energies that satisfy both an upper and a lower threshold. This
method works best for energies above 4 GeV.

Method of moments:

This intercalibration is carried out using MB events taken without zero
suppression by comparing the first (mean) and second (variance) moments of the
energy distribution in a calorimeter channel to the mean of the moments of the
energy distributions in the entire $i\eta$ ring. This method works best for
low energies, down to a fraction of a GeV.

By construction, these two methods use events from disjoint data samples and
are statistically independent.

The method of moments is performed using events with no zero suppression
(NZS). The measured energies include contributions both from genuine energy
deposits and from noise. The contributions due to noise are estimated from an
independent data sample taken when there were no beam collisions (pedestal
data), and subtracted from the measurements made using the collision data to
extract the contribution due to signal. The iterative method, on the other
hand, makes use of zero-suppressed events and is based on an estimation of
mean energy in an energy interval. The two methods symmetrize different energy
ranges and offer the best performance in the corresponding range.

###  0.5.1 Iterative method

The data used in this method are selected from events triggered by
subdetectors other than the HCAL to avoid trigger bias in the energy
measurement; only events collected with electron, photon, and muon triggers
are used (single-muon triggers with $\pt>24$and single-electron triggers with
$\pt>25$). Results obtained from different triggers are compared, and the
difference is used as input to the estimation of the systematic uncertainty.

This method utilizes energies recorded in HCAL channels that pass lower and
upper threshold requirements. Figure 2 shows a typical energy spectrum
obtained from reconstructed hit energy before the $\phi$ symmetry calibration.
It is shown for a single channel in the HB (left) and the HF (right)
calorimeters, along with the values of the lower and upper thresholds used for
these subdetectors.

Figure 2: Energy spectra used as an input to the iterative method of
equalizing the $\phi$ response of one typical $i\eta$ ring for an HB (left)
and HF (right) calorimeter channel. Thresholds on the collected energies used
for the energy estimation are shown with dashed lines. The legends show the
HCAL channel index in $i\eta$, $i\phi$, and depth segmentation units.

The reconstructed energies are obtained from zero-suppressed events after
pedestal subtraction, and the corresponding mean noise level is zero. The
value of the lower threshold ($E_{\text{low}}$) depends on the subdetector and
is chosen to be well above (approximately 20 times) the RMS of the noise
distribution for a single channel, which is a few hundred . The noise level is
determined from pedestal data and is found to be a few hundred . For the HB
and HE calorimeters, the threshold is 4, whereas it is 10for the HF
calorimeter.

The upper threshold ($E_{\text{high}}$) ensures that statistical fluctuations
in the tails of the energy distributions do not influence the mean. Its value
also depends on the subdetector. For $\Pp\Pp$ collision data, the threshold is
100 (150)for the HB (HE and HF).

For each channel, the total energy between the thresholds $E_{\text{tot}}$ is
calculated from the observed energy spectrum using:

$E_{\text{tot}}=\int_{E_{\text{low}}}^{E_{\text{high}}}\frac{\rd N(E)}{\rd
E}E\rd E,$ (1)

and the mean channel energy ($\langle E_{\text{tot}}\rangle$) is defined as
$E_{\text{tot}}$ divided by the number of events used in the $E_{\text{tot}}$
calculation. The scale factor is calculated as the inverse of the ratio of
$\langle E_{\text{tot}}\rangle$ for that channel to the mean $\langle
E_{\text{tot}}\rangle$ of all channels with the same $i\eta$ and depth, in
each iteration. These scale factors are then applied to the energy
measurement, and the whole process is repeated, including the selection of the
channels included in the determination of $E_{\text{tot}}$ through the
application of the energy thresholds. This procedure is repeated until the
mean change in the scale factor (over all channels) falls below a convergence
cutoff value.

Figure 3 shows $\langle E_{\text{tot}}\rangle$ as a function of $i\phi$ for
two typical $i\eta$ rings for the HB and HE calorimeters before and after the
corrections. The spread in the mean channel energy $\langle
E_{\text{tot}}\rangle$ is reduced from 4.7 to 0.3% for HB and from 6.2 to 0.2%
for HE. The uncertainties in the scale factors are estimated from the
statistical uncertainties in $\langle E_{\text{tot}}\rangle$ and the variation
from the last iteration, added in quadrature.

Figure 3: Mean channel energy $\langle E_{\text{tot}}\rangle$ for the
iterative method measured before (solid histogram) and after (open histogram)
correction as a function of $i\phi$ for a typical $i\eta$ ring of the HB
($i\eta=9$, $\text{depth}=1$, left) and of the HE ($i\eta=20$,
$\text{depth}=1$, right) calorimeters. Data triggered using electrons,
photons, and muon are used in this calibration procedure.

The statistical uncertainties, including the variations from the last
iteration, for the 2016 $\Pp\Pp$ collision data are of the order of 1% for the
HB, between 0.1 and 1.0% for the HE, and below 0.5% for the HF calorimeter
channels, depending on $i\eta$ and depth.

###  0.5.2 Method of moments

The first two central moments of the energy distributions are used to obtain
the scale factors for each channel. The main challenges in this method are (i)
the noise exceeds the size of a typical signal, which is a few MeV in the HB
channels and a few tens of MeV in the HE channels, and (ii) the variance of
the noise distribution, 0.04–0.092, differs considerably from one channel to
another. Conditions are more favorable in the HF, where the noise variance is
less than the signal variance and also less than the mean value of the signal.
Therefore, both the first and second moments for the HF calorimeter channels
are used in the determination of the intercalibration constants.

The analysis is done using MB events taken with a special trigger where zero
suppression is disabled in the HCAL readout. The noise in each channel is
measured separately by using an independent data set taken when the LHC was
not running and without any trigger requirements. The measured noise
distribution is subtracted from the energy distribution after suitable
normalization (pedestal subtraction).

The scale factor obtained using the first moment is given by

${\mathrm{C}}_{i\eta,i\phi}=\frac{\langle
E_{i\eta,i\phi}\rangle}{\frac{1}{N_{\phi}}\sum_{j\phi}\langle
E_{i\eta,j\phi}\rangle},$ (2)

where $N_{\phi}$ is the number of HCAL towers in a given $i\eta$ ring, and

$\langle E_{i\eta,i\phi}\rangle=\langle
E^{\text{signal}}_{i\eta,i\phi}\rangle+\langle
E^{\text{noise}}_{i\eta,i\phi}\rangle$ (3)

is the mean collected energy in the HCAL tower. After pedestal subtraction,
the data are consistent with $\langle E^{\text{noise}}_{i\eta,i\phi}\rangle=0$
and Eq. (3) becomes:

$\langle E_{i\eta,i\phi}\rangle=\langle
E^{\text{signal}}_{i\eta,i\phi}\rangle.$ (4)

The uncertainty in the estimation of the first moment is given by

$\sqrt{\Delta^{2}\left(\langle
E^{\text{signal}}_{i\eta,i\phi}\rangle\right)+\Delta^{2}\left(\langle
E^{\text{noise}}_{i\eta,i\phi}\rangle\right)},$ (5)

where $\Delta^{2}$ is the variance. It is dominated by the uncertainty in the
noise estimation. To achieve a precision better than 2% for channels in the
middle of HB ($\abs{i\eta}=1$), a few tens of millions of events are required.
While the method is straightforward to perform, a large amount of data is
required to use it effectively. Figure 4 shows the scale factors obtained for
two specific towers of HB and HE ($i\eta=9$, $\text{depth}=1$, and $i\eta=20$,
$\text{depth}=1$) using this method with data collected during 2016.

Figure 4: Calibration scale factor obtained using the method of moments with
the first moment of the energy distribution for one HB ($i\eta=9$,
$\text{depth}=1$ left)and one HE ($i\eta=20$. $\text{depth}=1$ right), as a
function of $i\phi$. Only statistical uncertainties in the measurements are
shown in these plots and the sizes of the vertical bars are typically smaller
than the marker size.

Estimation of the scale factor from the second central moment (variance) is
done after removing the noise by subtracting the variance of the noise from
the variance of the measured energy. The scale factor in this case is given
by:

$\mathrm{C}_{i\eta,i\phi}=\sqrt{\frac{\frac{1}{N_{\phi}}\sum_{j\phi}\Delta^{2}R_{i\eta,j\phi}}{\Delta^{2}R_{i\eta,i\phi}}},$
(6)

where

$\Delta^{2}R_{i\eta,i\phi}=\langle\Delta^{2}(E^{\text{signal}}_{i\eta,i\phi})+\Delta^{2}(E^{\text{noise}}_{i\eta,i\phi})\rangle-\langle\Delta^{2}(E^{\text{noise}}_{i\eta,i\phi})\rangle.$
(7)

Assuming no correlation between the noise and signal depositions in the
calorimeter,

$\Delta^{2}R_{i\eta,i\phi}=\langle\Delta^{2}(E^{\text{signal}}_{i\eta,i\phi})\rangle.$
(8)

The minimum sample size for achieving a 2% uncertainty in the signal variance
is determined by the residual noise contribution, and is of the order of a few
million events. The method based on the second moment requires substantially
smaller samples. Therefore, the second-moment method is used for the final
results for HB and HE, but the results are still sensitive to the noise level
in the channel, even when the noise levels are measured. During 2016, the
noise levels for channels in the HF calorimeter were not measured, and the
method based on the first moment is used for those channels.

Figure 5 shows the effect of using the first- or second-moment method on a
simulated sample of MB events for the HB (left) and HF (right). The structure
in the plot of HF scale factors as a function of $i\phi$ reflects the geometry
of the readout system (middle versus edge readouts) and of the passive
material between the calorimeter and the interaction point (support structure
and services for the detectors). The two methods of moments (mean and
variance) have different sensitivities to the amount of material in front of
the calorimeter. For HB and HE, the difference is typically $2.2\pm 0.1$% on
average, while for the HF calorimeter this difference is somewhat smaller,
$0.8\pm 0.1$% on average. This difference is assigned as a systematic
uncertainty in the scale factor determination from the method of moments. An
additional systematic uncertainty is assigned related to the determination of
electronic pedestals using various sets of noncollision data. This
uncertainty, which is negligible in the HB and varies in the 1.0–1.5% range in
the HE/HF, is added in quadrature to the uncertainty from the method of second
moment.

Figure 5: Derived calibration scale factors that equalize the $\phi$ response
in a simulated sample of minimum bias events for a channel in the HB
($i\eta=9$, $\text{depth}=1$, left) and HF ($i\eta=33$, $\text{depth}=1$,
right), as a function of $i\phi$ using two different methods: of first and of
second moment.

Figure 6 shows the ratios of scale factors obtained in different portions of
the 2016 data set from the second-moment method for two representative
channels: $i\eta=9$, $\text{depth}=1$ in the HB, and $i\eta=20$,
$\text{depth}=1$ in the HE. The plots indicate the level of stability of the
scale factors over approximately six months of data taking.

Figure 6: Ratio of the calibration scale factors for a typical HB ($i\eta=9$,
$\text{depth}=1$, left) and HE ($i\eta=20$, $\text{depth}=1$, right) channels
as a function of $i\phi$, in different data taking periods, to that obtained
in a sample corresponding to the first 8.3 of integrated luminosity for five
additional data taking periods during 2016. Although the dataset is divided
into five periods for the purpose of illustration, we end up with three sets
of constants over the entire run of 2016. Only statistical uncertainties in
the scale factors are shown.

###  0.5.3 Combination of the two methods

Figure 7: The ratio of $\phi$ intercalibration scale factors obtained with the
method of moments to that obtained with the iterative method for HB (left), HE
(center), and HF (right). The mean, RMS and width from Gaussian fit to the
distribution are $0.998\pm 0.001$, $0.058\pm 0.001$, $0.045\pm 0.001$ for the
HB, $0.998\pm 0.001$, $0.039\pm 0.001$, $0.030\pm 0.001$ for the HE, $1.004\pm
0.002$, $0.043\pm 0.001$, $0.037\pm 0.001$ for the HF.

Figure 7 shows the ratio of the scale factors measured using the two methods
(iterative method over method of moments) for the HB, HE, and HF calorimeters.
The two sets of measurements agree within 5% as indicated from the RMS of the
ratio distributions. The two sets of measurements agree, with the means of
their ratios being $0.998\pm 0.001$, $0.998\pm 0.001$, and $1.004\pm 0.002$
for the HB, HE, and HF calorimeters, respectively. The scale factor obtained
from the method of moments has better precision for low-energy depositions,
whereas those from the iterative method are better for high-energy
depositions. The statistical uncertainties from the two methods are comparable
for the HF calorimeter channels, whereas the iterative method gives smaller
uncertainties for the HB and HE calorimeter channels. The uncertainty-weighted
average of the scale factors from the two methods is used as the final scale
factor for the HCAL $\phi$ intercalibration. The arithmetic mean of the
corrections is used when the statistical uncertainties of both methods are
below 1%. The weighted average (with weight $w=1/\sigma^{2}$, where $\sigma$
is the uncertainty in the measurement) is used otherwise. The systematic
uncertainties in the interchannel calibration measured from the method of
moments are estimated using (i) the differences between constants found with
the first and the second moments, which amount to 2–3% for all three
detectors: HB, HE, and HF; and (ii) different sources of data: NZS (events
recorded without zero suppression) and single-muon trigger data, which yield
values less than 1%. The systematic uncertainties for the iterative method are
estimated using (i) different triggers: single-electron with $\pt>25\GeV$ and
single-muon with $\pt>24\GeV$, which yield values below 1%; and (ii) different
choices of the energy threshold in estimating $E_{\text{tot}}$, which result
in effects smaller than 3%. The difference in the coefficients derived using
the two methods provides a combination of systematic and statistical
uncertainties, which is 4% for the HB, and 3% for the HE and HF.

##  0.6 Absolute calibration using isolated tracks

The energies of charged hadrons in the central region of the CMS detector are
measured by two independent detector systems: the trackers and the
calorimeters. The precise calibration of the tracker system can be transferred
to the calorimeter by comparing the two measurements. Unlike the momentum
measurement by the tracker, the hadronic energy response of the HCAL is not
linear. The nonlinearity is more pronounced at lower energies [8]. The goal of
the HCAL absolute energy calibration is to set the relative energy scale to
unity for 50 charged hadrons that do not interact hadronically in the ECAL. In
practice, the calibration is done with tracks of momentum between 40 and 60.
The dominant systematic uncertainties are due to the contamination of the
calorimeter energy from other hadrons, produced either in the same interaction
that produced the isolated charged-hadron candidate or in pileup interactions.

The data used in this method come from two sources. The first sample is
selected using a trigger designed for this analysis. At L1, the event is
required to contain at least one jet with $\pt>60$. At the HLT, an isolated
track with an associated energy in the ECAL below 2 is required. The isolation
calculation utilizes information from tracks after extrapolation to the
calorimeter surface. The second sample uses events from the full, varied suite
of CMS triggers. Events are selected offline using a filter that requires a
track that satisfies very loose isolation criteria, has associated energy in
the ECAL less than 2, and momentum higher than 20.

Events are required to have at least one well-reconstructed primary vertex
[20] that is close to the nominal interaction point, with
$r(\equiv\sqrt{x^{2}+y^{2}})<2$and $\abs{z}<15$. Tracks considered as
candidate isolated hadrons are required to be associated with the primary
vertex and to satisfy quality requirements. Their impact parameters are
required to be close to the primary vertex in the transverse ($xy$) plane
($\Delta r<200$) as well as along the beam axis ($\Delta z<200$). The
$\chi^{2}$ of the track fit per degree of freedom is required to be less than
5, and the number of tracker layers used in the momentum measurement to be
greater than 8. To confidently select tracks that have not interacted before
reaching the calorimeter surface, tracks with missing hits in the inner and
outer layers of the tracker are rejected.

The analysis uses isolation in a cone around the track to reduce contamination
from neighboring hadrons, and to have a more accurate estimation of the hadron
energy. The cone algorithm clusters energy based on the linear distance from
the extrapolated track trajectory through the HCAL. For each HCAL tower, the
distance between two points is determined. The first point is the intersection
of the extrapolated track trajectory with the front face of the HCAL. The
second point is the intersection of the tower axis (the straight line joining
the center of the CMS and the center of the tower) with the plane
perpendicular to the extrapolated track trajectory. If this distance is
smaller than the radius of a circle on the surface of HCAL
($R_{\text{cone}}$), the energy from the HCAL tower is included in the
cluster. The signal is measured using $R_{\text{cone}}$ of 35, which contains
on average more than 99% of the energy deposited by a 50hadron.

The ECAL has a depth of approximately one interaction length and therefore
more than half of the hadrons undergo inelastic interactions before reaching
the HCAL. These hadrons are not used for calibration and are rejected by
requiring the energy deposited within a cone of radius 14around the impact
point for the ECAL to be less than 1. This requirement also removes a large
fraction of hadron candidates near a neutral particle, which deposit energy in
the signal cone that would otherwise contaminate the measurement.

To further reduce contamination due to neighboring particles, an isolation
requirement based on tracking information is used. The trajectories of all
charged particles are propagated to the ECAL surface. Hadron candidates are
vetoed if there are additional tracks above a momentum threshold impacting the
calorimeter surface within a circle of radius 64around the candidate’s impact
point. The momentum threshold depends on the desired selection efficiency and
purity. Two threshold values are used: a 10 loose isolation requirement is
used for the main analysis, whereas a 2 tight isolation requirement is used in
the assessment of systematic uncertainties. Although a stringent requirement
on the isolation from neighboring charged particles would reduce the
uncertainty caused by contamination of the signal energy from other nearby
hadrons, the large number of pileup interactions at high instantaneous
luminosity can result in a large amount of unrelated energy in the signal cone
in the endcap region of the HCAL. The loose isolation requirement increases
the efficiency for the track selection in the presence of pileup.

The calorimeter response is defined as the ratio

$E_{\mathrm{HCAL}}/(p_{\mathrm{track}}-E_{\mathrm{ECAL}}),$ (9)

where $E_{\mathrm{HCAL}}$ is the signal region energy of the HCAL cluster,
$E_{\mathrm{ECAL}}$ is the energy deposited in the ECAL in a cone of radius
14around the impact point of the track, and $p_{\mathrm{track}}$ is the
momentum of the track. To mitigate the contamination from pileup, we apply a
pileup correction, discussed below. The most probable value (MPV or mode) of
the response is extracted using an iterative two-step fitting procedure to a
Gaussian function. The two fits are performed in the intervals $\pm
1.5\sigma_{\mathrm{Tot}}$ and $\pm 1.5\sigma_{\mathrm{Fit}}$ around the mean
of the distribution or the fitted mean, where $\sigma_{\mathrm{Tot}}$ is the
sample RMS and $\sigma_{\mathrm{Fit}}$ is the width determined from the first
fit.

The contribution from pileup is subtracted on an event-by-event basis by
measuring the energy within an annular region beyond the signal cone. When
there is a large amount of pileup energy near the cone, a relatively high
pileup contribution is expected inside the cone. If so, the true particle
energy is lower than that reconstructed in the cone. If the energy deposit
outside the cone is caused by the particle itself, the true particle energy
will be higher than the reconstructed energy in the cone. The track under
consideration and the pileup originate from independent collisions within the
same bunch crossing. From a study using simulated single isolated high- pion
events, the pileup contribution is related to the energy in a region around
$R_{\text{cone}}$ with an annular radius of $+$10\. It does not depend on the
track momentum, and rather depends on the ratio of the energy in the cone to
the track momentum. The corrected energy $E_{\text{cor}}$ is calculated on an
event-by-event basis using Eq. (10),

$\displaystyle E_{\text{cor}}$
$\displaystyle=E\left(1+a_{1}\frac{E}{p}\left(\frac{\Delta}{p}+a_{2}\left(\frac{\Delta}{p}\right)^{2}\right)\right),$
(10) $\displaystyle(a_{1},a_{2})$
$\displaystyle=\begin{cases}(-0.35,-0.65)&\text{for}~{}\abs{i\eta}<25,\\\
(-0.35,-0.30)&\text{for}~{}\abs{i\eta}=25,\\\
(-0.45,-0.10)&\text{for}~{}\abs{i\eta}>25,\\\ \end{cases}$

where $E$ is the energy in the signal region cone $R_{\text{cone}}=35$around
the impact point of the selected isolated track, $p$ is the track momentum,
and $\Delta$ is the energy deposit in the annular region around the signal
cone. The values for the constant depend on $\abs{\eta}$ because of the
tracker coverage and the $\abs{\eta}$ dependence of the pileup particle
energies. The values of the coefficients $a_{1}$ and $a_{2}$ are extracted
using the dependence of the response on the ratio $\Delta/p$ by minimizing the
difference between the mean corrected response for simulated samples that are
processed two ways: with and without pileup. The pileup scale factors are
derived using single pion simulated samples with and without pileup, and
cross-checked with independent single pion simulated samples with pileup.

Figure 8 (left) shows the response distribution for the sample of simulated
pions without pileup before and after application of residual energy scale
corrections using the isolated track calibration technique. The bias in the
mode of the energy distribution for the simulated pion sample that is caused
by a pileup correction is less than 0.3%. The right plot in Fig. 8 shows the
ratio of the modal value from a simulated single pion sample with pileup to
that from a sample without pileup. The pions in the sample with pileup are
required to satisfy the isolation criteria, and their analysis utilizes the
pileup correction technique described above. The modes agree to within 1% for
the entire calorimeter, and within 0.5% in the barrel region.

Figure 8: (left) Distribution of the energy response in a simulated sample of
single isolated high- pions without pileup when the corrections for pileup
have (red squares) or have not (black circles) been applied. The plot also
shows the results of Gaussian fits. (right) The ratio of the mode of the
response distribution for a simulated pion sample with pileup, with loose
charged-particle isolation, and the correction for pileup applied (red
squares) to the mode from the sample without pileup. The uncertainties in the
mode without pileup are shown with the gray band. Only statistical
uncertainties are included. The dashed vertical lines show the boundaries
between the barrel and the endcaps.

The calibration method utilizes an iterative approach. At the $m$-th
iteration, the new scale factor $c_{i}^{(m+1)}$ is calculated using:

$c_{i}^{(m+1)}=c_{i}^{(m)}\left(1-\frac{\sum_{j}w_{ij}^{(m)}\left(\frac{E_{j}^{(m)}}{p_{j}-E_{j,\text{ECAL}}}-\text{RR}\right)}{\sum_{j}w_{ij}^{(m)}}\right),$
(11)

where the sum is over events that contribute to the towers at the $i$-th
$i\eta$ ring, RR is the reference to which the mean response is equalized,
$E_{j,\text{ECAL}}$ is the measured energy in the ECAL cluster around the
track $j$, $p_{j}$ is the track momentum, $w_{ij}^{(m)}$ is the weight of the
particular tower with measured energy $e_{ij}$ in the cluster energy
$E_{j}^{(m)}$:

$w_{ij}^{(m)}=\frac{c_{i}^{(m)}e_{ij}}{E_{j}^{(m)}},\quad\quad
E_{j}^{(m)}=\sum_{i=1}^{n_{j}}c_{i}^{(m)}e_{ij}.$ (12)

It follows from Eq. (11) that the iterative procedure results in equalization
of the mean response of the detector around the value RR, which equals 1 by
default. If the most probable value for the sample, mode${}_{\text{sample}}$,
differs from the sample mean, mean${}_{\text{sample}}$, the reference response
is set to $\text{RR}=\text{mean}_{\text{sample}}/\text{mode}_{\text{sample}}$.
The formulation in Eq. (11) makes the procedure stable with respect to
fluctuations of $E_{j}^{(m)}$.

The statistical uncertainty in the scale factor $\Delta c_{i}^{(m+1)}$ is
estimated from the measured RMS of the response distribution $\Delta
R_{i}^{(m)}$ for the subsample used for the $i$-th subdetector:

$\Delta c_{i}^{(m+1)}=\Delta
R_{i}^{(m)}\frac{\sqrt{\sum_{j}{(w^{(m)}_{ij})^{2}}}}{\sum_{j}{w^{(m)}_{ij}}}.$
(13)

The procedure is iterated until the difference between the scale factors in
subsequent steps becomes three times smaller than the statistical uncertainty.

The calibration procedure is applied to the 2016 collision data to obtain
scale factors for each $i\eta$ ring through $i\eta=23$. Scale factors for
rings beyond $i\eta=23$ in the HE are obtained by extrapolating these results.
We note that the determination of the scale factors cannot be extended all the
way to the boundaries of the tracker coverage, because the tracks used in the
calibration are required to satisfy isolation criteria with respect to other
particles, so the entire isolation cone is required to be within the tracker
acceptance. Different criteria for selection of isolated hadrons and different
methods for the pileup correction are tested, and the resulting correction
factors for each data taking period are compared. The initial and resulting
(after convergence) response distributions are shown in Fig. 9 for three HCAL
$\eta$ ranges. The resulting equalization of the mode is shown in Fig. 10.
Equalization within $\pm 2.5$% is achieved with the iterative procedure for
subdetectors up to $\abs{i\eta}=23$.

Figure 9: Response distributions for pions from 2016 data in three different
$\eta$ regions, $\abs{\eta}\leq 1.22$ (left), $1.22\leq\abs{\eta}\leq 1.48$
(middle), and $1.48\leq\abs{\eta}\leq 2.04$ (right), with loose charged-
particle isolation criterion: initial (black circles) and after convergence
(red squares). Only statistical uncertainties are shown on the data points.
Figure 10: Modes of the response with their statistical uncertainties versus
$i\eta$ from the 2016 data sample before (black circles) and after convergence
(red squares). The loose charged-particle isolation constraint is applied.

The statistical uncertainty in the scale factor, as obtained from data, is
typically below 2%. The uncertainty in the scale factor is computed from the
differences obtained (i) using tight and loose isolation criteria (estimated
to be $<$1% for $\abs{i\eta}<15$, and $<$2% for $\abs{i\eta}>14$); (ii) using
simulated isolated high-pion samples with and without pileup (estimated to be
less than 0.1% over the entire $i\eta$ range); and (iii) using the true
momenta versus the measured momenta of the charged particles in simulated
events (estimated to be also less than 0.1%). The overall uncertainty is
around 2%.

##  0.7 Calibration of the HF using $\PZ\to\Pe\Pe$ events

The initial calibration of the HF was based on test beam data [13]. The energy
scales of the long and short fibers in six HF wedges were set using the
responses from 100electrons and negative pions. The scale was transferred to
the rest of the wedges using radioactive source data.

The energy scale for the long fibers is validated using events from boson
decays. The dileptonic decays of the boson are useful tools for checking the
detector calibration, because the production cross section of the bosons at
the LHC is large, and the signature is almost background free. The dataset
used for this calibration consists of events with one electron candidate in
the HF and the other in the ECAL, which has been precisely calibrated. The
scale of the HF long fibers is adjusted so that the dielectron invariant mass
corresponding to the peak is consistent between simulation and data. The scale
of the HF short fibers utilizes the short-to-long ratio measured in an
analysis of 100test beam electron data.

Because the HF is outside of the tracker acceptance, the PF algorithm does not
identify electrons that impinge on this detector and they are instead
identified as photon candidates, which are clustered into PF jets. An isolated
high-electron, such as those produced in boson decays, would be identified as
a jet by this algorithm. The reconstruction of HF electrons for this analysis,
thus, starts with the PF jets. To select jets consistent with being isolated
electrons, jets with characteristics consistent with those of anomalous energy
deposits are rejected. Electron candidate jets must either have a nonzero
hadronic or nonzero electromagnetic energy after zero-suppression is applied.
The electromagnetic (EM) and hadronic (HAD) energies are defined by the
deposits in the long (L) and short (S) fibers in the following way: EM =
L$-$S, HAD = 2S.

Jets created from energy deposits of isolated electrons have a characteristic
shower size $\Delta
R\equiv\sqrt{\smash[b]{(\Delta\eta)^{2}+(\Delta\phi)^{2}}}$ of about 0.15–0.2,
where $\Delta\phi$ is in radians. Also, most of the energy is deposited in the
long fibers, and the energy in the short fibers is small. Thus, HF electron
candidates are required to have a “seed” for the core electron shower, chosen
as the constituent of the PF jet with the largest energy. This seed defines
the initial four-vector of the HF electron candidate. The four-vectors of
other constituents of the PF jet, ordered in energy, are added to the four-
vector of the HF electron candidate if the constituents are within $\Delta
R=0.15$ of the current four-vector of the core shower. The process stops when
there are no more constituents close enough to the core shower. The choice of
$\Delta R=0.15$ to sample the core energy of the HF electron candidate is used
to minimize the dependence on pileup. Because electrons from boson decays are
expected to be more energetic than electron candidates arising from the
misidentification of quark and gluon jets, the $\pt$ of HF electron candidates
are required to exceed 15. Furthermore, the electromagnetic fraction of the
candidate’s energy, calculated as a sum of all the EM energies of the PF
constituents that form the core four-vector of the HF electron candidate, must
be sufficiently large. The EM energy is required to be nonzero, and the ratio
of the HAD to EM energy is restricted to be less than 1.20. The HF electron
candidate is required to be isolated, as expected for leptons from boson
decays. The total energy of all the PF candidates found in the vicinity of the
shower, $0.15<\Delta R<0.30$, should not exceed 55% of the energy in the core
shower. These requirements result in an efficiency of 62% for the selection of
genuine electrons from boson decays, and retain only 1.5% of jets
misidentified as electrons, as estimated using simulated Drell–Yan events.

Candidate boson events are selected by requiring only one isolated electron in
the ECAL with $\pt>25$ and with $\abs{\eta}<2.5$ [34]. The events are further
required to have at least one HF electron candidate with $\pt>15$ and
$2.964<\abs{\eta}<5.191$.

Figure 11: Invariant mass of the two electrons in candidate $\PZ\to\Pe\Pe$
events for simulation (left), and for 2016 data (right). One candidate is
required to be in the ECAL, the other one in the HF. The mean ($\mu$) and the
width ($\sigma$) of the Gaussian fits are shown on the plots with their
uncertainties. The quality of the fits is sufficient to extract the mean and
the width to the necessary accuracies.

The dielectron invariant mass distributions ($M_{\EE}$) from candidate
$\PZ\to\Pe\Pe$ events are shown in Fig. 11 for simulation and 2016 data. The
boson mass is measured from a fit to a Gaussian function in a restricted mass
region around the peak position. In both simulation and data, the measured
boson mass is lower than the nominal mass (91.1876 [35]) because only the
long-fiber energies of towers within $\Delta R=0.15$ of the seed are used,
resulting in an underestimation of the shower energy due to energy leakage
outside of this region. The HF energy scale in data is not adjusted to match
the fitted mean from the invariant mass distribution using the simulation,
since both values are consistent within their uncertainties. Therefore, no
corrections are applied to the energy response in data. The 25% difference in
the width between data and simulation is not yet fully understood, but
selection of electron pairs in data from the boson decays, as well as shower
mismodeling in the simulation, could cause this discrepancy.

The bias in the energy due to pileup is estimated using events with different
numbers of pileup interactions. The result is consistent with a shift of the
measured boson mass of up to 1%.

Figure 12: (left) The results of the fits of the dielectron invariant mass to
a Gaussian function for different $\eta$ values of the HF electron candidates
obtained in simulation (black line, combined HF$+$ and HF$-$) and 2016 data,
split by HF$+$ and HF$-$ (red and blue, respectively). (right) The results of
the fits of the dielectron invariant mass to a Gaussian function for different
pseudorapidity $\eta$ values of the HF electron candidates obtained in data
corresponding to different run ranges. The dielectron mass in the denominator
comes from the first run range corresponding to 5.7. Errors on the data points
are statistical only.

Figure 12 shows the dielectron invariant mass as a function of the HF electron
candidate $\abs{i\eta}$, for $i\eta$ values between 30 and 38. The HF energy
response in data for both HF$+$ and HF$-$ are similar. The energy response in
the simulation has a shape similar to that of the data, but there is a visible
trend: for the towers with $\abs{i\eta}=33$–38, the data show a lower energy
scale than the simulation. This deviation is not completely understood and
therefore no specific corrections are considered at the moment.

##  0.8 Calibration of the HO calorimeter

The calibration of the HO calorimeter is carried out in two steps. The
intercalibration makes use of muons from collision data, as well as cosmic ray
muons that traverse the tiles of the HO. The determination of the absolute
energy scale makes use of dijet events.

###  0.8.1 Intercalibration of the HO towers

Before data taking began, the intercalibration of the HO towers was performed
by equalizing signals from cosmic ray muons. This method has a few drawbacks:

  * •

large statistical uncertainties in the calibration of the HO towers near
$i\phi=1$ and 37 because there are few horizontal muons in the cosmic muon
sample;

  * •

a large uncertainty in the extrapolated track position in the HO, which was
based on standalone muon reconstruction using information from muon chambers
alone; this leads to a large uncertainty in the resulting calibration.

The intercalibration is improved using muons from and decays in $\Pp\Pp$
collisions. Muon candidates [36] are selected using the following criteria:

  * •

the associated track is well measured and satisfies reconstruction quality
criteria;

  * •

the energy in the calorimeter tower traversed by the muon is consistent with
the expectation of a minimum ionizing particle;

  * •

the muon is isolated: no other reconstructed muon is present within a
$15^{\circ}$ cone around the muon under study and the scalar $\pt$ sum of all
other tracks within $\Delta R<0.3$ relative to the muon is less than 4;

  * •

the momentum of the muon after extrapolation to the HO face is more than 15,
17, and 20 for towers in rings 0, $\pm$1, and $\pm$2, respectively;

  * •

the cosine of the angle made by the muon trajectory with respect to the HO
scintillator surface is more than 0.6 (0.4) for ring 0 (other rings);

  * •

the location of the muon after extrapolation to the HO is at least 2away from
the tower boundary; and

  * •

the time of the HO energy deposits is within 30 (20)ns of the beam crossing
time for towers in ring 0 (other rings).

Figure 13: Energy distributions for HO towers impacted by a high-muon for the
central ring ($i\eta=-4$, $i\phi=4$, left) and for a side ring ($i\eta=-8$,
$i\phi=4$, right), fitted with a combination of a Gaussian function for the
pedestal region (shown as red lines) and a convolution of a Gaussian and a
Landau function for the signal region (the combined fits are shown as the
green lines). The parameters $\mu$, $\Gamma$ and $\sigma$ are the most
probable values and widths of the Landau and the Gaussian functions, and ndf
is number of degrees of freedom in the fit.

Figure 13 shows a typical distribution of the energy deposited in a tower
impacted by a muon, for two typical HO towers. The signal distribution is
parametrized in the pedestal region with a Gaussian function whose mean and
width are set to values obtained from pedestal data, and with a convolution of
Gaussian and Landau functions in the signal region, where width ($\sigma$) of
the Gaussian function, the most probable value ($\mu$), and width ($\Gamma$)
of the Landau function are free parameters of the fit. The most probable
values from the fits to the convoluted function are calculated for all towers.
In parallel, the product of the path length of each muon trajectory in the
tower and the ratio of the expected energy loss of the muon with respect to
the energy loss at a fixed momentum (8) is calculated. These observed
responses are then normalized over all ($i\eta$, $i\phi$) channels. These
responses are then symmetrized over all $i\phi$ channels for a given $i\eta$
ring.

###  0.8.2 Absolute scale in the HO

In a test beam experiment [8], a 150muon beam was used to obtain the
conversion factors from charge to energy for both the HB and HO. However,
because the HO measures the energy deposited in the tail of a hadronic shower,
which typically contains low-energy secondary particles, the energy resolution
can be improved with the application of a weight factor to the HO energies.
This weight factor was estimated using a test beam of 300 $\PGpm$ mesons [7].

The relative weight factor derived from a single particle test beam may not be
optimal for jets, so the utility of an extra weight factor ${w_{\text{HO}}}$
is explored. The value of ${w_{\text{HO}}}$ is tested with collision data by
balancing energies in dijet events, where one of the jets produces a
substantial energy deposit in the HO. The analysis varies the scale of the HO
energy deposits contributing to a PF jet to find the weight factor which gives
the best jet energy resolution.

Events are selected with the following criteria:

  * •

if there are more than two jets in the event, the third jet $\pt$ is required
to be less than 30,

  * •

the two leading jets must have $\Delta\phi>\pi/2$, and

  * •

the event must not contain any isolated photon, electron, or muon with
$\pt>20$.

This analysis uses jets in $\abs{\eta}<0.34$ (which corresponds to the central
HO ring) with the highest energy HO cluster. Figure 14 shows the width of the
dijet energy balance distribution as a function of ${w_{\text{HO}}}$, where
the energy balance ($E_{\mathrm{b}}$) is defined as

$E_{\mathrm{b}}=2\frac{(p_{\text{T1}}-p_{\text{T2}})}{(p_{\text{T1}}+p_{\text{T2}})},$
(14)

with $p_{\text{T1}}$ and $p_{\text{T2}}$ being the transverse momenta of the
leading and the subleading jet, respectively. The smooth curves are results
from fits to asymmetric parabolic function through the points, which is
defined as,

$\sigma(E_{\mathrm{b}})=p_{0}+\alpha_{1,2}\left({w_{\text{HO}}}-p_{2}\right)^{2},$
(15)

where $\alpha_{1,2}$ are used for $\left({w_{\text{HO}}}-p_{2}\right)\geq 0$
and $\left({w_{\text{HO}}}-p_{2}\right)<0$, respectively, and $p_{0}$,
$\alpha_{1}$, $\alpha_{2}$, and $p_{2}$ are parameters of the fit. Events
containing jets with higher HO energy provide a better sensitivity to
${w_{\text{HO}}}$. The results imply that within the uncertainty of
measurements the relative weight factor of HO in these events is the same as
in the test beam. Similar analyses are carried out for the other HO rings
($\pm$1, $\pm$2) as well.

Figure 14: The Gaussian width of the relative difference of $\pt$ of the two
jets in dijet events as a function of HO weight factor in different ranges of
energy contained in the HO cluster of the jet carrying the highest HO energy,
when that jet is in ring 0. The smooth curves are results from fits to
asymmetric parabolic function (Eq. (15)) through the points. Uncertainties are
statistical only.

##  0.9 Summary

The CMS experiment utilizes a variety of data to calibrate the energy
measurements obtained from its hadron calorimeter systems. The strategy
utilizes different approaches since the calorimeter subdetectors make use of
multiple technologies, have different radiation environments, and probe a
large range of particle energies. Mean noise levels (pedestals) of all the
channels are monitored for each fill of LHC and the scale factors are checked
for each run period, which typically spans one month. The mean noise levels as
well as the correction factors are updated whenever any of the correction
factors change by more than 3%. The calibration is generally performed in two
steps: the azimuthal ($\phi$) intercalibration of the channels, followed by
the determination of an absolute energy scale.

In the barrel, endcap, and forward calorimeters, the $\phi$ symmetry of
minimum bias events is used to carry out an interdetector calibration, whereas
the hadron outer calorimeter utilizes reconstructed muons for this purpose.
The absolute energy calibration of the barrel and endcap calorimeters is based
on isolated charged hadrons with momenta between 40 and 60, whereas the
calibration of the forward calorimeter relies on a validation based on
$\PZ\to\Pe\Pe$ events. The nonlinearity in the energy measurement of hadrons
is addressed during the particle-flow reconstruction by using the predicted
dependence on transverse momentum and pseudorapidity from a -based simulation.
Residual nonlinearities that affect the energy scale of reconstructed jets are
reduced during the calibration of the jet energy scale. The calibration of the
hadron outer calorimeter relies on the energy balance in dijet events.

The methods use proton-proton collision data at $\sqrt{s}=13\TeV$ collected
with the CMS detector in 2016, and corresponding to integrated luminosities up
to 35.9. The results are applied to the final reconstruction of events
collected during that period. The systematic uncertainties in these
measurements are dominated by the uncertainties in the amount of material
between the interaction point and the detectors, including their dependence on
azimuthal angle, and by the systematic uncertainties from the simulation of
the effect of noise on the readout signal. Systematic uncertainties in the
intercalibration are below 3% for HB, HE, HF and HO. The uncertainty in the
absolute energy scale, as determined here for HB and HE, is below 2%. Absolute
energy scale for HF and HO are kept at their nominal values and validated
during 2016 using the methods described in the text. These techniques lead to
the typical calibration precision of less than 3%.

###### Acknowledgements.

We congratulate our colleagues in the CERN accelerator departments for the
excellent performance of the LHC and thank the technical and administrative
staffs at CERN and at other CMS institutes for their contributions to the
success of the CMS effort. In addition, we gratefully acknowledge the
computing centers and personnel of the Worldwide LHC Computing Grid for
delivering so effectively the computing infrastructure essential to our
analyses. Finally, we acknowledge the enduring support for the construction
and operation of the LHC and the CMS detector provided by the following
funding agencies: BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq,
CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST,
and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF
(Cyprus); SENESCYT (Ecuador); MoER, ERC IUT, PUT and ERDF (Estonia); Academy
of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG,
and HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM
(Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES
(Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT,
LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC
(Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom,
RAS, RFBR, and NRC KI (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER
(Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST
(Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK
(Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA).
Individuals have received support from the Marie-Curie program and the
European Research Council and Horizon 2020 Grant, contract Nos. 675440,
752730, and 765710 (European Union); the Leventis Foundation; the A.P. Sloan
Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science
Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et
dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door
Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium)
under the “Excellence of Science – EOS” – be.h project n. 30820817; the
Beijing Municipal Science & Technology Commission, No. Z181100004218003; the
Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the
Lendület (“Momentum”) Program and the János Bolyai Research Scholarship of the
Hungarian Academy of Sciences, the New National Excellence Program ÚNKP, the
NKFIA research grants 123842, 123959, 124845, 124850, 125105, 128713, 128786,
and 129058 (Hungary); the Council of Science and Industrial Research, India;
the HOMING PLUS program of the Foundation for Polish Science, cofinanced from
European Union, Regional Development Fund, the Mobility Plus program of the
Ministry of Science and Higher Education, the National Science Center
(Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543,
2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406;
the National Priorities Research Program by Qatar National Research Fund; the
Ministry of Science and Education, grant no. 3.2989.2017 (Russia); the
Programa Estatal de Fomento de la Investigación Científica y Técnica de
Excelencia María de Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa
del Principado de Asturias; the Thalis and Aristeia programs cofinanced by EU-
ESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral
Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its
2nd Century Project Advancement Project (Thailand); the Nvidia Corporation;
the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA).

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##  .10 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan${}^{\textrm{\textdagger}}$, A. Tumasyan Institut für
Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, T. Bergauer, J. Brandstetter, M. Dragicevic, J. Erö, A.
Escalante Del Valle, M. Flechl, R. Frühwirth1, M. Jeitler1, N. Krammer, I.
Krätschmer, D. Liko, T. Madlener, I. Mikulec, N. Rad, J. Schieck1, R.
Schöfbeck, M. Spanring, D. Spitzbart, W. Waltenberger, C.-E. Wulz1, M. Zarucki
Institute for Nuclear Problems, Minsk, Belarus
V. Chekhovsky, A. Litomin, V. Mossolov Universiteit Antwerpen, Antwerpen,
Belgium
M.R. Darwish, E.A. De Wolf, D. Di Croce, X. Janssen, A. Lelek, M. Pieters, H.
Rejeb Sfar, H. Van Haevermaet, P. Van Mechelen, S. Van Putte, N. Van Remortel
Vrije Universiteit Brussel, Brussel, Belgium
F. Blekman, E.S. Bols, S.S. Chhibra, J. D’Hondt, J. De Clercq, D. Lontkovskyi,
S. Lowette, I. Marchesini, S. Moortgat, 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, L. Favart, A. Grebenyuk, A.K. Kalsi, A. Popov, N. Postiau, E.
Starling, L. Thomas, C. Vander Velde, P. Vanlaer, D. Vannerom Ghent
University, Ghent, Belgium
T. Cornelis, D. Dobur, I. Khvastunov2, M. Niedziela, C. Roskas, D. Trocino, M.
Tytgat, W. Verbeke, B. Vermassen, M. Vit, N. Zaganidis Université Catholique
de Louvain, Louvain-la-Neuve, Belgium
O. Bondu, G. Bruno, C. Caputo, P. David, C. Delaere, M. Delcourt, A.
Giammanco, V. Lemaitre, A. Magitteri, J. Prisciandaro, A. Saggio, M. Vidal
Marono, P. Vischia, J. Zobec Centro Brasileiro de Pesquisas Fisicas, Rio de
Janeiro, Brazil
F.L. Alves, G.A. Alves, G. Correia Silva, C. Hensel, A. Moraes, P. Rebello
Teles Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato3, E. Coelho, E.M.
Da Costa, G.G. Da Silveira4, D. De Jesus Damiao, C. De Oliveira Martins, S.
Fonseca De Souza, L.M. Huertas Guativa, H. Malbouisson, J. Martins5, D. Matos
Figueiredo, M. Medina Jaime6, M. Melo De Almeida, C. Mora Herrera, L. Mundim,
H. Nogima, W.L. Prado Da Silva, L.J. Sanchez Rosas, A. Santoro, A. Sznajder,
M. Thiel, E.J. Tonelli Manganote3, F. Torres Da Silva De Araujo, A. Vilela
Pereira Universidade Estadual Paulista a, Universidade Federal do ABC b, São
Paulo, Brazil
C.A. Bernardesa, L. Calligarisa, T.R. Fernandez Perez Tomeia, E.M. Gregoresb,
D.S. Lemos, P.G. Mercadanteb, S.F. Novaesa, SandraS. Padulaa Institute for
Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia,
Bulgaria
A. Aleksandrov, G. Antchev, R. Hadjiiska, P. Iaydjiev, A. Marinov, M. Misheva,
M. Rodozov, M. Shopova, G. Sultanov University of Sofia, Sofia, Bulgaria
M. Bonchev, A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov Beihang
University, Beijing, China
W. Fang7, X. Gao7, L. Yuan Department of Physics, Tsinghua University,
Beijing, China
Z. Hu, Y. Wang Institute of High Energy Physics, Beijing, China
M. Ahmad, G.M. Chen, H.S. Chen, M. Chen, C.H. Jiang, D. Leggat, H. Liao, Z.
Liu, S.M. Shaheen8, A. Spiezia, J. Tao, E. Yazgan, H. Zhang, S. Zhang8, J.
Zhao State Key Laboratory of Nuclear Physics and Technology, Peking
University, Beijing, China
A. Agapitos, Y. Ban, G. Chen, A. Levin, J. Li, L. Li, Q. Li, Y. Mao, S.J.
Qian, D. Wang, Q. Wang Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, L.F. Chaparro Sierra, C. Florez, C.F. González
Hernández, M.A. Segura Delgado Universidad de Antioquia, Medellin, Colombia
J. Mejia Guisao, J.D. Ruiz Alvarez, C.A. Salazar González, N. Vanegas Arbelaez
University of Split, Faculty of Electrical Engineering, Mechanical Engineering
and Naval Architecture, Split, Croatia
D. Giljanović, N. Godinovic, D. Lelas, I. Puljak, T. Sculac University of
Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, S. Ceci, D. Ferencek, K. Kadija, B. Mesic, M. Roguljic, A.
Starodumov9, T. Susa University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, E. Erodotou, A. Ioannou, M. Kolosova, S. Konstantinou,
G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H.
Rykaczewski, D. Tsiakkouri Charles University, Prague, Czech Republic
M. Finger10, M. Finger Jr.10, A. Kveton, J. Tomsa Escuela Politecnica
Nacional, Quito, Ecuador
E. Ayala Universidad San Francisco de Quito, Quito, Ecuador
E. Carrera Jarrin Academy of Scientific Research and Technology of the Arab
Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
Y. Assran11,12, S. Elgammal12 National Institute of Chemical Physics and
Biophysics, Tallinn, Estonia
S. Bhowmik, A. Carvalho Antunes De Oliveira, R.K. Dewanjee, K. Ehataht, M.
Kadastik, M. Raidal, C. Veelken Department of Physics, University of Helsinki,
Helsinki, Finland
P. Eerola, L. Forthomme, H. Kirschenmann, K. Osterberg, M. Voutilainen
Helsinki Institute of Physics, Helsinki, Finland
F. Garcia, J. Havukainen, J.K. Heikkilä, T. Järvinen, V. Karimäki, M.S. Kim,
R. Kinnunen, T. Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, P.
Luukka, T. Mäenpää, H. Siikonen, E. Tuominen, J. Tuominiemi Lappeenranta
University of Technology, Lappeenranta, Finland
T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, B. Fabbro, J.L. Faure, F.
Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
C. Leloup, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin, A. Savoy-
Navarro13, M. Titov Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole
Polytechnique, Institut Polytechnique de Paris
S. Ahuja, C. Amendola, F. Beaudette, P. Busson, C. Charlot, B. Diab, G.
Falmagne, R. Granier de Cassagnac, I. Kucher, A. Lobanov, C. Martin Perez, M.
Nguyen, C. Ochando, P. Paganini, J. Rembser, R. Salerno, J.B. Sauvan, Y.
Sirois, A. Zabi, A. Zghiche Université de Strasbourg, CNRS, IPHC UMR 7178,
Strasbourg, France
J.-L. Agram14, J. Andrea, D. Bloch, G. Bourgatte, J.-M. Brom, E.C. Chabert, C.
Collard, E. Conte14, J.-C. Fontaine14, D. Gelé, U. Goerlach, M. Jansová, A.-C.
Le Bihan, N. Tonon, P. Van Hove Centre de Calcul de l’Institut National de
Physique Nucleaire et de Physique des Particules, CNRS/IN2P3, Villeurbanne,
France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, C. Bernet, G. Boudoul, C. Camen, N. Chanon, R. Chierici, D.
Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M. Gouzevitch, B.
Ille, Sa. Jain, F. Lagarde, I.B. Laktineh, H. Lattaud, A. Lesauvage, M.
Lethuillier, L. Mirabito, S. Perries, V. Sordini, L. Torterotot, G. Touquet,
M. Vander Donckt, S. Viret Georgian Technical University, Tbilisi, Georgia
G. Adamov Tbilisi State University, Tbilisi, Georgia
Z. Tsamalaidze10 RWTH Aachen University, I. Physikalisches Institut, Aachen,
Germany
C. Autermann, L. Feld, M.K. Kiesel, K. Klein, M. Lipinski, D. Meuser, A.
Pauls, M. Preuten, M.P. Rauch, C. Schomakers, J. Schulz, M. Teroerde, B.
Wittmer RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
A. Albert, M. Erdmann, S. Erdweg, T. Esch, B. Fischer, R. Fischer, S. Ghosh,
T. Hebbeker, K. Hoepfner, H. Keller, L. Mastrolorenzo, M. Merschmeyer, A.
Meyer, P. Millet, G. Mocellin, S. Mondal, S. Mukherjee, D. Noll, A. Novak, T.
Pook, A. Pozdnyakov, T. Quast, M. Radziej, Y. Rath, H. Reithler, M. Rieger, J.
Roemer, A. Schmidt, S.C. Schuler, A. Sharma, S. Thüer, S. Wiedenbeck, S.
Zaleski RWTH Aachen University, III. Physikalisches Institut B, Aachen,
Germany
G. Flügge, W. Haj Ahmad15, O. Hlushchenko, T. Kress, T. Müller, A. Nehrkorn,
A. Nowack, C. Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl16 Deutsches
Elektronen-Synchrotron, Hamburg, Germany
M. Aldaya Martin, P. Asmuss, I. Babounikau, H. Bakhshiansohi, K. Beernaert, O.
Behnke, U. Behrens, A. Bermúdez Martínez, D. Bertsche, A.A. Bin Anuar, K.
Borras17, V. Botta, A. Campbell, A. Cardini, P. Connor, S. Consuegra
Rodríguez, C. Contreras-Campana, V. Danilov, A. De Wit, M.M. Defranchis, C.
Diez Pardos, D. Domínguez Damiani, G. Eckerlin, D. Eckstein, T. Eichhorn, A.
Elwood, E. Eren, E. Gallo18, A. Geiser, J.M. Grados Luyando, A. Grohsjean, M.
Guthoff, M. Haranko, A. Harb, A. Jafari, N.Z. Jomhari, H. Jung, A. Kasem17, M.
Kasemann, H. Kaveh, J. Keaveney, C. Kleinwort, J. Knolle, D. Krücker, W.
Lange, T. Lenz, J. Leonard, J. Lidrych, K. Lipka, W. Lohmann19, R. Mankel,
I.-A. Melzer-Pellmann, A.B. Meyer, M. Meyer, M. Missiroli, G. Mittag, J.
Mnich, A. Mussgiller, V. Myronenko, D. Pérez Adán, S.K. Pflitsch, D. Pitzl, A.
Raspereza, A. Saibel, M. Savitskyi, V. Scheurer, P. Schütze, C.
Schwanenberger, R. Shevchenko, A. Singh, H. Tholen, O. Turkot, A. Vagnerini,
M. Van De Klundert, G.P. Van Onsem, R. Walsh, Y. Wen, K. Wichmann, C. Wissing,
O. Zenaiev, R. Zlebcik University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, V. Blobel, T. Dreyer, A.
Ebrahimi, A. Fröhlich, C. Garbers, E. Garutti, D. Gonzalez, P. Gunnellini, J.
Haller, A. Hinzmann, A. Karavdina, G. Kasieczka, R. Klanner, R. Kogler, N.
Kovalchuk, S. Kurz, V. Kutzner, J. Lange, T. Lange, A. Malara, J. Multhaup,
C.E.N. Niemeyer, A. Perieanu, A. Reimers, O. Rieger, C. Scharf, P. Schleper,
S. Schumann, J. Schwandt, J. Sonneveld, H. Stadie, G. Steinbrück, F.M. Stober,
M. Stöver, B. Vormwald, I. Zoi Karlsruher Institut fuer Technologie,
Karlsruhe, Germany
M. Akbiyik, C. Barth, M. Baselga, S. Baur, T. Berger, E. Butz, R. Caspart, T.
Chwalek, W. De Boer, A. Dierlamm, K. El Morabit, N. Faltermann, M. Giffels, P.
Goldenzweig, A. Gottmann, M.A. Harrendorf, F. Hartmann16, U. Husemann, S.
Kudella, S. Mitra, M.U. Mozer, D. Müller, Th. Müller, M. Musich, A. Nürnberg,
G. Quast, K. Rabbertz, M. Schröder, I. Shvetsov, H.J. Simonis, R. Ulrich, M.
Wassmer, M. Weber, C. Wöhrmann, R. Wolf Institute of Nuclear and Particle
Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, P. Asenov, G. Daskalakis, T. Geralis, A. Kyriakis, D. Loukas,
G. Paspalaki National and Kapodistrian University of Athens, Athens, Greece
M. Diamantopoulou, G. Karathanasis, P. Kontaxakis, A. Manousakis-katsikakis,
A. Panagiotou, I. Papavergou, N. Saoulidou, A. Stakia, K. Theofilatos, K.
Vellidis, E. Vourliotis National Technical University of Athens, Athens,
Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis University of
Ioánnina, Ioánnina, Greece
I. Evangelou, C. Foudas, P. Gianneios, P. Katsoulis, P. Kokkas, S. Mallios, K.
Manitara, N. Manthos, I. Papadopoulos, J. Strologas, F.A. Triantis, D.
Tsitsonis MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary
M. Bartók20, R. Chudasama, M. Csanad, P. Major, K. Mandal, A. Mehta, M.I.
Nagy, G. Pasztor, O. Surányi, G.I. Veres Wigner Research Centre for Physics,
Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath21, F. Sikler, T.Á. Vámi, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi20, A. Makovec, J. Molnar, Z. Szillasi
Institute of Physics, University of Debrecen, Debrecen, Hungary
P. Raics, D. Teyssier, Z.L. Trocsanyi, B. Ujvari Eszterhazy Karoly University,
Karoly Robert Campus, Gyongyos, Hungary
T. Csorgo, W.J. Metzger, F. Nemes, T. Novak Indian Institute of Science
(IISc), Bangalore, India
S. Choudhury, J.R. Komaragiri, P.C. Tiwari National Institute of Science
Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati23, C. Kar, G. Kole, P. Mal, V.K. Muraleedharan Nair Bindhu, A.
Nayak24, D.K. Sahoo23, S.K. Swain Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, R. Chawla, N. Dhingra, R.
Gupta, A. Kaur, M. Kaur, S. Kaur, P. Kumari, M. Lohan, M. Meena, K. Sandeep,
S. Sharma, J.B. Singh, A.K. Virdi, G. Walia University of Delhi, Delhi, India
A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, Ashok Kumar, S.
Malhotra, M. Naimuddin, P. Priyanka, K. Ranjan, Aashaq Shah, R. Sharma Saha
Institute of Nuclear Physics, HBNI, Kolkata, India
R. Bhardwaj25, M. Bharti25, R. Bhattacharya, S. Bhattacharya, U. Bhawandeep25,
D. Bhowmik, S. Dey, S. Dutta, S. Ghosh, M. Maity26, K. Mondal, S. Nandan, A.
Purohit, P.K. Rout, G. Saha, S. Sarkar, T. Sarkar26, M. Sharan, B. Singh25, S.
Thakur25 Indian Institute of Technology Madras, Madras, India
P.K. Behera, P. Kalbhor, A. Muhammad, P.R. Pujahari, A. Sharma, A.K. Sikdar
Bhabha Atomic Research Centre, Mumbai, India
D. Dutta, V. Jha, V. Kumar, D.K. Mishra, P.K. Netrakanti, L.M. Pant, P. Shukla
Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, G.B. Mohanty, P. Shingade, N. Sur, RavindraKumar
Verma Tata Institute of Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, S. Karmakar,
M.M. Kolwalkar, S. Kumar, G. Majumder, K. Mazumdar, P. Patel, P. Pathare, M.R.
Patil, N. Sahoo, S. Sawant Indian Institute of Science Education and Research
(IISER), Pune, India
S. Chauhan, S. Dube, V. Hegde, B. Kansal, A. Kapoor, K. Kothekar, S. Pandey,
A. Rane, A. Rastogi, S. Sharma Institute for Research in Fundamental Sciences
(IPM), Tehran, Iran
S. Chenarani27, E. Eskandari Tadavani, S.M. Etesami27, M. Khakzad, M.
Mohammadi Najafabadi, M. Naseri, F. Rezaei Hosseinabadi University College
Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari a, Università di Bari b,
Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, R. Alya,b,28, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, L.
Cristellaa,b, N. De Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, L. Fiorea,
A. Gelmia,b, G. Iasellia,c, M. Incea,b, S. Lezkia,b, G. Maggia,c, M. Maggia,
G. Minielloa,b, S. Mya,b, S. Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, R.
Radognaa, A. Ranieria, G. Selvaggia,b, L. Silvestrisa, R. Vendittia, P.
Verwilligena INFN Sezione di Bologna a, Università di Bologna b, Bologna,
Italy
G. Abbiendia, C. Battilanaa,b, D. Bonacorsia,b, L. Borgonovia,b, S. Braibant-
Giacomellia,b, R. Campaninia,b, P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa,
C. Cioccaa, G. Codispotia,b, M. Cuffiania,b, G.M. Dallavallea, F. Fabbria, A.
Fanfania,b, E. Fontanesia,b, P. Giacomellia, C. Grandia, L. Guiduccia,b, F.
Iemmia,b, S. Lo Meoa,29, S. Marcellinia, G. Masettia, F.L. Navarriaa,b, A.
Perrottaa, F. Primaveraa,b, A.M. Rossia,b, T. Rovellia,b, G.P. Sirolia,b, N.
Tosia INFN Sezione di Catania a, Università di Catania b, Catania, Italy
S. Albergoa,b,30, S. Costaa,b, A. Di Mattiaa, R. Potenzaa,b, A. Tricomia,b,30,
C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b, Firenze, Italy
G. Barbaglia, A. Cassese, R. Ceccarelli, K. Chatterjeea,b, V. Ciullia,b, C.
Civininia, R. D’Alessandroa,b, E. Focardia,b, G. Latinoa,b, P. Lenzia,b, M.
Meschinia, S. Paolettia, G. Sguazzonia, D. Stroma, L. Viliania INFN Laboratori
Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, D. Piccolo INFN Sezione di Genova a, Università di
Genova b, Genova, Italy
M. Bozzoa,b, F. Ferroa, R. Mulargiaa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschia,b, F. Brivioa,b, V. Cirioloa,b,16, S. Di Guidaa,b,16,
M.E. Dinardoa,b, P. Dinia, S. Gennaia, A. Ghezzia,b, P. Govonia,b, L.
Guzzia,b, M. Malbertia, S. Malvezzia, D. Menascea, F. Montia,b, L. Moronia, G.
Ortonaa,b, M. Paganonia,b, D. Pedrinia, S. Ragazzia,b, T. Tabarelli de
Fatisa,b, D. Zuoloa,b INFN Sezione di Napoli a, Università di Napoli ’Federico
II’ b, Napoli, Italy, Università della Basilicata c, Potenza, Italy,
Università G. Marconi d, Roma, Italy
S. Buontempoa, N. Cavalloa,c, A. De Iorioa,b, A. Di Crescenzoa,b, F.
Fabozzia,c, F. Fiengaa, G. Galatia, A.O.M. Iorioa,b, L. Listaa,b, S.
Meolaa,d,16, P. Paoluccia,16, B. Rossia, C. Sciaccaa,b, E. Voevodinaa,b INFN
Sezione di Padova a, Università di Padova b, Padova, Italy, Università di
Trento c, Trento, Italy
P. Azzia, N. Bacchettaa, D. Biselloa,b, A. Bolettia,b, A. Bragagnoloa,b, R.
Carlina,b, P. Checchiaa, P. De Castro Manzanoa, T. Dorigoa, U. Dossellia, F.
Gasparinia,b, U. Gasparinia,b, A. Gozzelinoa, S.Y. Hoha,b, P. Lujana, M.
Margonia,b, A.T. Meneguzzoa,b, J. Pazzinia,b, M. Presillab, P. Ronchesea,b, R.
Rossina,b, F. Simonettoa,b, A. Tikoa, M. Tosia,b, M. Zanettia,b, P. Zottoa,b,
G. Zumerlea,b INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
A. Braghieria, P. Montagnaa,b, S.P. Rattia,b, V. Rea, M. Ressegottia,b, C.
Riccardia,b, P. Salvinia, I. Vaia,b, P. Vituloa,b INFN Sezione di Perugia a,
Università di Perugia b, Perugia, Italy
M. Biasinia,b, G.M. Bileia, D. Ciangottinia,b, L. Fanòa,b, P. Laricciaa,b, R.
Leonardia,b, G. Mantovania,b, V. Mariania,b, M. Menichellia, A. Rossia,b, A.
Santocchiaa,b, D. Spigaa INFN Sezione di Pisa a, Università di Pisa b, Scuola
Normale Superiore di Pisa c, Pisa, Italy
K. Androsova, P. Azzurria, G. Bagliesia, V. Bertacchia,c, L. Bianchinia, T.
Boccalia, R. Castaldia, M.A. Cioccia,b, R. Dell’Orsoa, G. Fedia, L.
Gianninia,c, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c, G.
Mandorlia,c, A. Messineoa,b, F. Pallaa, A. Rizzia,b, G. Rolandi31, S. Roy
Chowdhury, A. Scribanoa, P. Spagnoloa, R. Tenchinia, G. Tonellia,b, N. Turini,
A. Venturia, P.G. Verdinia INFN Sezione di Roma a, Sapienza Università di Roma
b, Rome, Italy
F. Cavallaria, M. Cipriania,b, D. Del Rea,b, E. Di Marcoa,b, M. Diemoza, E.
Longoa,b, B. Marzocchia,b, P. Meridiania, G. Organtinia,b, F. Pandolfia, R.
Paramattia,b, C. Quarantaa,b, S. Rahatloua,b, C. Rovellia, F.
Santanastasioa,b, L. Soffia,b INFN Sezione di Torino a, Università di Torino
b, Torino, Italy, Università del Piemonte Orientale c, Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, C. Biinoa, A. Cappatia,b, N. Cartigliaa, S. Comettia, M.
Costaa,b, R. Covarellia,b, N. Demariaa, B. Kiania,b, C. Mariottia, S.
Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M. Montenoa, M.M.
Obertinoa,b, L. Pachera,b, N. Pastronea, M. Pelliccionia, G.L. Pinna
Angionia,b, A. Romeroa,b, M. Ruspaa,c, R. Sacchia,b, R. Salvaticoa,b, V.
Solaa, A. Solanoa,b, D. Soldia,b, A. Staianoa INFN Sezione di Trieste a,
Università di Trieste b, Trieste, Italy
S. Belfortea, V. Candelisea,b, M. Casarsaa, F. Cossuttia, A. Da Rolda,b, G.
Della Riccaa,b, F. Vazzolera,b, A. Zanettia Kyungpook National University,
Daegu, Korea
B. Kim, D.H. Kim, G.N. Kim, J. Lee, S.W. Lee, C.S. Moon, Y.D. Oh, S.I. Pak, S.
Sekmen, D.C. Son, Y.C. Yang Chonnam National University, Institute for
Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon, G. Oh Hanyang University, Seoul, Korea
B. Francois, T.J. Kim, J. Park Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, D. Gyun, S. Ha, B. Hong, K. Lee, K.S. Lee, J. Lim, J.
Park, S.K. Park, Y. Roh, J. Yoo Kyung Hee University, Department of Physics
J. Goh Sejong University, Seoul, Korea
H.S. Kim Seoul National University, Seoul, Korea
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, H. Lee, K. Lee, S.
Lee, K. Nam, M. Oh, S.B. Oh, B.C. Radburn-Smith, U.K. Yang, H.D. Yoo, I. Yoon,
G.B. Yu University of Seoul, Seoul, Korea
D. Jeon, H. Kim, J.H. Kim, J.S.H. Lee, I.C. Park, I. Watson Sungkyunkwan
University, Suwon, Korea
Y. Choi, C. Hwang, Y. Jeong, J. Lee, Y. Lee, I. Yu Riga Technical University,
Riga, Latvia
V. Veckalns32 Vilnius University, Vilnius, Lithuania
V. Dudenas, A. Juodagalvis, G. Tamulaitis, J. Vaitkus National Centre for
Particle Physics, Universiti Malaya, Kuala Lumpur, Malaysia
Z.A. Ibrahim, F. Mohamad Idris33, W.A.T. Wan Abdullah, M.N. Yusli, Z. Zolkapli
Universidad de Sonora (UNISON), Hermosillo, Mexico
J.F. Benitez, A. Castaneda Hernandez, J.A. Murillo Quijada, L. Valencia Palomo
Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-De La Cruz34, R. Lopez-
Fernandez, A. Sanchez-Hernandez Universidad Iberoamericana, Mexico City,
Mexico
S. Carrillo Moreno, C. Oropeza Barrera, M. Ramirez-Garcia, F. Vazquez Valencia
Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Montenegro, Podgorica, Montenegro
N. Raicevic University of Auckland, Auckland, New Zealand
D. Krofcheck University of Canterbury, Christchurch, New Zealand
S. Bheesette, P.H. Butler National Centre for Physics, Quaid-I-Azam
University, Islamabad, Pakistan
A. Ahmad, M. Ahmad, Q. Hassan, H.R. Hoorani, W.A. Khan, M.A. Shah, M. Shoaib,
M. Waqas AGH University of Science and Technology Faculty of Computer Science,
Electronics and Telecommunications, Krakow, Poland
V. Avati, L. Grzanka, M. Malawski National Centre for Nuclear Research,
Swierk, Poland
H. Bialkowska, M. Bluj, B. Boimska, M. Górski, M. Kazana, M. Szleper, P.
Zalewski Institute of Experimental Physics, Faculty of Physics, University of
Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk35, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Misiura, M. Olszewski, A. Pyskir, M. Walczak Laboratório de
Instrumentação e Física Experimental de Partículas, Lisboa, Portugal
M. Araujo, P. Bargassa, D. Bastos, A. Di Francesco, P. Faccioli, B. Galinhas,
M. Gallinaro, J. Hollar, N. Leonardo, J. Seixas, K. Shchelina, G. Strong, O.
Toldaiev, J. Varela Joint Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, P. Bunin, M. Gavrilenko, I. Golutvin, I. Gorbunov, A. Kamenev,
V. Karjavine, A. Lanev, A. Malakhov, V. Matveev36,37, P. Moisenz, V. Palichik,
V. Perelygin, M. Savina, S. Shmatov, S. Shulha, N. Skatchkov, V. Smirnov, N.
Voytishin, A. Zarubin Petersburg Nuclear Physics Institute, Gatchina (St.
Petersburg), Russia
L. Chtchipounov, V. Golovtcov, Y. Ivanov, V. Kim38, E. Kuznetsova39, P.
Levchenko, V. Murzin, V. Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L.
Uvarov, A. Vorobyev Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, D. Tlisov, A. Toropin Institute for Theoretical
and Experimental Physics named by A.I. Alikhanov of NRC ‘Kurchatov Institute’,
Moscow, Russia
V. Epshteyn, V. Gavrilov, N. Lychkovskaya, A. Nikitenko40, V. Popov, I.
Pozdnyakov, G. Safronov, A. Spiridonov, A. Stepennov, M. Toms, E. Vlasov, A.
Zhokin Moscow Institute of Physics and Technology, Moscow, Russia
T. Aushev National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
M. Chadeeva41, P. Parygin, D. Philippov, E. Popova, V. Rusinov P.N. Lebedev
Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin, M. Kirakosyan, A. Terkulov Skobeltsyn
Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow,
Russia
A. Belyaev, E. Boos, A. Demiyanov, L. Dudko, A. Ershov, A. Gribushin, A.
Kaminskiy42, V. Klyukhin, O. Kodolova, I. Lokhtin, S. Obraztsov, S.
Petrushanko, V. Savrin Novosibirsk State University (NSU), Novosibirsk, Russia
A. Barnyakov43, V. Blinov43, T. Dimova43, L. Kardapoltsev43, Y. Skovpen43
Institute for High Energy Physics of National Research Centre ‘Kurchatov
Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, S. Bitioukov, V. Kachanov, D. Konstantinov, P.
Mandrik, V. Petrov, R. Ryutin, S. Slabospitskii, A. Sobol, S. Troshin, N.
Tyurin, A. Uzunian, A. Volkov National Research Tomsk Polytechnic University,
Tomsk, Russia
A. Babaev, A. Iuzhakov, V. Okhotnikov Tomsk State University, Tomsk, Russia
V. Borchsh, V. Ivanchenko, E. Tcherniaev University of Belgrade: Faculty of
Physics and VINCA Institute of Nuclear Sciences
P. Adzic44, P. Cirkovic, D. Devetak, M. Dordevic, P. Milenovic, J. Milosevic,
M. Stojanovic Centro de Investigaciones Energéticas Medioambientales y
Tecnológicas (CIEMAT), Madrid, Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M.
Barrio Luna, J.A. Brochero Cifuentes, C.A. Carrillo Montoya, M. Cepeda, M.
Cerrada, N. Colino, B. De La Cruz, A. Delgado Peris, C. Fernandez Bedoya, J.P.
Fernández Ramos, J. Flix, M.C. Fouz, O. Gonzalez Lopez, S. Goy Lopez, J.M.
Hernandez, M.I. Josa, D. Moran, Á. Navarro Tobar, A. Pérez-Calero Yzquierdo,
J. Puerta Pelayo, I. Redondo, L. Romero, S. Sánchez Navas, M.S. Soares, A.
Triossi, C. Willmott Universidad Autónoma de Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz Universidad de Oviedo, Instituto Universitario de
Ciencias y Tecnologías Espaciales de Asturias (ICTEA), Oviedo, Spain
B. Alvarez Gonzalez, J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras,
I. Gonzalez Caballero, J.R. González Fernández, E. Palencia Cortezon, V.
Rodríguez Bouza, S. Sanchez Cruz Instituto de Física de Cantabria (IFCA),
CSIC-Universidad de Cantabria, Santander, Spain
I.J. Cabrillo, A. Calderon, B. Chazin Quero, J. Duarte Campderros, M.
Fernandez, P.J. Fernández Manteca, A. García Alonso, G. Gomez, C. Martinez
Rivero, P. Martinez Ruiz del Arbol, F. Matorras, J. Piedra Gomez, C. Prieels,
T. Rodrigo, A. Ruiz-Jimeno, L. Russo45, L. Scodellaro, N. Trevisani, I. Vila,
J.M. Vizan Garcia University of Colombo, Colombo, Sri Lanka
K. Malagalage University of Ruhuna, Department of Physics, Matara, Sri Lanka
W.G.D. Dharmaratna, N. Wickramage CERN, European Organization for Nuclear
Research, Geneva, Switzerland
D. Abbaneo, B. Akgun, E. Auffray, G. Auzinger, J. Baechler, P. Baillon, A.H.
Ball, D. Barney, J. Bendavid, M. Bianco, A. Bocci, P. Bortignon, E. Bossini,
C. Botta, E. Brondolin, T. Camporesi, A. Caratelli, G. Cerminara, E. Chapon,
G. Cucciati, D. d’Enterria, A. Dabrowski, N. Daci, V. Daponte, A. David, O.
Davignon, A. De Roeck, N. Deelen, M. Deile, M. Dobson, M. Dünser, N. Dupont,
A. Elliott-Peisert, F. Fallavollita46, D. Fasanella, S. Fiorendi, G. Franzoni,
J. Fulcher, W. Funk, S. Giani, D. Gigi, A. Gilbert, K. Gill, F. Glege, M.
Gruchala, M. Guilbaud, D. Gulhan, J. Hegeman, C. Heidegger, Y. Iiyama, V.
Innocente, P. Janot, O. Karacheban19, J. Kaspar, J. Kieseler, M. Krammer1, C.
Lange, P. Lecoq, C. Lourenço, L. Malgeri, M. Mannelli, A. Massironi, F.
Meijers, J.A. Merlin, S. Mersi, E. Meschi, F. Moortgat, M. Mulders, J.
Ngadiuba, S. Nourbakhsh, S. Orfanelli, L. Orsini, F. Pantaleo16, L. Pape, E.
Perez, M. Peruzzi, A. Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, F.M.
Pitters, D. Rabady, A. Racz, M. Rovere, H. Sakulin, C. Schäfer, C. Schwick, M.
Selvaggi, A. Sharma, P. Silva, W. Snoeys, P. Sphicas47, J. Steggemann, S.
Summers, V.R. Tavolaro, D. Treille, A. Tsirou, A. Vartak, M. Verzetti, W.D.
Zeuner Paul Scherrer Institut, Villigen, Switzerland
L. Caminada48, K. Deiters, W. Erdmann, R. Horisberger, Q. Ingram, H.C.
Kaestli, D. Kotlinski, U. Langenegger, T. Rohe, S.A. Wiederkehr ETH Zurich -
Institute for Particle Physics and Astrophysics (IPA), Zurich, Switzerland
M. Backhaus, P. Berger, N. Chernyavskaya, G. Dissertori, M. Dittmar, M.
Donegà, C. Dorfer, T.A. Gómez Espinosa, C. Grab, D. Hits, T. Klijnsma, W.
Lustermann, R.A. Manzoni, M. Marionneau, M.T. Meinhard, F. Micheli, P.
Musella, F. Nessi-Tedaldi, F. Pauss, G. Perrin, L. Perrozzi, S. Pigazzini,
M.G. Ratti, M. Reichmann, C. Reissel, T. Reitenspiess, D. Ruini, D.A. Sanz
Becerra, M. Schönenberger, L. Shchutska, M.L. Vesterbacka Olsson, R. Wallny,
D.H. Zhu Universität Zürich, Zurich, Switzerland
T.K. Aarrestad, C. Amsler49, D. Brzhechko, M.F. Canelli, A. De Cosa, R. Del
Burgo, S. Donato, B. Kilminster, S. Leontsinis, V.M. Mikuni, I. Neutelings, G.
Rauco, P. Robmann, D. Salerno, K. Schweiger, C. Seitz, Y. Takahashi, S. Wertz,
A. Zucchetta National Central University, Chung-Li, Taiwan
T.H. Doan, C.M. Kuo, W. Lin, A. Roy, S.S. Yu National Taiwan University (NTU),
Taipei, Taiwan
P. Chang, Y. Chao, K.F. Chen, P.H. Chen, W.-S. Hou, Y.y. Li, R.-S. Lu, E.
Paganis, A. Psallidas, A. Steen Chulalongkorn University, Faculty of Science,
Department of Physics, Bangkok, Thailand
B. Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas, N. Suwonjandee Çukurova
University, Physics Department, Science and Art Faculty, Adana, Turkey
D. Agyel, S. Anagul, M.N. Bakirci50, A. Bat, F. Bilican, F. Boran, A. Celik51,
S. Cerci52, S. Damarseckin53, Z.S. Demiroglu, F. Dolek, C. Dozen, I.
Dumanoglu, E. Eskut, G. Gokbulut, EmineGurpinar Guler54, Y. Guler, I. Hos55,
C. Isik, E.E. Kangal56, O. Kara, A. Kayis Topaksu, U. Kiminsu, M. Oglakci, G.
Onengut, K. Ozdemir57, S. Ozturk50, A. Polatoz, A.E. Simsek, Ü. Sözbilir, D.
Sunar Cerci52, B. Tali52, U.G. Tok, H. Topakli50, S. Turkcapar, E. Uslan, I.S.
Zorbakir, C. Zorbilmez Middle East Technical University, Physics Department,
Ankara, Turkey
B. Isildak58, G. Karapinar59, M. Yalvac Bogazici University, Istanbul, Turkey
I.O. Atakisi, E. Gülmez, M. Kaya60, O. Kaya61, B. Kaynak, Ö. Özçelik, S.
Tekten, E.A. Yetkin62 Istanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak, Y. Komurcu, S. Sen63 Istanbul University, Istanbul,
Turkey
S. Ozkorucuklu Institute for Scintillation Materials of National Academy of
Science of Ukraine, Kharkov, Ukraine
B. Grynyov National Scientific Center, Kharkov Institute of Physics and
Technology, Kharkov, Ukraine
L. Levchuk University of Bristol, Bristol, United Kingdom
F. Ball, E. Bhal, S. Bologna, J.J. Brooke, D. Burns64, E. Clement, D. Cussans,
H. Flacher, J. Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, S. Paramesvaran,
B. Penning, T. Sakuma, S. Seif El Nasr-Storey, D. Smith64, V.J. Smith, J.
Taylor, A. Titterton Rutherford Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev65, C. Brew, R.M. Brown, D. Cieri, D.J.A. Cockerill, J.A.
Coughlan, K. Harder, S. Harper, J. Linacre, K. Manolopoulos, D.M. Newbold, E.
Olaiya, D. Petyt, T. Reis, T. Schuh, C.H. Shepherd-Themistocleous, A. Thea,
I.R. Tomalin, T. Williams, W.J. Womersley Imperial College, London, United
Kingdom
R. Bainbridge, P. Bloch, J. Borg, S. Breeze, O. Buchmuller, A. Bundock,
GurpreetSingh CHAHAL66, D. Colling, P. Dauncey, G. Davies, M. Della Negra, R.
Di Maria, P. Everaerts, G. Hall, G. Iles, T. James, M. Komm, C. Laner, L.
Lyons, A.-M. Magnan, S. Malik, A. Martelli, V. Milosevic, J. Nash67, V.
Palladino, M. Pesaresi, D.M. Raymond, A. Richards, A. Rose, E. Scott, C. Seez,
A. Shtipliyski, M. Stoye, T. Strebler, A. Tapper, K. Uchida, T. Virdee16, N.
Wardle, D. Winterbottom, J. Wright, A.G. Zecchinelli, S.C. Zenz Brunel
University, Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, C.K. Mackay, A. Morton, I.D. Reid,
L. Teodorescu, S. Zahid Baylor University, Waco, USA
K. Call, J. Dittmann, K. Hatakeyama, C. Madrid, B. McMaster, N. Pastika, C.
Smith Catholic University of America, Washington, DC, USA
R. Bartek, A. Dominguez, R. Uniyal The University of Alabama, Tuscaloosa, USA
A. Buccilli, S.I. Cooper, C. Henderson, P. Rumerio, C. West Boston University,
Boston, USA
D. Arcaro, C. Cosby, Z. Demiragli, D. Gastler, S. Girgis, E. Hazen, D. Pinna,
C. Richardson, J. Rohlf, D. Sperka, I. Suarez, L. Sulak, S. Wu, D. Zou Brown
University, Providence, USA
G. Benelli, B. Burkle, X. Coubez17, D. Cutts, Y.t. Duh, M. Hadley, J. Hakala,
U. Heintz, J.M. Hogan68, K.H.M. Kwok, E. Laird, G. Landsberg, J. Lee, Z. Mao,
M. Narain, S. Sagir69, R. Syarif, E. Usai, D. Yu, W. Zhang University of
California, Davis, Davis, USA
R. Band, C. Brainerd, R. Breedon, M. Calderon De La Barca Sanchez, M. Chertok,
J. Conway, R. Conway, P.T. Cox, R. Erbacher, C. Flores, G. Funk, F. Jensen, W.
Ko, O. Kukral, R. Lander, M. Mulhearn, D. Pellett, J. Pilot, M. Shi, D.
Taylor, K. Tos, M. Tripathi, Z. Wang, F. Zhang University of California, Los
Angeles, USA
M. Bachtis, C. Bravo, R. Cousins, A. Dasgupta, A. Florent, J. Hauser, M.
Ignatenko, N. Mccoll, W.A. Nash, S. Regnard, D. Saltzberg, C. Schnaible, B.
Stone, V. Valuev University of California, Riverside, Riverside, USA
K. Burt, Y. Chen, R. Clare, J.W. Gary, S.M.A. Ghiasi Shirazi, G. Hanson, G.
Karapostoli, E. Kennedy, O.R. Long, M. Olmedo Negrete, M.I. Paneva, W. Si, L.
Wang, H. Wei, S. Wimpenny, B.R. Yates, Y. Zhang University of California, San
Diego, La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, M. Derdzinski, R. Gerosa, D. Gilbert, B.
Hashemi, D. Klein, V. Krutelyov, J. Letts, M. Masciovecchio, S. May, S. Padhi,
M. Pieri, V. Sharma, M. Tadel, F. Würthwein, A. Yagil, G. Zevi Della Porta
University of California, Santa Barbara - Department of Physics, Santa
Barbara, USA
N. Amin, R. Bhandari, C. Campagnari, M. Citron, V. Dutta, M. Franco Sevilla,
L. Gouskos, J. Incandela, B. Marsh, H. Mei, A. Ovcharova, H. Qu, J. Richman,
U. Sarica, D. Stuart, S. Wang California Institute of Technology, Pasadena,
USA
D. Anderson, A. Bornheim, O. Cerri, I. Dutta, J.M. Lawhorn, N. Lu, J. Mao,
H.B. Newman, T.Q. Nguyen, J. Pata, M. Spiropulu, J.R. Vlimant, S. Xie, Z.
Zhang, R.Y. Zhu Carnegie Mellon University, Pittsburgh, USA
M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, M. Sun, I. Vorobiev, M.
Weinberg University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, A. Johnson, E. MacDonald, T. Mulholland, R. Patel, A.
Perloff, K. Stenson, K.A. Ulmer, S.R. Wagner Cornell University, Ithaca, USA
J. Alexander, J. Chaves, Y. Cheng, J. Chu, A. Datta, A. Frankenthal, K.
Mcdermott, J.R. Patterson, D. Quach, A. Rinkevicius70, A. Ryd, S.M. Tan, Z.
Tao, J. Thom, P. Wittich, M. Zientek Fairfield University, Fairfield, USA
D. Winn Fermi National Accelerator Laboratory, Batavia, USA
S. Abdullin, M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S.
Banerjee, L.A.T. Bauerdick, A. Beretvas, J. Berryhill, P.C. Bhat, K. Burkett,
J.N. Butler, A. Canepa, G.B. Cerati, H.W.K. Cheung, F. Chlebana, M. Cremonesi,
J. Duarte, V.D. Elvira, J. Freeman, Z. Gecse, E. Gottschalk, L. Gray, D.
Green, S. Grünendahl, O. Gutsche, AllisonReinsvold Hall, J. Hanlon, R.M.
Harris, S. Hasegawa, R. Heller, J. Hirschauer, B. Jayatilaka, S. Jindariani,
M. Johnson, U. Joshi, B. Klima, M.J. Kortelainen, B. Kreis, S. Lammel, J.
Lewis, D. Lincoln, R. Lipton, M. Liu, T. Liu, J. Lykken, K. Maeshima, J.M.
Marraffino, D. Mason, P. McBride, P. Merkel, S. Mrenna, S. Nahn, V. O’Dell, V.
Papadimitriou, K. Pedro, C. Pena, G. Rakness, F. Ravera, L. Ristori, B.
Schneider, E. Sexton-Kennedy, N. Smith, A. Soha, W.J. Spalding, L. Spiegel, S.
Stoynev, J. Strait, N. Strobbe, L. Taylor, S. Tkaczyk, N.V. Tran, L. Uplegger,
E.W. Vaandering, C. Vernieri, M. Verzocchi, R. Vidal, M. Wang, H.A. Weber
University of Florida, Gainesville, USA
D. Acosta, P. Avery, D. Bourilkov, A. Brinkerhoff, L. Cadamuro, A. Carnes, V.
Cherepanov, D. Curry, F. Errico, R.D. Field, S.V. Gleyzer, B.M. Joshi, M. Kim,
J. Konigsberg, A. Korytov, K.H. Lo, P. Ma, K. Matchev, N. Menendez, G.
Mitselmakher, D. Rosenzweig, K. Shi, J. Wang, S. Wang, X. Zuo Florida
International University, Miami, USA
Y.R. Joshi Florida State University, Tallahassee, USA
T. Adams, A. Askew, S. Hagopian, V. Hagopian, K.F. Johnson, R. Khurana, T.
Kolberg, G. Martinez, T. Perry, H. Prosper, C. Schiber, R. Yohay, J. Zhang
Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, V. Bhopatkar, M. Hohlmann, D. Noonan, M. Rahmani, M. Saunders,
F. Yumiceva University of Illinois at Chicago (UIC), Chicago, USA
M.R. Adams, L. Apanasevich, D. Berry, R.R. Betts, R. Cavanaugh, X. Chen, S.
Dittmer, O. Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, K. Jung, C.
Mills, T. Roy, M.B. Tonjes, N. Varelas, J. Viinikainen, H. Wang, X. Wang, Z.
Wu The University of Iowa, Iowa City, USA
M. Alhusseini, B. Bilki54, W. Clarida, P. Debbins, K. Dilsiz71, S. Durgut, L.
Emediato, R.P. Gandrajula, M. Haytmyradov, V. Khristenko, O.K. Köseyan, T.
Mcdowell, J.-P. Merlo, A. Mestvirishvili72, M.J. Miller, A. Moeller, J.
Nachtman, H. Ogul73, Y. Onel, F. Ozok74, A. Penzo, C. Rude, I. Schmidt, C.
Snyder, D. Southwick, E. Tiras, J. Wetzel, K. Yi Johns Hopkins University,
Baltimore, USA
B. Blumenfeld, A. Cocoros, N. Eminizer, D. Fehling, L. Feng, A.V. Gritsan,
W.T. Hung, P. Maksimovic, J. Roskes, M. Swartz, M. Xiao The University of
Kansas, Lawrence, USA
C. Baldenegro Barrera, P. Baringer, A. Bean, S. Boren, J. Bowen, A. Bylinkin,
T. Isidori, S. Khalil, J. King, G. Krintiras, A. Kropivnitskaya, C. Lindsey,
D. Majumder, W. Mcbrayer, N. Minafra, M. Murray, C. Rogan, C. Royon, S.
Sanders, E. Schmitz, J.D. Tapia Takaki, Q. Wang, J. Williams, G. Wilson Kansas
State University, Manhattan, USA
S. Duric, A. Ivanov, K. Kaadze, D. Kim, Y. Maravin, D.R. Mendis, T. Mitchell,
A. Modak, A. Mohammadi Lawrence Livermore National Laboratory, Livermore, USA
F. Rebassoo, D. Wright University of Maryland, College Park, USA
A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, T. Grassi, N.J. Hadley, S.
Jabeen, G.Y. Jeng, R.G. Kellogg, J. Kunkle, A.C. Mignerey, S. Nabili, F.
Ricci-Tam, M. Seidel, Y.H. Shin, A. Skuja, S.C. Tonwar, K. Wong Massachusetts
Institute of Technology, Cambridge, USA
D. Abercrombie, B. Allen, A. Baty, R. Bi, S. Brandt, W. Busza, I.A. Cali, M.
D’Alfonso, G. Gomez Ceballos, M. Goncharov, P. Harris, D. Hsu, M. Hu, M.
Klute, D. Kovalskyi, Y.-J. Lee, P.D. Luckey, B. Maier, A.C. Marini, C. Mcginn,
C. Mironov, S. Narayanan, X. Niu, C. Paus, D. Rankin, C. Roland, G. Roland, Z.
Shi, G.S.F. Stephans, K. Sumorok, K. Tatar, D. Velicanu, J. Wang, T.W. Wang,
B. Wyslouch University of Minnesota, Minneapolis, USA
A.C. Benvenuti${}^{\textrm{\textdagger}}$, R.M. Chatterjee, A. Evans, S. Guts,
P. Hansen, J. Hiltbrand, Y. Kubota, Z. Lesko, J. Mans, R. Rusack, M.A. Wadud
University of Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
K. Bloom, D.R. Claes, C. Fangmeier, L. Finco, F. Golf, R. Gonzalez Suarez, R.
Kamalieddin, I. Kravchenko, J.E. Siado, G.R. Snow, B. Stieger, W. Tabb State
University of New York at Buffalo, Buffalo, USA
G. Agarwal, C. Harrington, I. Iashvili, A. Kharchilava, C. McLean, D. Nguyen,
A. Parker, J. Pekkanen, S. Rappoccio, B. Roozbahani Northeastern University,
Boston, USA
G. Alverson, E. Barberis, C. Freer, Y. Haddad, A. Hortiangtham, G. Madigan,
D.M. Morse, T. Orimoto, L. Skinnari, A. Tishelman-Charny, T. Wamorkar, B.
Wang, A. Wisecarver, D. Wood Northwestern University, Evanston, USA
S. Bhattacharya, J. Bueghly, T. Gunter, K.A. Hahn, N. Odell, M.H. Schmitt, K.
Sung, M. Trovato, M. Velasco University of Notre Dame, Notre Dame, USA
R. Bucci, N. Dev, R. Goldouzian, A.H. Heering, M. Hildreth, K. Hurtado Anampa,
C. Jessop, D.J. Karmgard, K. Lannon, W. Li, N. Loukas, N. Marinelli, I.
Mcalister, F. Meng, C. Mueller, Y. Musienko36, M. Planer, R. Ruchti, P.
Siddireddy, G. Smith, S. Taroni, M. Wayne, A. Wightman, M. Wolf, A. Woodard
The Ohio State University, Columbus, USA
J. Alimena, B. Bylsma, L.S. Durkin, S. Flowers, B. Francis, C. Hill, W. Ji, A.
Lefeld, T.Y. Ling, B.L. Winer Princeton University, Princeton, USA
S. Cooperstein, G. Dezoort, P. Elmer, J. Hardenbrook, N. Haubrich, S.
Higginbotham, A. Kalogeropoulos, S. Kwan, D. Lange, M.T. Lucchini, J. Luo, D.
Marlow, K. Mei, I. Ojalvo, J. Olsen, C. Palmer, P. Piroué, J. Salfeld-Nebgen,
D. Stickland, C. Tully, Z. Wang University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
A. Barker, V.E. Barnes, S. Das, L. Gutay, M. Jones, A.W. Jung, A. Khatiwada,
B. Mahakud, D.H. Miller, G. Negro, N. Neumeister, C.C. Peng, S. Piperov, H.
Qiu, J.F. Schulte, J. Sun, F. Wang, R. Xiao, W. Xie Purdue University
Northwest, Hammond, USA
T. Cheng, J. Dolen, N. Parashar Rice University, Houston, USA
K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Kilpatrick, Arun Kumar, W. Li, B.P.
Padley, R. Redjimi, J. Roberts, J. Rorie, W. Shi, A.G. Stahl Leiton, Z. Tu, A.
Zhang University of Rochester, Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, J.L. Dulemba, C. Fallon, T. Ferbel, M.
Galanti, A. Garcia-Bellido, J. Han, O. Hindrichs, A. Khukhunaishvili, E.
Ranken, P. Tan, R. Taus The Rockefeller University, New York, USA
R. Ciesielski Rutgers, The State University of New Jersey, Piscataway, USA
B. Chiarito, J.P. Chou, A. Gandrakota, Y. Gershtein, E. Halkiadakis, A. Hart,
M. Heindl, E. Hughes, S. Kaplan, S. Kyriacou, I. Laflotte, A. Lath, R.
Montalvo, K. Nash, M. Osherson, H. Saka, S. Salur, S. Schnetzer, D. Sheffield,
S. Somalwar, R. Stone, S. Thomas, P. Thomassen University of Tennessee,
Knoxville, USA
H. Acharya, A.G. Delannoy, G. Riley, S. Spanier Texas A&M University, College
Station, USA
O. Bouhali75, M. Dalchenko, M. De Mattia, A. Delgado, S. Dildick, R. Eusebi,
J. Gilmore, T. Huang, T. Kamon76, S. Luo, D. Marley, R. Mueller, D. Overton,
L. Perniè, D. Rathjens, A. Safonov Texas Tech University, Lubbock, USA
N. Akchurin, J. Damgov, F. De Guio, S. Kunori, K. Lamichhane, S.W. Lee, T.
Mengke, S. Muthumuni, T. Peltola, S. Undleeb, I. Volobouev, Z. Wang, A.
Whitbeck Vanderbilt University, Nashville, USA
S. Greene, A. Gurrola, R. Janjam, W. Johns, C. Maguire, A. Melo, H. Ni, K.
Padeken, F. Romeo, P. Sheldon, S. Tuo, J. Velkovska, M. Verweij University of
Virginia, Charlottesville, USA
M.W. Arenton, P. Barria, B. Cox, G. Cummings, R. Hirosky, M. Joyce, A.
Ledovskoy, C. Neu, B. Tannenwald, Y. Wang, E. Wolfe, F. Xia Wayne State
University, Detroit, USA
R. Harr, P.E. Karchin, N. Poudyal, J. Sturdy, P. Thapa University of Wisconsin
- Madison, Madison, WI, USA
T. Bose, J. Buchanan, C. Caillol, D. Carlsmith, S. Dasu, I. De Bruyn, L. Dodd,
F. Fiori, C. Galloni, B. Gomber77, H. He, M. Herndon, A. Hervé, U. Hussain, P.
Klabbers, A. Lanaro, A. Loeliger, K. Long, R. Loveless, J. Madhusudanan
Sreekala, T. Ruggles, A. Savin, V. Sharma, W.H. Smith, D. Teague, S. Trembath-
reichert, N. Woods †: Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
3: Also at Universidade Estadual de Campinas, Campinas, Brazil
4: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
5: Also at UFMS, Nova Andradina, Brazil
6: Also at Universidade Federal de Pelotas, Pelotas, Brazil
7: Also at Université Libre de Bruxelles, Bruxelles, Belgium
8: Also at University of Chinese Academy of Sciences, Beijing, China
9: Also at Institute for Theoretical and Experimental Physics named by A.I.
Alikhanov of NRC ‘Kurchatov Institute’, Moscow, Russia
10: Also at Joint Institute for Nuclear Research, Dubna, Russia
11: Also at Suez University, Suez, Egypt
12: Now at British University in Egypt, Cairo, Egypt
13: Also at Purdue University, West Lafayette, USA
14: Also at Université de Haute Alsace, Mulhouse, France
15: Also at Erzincan Binali Yildirim University, Erzincan, Turkey
16: Also at CERN, European Organization for Nuclear Research, Geneva,
Switzerland
17: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
18: Also at University of Hamburg, Hamburg, Germany
19: Also at Brandenburg University of Technology, Cottbus, Germany
20: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary,
Debrecen, Hungary
21: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
22: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary, Budapest, Hungary
23: Also at IIT Bhubaneswar, Bhubaneswar, India, Bhubaneswar, India
24: Also at Institute of Physics, Bhubaneswar, India
25: Also at Shoolini University, Solan, India
26: Also at University of Visva-Bharati, Santiniketan, India
27: Also at Isfahan University of Technology, Isfahan, Iran
28: Now at INFN Sezione di Bari a, Università di Bari b, Politecnico di Bari
c, Bari, Italy
29: Also at Italian National Agency for New Technologies, Energy and
Sustainable Economic Development, Bologna, Italy
30: Also at Centro Siciliano di Fisica Nucleare e di Struttura Della Materia,
Catania, Italy
31: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy
32: Also at Riga Technical University, Riga, Latvia, Riga, Latvia
33: Also at Malaysian Nuclear Agency, MOSTI, Kajang, Malaysia
34: Also at Consejo Nacional de Ciencia y Tecnología, Mexico City, Mexico
35: Also at Warsaw University of Technology, Institute of Electronic Systems,
Warsaw, Poland
36: Also at Institute for Nuclear Research, Moscow, Russia
37: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
38: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
39: Also at University of Florida, Gainesville, USA
40: Also at Imperial College, London, United Kingdom
41: Also at P.N. Lebedev Physical Institute, Moscow, Russia
42: Also at INFN Sezione di Padova a, Università di Padova b, Padova, Italy,
Università di Trento c, Trento, Italy, Padova, Italy
43: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
44: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
45: Also at Università degli Studi di Siena, Siena, Italy
46: Also at INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy,
Pavia, Italy
47: Also at National and Kapodistrian University of Athens, Athens, Greece
48: Also at Universität Zürich, Zurich, Switzerland
49: Also at Stefan Meyer Institute for Subatomic Physics, Vienna, Austria,
Vienna, Austria
50: Also at Gaziosmanpasa University, Tokat, Turkey
51: Also at Burdur Mehmet Akif Ersoy University, BURDUR, Turkey
52: Also at Adiyaman University, Adiyaman, Turkey
53: Also at Şırnak University, Sirnak, Turkey
54: Also at Beykent University, Istanbul, Turkey, Istanbul, Turkey
55: Also at Istanbul Aydin University, Application and Research Center for
Advanced Studies (App. & Res. Cent. for Advanced Studies), Istanbul, Turkey
56: Also at Mersin University, Mersin, Turkey
57: Also at Piri Reis University, Istanbul, Turkey
58: Also at Ozyegin University, Istanbul, Turkey
59: Also at Izmir Institute of Technology, Izmir, Turkey
60: Also at Marmara University, Istanbul, Turkey
61: Also at Kafkas University, Kars, Turkey
62: Also at Istanbul Bilgi University, Istanbul, Turkey
63: Also at Hacettepe University, Ankara, Turkey
64: Also at Vrije Universiteit Brussel, Brussel, Belgium