EGM-18-001

EGM-18-001

# Reconstruction of signal amplitudes in the CMS electromagnetic calorimeter
in the presence of overlapping proton-proton interactions

###### Abstract

A template fitting technique for reconstructing the amplitude of signals
produced by the lead tungstate crystals of the CMS electromagnetic calorimeter
is described. This novel approach is designed to suppress the contribution to
the signal of the increased number of out-of-time interactions per beam
crossing following the reduction of the accelerator bunch spacing from 50 to
25ns at the start of Run 2 of the LHC. Execution of the algorithm is
sufficiently fast for it to be employed in the CMS high-level trigger. It is
also used in the offline event reconstruction. Results obtained from
simulations and from Run 2 collision data (2015–2018) demonstrate a
substantial improvement in the energy resolution of the calorimeter over a
range of energies extending from a few GeV to several tens of GeV.

###### Contents

  1. 0.1 Introduction
  2. 0.2 Data and simulated samples
  3. 0.3 The electromagnetic calorimeter readout
  4. 0.4 The multifit method
    1. 0.4.1 The Run 1 amplitude reconstruction of ECAL signals
    2. 0.4.2 The multifit algorithm
  5. 0.5 Determination of the multifit parameters
    1. 0.5.1 Pulse shape templates
    2. 0.5.2 Pedestals and electronic noise
  6. 0.6 Sensitivity of the amplitude reconstruction to  pulse timing and pedestal drifts
  7. 0.7 Performance with simulations and collision data
    1. 0.7.1 Suppression of out-of-time pileup signals
    2. 0.7.2 Energy reconstruction with simulated data
    3. 0.7.3 Energy reconstruction with Run 2 data
    4. 0.7.4 Reconstruction of cluster shape variables
  8. 0.8 Summary
  9. .9 The CMS Collaboration

##  0.1 Introduction

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 (PbWO4) crystal
electromagnetic calorimeter (ECAL), which is the focus of this paper, and a
brass and scintillator hadron calorimeter (HCAL), each composed of a barrel
and two endcap sections. Forward calorimeters extend the pseudorapidity
coverage provided by the barrel and endcap detectors. Muons are detected in
gas-ionization chambers embedded in the steel flux-return yoke outside the
solenoid. A more detailed description of the CMS detector is given in Ref.
[1].

The ECAL consists of 61 200 PbWO4 crystals mounted in the barrel section (EB),
covering the range of pseudorapidity $\abs{\eta}<1.48$, closed by 7324
crystals in each of the two endcaps (EE), covering the range
$1.48<\abs{\eta}<3.0$. The EB uses 23cm long crystals with front-face cross
sections of approximately $2.2{\times}2.2$cm2, while the EE contains 22cm long
crystals with a front-face cross section of $2.86{\times}2.86$cm2. The
scintillation light is detected by avalanche photodiodes (APDs) in the EB and
by vacuum phototriodes (VPTs) in the EE. The PbWO4 crystals have a Molière
radius of 2.19cm, approximately matching the transverse dimensions of the
crystals. A preshower detector consisting of two planes of silicon sensors
interleaved with lead for a total of 3 radiation lengths is located in front
of EE [2]. A crystal transparency monitoring system, based on the injection of
laser light at 447nm, close to the emission peak of scintillation light from
PbWO4, is used to track and correct for response changes during LHC operation
[3, 4].

The LHC operating conditions during Run 2 data taking (2015–2018) were more
challenging than those of Run 1 (2010–2013) in several respects. The center-
of-mass energy of the collisions was raised from 8 to 13, the bunch spacing
(the time interval between neighboring bunches), was halved from 50ns to the
design value of 25ns, and the instantaneous luminosity reached
$2.1{\times}10^{34}\percms$ compared to $0.75{\times}10^{34}\percms$ achieved
in 2012.

The mean number of additional interactions in a single bunch crossing (BX),
termed pileup (PU), in Run 2 was 34, with the tail of the distribution
extending up to 80. The average values for 2016, 2017 and 2018 were 27, 38 and
37, respectively. For the results shown in this paper, obtained from
simulations, an average number of 40 interactions per bunch crossing is used.
For comparison, during Run 1 in 2012, the mean value was 21 interactions per
BX, with an extreme value of 40\. After shaping by the electronics, the ECAL
signals extend over several hundred nanoseconds. Consequently, the decrease in
the LHC bunch spacing from 50 to 25ns results in an increased number of
overlapping signals from neighboring BXs, referred to as out-of-time (OOT)
pileup. These spurious signals effectively add to the electronic noise and
degrade the energy resolution of the calorimeter. To reduce these effects, an
innovative ECAL amplitude reconstruction procedure, based on a template
fitting technique, named “multifit”, was introduced in 2015, before the start
of Run 2. The new algorithm replaces the one used during Run 1 (“weights”
method) [5], which was based on a digital-filtering technique. The original
algorithm performed well under the conditions of Run 1, but was not suitable
for Run 2 because of the increased OOT pileup.

##  0.2 Data and simulated samples

The results shown in this paper are based on subsets of the data samples
recorded by the CMS experiment in proton-proton ($\Pp\Pp$) collisions at a
center-of-mass energy of 13. Calibration samples are recorded by using special
data streams, based either on a minimal single-crystal energy deposit, or on
diphoton invariant mass, to profit from the copious production of $\PGpz$
mesons subsequently decaying into $\gamma\gamma$. Performances are evaluated
on a subset of the standard physics stream of the high-level trigger (HLT), by
using electrons from $\cPZ$-boson decays ($\cPZ\to\Pep\Pem$).

In addition to data samples from calibration sources and collision data, two
kinds of Monte Carlo (MC) samples are used. One is the full detector
simulation used for physics analyses, implemented with [6], of single photons
within the CMS detector with a uniform distribution in $\eta$ and a flat
transverse momentum $\pt$ spectrum extending from 1 to 100. These events are
generated with the $\PYTHIA$ 8.226 [7] package and its CUETP8M1 [8] tune for
parton showering, hadronization, and underlying event simulation. These events
are used to study the performance of the algorithm when the showering of an
electromagnetic particle spreads across more than a single crystal, which is
typical of most energy deposits in the ECAL. The second set of MC samples is
produced by a fast stand-alone simulation, where the single-crystal amplitudes
are generated by pseudo-experiments using a parametric representation of the
pulse shape and the measured covariance matrix. Energy deposits typical of the
PU present in Run 2 are then added to these signals. Additional $\Pp\Pp$
interactions in the same or adjacent BXs are added to each simulated event
sample, with an average number of 40.

##  0.3 The electromagnetic calorimeter readout

The electrical signal from the photodetectors is amplified and shaped using a
multigain preamplifier (MGPA), which provides three simultaneous analogue
outputs that are shaped to have a rise time of approximately 50ns and fall to
10% of the peak value in 400ns[2]. The shaped signals are sampled at the LHC
bunch-crossing frequency of 40MHz and digitized by a system of three channels
of floating-point Analog-to-Digital Converters (ADCs). The channel with the
gain that gives the highest nonsaturated value is selected sample-by-sample,
thus providing a dynamic range from 35to 1.7in the barrel. A time frame of 10
consecutive samples is read out every 25ns, in synchronization with the
triggered LHC BX [2]. The convention used throughout this report is to number
samples starting from 0. The phase of the readout is adjusted such that the
time of the in-time pulse maximum value coincides with the fifth digitized
sample. The first three samples are read out before the signal pulse rises
significantly from the pedestal baseline (presamples). The 50ns rise time of
the signal pulse after amplification results from the convolution of the 10ns
decay time of the crystal scintillation emission and the 40ns shaping time of
the MGPA [2, 5, 1].

##  0.4 The multifit method

###  0.4.1 The Run 1 amplitude reconstruction of ECAL signals

During LHC Run 1, a weighting algorithm [5] was used to estimate the ECAL
signal amplitudes, both online in the HLT [9] and in the offline
reconstruction. With that algorithm the amplitude is estimated as a linear
combination of 10 samples, $s_{i}$:

$\hat{\cal A}=\sum_{i=0}^{9}w_{i}s_{i},$ (1)

where the weights $w_{i}$ are calculated by minimizing the variance of
$\hat{\cal A}$. This algorithm was developed to provide an optimal reduction
of the electronics noise and a dynamic subtraction of the pedestal, which is
estimated on an event-by-event basis by the average of the presamples.

The LHC Run 2 conditions placed stringent requirements on the ECAL pulse
reconstruction algorithm. Several methods were investigated to mitigate the
effect of the increased OOT pileup, to achieve optimal noise performance. The
methods that were studied included: using a single sample at the signal pulse
maximum, a deconvolution method converting the discrete time signal into the
frequency domain [10], and the multifit. The first one uses a minimal
information from the pulse shape and, although being robust against OOT
pileup, results in a degradation of energy resolution for most of the energy
range below $\approx$100\. The second was the subject of a pilot study and was
never fully developed. The last one is the subject of this paper.

###  0.4.2 The multifit algorithm

The multifit method uses a template fit with $N_{\text{BX}}$ parameters,
comprising one in-time (IT) and up to nine OOT amplitudes, up to five
occurring before, and up to four after the IT pulse: $N_{\text{BX}}\in[1-10]$.
The fit minimizes the $\chi^{2}$ defined as:

$\chi^{2}=\left(\sum_{j=0}^{N_{\text{BX}}}A_{j}\vec{p}_{j}-\vec{S}\right)^{T}\mathbf{C}^{-1}\left(\sum_{j=0}^{N_{\text{BX}}}A_{j}\vec{p}_{j}-\vec{S}\right),$
(2)

where the vector $\vec{S}$ comprises the 10 readout samples, $s_{i}$, after
having subtracted the pedestal value, $\vec{p}_{j}$ are the pulse templates
for each BX, and $A_{j}$, which are obtained by the fit, are the signal pulse
amplitudes in ten consecutive BXs, with $A_{5}$ corresponding to the IT BX.
The pulse templates $\vec{p}_{j}$ for each BX have the same shape, but are
shifted in time by $j$ multiples of 25ns. The pulse templates are described by
binned distributions with 15 bins of width 25ns. An extension of five
additional time samples after the $10^{th}$ sample (the last digitized one) is
used to obtain an accurate description of the contribution to the signal from
early OOT pulses with tails that overlap the IT pulse.

The total covariance matrix $\mathbf{C}$ used in the $\chi^{2}$ minimization
of Eq. (2) includes the correlation of the noise and the signal between the
different time samples. It is defined as the quadratic sum of two
contributions:

$\mathbf{C}=\mathbf{C}_{\text{noise}}\oplus\sum_{j=0}^{N_{\text{BX}}}A_{j}^{2}\mathbf{C}_{\text{pulse}}^{j},$
(3)

where $\mathbf{C}_{\text{noise}}$ is the covariance matrix associated with the
electronics noise and $\mathbf{C}_{\text{pulse}}^{j}$ is the one associated
with the pulse shape template. Each channel of the ECAL, , a crystal with its
individual readout, is assigned its own covariance matrix. Quadratic summation
of the two components is justified since the variance for the pulse templates
is uncorrelated with the electronic noise. In fact, the uncertainty in the
shape of the signal pulses for a given channel is dominated by event-by-event
fluctuations of the beam spot position along the $z$-axis, of order several
cms [11], which affect the arrival time of particles at the front face of
ECAL.

The $\mathbf{C}_{\text{pulse}}$ matrix is calculated as:

$C_{\text{pulse}}^{i,k}=\frac{\sum_{n=1}^{N_{\text{events}}}\tilde{s}_{i}(n)\tilde{s}_{k}(n)}{N_{\text{events}}},$
(4)

where the $\tilde{s}_{i}(n)$ are the pedestal-subtracted sample values,
$s_{i}(n)-P$, scaled for each event $n$, such that $\tilde{s}_{5}(n)=1$. The
value of $P$ equals the average of the three unscaled presamples over many
events. Both the templates and their covariance matrices are estimated from
collision data and may vary with time, for reasons described in Section 0.5.1.
The electronics noise dominates the uncertainty for low-energy pulses, whereas
the uncertainty in the template shape dominates for higher energies. The
determination of $\mathbf{C}_{\text{noise}}$, which is calculated analogously
as $\mathbf{C}_{\text{pulse}}$, but with dedicated data, is described in
Section 0.5.2.

The minimization of the $\chi^{2}$ in Eq. (2) has to be robust and fast to use
both in the offline CMS reconstruction and at the HLT. In particular, the
latter has tight computation time constraints, especially in the EB, where the
number of channels that are read out (typically 1000 and as high as 4000) for
every triggered BX, poses a severe limitation on the time allowed for each
minimization. Therefore, the possibility of using minimization algorithms like
minuit [12] to perform the $10{\times}10$ matrix inversion is excluded.
Instead, the technique of nonnegative least squares [13] is used, with the
constraint that the fitted amplitudes $A_{j}$ are all positive. The $\chi^{2}$
minimization is performed iteratively. First, all the amplitudes are set to
zero, and one nonzero amplitude at a time is added. The evaluation of the
inverse matrix $\mathbf{C}^{-1}$, which is the computationally intensive
operation, is iterated until the $\chi^{2}$ value in Eq. (2) converges
($\Delta\chi^{2}<10^{-3}$) [14]. Usually the convergence is reached with fewer
than 10 nonzero fitted amplitudes, so the system is, in general, over-
constrained. Examples of fitted pulses in single crystals of the EB and EE are
shown in Fig. 1 (right) and (left), respectively. They are obtained from a
full detector simulation of photons with transverse momentum $\pt=10\GeV$.

Figure 1: Two examples of fitted pulses for simulated events with 20 average
pileup interactions and 25ns bunch spacing. Signals from individual crystals
are shown. They arise from a $\pt=10\GeV$ photon shower in the barrel (left)
and in an endcap (right). In the left panel, one OOT pulse, in addition to the
IT pulse, is fitted. In the right panel, six OOT pulses, in addition to the IT
pulse, are fitted. Filled circles with error bars represent the 10 digitized
samples, the red dashed distributions (dotted multicolored distributions)
represent the fitted in-time (out-of time) pulses with positive amplitudes.
The solid dark blue histograms represent the sum of all the fitted
contributions. Within the dotted distributions, the color distinguishes the
fitted out-of-time pulses with different BX, while the legend represent them
as a generic gray dotted line.

Since the only unknown quantities are the fitted amplitudes, the minimization
corresponds to the solution of a system of linear equations with respect to a
maximum of 10 nonnegative $A_{j}$ values. The implementation uses a C++
template linear algebra library, eigen [15], which is versatile and highly
optimized. The time required to compute the amplitude of all the channels in
one event is approximately 10ms for typical Run 2 events where the bunch
spacing was 25ns and there is an average of 40 PU interactions per BX. The
timing has been measured on an Intel Xeon E5-2650v2 processor, which was used
for the benchmark tests of the CMS HLT farm at the beginning of Run 2 in 2015
[16]. The CPU time needed is about 100 times less than that which was used to
perform the equivalent minimization using minuit, and for all events is much
less than the maximum time of 100ms/event allowed for the HLT. The algorithm
implementation has also been adapted for execution on GPUs for the new
processor farm, which will be used for LHC Run 3, which is planned to begin in
2022.

##  0.5 Determination of the multifit parameters

###  0.5.1 Pulse shape templates

The templates for the $\vec{p}_{j}$ term in Eq. (2) are histograms with 15
bins, and represent the expected time distribution of a signal from an energy
deposit in the ECAL crystals. The first 10 bins correspond to the samples that
are read out in a triggered event. Bins 10–14 describe the tail of the signal
pulse shape.

The pulse template differs from crystal to crystal, both because of intrinsic
pulse shape differences and, more importantly, because of differences in the
relative time position of the pulse maximum, $T_{\text{max}}$, between
channels. The pulse templates have also been found to vary with time, during
Run 2, as a result of crystal irradiation. Both of these effects must be
corrected for, and the time variation requires regular updates to the pulse
shape templates during data taking.

The pulse templates are constructed in situ from collision data acquired with
a zero-bias trigger, , a beam activity trigger [9], and events recorded with a
dedicated high-rate calibration data stream [17]. In the calibration data
stream, the ten digitized samples from all single-crystal energy deposits
above a predefined noise threshold are recorded, while the rest of the event
is dropped to limit the trigger bandwidth. The energy deposits in these events
receive contributions from both IT and OOT interactions. In a fraction of the
LHC fills, the circulating beams are configured so that a few of the bunch
collisions are isolated, , occur between bunches that are not surrounded by
other bunches. In these collisions, the nominal single-bunch intensity is
achieved without OOT pileup, so a special trigger requirement to record them
was developed. This allows a clean measurement of the templates of IT pulses
only. An amplitude-weighted average pulse template is obtained, and only hits
with amplitudes larger than approximately five times the root-mean-square
spread of the noise are used.

During 2017, the pulse templates were recalibrated about 30 times. The LHC
implemented collisions with isolated bunches only when the LHC was not
completely filled with bunches, during the intensity ramp up, typically at the
beginning of the yearly data taking and after each technical stop, , a
scheduled period of several days without collisions exploited by the LHC for
accelerator developments. For all other updates, normal bunch collisions were
used. For these, a minimum amplitude threshold was imposed at the level of 1,
or $5\sigma_{\text{noise}}$ when this was greater, and the amplitude-weighted
average of the templates suppressed the relative contribution of OOT PU
pulses. It was verified that the pulse templates derived from isolated bunches
are consistent with those obtained from nonisolated bunches. Anomalous signals
in the APDs, which have a distorted pulse shape, are rejected on the basis of
the single-crystal timing and the spatial distribution of the energy deposit
among neighboring crystals [18, 17].

The average pulse shape measured in the digitized time window of 10 BXs is
extended by five additional time samples to model the falling tail of the
pulse, which is used to fit for the contribution of early OOT pileup. This is
achieved by fitting the average template with a function of the form [19]:

$A(t)=A\left(1+\frac{\Delta t}{\alpha\beta}\right)^{\alpha}\re^{-\Delta
t/\beta}$ (5)

where $A$ represents the hit amplitude, $\Delta t=t-T_{\text{max}}$ the time
position relative to the peak, and $\alpha$, $\beta$ are two shape parameters.
Examples of two average pulse shapes, obtained using this method, are shown in
Fig. 2. The extrapolation of the pulse templates outside of the readout window
was checked by injecting laser light into the crystals, with a shifted readout
phase. The tail of the pulse, measured in this way, agrees with the
extrapolated templates.

Figure 2: Pulse shape binned templates, measured in collision data recorded
during June 2017 in a typical LHC fill, for a channel in the barrel (left) and
in an endcap (right). The first 3 bins are the pedestal samples, and their
values equal zero by construction. The following 7 bins are estimated from the
average of the digitized samples on many hits, while the rightmost 5 bins are
estimated by extrapolating the distribution using the function of Eq. (5)
(blue solid line).

The covariance matrix associated with the pulse template,
$\mathbf{C}_{\text{pulse}}$, is computed using Eq. (4), with the same sample
of digitized templates used to determine the average pulse template and with
the same normalization and weighting strategy. The correlation matrix of the
pulse template, $\boldsymbol{\rho}_{\text{pulse}}$, shown in Fig. 3, is
defined as
$\rho^{i,k}_{\text{pulse}}=C^{i,k}_{\text{pulse}}/(\sigma^{i}_{\text{pulse}}\sigma^{k}_{\text{pulse}})$,
where $\sigma^{i,k}_{\text{pulse}}$ is the square root of the variance of the
pulse shape for the $i,k$ bin of the template. The values of
$\sigma^{i}_{\text{pulse}}$ are in the range
$\left[5{\times}10^{-4}-1{\times}10^{-3}\right]$, the largest values relative
to samples in the tail of the pulse template. The elements of the covariance
matrix outside the digitization window, ${C_{\text{pulse}}}^{i,k}$ with $i>9$
or $k>9$, are estimated from simulations of single-photon events with the
interaction time shifted by an integer number of BXs. It was checked that this
simulation reproduces well the covariance matrix for the samples inside the
readout window.

Figure 3: Correlation matrix of the pulse shape binned templates,
$\boldsymbol{\rho}_{\text{pulse}}$, measured in collision data recorded during
June 2017 in a typical LHC fill, for one channel in the barrel (left) and in
an endcap (right). The elements with $i=5$ or $k=5$ have zero variance by
definition, since $S_{5}=1$ for all the hits. The elements
$\rho_{\text{pulse}}^{i,k}$ with $i<3$ or $k<3$ are the presamples, where no
signal is expected, and are set to zero. Those with $i>9$ or $k>9$ are
estimated from simulations with a shifted BX. The others are measured in
collision data, as described in the text. All the $\rho_{\text{pulse}}^{i,k}$
values in the figure are expressed in percent for legibility.

The $\mathbf{C}_{\text{pulse}}$ matrix shows a strong correlation between the
time samples within either the rising edge or the falling tail of the pulse.
An anti-correlation is also observed between the time samples of the rising
edge and of the falling tail that is mostly due to the spread in the particle
arrival time at the ECAL surface, which reflects the spatial and temporal
distribution of the LHC beam spot in CMS [20]. For the measured samples, the
correlations between $S_{9}$ and $S_{8},S_{7},S_{6}$, are all close to 1, with
values in the range (0.90–0.97). For the extrapolated samples, the
correlations change from bin to bin: between $S_{14}$ and
$S_{13},S_{12},S_{11}$ they are 0.69, 0.56, 0.45, respectively, in the case of
the barrel.

###  0.5.2 Pedestals and electronic noise

The pedestal mean is used in the multifit method to compute the pedestal-
subtracted template amplitudes $A_{j}$ in Eq. (2). A bias in its measurement
would reflect almost linearly in a bias of the fitted amplitude, as discussed
in Section 0.6.

The covariance matrix associated with the electronic noise enters the total
covariance matrix of Eq. (3). It is constructed as
$\mathbf{C}_{\text{noise}}=\sigma^{2}_{\text{noise}}\boldsymbol{\rho}_{\text{noise}}$,
where $\sigma_{\text{noise}}$ is the measured single-sample noise of the
channels, and $\boldsymbol{\rho}_{\text{noise}}$ is the noise correlation
matrix. The $\mathbf{C}_{\text{noise}}$ is calculated with Eq. (4), where
$i,k$ are the sample indices, $\tilde{s}_{i}$ and $\tilde{s}_{j}$ are the
measured sample values, normalized to $\tilde{s}_{5}$, and $P$ is the expected
value in the absence of any signal, calculated, as for Eq. (4), by averaging
the three unscaled presamples over many events. Each element of the noise
covariance matrix is the mean over a large number of events. The noise
correlation matrix is defined as:

$\rho_{\text{noise}}^{i,k}=\frac{C^{i,k}_{\text{noise}}}{\sigma_{\text{noise}}^{i}\sigma_{\text{noise}}^{k}}.$
(6)

The average pedestal value and the electronic noise are measured separately
for the three MGPA gains. For the highest gain value, data from empty LHC
bunches [3, 4] are used. These are obtained by injecting laser light into the
ECAL crystals in coincidence with the bunch crossings. This gain value is used
for the vast majority of the reconstructed pulses (up to 150), and is very
sensitive to the electronics noise. One measurement per channel is acquired
approximately every 40 minutes. For the other two MGPA gains, the pedestal
mean and its fluctuations are measured from dedicated runs without LHC beams
present.

The time evolution of the pedestal mean in the EB during Run 2 is shown for
the highest MGPA gain in Fig. 4 (left). A long-term, monotonic drift upwards
is visible. Short term (interfill) luminosity related effects are also
visible. The short-term variations are smaller when the LHC luminosity is
lower. The long-term drift depends on the integrated luminosity, while the
short-term effects depend on the instantaneous luminosity, and related to
variations inside the readout electronics. The behavior of the variation of
the pedestal value with time is similar at any $\abs{\eta}$ of the crystal,
while the magnitude of it increases with the pseudorapidity, reflecting the
higher irradiation.

Figure 4: History of the pedestal mean value for the ECAL barrel (left) and
its noise (right), measured for the highest MGPA gain in collision or
noncollision runs taken during the 2016–2018 data taking period. The inset in
the left panel shows an enlargement of two days in August 2018, to show in
more detail the variation of the pedestal mean during LHC fills.

The evolution of the electronic noise in the barrel is shown in Fig. 4
(right). It shows a monotonic increase with time, related to the increase of
the APD dark current due to the larger radiation dose; no short-term
luminosity-related effects are visible. For the barrel, where 1ADC count
$\cong$ 40, this translates to an energy-equivalent noise of about 65at the
beginning of 2017 and 80at the end of the proton-proton running in the same
year. A small decrease in the noise induced by the APD dark current is visible
after long periods without irradiation, , after the year-end LHC stops. For
the endcaps, the single-channel noise related to the VPT signal does not
evolve with time, and is approximately 2ADC counts. Nevertheless, the energy-
equivalent noise increases with time and with absolute pseudorapidity
$\abs{\eta}$ of the crystal because of the strong dependence of the crystal
transparency loss on $\abs{\eta}$ and time, due to higher irradiation level.
Consequently, the average noise at the end of 2017 in the endcaps translates
to roughly 150up to $\abs{\eta}\approx 2$, whereas it increases to as much as
500at the limit of the CMS tracker acceptance ($\abs{\eta}\approx 2.5$). Thus,
the relative contribution of $\mathbf{C}_{\text{noise}}$ in the total
covariance matrix strongly depends on $\abs{\eta}$. For hits with amplitude
larger than $\approx$20ADC counts, equivalent to an energy $\approx$1before
applying the light transparency corrections, $\mathbf{C}_{\text{pulse}}$
dominates the covariance matrix for the whole ECAL.

The covariance matrix for the noise used in the multifit is obtained by
multiplying the time independent correlation matrix in Eq. (6) by the time
dependent squared single sample noise, $\sigma^{2}_{\text{noise}}$. The time
evolution is automatically accounted for by updating the values in the
conditions database [21], with the measurements obtained in situ.

Correlations between samples exist because of (1) the presence of low-
frequency (less than 4MHz) noise that has been observed during CMS operation
[19], and (2) the effect of the feedback resistor in the MGPA circuit [22].
The correlation matrix of the electronic noise was measured with dedicated
pedestal runs; it is very similar for all channels within either the EB or the
EE, and stable with time. Consequently, it has been averaged over all the
channels within a single subsystem. The matrix for the highest gain of the
MGPA is shown in Fig. 5.

Figure 5: Correlation matrix of the electronics noise,
$\boldsymbol{\rho}_{\text{noise}}$, measured in dedicated pedestal runs in Run
2, averaged over all the channels of the barrel (left) or endcaps (right). All
the $\rho_{\text{noise}}^{i,k}$ values in the figure are expressed in percent
for legibility.

The MGPA component of the noise is such that the correlation depends almost
solely on the time distance between the two samples, following an exponential
relationship. For $\Delta t>100$ns, it flattens to a plateau corresponding to
the low frequency noise.

##  0.6 Sensitivity of the amplitude reconstruction to
pulse timing and pedestal drifts

The multifit amplitude reconstruction utilizes as inputs pedestal baseline
values and signal pulse templates that are determined from dedicated periodic
measurements. Thus, it is sensitive to their possible changes with time.

Figure 6 shows the absolute amplitude bias for pulses corresponding to a
50energy deposit ($E$) in one crystal in the barrel, as a function of the
pedestal baseline shift. The dependence for deposits in the endcaps is the
same. A shift of $\pm$1ADC count produces an amplitude bias up to 0.3ADC
counts in a single crystal, corresponding, in the barrel, to an energy-
equivalent shift of about 300in a $5{\times}5$ crystal matrix. Since the drift
of the pedestal baseline with time can be as much as 2ADC counts in one year
of data taking, as shown in Fig. 4 (left), and is coherent in all crystals,
the induced bias is significant, in the range $\approx$(0.5–1)%, even in the
typical energy range of decay products of the $\PW$, $\PZ$, and Higgs bosons.
Therefore, it is important to monitor and periodically correct the pedestals
in the reconstruction inputs.

Figure 6: Reconstructed amplitude bias for the IT amplitude, $\langle
A\rangle-A_{\text{true}}$, as a function of pedestal shifts $\Delta P$, for a
single-crystal pulse of $E=50\GeV$ in the EB.

The IT amplitude resulting from the $\chi^{2}$ minimization of Eq. (2) is also
more sensitive to a shift in the position of the maximum, $T_{\text{max}}$ of
the signal pulse, compared to that obtained from the weights method [5]. This
timing shift can be caused by variations of the pulse shapes over time, both
independently from crystal to crystal and coherently, as discussed in Section
0.5.1. A difference in the pulse maximum position between the measured signal
pulse and the binned template will be absorbed into the $\chi^{2}$ as nonzero
OOT amplitudes, $A_{j}$, with $j\neq 5$.

To estimate the sensitivity of the reconstructed amplitude to changes in the
template timing $\Delta T_{\text{max}}$, the amplitude of a given pulse is
reconstructed several times, with increasing values of $\Delta
T_{\text{max}}$. The observed changes in the ratio of the reconstructed
amplitude to the true amplitude, $\langle A\rangle/A_{\text{true}}$, as a
function of $\Delta T_{\text{max}}$, for single-crystal pulses of 50in the EB
and EE, are shown in Fig. 7 (left) and (right), respectively. The difference
in shape for positive and negative time shifts is related to the asymmetry of
the pulse shape with respect to the maximum: spurious OOT amplitudes can be
fitted more accurately using the time samples preceding the rising edge, where
pedestal-only samples are expected, compared to using those on the falling
tail. For positive $\Delta T_{\text{max}}$, the net change is positive because
the effect of an increase in the IT contribution is larger than the decrease
in the signal amplitude caused by the misalignment of the template. The change
in reconstructed amplitude at a given $\Delta T_{\text{max}}$ is similar for
the barrel and the endcaps. Small differences arise mostly from the slightly
different rise time of the barrel and endcap pulses and the difference in
energy distributions from PU interactions in a single crystal in the two
regions. For the endcaps, the residual offset of $\approx$0.2% for $\Delta
T_{\text{max}}=0$ has two sources. First, the larger occupancy of OOT pileup
amplitudes per channel contributes energy coherently to all of the samples
within the readout window. Second, the higher electronics noise leads to a
looser amplitude constraint in the $\chi^{2}$ minimization of Eq. 2, allowing
a larger amplitude to be fitted. This offset is reabsorbed in the subsequent
absolute energy calibration and it does not affect the energy resolution.

Figure 7: Reconstructed amplitude over true amplitude, $\langle
A\rangle/A_{\text{true}}$, as a function of the timing shift of the pulse
template, $\Delta T_{\text{max}}$, for a single-crystal pulse of $E=50\GeV$ in
the EB (left) and EE (right). The insets show an enlargement in the $\pm 1$ns
range with a finer $\Delta T_{\text{max}}$ granularity.

The effects of small channel-dependent differences between actual pulse shapes
and the assumed templates are absorbed by the crystal-to-crystal energy
intercalibrations. However, any changes with time in the relative position of
the template will affect the reconstructed amplitudes, worsening the energy
resolution. This implies the need to monitor $T_{\text{max}}$ and periodically
correct the templates for any observed drifts. The average correlated drift of
$T_{\text{max}}$ was constantly monitored throughout Run 2, measured with the
algorithm of Ref. [23]. Its evolution during 2017 is shown in Fig. 8. The
coherent variation can be up to 1ns. The repeated sharp changes in
$T_{\text{max}}$ occur when data taking is resumed after a technical stop of
the LHC. They are caused by a partial recovery in crystal transparency while
the beam is off, followed by a rapid return to the previous value when
irradiation resumes. A similar trend was measured in the other years of data-
taking during Run 2.

Figure 8: Average timing of ECAL pulses in proton-proton collisions collected
in 2017, as measured in Ref. [23]. For each point, the average of the hits
reconstructed in all barrel and endcaps channels is used. The sharp changes in
$T_{\text{max}}$ correspond to restarts of data taking following LHC technical
stops, as discussed in the text. At the beginning of the yearly data taking,
the timing is calibrated so that the average $T_{\text{max}}=0$.

The measured time variation is crystal dependent, since the integrated
radiation dose depends on the crystal position, and since there are small
differences in the effect between crystals at the same $\eta$. For this reason
the pulse templates are measured in situ multiple times during periods with
collision data, and a specific pulse template is used for each channel. The
measurement described in Section 0.5.1 is repeated after every LHC technical
stop, when a change of the templates is expected because of partial recovery
of the crystal transparency, or when the $\abs{\Delta T_{\text{max}}}$ was
larger than 250ps.

##  0.7 Performance with simulations and collision data

In this section, the performance of the ECAL local reconstruction with the
multifit algorithm is compared with the weights method [5]. Simulated events
with a PU typical of Run 2 (a Poisson distribution with a mean of 40) and
collision data collected in 2016–2018 are used. The data comparisons are
performed for low-energy photons from $\PGpz\to\PGg\PGg$ decays, and for high-
energy electrons from $\PZ\to\Pep\Pem$ decays.

###  0.7.1 Suppression of out-of-time pileup signals

The motivation for implementing the multifit reconstruction is to suppress the
OOT pileup energy contribution, while reconstructing IT amplitudes as
accurately as possible. To show how well the multifit reconstruction performs,
the resolution of the estimated IT energy is compared for single crystals, as
a function of the average number of PU interactions. This study was performed
using simple pseudo-experiments, where the pulse shape is generated according
to the measured template for a barrel crystal at $\abs{\eta}\approx 0$. The
appropriate electronics noise, equal to the average value measured in Run 2,
together with its covariance matrix, is included. The effect of the PU is
simulated assuming that the number of additional interactions has a Poisson
distribution about the mean expected value and that these interactions have an
energy distribution corresponding to that expected for minimum bias events at
the particular value of $\eta$ of the crystal. The pseudo-experiments are
performed for two fixed single-crystal energies: 2 and 50. For a single
crystal, the amplitude is related directly to the energy only through a
constant calibration factor, thus the resolution of the uncalibrated amplitude
equals the energy resolution. The resolution of a cluster receives other
contributions that may degrade the intrinsic single-crystal energy measurement
precision, such as a nonuniform response across several crystals, within the
calibration uncertainties. These considerations are outside the scope of this
paper.

The amplitude resolution is estimated as the effective standard deviation
$\sigma_{\text{eff}}$, calculated as half of the smallest symmetrical interval
around the peak position containing 68.3% of the events. The PU energy from IT
interactions constitutes an irreducible background for both energy
reconstruction methods. It is expected that event-by-event fluctuations of
this component degrade the energy resolution in both cases as the PU
increases. On the other hand, the fluctuations in the energy from all the OOT
interactions are suppressed significantly by the multifit algorithm, in
contrast to the situation for the weights reconstruction, where they
contribute further to the energy resolution deterioration at large average PU.
This is shown in Fig. 9, for the two energies considered in this study. The
reconstructed energy is compared with either the true generated energy
(corrected for both IT and OOT PU) or the sum of the energy from the IT pileup
and the true energy (corrected only for the effect of OOT PU). In the latter
case, the amplitude resolution for the multifit reconstruction does not depend
on the number of interactions, showing that this algorithm effectively
suppresses the contributions of the OOT PU. The offset in resolution in the
case of no PU between the two methods, in this ideal case, is due to the
improved suppression of the electronic noise resulting from the use of a fixed
pedestal rather than the event-by-event estimate used in the weights method.
In the data, additional sources of miscalibration may further worsen the
energy resolution. Such effects are considered in the full detector simulation
used for physics analyses, described below, but are not included in this
stand-alone simulation.

Figure 9: Measured amplitude resolution for two generated energy deposits
($E=2\GeV$ or $E=50\GeV$) in a single ECAL barrel crystal, at $\eta=0$,
reconstructed with either the multifit or the weights algorithm. Filled points
show the effective resolution expressed as the difference between the
reconstructed energy and the true energy, divided by the true energy. Open
points show the percent resolution estimated when the true energy is replaced
with the sum of the true energy and the in-time pileup energy.

Simulations performed for an upgraded EB, planned for the high-luminosity
phase of the LHC [24], have shown that the multifit algorithm can subtract OOT
PU for energies down to the level of the electronic noise, for
$\sigma_{\text{noise}}>10\MeV$, for PU values up to 200 with 25ns bunch
spacing. This future reconstruction method will benefit from a more frequent
sampling of the pulse shape, at 160 MHz, and from a narrower signal pulse to
be achieved with the upgraded front-end electronics [25].

###  0.7.2 Energy reconstruction with simulated data

The ability of the multifit algorithm to estimate the OOT amplitudes and,
consequently, to estimate the IT amplitude is demonstrated in Fig. 10 (left).
Simulated events are generated with an average of 40 PU interactions, with an
energy spectrum per EB crystal as shown in Fig. 10 (right). The reconstructed
energy assigned by the multifit algorithm to each BX from $-$5 to $+$4 is
compared with the generated value. The IT contribution corresponds to BX $=$
0\. Amplitudes are included with energy larger than 50, a value corresponding
approximatively to one standard deviation of the electronic noise [26]. The
mode of the distribution of the ratio between the reconstructed and true
energies of OOT PU pulses and true energies,
$A^{\mathrm{PU}}_{\mathrm{BX}}/A^{\text{true}}_{\mathrm{BX}}$, with BX in the
range $[-5,$…$,+4]$, is equal to unity within $\pm 2.5\%$ for all the BXs. The
OOT interactions simulated in these events cover a range from 12 BXs before to
3 BXs after the IT interaction, as is done in the full simulation used in CMS.
The distribution of the measured to true energy becomes asymmetric at the
boundaries of the pulse readout window ($\mathrm{BX}=-5$, $-$4, and $-$3),
because the contributions of earlier interactions cannot be resolved with the
information provided by the 10 digitized samples. However, this does not
introduce a bias in the IT amplitude since the energy contribution from very
early BXs below the maximum of the IT pulse is negligible. The remaining
offset of $\approx$0.2% in the median of
$A^{\mathrm{PU}}_{\mathrm{BX}}/A^{\text{true}}_{\mathrm{BX}}$ for BXs close to
zero is due to the requirement that all the $A_{j}$ values are nonnegative, ,
any spuriously fitted OOT pulse can only subtract part of the in-time
amplitude. This offset is absorbed in the absolute energy scale calibration
and does not affect the energy resolution.

Figure 10: Left: bias in the out-of-time amplitude estimated by the multifit
algorithm as a function of BX, for the bunch crossings
$-5\leq\mathrm{BX}\leq+4$. The in-time interaction corresponds to BX $=$ 0 in
the figure. The bias is estimated as the mode of the distribution of the ratio
between the measured and the true energy. Only statistical uncertainties are
shown. Right: energy spectrum in an ECAL barrel crystal, at $\eta\approx 0$.

The energy from an electromagnetic shower for a high-momentum electron or
photon is deposited in several adjacent ECAL crystals. A clustering algorithm
is required to sum together the deposits of adjacent channels that are
associated with a single electromagnetic shower. Corrections are applied to
rectify the cluster partial containment effects. In the present work, we use a
simple clustering algorithm that sums the energy in a $5{\times}5$ crystal
matrix centered on the crystal with the maximum energy deposit. This approach
is adequate for comparing the performance of the two reconstruction
algorithms, especially in regions with low tracker material (,
$\abs{\eta}<0.8$), where the fraction of energy lost by electrons by
bremsstrahlung (and subsequent photon conversions) is small. Here, more than
95% of the energy is contained in a $5{\times}5$ matrix. To reduce the
fraction of events with partial cluster containment caused by early
bremsstrahlung and photon conversion, a selection is applied to the electrons
and photons. In the simulation, events with photon conversions are rejected
using Monte Carlo information, whereas in data a variable that uses only
information from the tracker is adopted, as described later.

The relative performance of the two reconstruction algorithms is evaluated on
a simulated sample of single-photon events generated by with a uniform
distribution in $\eta$ and a flat transverse momentum $\pt$ spectrum extending
from 1 to 100. The photons not undergoing a conversion before the ECAL surface
are selected by excluding those that match geometrically electron-positron
pair tracks from conversions in the simulation. For the retained photons, the
energy is mostly contained in a $5{\times}5$ matrix of crystals, and no
additional corrections are applied.

The ratio between the reconstructed energy in the $5{\times}5$ crystal matrix
and the generated photon energy, $E_{5{\times}5}/E_{\text{true}}$, for
nonconverted photons with a uniform distribution in the range
$1<\pt^{\text{true}}<100\GeV$ is histogramed. For both reconstruction
algorithms, the distributions show a non-Gaussian tail towards lower values,
caused by the energy leakage out of the $5{\times}5$ crystal matrix, which is
not corrected for. To account for this, $\sigma_{\text{eff}}$, as defined in
Section 0.7.1, is used to quantify the energy resolution. The average energy
scale of the reconstructed clusters is shifted downwards for the multifit
method, whereas it is approximately unity for the weights reconstruction. As
stated earlier, this is because the amplitudes for the OOT pulses ($A_{j}$
with $j\neq 5$) are constrained to be positive. In the reconstruction of
photons used by CMS such a shift is corrected for, a posteriori, by a
dedicated multivariate regression, which simultaneously corrects the residual
dependence of the energy scale on the cluster containment and IT pileup. This
correction is applied in the HLT and, with a more refined algorithm, in the
offline event reconstruction. This type of cluster containment correction was
developed in Run 1 [27, 26] and has been used subsequently. In this approach,
the shift of the $E_{5{\times}5}/E_{\text{true}}$ distribution is corrected by
rescaling the resolution estimator, $\sigma_{\text{eff}}$, by $m$, estimated
as the mean of a Gaussian function fitting the bulk of the distribution, and
expressed in percent. The variation of $\sigma_{\text{eff}}$ as a function of
the true of the photon, is shown in Fig. 11.

Figure 11: Effective energy resolutions for nonconverted photons in barrel
(left) and endcaps (right) as a function of the generated of the photon. The
photons are generated with a uniform distribution and their interaction is
obtained with the full detector simulation. The average number of PU
interactions is 40\. The horizontal error bars represent the bin width. The
statistical uncertainties are too small to be displayed.

The improvement in the precision of the energy measurement is significant for
the full range of $\pt$ considered. Expressed as a quadratic contribution to
the total, it varies from 10 (15)% in the barrel (endcaps) for photons with
$\pt<5\GeV$, to 0.5 (1.0)% at $\pt=100\GeV$. The improvement is larger at low
$\pt$, since the relative contribution of the energy deposits from PU
interactions, which have the characteristic momentum spectrum shown in Fig. 10
(right), is relatively larger. This is particularly relevant for suppressing
the PU contribution to low-$\pt$ particles that enter the reconstruction of
jets and missing transverse momentum with the particle-flow algorithm used in
CMS [28], thus preserving the resolution achieved during Run 1 [29, 30, 31].
The improvement grows with $\abs{\eta}$ both within the EB and within the EE,
because of the increasing probability of overlapping pulses from PU. The
improvement is larger in the barrel, even though the PU contribution is
smaller than in the endcaps, because the lower electronic noise allows a more
stringent constraint of the amplitudes in the multifit. For photons, the
improvement extends above $\pt\approx 50\GeV$, because of the higher number of
digitized samples of the pulse shape used, and the suppression of the residual
OOT PU contribution. The energy resolution becomes constant at very high
energies, above a few hundred , where it is dominated by sources other than
the relatively tiny contribution of OOT pileup energy, such as nonuniformities
in the energy response of different crystals belonging to the same cluster.
The improvement in energy resolution is also expected to be valid for
electrons with $\pt>20(10)$in the barrel (endcaps), since the electron
momentum resolution is dominated by the ECAL cluster measurement above these
values [27].

###  0.7.3 Energy reconstruction with Run 2 data

#### Effect on low energy deposits using $\Pgpz\to\gamma\gamma$

The improvement in the energy resolution for low-energy clusters is quantified
in data using $\PGpz$ mesons decaying into two photons. The $\pt$ spectrum of
the photons, selected by a dedicated calibration trigger [17], falls very fast
and most of the photons have a in the range of 1–2. The photon energy in this
case is reconstructed summing the energy of the crystals in a $3{\times}3$
matrix. Figure 12 shows the diphoton invariant masses when both clusters are
in the EB (left) and when both are in EE (right). The invariant mass
distributions obtained with the weights and the multifit methods are compared,
using a subset of the $\PGpz$ calibration data collected during 2018. The
position of the peak, $M$, is affected by OOT PU differently in the multifit
method and in the weights algorithm. Since the $\PGpz\to\PGg\PGg$ process is
only used to calibrate the relative response of a crystal with respect to
others, the absolute energy scale is not important here. The energy scale is
determined separately by comparing the position of the $\PZ\to\Pep\Pem$ mass
peak in data and simulation. On the other hand, the improvement in mass
resolution, $\sigma/M$, is significant, 4.5% (8.8%) in quadrature in the
barrel (endcaps).

Figure 12: The invariant mass distribution of the two photons for the selected
$\PGpz\to\gamma\gamma$ candidates in the barrel (left) and endcaps (right),
for the single-crystal amplitudes measured with either the weights or the
multifit reconstruction. A portion of collision data with typical Run 2
conditions, recorded during July 2018, is used. Vertical error bars represent
the statistical uncertainty. The result of the fit with a Gaussian
distribution (green dotted line) plus a polynomial function (red dashed line)
is superimposed on the measured distributions for the multifit case (dark blue
solid line). For the weights case the same model is used, but only the total
likelihood is shown superimposed (light orange solid line).

At the end of 2017, the LHC operated for a period of about 1 month with a
filling scheme with trains of 8 bunches alternated with 4 empty BXs. The
resilience of the multifit method to OOT pileup had a particularly positive
effect in this period, since the bunch-to-bunch variations in OOT PU are
larger than with the standard LHC filling schemes used in Run 2. All the
bunches of a given train provide approximately the same luminosity, about
$5.5{\times}10^{27}\percms$, so the average number of PU interactions is the
typical one of Run 2 (about 34, with peaks up to 80). Data from this period is
used to assess the sensitivity of the algorithms to OOT interactions by
estimating the invariant mass peak position of the $\PGpz$ mesons as a
function of BX within each LHC bunch train. The measured invariant mass,
normalized to that measured in the first BX of the train, is shown in Fig. 13
(left). The peak position, estimated with the weights algorithm, increases for
BXs towards the middle of the bunch train, where the contribution from OOT
collisions is larger, and then decreases again towards the end of the train.
In contrast, for the multifit reconstruction, the peak position remains stable
within $\pm 0.4\%$ with respect to the value observed in the first BX of the
train. The overall resolution in the diphoton invariant mass improves
significantly using the multifit algorithm, and, within the precision of the
measurement, is insensitive to the variations of OOT PU for different BX
within the train. This is shown in Fig. 13 (right).

Figure 13: Peak position, normalized to the mass measured in the first BX of
the train, (left) and Gaussian resolution $\sigma_{m({\PGg\PGg})}$ (right) of
the invariant mass distribution of $\PGpz\to\PGg\PGg$ decays with both photons
in the EB, within a bunch train of 8 colliding bunches from an LHC fill in
October 2017. Error bars represent the statistical uncertainty. The single-
crystal energy is reconstructed either with the weights method (open circles)
or with the multifit method (filled circles). Each point is obtained by
fitting the diphoton invariant mass distribution in collisions selected from a
single BX of the train.

#### Effect on high energy deposits using $\cPZ\to\Pep\Pem$

The performance of the two algorithms for high-energy electromagnetic deposits
is estimated using electrons from $\PZ\to\Pep\Pem$ decays. Electrons with
$\pt>25\GeV$ are identified with tight electron identification criteria, using
a discriminant based on a multivariate approach [27]. To decouple the effects
of cluster containment corrections from the single-crystal resolution,
$5{\times}5$ crystal matrices are used to form clusters. The sample is
enriched in low-bremsstrahlung electrons by selecting with an observable using
only tracker information, $f_{\text{brem}}$, which represents the fraction of
momentum, estimated from the track, lost before reaching the ECAL. It is
defined as $f_{\text{brem}}=(p_{\text{in}}-p_{\text{out}})/p_{\text{in}}$,
where $p_{\text{in}}$ and $p_{\text{out}}$ are the momenta of the track
extrapolated to the point of closest approach to the beam spot and estimated
from the track at the last sensitive layer of the tracker, respectively. The
variable $f_{\text{brem}}$ is required to be smaller than 20%. In the range
$0.8<\abs{\eta}<2.5$ [27], the resolution is dominated by the incomplete
containment of the $5{\times}5$ crystal matrix caused by the larger amount of
tracker material in this region. Therefore, detailed performance comparisons
are restricted to events with electromagnetic showers occurring in the central
region of the EB.

Figure 14 shows the invariant mass of $5{\times}5$ cluster pairs, for a
portion of the 2016 data, selecting pairs of electrons, $\Pe_{1}$ and
$\Pe_{2}$, that lie within a representative central region of the barrel
($0.200<\text{max}(\abs{\eta_{1}},\abs{\eta_{2}})<0.435$). The outcome is
similar in other regions with low tracker material. The shift in the absolute
energy scale for the simplified $5{\times}5$ clustering, caused by the
multifit $A_{j}$ being nonnegative for each BX, is not corrected for. The
improvement is still significant for the $\pt$ range characteristic of
$\PZ\to\Pep\Pem$ decays, matching the expectation from the simulation, shown
in Fig. 11, namely an improvement in resolution of $\approx$1% in quadrature,
after unfolding the natural width of the $\PZ$ boson, for electrons and
photons with $30<\pt<100\GeV$.

Figure 14: Example of the $\PZ\to\Pep\Pem$ invariant mass distribution in a
central region of the barrel
($0.200<\text{max}(\abs{\eta_{1}},\abs{\eta_{2}})<0.435$) with the single-
crystal amplitude estimated using either the weights or the multifit method. A
portion of collision data with typical Run 2 conditions, recorded during
October 2016, is used. Error bars represent the statistical uncertainty. The
energy is summed over a $5{\times}5$ crystal matrix. The reported values of
$\sigma_{\text{eff}}$ include the natural width of the $\PZ$ boson, and are
expressed as a percent of the position of the peak, $m$, of the corresponding
invariant mass distribution.

A full comparison of the performance of the multifit algorithm in Run 2 with
that of the weights algorithm in Run 1 would require a reanalysis of the Run 1
data, applying the more sophisticated clustering techniques used in Run 2.
Nevertheless, it is instructive to make a straightforward comparison. For Run
1, where the crystal energy was reconstructed with the default weights method,
the electron energy was estimated with the simple $5{\times}5$ crystal
cluster, and using the optimal calibrations of the 2012 data set ($\sqrt{s}$ =
8and 50ns LHC bunch spacing) [27]. The effective resolution of the dielectron
invariant mass distribution, normalized to its peak, is
$\sigma_{\text{eff}}/m=4.59$%. This is consistent with the value of 4.56%
obtained in Run 2 with the multifit algorithm, shown in Fig. 14. This
indicates that the multifit method can maintain the ECAL performance obtained
during Run 1, in the range $\approx$(5–100), relevant for most data analyses
performed with CMS, despite the substantially larger PU present in Run 2.

#### Effect on jets

The contribution to the average offset of the jet energy scale, from the
reconstructed electromagnetic component of each additional PU interaction, was
estimated in a simulated sample of pure noise in the CMS detector by
considering the energy contained in cones randomly chosen within the detector
acceptance. This shows that the contribution to the offset from ECAL signals
is reduced to a value of less than 10%, similar to that obtained in Run 1.
Further details are given in Ref. [30].

###  0.7.4 Reconstruction of cluster shape variables

The relative contribution of the PU energy within a cluster for electrons from
$\PZ$ boson decays is less than for clusters from $\PGpz$ meson decays, and
the sample of events is smaller. For these reasons, it is difficult to
estimate the variation of the energy scale within one LHC fill arising from
this contribution. The effect on the cluster shapes is still significant,
since they are computed using all the hits in a cluster, including the low-
energy ones. One example is provided by the evolution, within an LHC fill, of
the variable $R_{9}$, defined as the ratio of the energy in a $3{\times}3$
crystal matrix centered on the seed hit of the cluster, divided by the total
energy of the cluster. This variable is an important measure of cluster shape,
since it is often used to distinguish between showering or converted photons,
and those not undergoing a bremsstrahlung process or conversion within the
tracker. For example, in studies of Higgs boson physics, it is used to
separate $\PH\to\PGg\PGg$ events into categories with different $m_{\PGg\PGg}$
effective mass resolutions. Thus it is important that the $R_{9}$ variable
remains stable over time. Figure 15 shows the median of the $R_{9}$
distribution for clusters from electron pairs in the barrel having a mass
consistent with that of the $\PZ$ boson, during an LHC fill in 2016 with an
average PU decreasing from a value of 42 at the beginning of the fill to a
value of 13 at the end. The stability of the cluster shape as a function of
instantaneous luminosity, obtained with the multifit algorithm, is clearly
better than the one obtained with the weights reconstruction. The main reason
the median $R_{9}$ values drift up during a fill is that the denominator of
the $R_{9}$ ratio, which includes contributions from low-energy hits located
outside of the $3{\times}3$ matrix, decreases in the weights algorithm when
the instantaneous luminosity (and the PU) decreases.

Figure 15: History of the median of the $R_{9}$ cluster shape for electrons
from $\PZ\to\Pep\Pem$ decays during one typical LHC fill in 2016\. Hits are
reconstructed with either the multifit (filled circles) or the weights
algorithm (open circles). Each point represents the median of the distribution
for a 5 hour period during the considered LHC fill. Error bars represent the
statistical uncertainty on the median. The bottom panel shows the
instantaneous luminosity delivered by the LHC as a function of time. The steps
in the luminosity occurring about every two hours correspond to changes in the
LHC beam crossing angle, which changes the overlap area of the bunches. Larger
brief drops could indicate emittance scans during the fill.

Another effect that has been checked in data is the rejection power for
anomalous signals ascribed to direct energy deposition in the APDs [18] by
traversing particles. Unlike the hits in an electromagnetic shower, the
anomalous signals generally occur in single channels of the calorimeter. They
are rejected by a combination of a topological selection and a requirement on
the hit timing. The topological selection rejects hits for which the value of
the quantity $(1-E_{4}/E_{1})$ is close to 1, where $E_{1}$ is the energy of
the crystal and $E_{4}$ is the energy sum of the four nearest neighboring
crystals. A simulation of anomalous signals in the APDs is used, and the
efficiency is defined as the fraction of the reconstructed hits in crystals
with anomalous signals identified as such by the offline reconstruction. The
rejection efficiency obtained when using the multifit reconstruction is
improved by as much as 15% compared to the weights method for hits with
$E<15\GeV$. The probability of rejecting hits from genuine energy deposits has
been checked on data with hits within clusters of $\PZ\to\Pep\Pem$ and is
lower than $10^{-3}$ over the entire $\pt$ spectrum of electrons from $\PZ$
boson decays for both methods.

##  0.8 Summary

A multifit algorithm that uses a template fitting technique to reconstruct the
energy of single hits in the CMS electromagnetic calorimeter has been
presented. This algorithm was implemented before the start of the Run 2 data
taking period of the LHC, replacing the weights method used in Run 1. The
change was motivated by the reduction of the LHC bunch spacing from 50 to
25ns, and by the higher instantaneous luminosity of Run 2, which led to a
substantial increase in both the in-time and out-of-time pileup. Procedures
have been developed to provide regular updates of input parameters to ensure
the stability of energy reconstruction over time.

Studies based on $\PGpz\to\PGg\PGg$ and $\PZ\to\Pep\Pem$ control samples in
data show that the energy resolution for deposits ranging from a few to
several tens of is improved. The gain is more significant for lower energy
electromagnetic deposits, for which the relative contribution of pileup is
larger. This enhances the reconstruction of jets and missing transverse energy
with the particle-flow algorithm used in CMS. These results have been
reproduced with simulation studies, which show that an improvement relative to
the weights method is obtained at all energies, including those relevant for
photons from Higgs boson decays.

Simulation studies show that the new algorithm will perform successfully at
the high-luminosity LHC, where a peak pileup of about 200 interactions per
bunch crossing, with 25ns bunch spacing, is expected.

###### 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); RIF
(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. Z191100007219010; the
Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the
Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy –
EXC 2121 “Quantum Universe” – 390833306; 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
Higher Education, project no. 02.a03.21.0005 (Russia); the Tomsk Polytechnic
University Competitiveness Enhancement Program and “Nauka” Project
FSWW-2020-0008 (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
Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the
Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA).

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##  .9 The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
A.M. Sirunyan${}^{\textrm{\textdagger}}$, A. Tumasyan Institut für
Hochenergiephysik, Wien, Austria
W. Adam, F. Ambrogi, T. Bergauer, M. Dragicevic, J. Erö, A. Escalante Del
Valle, R. Frühwirth1, M. Jeitler1, N. Krammer, L. Lechner, D. Liko, T.
Madlener, I. Mikulec, F.M. Pitters, N. Rad, J. Schieck1, R. Schöfbeck, M.
Spanring, S. Templ, W. Waltenberger, C.-E. Wulz1, M. Zarucki Institute for
Nuclear Problems, Minsk, Belarus
V. Chekhovsky, A. Litomin, V. Makarenko, J. Suarez Gonzalez Universiteit
Antwerpen, Antwerpen, Belgium
M.R. Darwish2, E.A. De Wolf, D. Di Croce, X. Janssen, T. Kello3, 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, A. Morton, Q. Python, S. Tavernier, W.
Van Doninck, P. Van Mulders Université Libre de Bruxelles, Bruxelles, Belgium
D. Beghin, B. Bilin, B. Clerbaux, G. De Lentdecker, H. Delannoy, B. Dorney, L.
Favart, A. Grebenyuk, A.K. Kalsi, I. Makarenko, L. Moureaux, L. Pétré, A.
Popov, N. Postiau, E. Starling, L. Thomas, C. Vander Velde, P. Vanlaer, D.
Vannerom, L. Wezenbeek Ghent University, Ghent, Belgium
T. Cornelis, D. Dobur, M. Gruchala, I. Khvastunov4, M. Niedziela, C. Roskas,
K. Skovpen, M. Tytgat, W. Verbeke, B. Vermassen, M. Vit Université Catholique
de Louvain, Louvain-la-Neuve, Belgium
G. Bruno, F. Bury, C. Caputo, P. David, C. Delaere, M. Delcourt, I.S.
Donertas, A. Giammanco, V. Lemaitre, K. Mondal, J. Prisciandaro, A. Taliercio,
M. Teklishyn, P. Vischia, S. Wuyckens, J. Zobec Centro Brasileiro de Pesquisas
Fisicas, Rio de Janeiro, Brazil
G.A. Alves, G. Correia Silva, C. Hensel, A. Moraes Universidade do Estado do
Rio de Janeiro, Rio de Janeiro, Brazil
W.L. Aldá Júnior, E. Belchior Batista Das Chagas, H. BRANDAO MALBOUISSON, W.
Carvalho, J. Chinellato5, E. Coelho, E.M. Da Costa, G.G. Da Silveira6, D. De
Jesus Damiao, S. Fonseca De Souza, J. Martins7, D. Matos Figueiredo, M. Medina
Jaime8, M. Melo De Almeida, C. Mora Herrera, L. Mundim, H. Nogima, P. Rebello
Teles, L.J. Sanchez Rosas, A. Santoro, S.M. Silva Do Amaral, A. Sznajder, M.
Thiel, E.J. Tonelli Manganote5, 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. Lemosa, P.G. Mercadanteb, S.F. Novaesa, Sandra S. Padulaa Institute for
Nuclear Research and Nuclear Energy, Bulgarian Academy of Sciences, Sofia,
Bulgaria
A. Aleksandrov, G. Antchev, I. Atanasov, R. Hadjiiska, P. Iaydjiev, M.
Misheva, M. Rodozov, M. Shopova, G. Sultanov University of Sofia, Sofia,
Bulgaria
M. Bonchev, A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov, A. Petrov
Beihang University, Beijing, China
W. Fang3, Q. Guo, H. Wang, L. Yuan Department of Physics, Tsinghua University,
Beijing, China
M. Ahmad, Z. Hu, Y. Wang Institute of High Energy Physics, Beijing, China
E. Chapon, G.M. Chen9, H.S. Chen9, M. Chen, D. Leggat, H. Liao, Z. Liu, R.
Sharma, A. Spiezia, J. Tao, J. Thomas-wilsker, J. Wang, H. Zhang, S. Zhang9,
J. Zhao State Key Laboratory of Nuclear Physics and Technology, Peking
University, Beijing, China
A. Agapitos, Y. Ban, C. Chen, A. Levin, J. Li, Q. Li, M. Lu, X. Lyu, Y. Mao,
S.J. Qian, D. Wang, Q. Wang, J. Xiao Sun Yat-Sen University, Guangzhou, China
Z. You Institute of Modern Physics and Key Laboratory of Nuclear Physics and
Ion-beam Application (MOE) - Fudan University, Shanghai, China
X. Gao3 Zhejiang University, Hangzhou, China
M. Xiao Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, C. Florez, J. Fraga, A. Sarkar, M.A. Segura Delgado
Universidad de Antioquia, Medellin, Colombia
J. Jaramillo, J. Mejia Guisao, F. Ramirez, 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. Giljanovic, N. Godinovic, D. Lelas, I. Puljak, T. Sculac University of
Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, D. Ferencek, D. Majumder, B. Mesic, M. Roguljic, A.
Starodumov10, T. Susa University of Cyprus, Nicosia, Cyprus
M.W. Ather, A. Attikis, E. Erodotou, A. Ioannou, G. Kole, M. Kolosova, S.
Konstantinou, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A.
Razis, H. Rykaczewski, H. Saka, D. Tsiakkouri Charles University, Prague,
Czech Republic
M. Finger11, M. Finger Jr.11, 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. Assran12,13, A. Mohamed14, E. Salama13,15 Center for High Energy Physics
(CHEP-FU), Fayoum University, El-Fayoum, Egypt
M.A. Mahmoud, Y. Mohammed16 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
E. Brücken, F. Garcia, J. Havukainen, V. Karimäki, M.S. Kim, R. Kinnunen, T.
Lampén, K. Lassila-Perini, S. Laurila, S. Lehti, T. Lindén, H. Siikonen, E.
Tuominen, J. Tuominiemi Lappeenranta University of Technology, Lappeenranta,
Finland
P. Luukka, T. Tuuva IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
C. Amendola, M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J.L. Faure, F.
Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
B. Lenzi, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M.Ö. Sahin, A. Savoy-
Navarro17, M. Titov, G.B. Yu Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole
Polytechnique, Institut Polytechnique de Paris, Paris, France
S. Ahuja, F. Beaudette, M. Bonanomi, A. Buchot Perraguin, P. Busson, C.
Charlot, O. Davignon, B. Diab, G. Falmagne, R. Granier de Cassagnac, A.
Hakimi, 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. Agram18, J. Andrea, D. Bloch, G. Bourgatte, J.-M. Brom, E.C. Chabert, C.
Collard, J.-C. Fontaine18, D. Gelé, U. Goerlach, C. Grimault, A.-C. Le Bihan,
P. Van Hove Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3,
Institut de Physique Nucléaire de Lyon, Villeurbanne, France
E. Asilar, S. Beauceron, C. Bernet, G. Boudoul, C. Camen, A. Carle, N. Chanon,
D. Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, M. Gouzevitch, B.
Ille, Sa. Jain, I.B. Laktineh, H. Lattaud, A. Lesauvage, M. Lethuillier, L.
Mirabito, L. Torterotot, G. Touquet, M. Vander Donckt, S. Viret Georgian
Technical University, Tbilisi, Georgia
G. Adamov, Z. Tsamalaidze11 RWTH Aachen University, I. Physikalisches
Institut, Aachen, Germany
L. Feld, K. Klein, M. Lipinski, D. Meuser, A. Pauls, M. Preuten, M.P. Rauch,
J. Schulz, M. Teroerde RWTH Aachen University, III. Physikalisches Institut A,
Aachen, Germany
D. Eliseev, M. Erdmann, P. Fackeldey, B. Fischer, S. Ghosh, T. Hebbeker, K.
Hoepfner, H. Keller, L. Mastrolorenzo, M. Merschmeyer, A. Meyer, P. Millet, G.
Mocellin, S. Mondal, S. Mukherjee, D. Noll, A. Novak, T. Pook, A. Pozdnyakov,
T. Quast, M. Radziej, Y. Rath, H. Reithler, J. Roemer, A. Schmidt, S.C.
Schuler, A. Sharma, S. Wiedenbeck, S. Zaleski RWTH Aachen University, III.
Physikalisches Institut B, Aachen, Germany
C. Dziwok, G. Flügge, W. Haj Ahmad19, O. Hlushchenko, T. Kress, A. Nowack, C.
Pistone, O. Pooth, D. Roy, H. Sert, A. Stahl20, T. Ziemons Deutsches
Elektronen-Synchrotron, Hamburg, Germany
H. Aarup Petersen, M. Aldaya Martin, P. Asmuss, I. Babounikau, S. Baxter, O.
Behnke, A. Bermúdez Martínez, A.A. Bin Anuar, K. Borras21, V. Botta, D.
Brunner, A. Campbell, A. Cardini, P. Connor, S. Consuegra Rodríguez, V.
Danilov, A. De Wit, M.M. Defranchis, L. Didukh, D. Domínguez Damiani, G.
Eckerlin, D. Eckstein, T. Eichhorn, A. Elwood, L.I. Estevez Banos, E. Gallo22,
A. Geiser, A. Giraldi, A. Grohsjean, M. Guthoff, A. Harb, A. Jafari23, N.Z.
Jomhari, H. Jung, A. Kasem21, M. Kasemann, H. Kaveh, C. Kleinwort, J. Knolle,
D. Krücker, W. Lange, T. Lenz, J. Lidrych, K. Lipka, W. Lohmann24, R. Mankel,
I.-A. Melzer-Pellmann, J. Metwally, A.B. Meyer, M. Meyer, M. Missiroli, J.
Mnich, A. Mussgiller, V. Myronenko, Y. Otarid, D. Pérez Adán, S.K. Pflitsch,
D. Pitzl, A. Raspereza, A. Saggio, A. Saibel, M. Savitskyi, V. Scheurer, P.
Schütze, C. Schwanenberger, R. Shevchenko, A. Singh, R.E. Sosa Ricardo, H.
Tholen, N. Tonon, O. Turkot, A. Vagnerini, M. Van De Klundert, R. Walsh, D.
Walter, Y. Wen, K. Wichmann, C. Wissing, S. Wuchterl, O. Zenaiev, R. Zlebcik
University of Hamburg, Hamburg, Germany
R. Aggleton, S. Bein, L. Benato, A. Benecke, K. De Leo, T. Dreyer, A.
Ebrahimi, F. Feindt, A. Fröhlich, C. Garbers, E. Garutti, P. Gunnellini, J.
Haller, A. Hinzmann, A. Karavdina, G. Kasieczka, R. Klanner, R. Kogler, V.
Kutzner, J. Lange, T. Lange, A. Malara, J. Multhaup, C.E.N. Niemeyer, A.
Nigamova, K.J. Pena Rodriguez, O. Rieger, P. Schleper, S. Schumann, J.
Schwandt, D. Schwarz, J. Sonneveld, H. Stadie, G. Steinbrück, B. Vormwald, I.
Zoi Karlsruher Institut fuer Technologie, Karlsruhe, Germany
M. Baselga, S. Baur, J. Bechtel, T. Berger, E. Butz, R. Caspart, T. Chwalek,
W. De Boer, A. Dierlamm, A. Droll, K. El Morabit, N. Faltermann, K. Flöh, M.
Giffels, A. Gottmann, F. Hartmann20, C. Heidecker, U. Husemann, M.A. Iqbal, I.
Katkov25, P. Keicher, R. Koppenhöfer, S. Maier, M. Metzler, S. Mitra, M.U.
Mozer, D. Müller, Th. Müller, M. Musich, G. Quast, K. Rabbertz, J. Rauser, D.
Savoiu, D. Schäfer, M. Schnepf, M. Schröder, D. Seith, I. Shvetsov, H.J.
Simonis, R. Ulrich, M. Wassmer, M. Weber, C. Wöhrmann, R. Wolf, S. Wozniewski
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, A. Stakia National and Kapodistrian University of Athens,
Athens, Greece
M. Diamantopoulou, D. Karasavvas, G. Karathanasis, P. Kontaxakis, C.K. Koraka,
A. Manousakis-katsikakis, A. Panagiotou, I. Papavergou, N. Saoulidou, K.
Theofilatos, K. Vellidis, E. Vourliotis National Technical University of
Athens, Athens, Greece
G. Bakas, K. Kousouris, I. Papakrivopoulos, G. Tsipolitis, A. Zacharopoulou
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 MTA-ELTE Lendület CMS
Particle and Nuclear Physics Group, Eötvös Loránd University, Budapest,
Hungary
M. Bartók26, R. Chudasama, M. Csanad, M.M.A. Gadallah27, S. Lökös28, P. Major,
K. Mandal, A. Mehta, G. Pasztor, O. Surányi, G.I. Veres Wigner Research Centre
for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, D. Horvath29, F. Sikler, V. Veszpremi, G.
Vesztergombi${}^{\textrm{\textdagger}}$ Institute of Nuclear Research ATOMKI,
Debrecen, Hungary
S. Czellar, J. Karancsi26, J. Molnar, Z. Szillasi, D. Teyssier Institute of
Physics, University of Debrecen, Debrecen, Hungary
P. Raics, Z.L. Trocsanyi, B. Ujvari Eszterhazy Karoly University, Karoly
Robert Campus, Gyongyos, Hungary
T. Csorgo, F. Nemes, T. Novak Indian Institute of Science (IISc), Bangalore,
India
S. Choudhury, J.R. Komaragiri, D. Kumar, L. Panwar, P.C. Tiwari National
Institute of Science Education and Research, HBNI, Bhubaneswar, India
S. Bahinipati30, D. Dash, C. Kar, P. Mal, T. Mishra, V.K. Muraleedharan Nair
Bindhu, A. Nayak31, D.K. Sahoo30, N. Sur, S.K. Swain Panjab University,
Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, S. Chauhan, N. Dhingra32, R. Gupta, A.
Kaur, S. Kaur, P. Kumari, M. Lohan, M. Meena, K. Sandeep, S. Sharma, J.B.
Singh, A.K. Virdi University of Delhi, Delhi, India
A. Ahmed, A. Bhardwaj, B.C. Choudhary, R.B. Garg, M. Gola, S. Keshri, A.
Kumar, M. Naimuddin, P. Priyanka, K. Ranjan, A. Shah Saha Institute of Nuclear
Physics, HBNI, Kolkata, India
M. Bharti33, R. Bhattacharya, S. Bhattacharya, D. Bhowmik, S. Dutta, S. Ghosh,
B. Gomber34, M. Maity35, S. Nandan, P. Palit, A. Purohit, P.K. Rout, G. Saha,
S. Sarkar, M. Sharan, B. Singh33, S. Thakur33 Indian Institute of Technology
Madras, Madras, India
P.K. Behera, S.C. Behera, P. Kalbhor, A. Muhammad, R. Pradhan, P.R. Pujahari,
A. Sharma, A.K. Sikdar Bhabha Atomic Research Centre, Mumbai, India
D. Dutta, V. Jha, V. Kumar, D.K. Mishra, K. Naskar36, P.K. Netrakanti, L.M.
Pant, P. Shukla Tata Institute of Fundamental Research-A, Mumbai, India
T. Aziz, M.A. Bhat, S. Dugad, R. Kumar Verma, U. Sarkar Tata Institute of
Fundamental Research-B, Mumbai, India
S. Banerjee, S. Bhattacharya, S. Chatterjee, P. Das, M. Guchait, S. Karmakar,
S. Kumar, G. Majumder, K. Mazumdar, S. Mukherjee, D. Roy, N. Sahoo Indian
Institute of Science Education and Research (IISER), Pune, India
S. Dube, B. Kansal, A. Kapoor, K. Kothekar, S. Pandey, A. Rane, A. Rastogi, S.
Sharma Department of Physics, Isfahan University of Technology, Isfahan, Iran
H. Bakhshiansohi37 Institute for Research in Fundamental Sciences (IPM),
Tehran, Iran
S. Chenarani38, S.M. Etesami, M. Khakzad, M. Mohammadi Najafabadi 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,39, C. Arutaa,b, A. Colaleoa, D. Creanzaa,c, N. De
Filippisa,c, M. De Palmaa,b, A. Di Florioa,b, A. Di Pilatoa,b, W.
Elmetenaweea,b, L. Fiorea, A. Gelmia,b, M. Gula, G. Iasellia,c, M. Incea,b, S.
Lezkia,b, G. Maggia,c, M. Maggia, I. Margjekaa,b, J.A. Merlina, S. Mya,b, S.
Nuzzoa,b, A. Pompilia,b, G. Pugliesea,c, A. Ranieria, G. Selvaggia,b, L.
Silvestrisa, F.M. Simonea,b, 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, M. Cuffiania,b, G.M. Dallavallea, T. Diotalevia,b, F. Fabbria, A.
Fanfania,b, E. Fontanesia,b, P. Giacomellia, C. Grandia, L. Guiduccia,b, F.
Iemmia,b, S. Lo Meoa,40, 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,41, S. Costaa,b, A. Di Mattiaa, R. Potenzaa,b, A. Tricomia,b,41,
C. Tuvea,b INFN Sezione di Firenze a, Università di Firenze b, Firenze, Italy
G. Barbaglia, A. Cassesea, R. Ceccarellia,b, V. Ciullia,b, C. Civininia, R.
D’Alessandroa,b, F. Fioria, E. Focardia,b, G. Latinoa,b, P. Lenzia,b, M.
Lizzoa,b, M. Meschinia, S. Paolettia, R. Seiditaa,b, G. Sguazzonia, L.
Viliania INFN Laboratori Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, D. Piccolo INFN Sezione di Genova a, Università di
Genova b, Genova, Italy
M. Bozzoa,b, F. Ferroa, R. Mulargiaa,b, E. Robuttia, S. Tosia,b INFN Sezione
di Milano-Bicocca a, Università di Milano-Bicocca b, Milano, Italy
A. Benagliaa, A. Beschia,b, F. Brivioa,b, F. Cetorellia,b, V. Cirioloa,b,20,
F. De Guioa,b, 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, M. Paganonia,b, D. Pedrinia, S. Ragazzia,b, T. Tabarelli de
Fatisa,b, D. Valsecchia,b,20, 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, F. Fabozzia,c, F. Fiengaa,
A.O.M. Iorioa,b, L. Layera,b, L. Listaa,b, S. Meolaa,d,20, P. Paoluccia,20, 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. De Castro Manzanoa, T. Dorigoa, F. Gasparinia,b, U.
Gasparinia,b, S.Y. Hoha,b, M. Margonia,b, A.T. Meneguzzoa,b, M. Presillab, P.
Ronchesea,b, R. Rossina,b, F. Simonettoa,b, G. Strong, A. Tikoa, M. Tosia,b,
H. YARARa,b, M. Zanettia,b, P. Zottoa,b, A. Zucchettaa,b, G. Zumerlea,b INFN
Sezione di Pavia a, Università di Pavia b, Pavia, Italy
A. Braghieria, S. Calzaferria,b, D. Fiorinaa,b, P. Montagnaa,b, S.P. Rattia,b,
V. Rea, M. Ressegottia,b, C. Riccardia,b, P. Salvinia, I. Vaia, 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, G.
Mantovania,b, V. Mariania,b, M. Menichellia, F. Moscatellia, A. Rossia,b, A.
Santocchiaa,b, D. Spigaa, T. Tedeschia,b 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, M.R. Di Domenicoa,b, S.
Donatoa, L. Gianninia,c, A. Giassia, M.T. Grippoa, F. Ligabuea,c, E. Mancaa,c,
G. Mandorlia,c, A. Messineoa,b, F. Pallaa, G. Ramirez-Sancheza,c, A. Rizzia,b,
G. Rolandia,c, S. Roy Chowdhurya,c, A. Scribanoa, N. Shafieia,b, P. Spagnoloa,
R. Tenchinia, G. Tonellia,b, N. Turinia, 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, M. Diemoza, E.
Longoa,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, R.
Tramontanoa,b INFN Sezione di Torino a, Università di Torino b, Torino, Italy,
Università del Piemonte Orientale c, Novara, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, N. Bartosika,
R. Bellana,b, A. Belloraa,b, C. Biinoa, A. Cappatia,b, N. Cartigliaa, S.
Comettia, M. Costaa,b, R. Covarellia,b, N. Demariaa, B. Kiania,b, F. Leggera,
C. Mariottia, S. Masellia, E. Migliorea,b, V. Monacoa,b, E. Monteila,b, M.
Montenoa, M.M. Obertinoa,b, G. Ortonaa, L. Pachera,b, N. Pastronea, M.
Pelliccionia, G.L. Pinna Angionia,b, M. Ruspaa,c, R. Salvaticoa,b, F.
Sivieroa,b, V. Solaa, A. Solanoa,b, D. Soldia,b, A. Staianoa, D. Trocinoa,b
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 Kyungpook National University, Daegu, Korea
S. Dogra, C. Huh, B. Kim, D.H. Kim, G.N. Kim, J. Lee, S.W. Lee, C.S. Moon,
Y.D. Oh, S.I. Pak, S. Sekmen, Y.C. Yang Chonnam National University, Institute
for Universe and Elementary Particles, Kwangju, Korea
H. Kim, D.H. Moon Hanyang University, Seoul, Korea
B. Francois, T.J. Kim, J. Park Korea University, Seoul, Korea
S. Cho, S. Choi, Y. Go, S. Ha, B. Hong, K. Lee, K.S. Lee, J. Lim, J. Park,
S.K. Park, J. Yoo Kyung Hee University, Department of Physics, Seoul, Republic
of Korea
J. Goh, A. Gurtu Sejong University, Seoul, Korea
H.S. Kim, Y. Kim Seoul National University, Seoul, Korea
J. Almond, J.H. Bhyun, J. Choi, S. Jeon, J. Kim, J.S. Kim, S. Ko, H. Kwon, H.
Lee, K. Lee, S. Lee, K. Nam, B.H. Oh, M. Oh, S.B. Oh, B.C. Radburn-Smith, H.
Seo, U.K. Yang, I. Yoon University of Seoul, Seoul, Korea
D. Jeon, J.H. Kim, B. Ko, J.S.H. Lee, I.C. Park, Y. Roh, D. Song, I.J. Watson
Yonsei University, Department of Physics, Seoul, Korea
H.D. Yoo Sungkyunkwan University, Suwon, Korea
Y. Choi, C. Hwang, Y. Jeong, H. Lee, Y. Lee, I. Yu Riga Technical University,
Riga, Latvia
V. Veckalns42 Vilnius University, Vilnius, Lithuania
A. Juodagalvis, A. Rinkevicius, G. Tamulaitis National Centre for Particle
Physics, Universiti Malaya, Kuala Lumpur, Malaysia
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 Cruz43, R. Lopez-
Fernandez, A. Sanchez-Hernandez Universidad Iberoamericana, Mexico City,
Mexico
S. Carrillo Moreno, C. Oropeza Barrera, M. Ramirez-Garcia, F. Vazquez Valencia
Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
J. Eysermans, I. Pedraza, H.A. Salazar Ibarguen, C. Uribe Estrada Universidad
Autónoma de San Luis Potosí, San Luis Potosí, Mexico
A. Morelos Pineda University of Montenegro, Podgorica, Montenegro
J. Mijuskovic4, 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.I. Asghar, M.I.M. Awan, 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, T. Frueboes, M. Górski, M. Kazana, M.
Szleper, P. Traczyk, P. Zalewski Institute of Experimental Physics, Faculty of
Physics, University of Warsaw, Warsaw, Poland
K. Bunkowski, A. Byszuk44, K. Doroba, A. Kalinowski, M. Konecki, J.
Krolikowski, M. Olszewski, M. Walczak Laboratório de Instrumentação e Física
Experimental de Partículas, Lisboa, Portugal
M. Araujo, P. Bargassa, D. Bastos, P. Faccioli, M. Gallinaro, J. Hollar, N.
Leonardo, T. Niknejad, J. Seixas, K. Shchelina, 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. Matveev45,46, P. Moisenz, V. Palichik,
V. Perelygin, M. Savina, D. Seitova, V. Shalaev, S. Shmatov, S. Shulha, V.
Smirnov, O. Teryaev, N. Voytishin, A. Zarubin, I. Zhizhin Petersburg Nuclear
Physics Institute, Gatchina (St. Petersburg), Russia
G. Gavrilov, V. Golovtcov, Y. Ivanov, V. Kim47, E. Kuznetsova48, V. Murzin, V.
Oreshkin, I. Smirnov, D. Sosnov, V. Sulimov, L. Uvarov, S. Volkov, A. Vorobyev
Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov,
N. Krasnikov, A. Pashenkov, G. Pivovarov, 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. Nikitenko49, 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. Danilov50, 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, A. Ershov, A. Gribushin, A. Kaminskiy51, V.
Klyukhin, O. Kodolova, I. Lokhtin, S. Obraztsov, S. Petrushanko, V. Savrin, A.
Snigirev Novosibirsk State University (NSU), Novosibirsk, Russia
V. Blinov52, T. Dimova52, L. Kardapoltsev52, I. Ovtin52, Y. Skovpen52
Institute for High Energy Physics of National Research Centre ‘Kurchatov
Institute’, Protvino, Russia
I. Azhgirey, I. Bayshev, V. Kachanov, A. Kalinin, D. Konstantinov, V. Petrov,
R. Ryutin, A. Sobol, S. Troshin, N. Tyurin, A. Uzunian, A. Volkov National
Research Tomsk Polytechnic University, Tomsk, Russia
A. Babaev, A. Iuzhakov, V. Okhotnikov, L. Sukhikh Tomsk State University,
Tomsk, Russia
V. Borchsh, V. Ivanchenko, E. Tcherniaev University of Belgrade: Faculty of
Physics and VINCA Institute of Nuclear Sciences, Belgrade, Serbia
P. Adzic53, P. Cirkovic, M. Dordevic, P. Milenovic, J. Milosevic Centro de
Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid,
Spain
M. Aguilar-Benitez, J. Alcaraz Maestre, A. Álvarez Fernández, I. Bachiller, M.
Barrio Luna, Cristina F. Bedoya, J.A. Brochero Cifuentes, C.A. Carrillo
Montoya, M. Cepeda, M. Cerrada, N. Colino, B. De La Cruz, A. Delgado Peris,
J.P. Fernández Ramos, J. Flix, M.C. Fouz, A. García Alonso, O. Gonzalez Lopez,
S. Goy Lopez, J.M. Hernandez, M.I. Josa, J. León Holgado, D. Moran, Á. Navarro
Tobar, A. Pérez-Calero Yzquierdo, J. Puerta Pelayo, I. Redondo, L. Romero, S.
Sánchez Navas, M.S. Soares, A. Triossi, C. Willmott Universidad Autónoma de
Madrid, Madrid, Spain
C. Albajar, J.F. de Trocóniz, R. Reyes-Almanza Universidad de Oviedo,
Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias
(ICTEA), Oviedo, Spain
B. Alvarez Gonzalez, J. Cuevas, C. Erice, J. Fernandez Menendez, S. Folgueras,
I. Gonzalez Caballero, E. Palencia Cortezon, C. Ramón Álvarez, J. Ripoll Sau,
V. Rodríguez Bouza, S. Sanchez Cruz, A. Trapote 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, G. Gomez, C. Martinez Rivero, P. Martinez
Ruiz del Arbol, F. Matorras, J. Piedra Gomez, C. Prieels, F. Ricci-Tam, T.
Rodrigo, A. Ruiz-Jimeno, L. Russo54, L. Scodellaro, I. Vila, J.M. Vizan Garcia
University of Colombo, Colombo, Sri Lanka
MK Jayananda, B. Kailasapathy55, D.U.J. Sonnadara, DDC Wickramarathna
University of Ruhuna, Department of Physics, Matara, Sri Lanka
W.G.D. Dharmaratna, K. Liyanage, N. Perera, N. Wickramage CERN, European
Organization for Nuclear Research, Geneva, Switzerland
T.K. Aarrestad, D. Abbaneo, B. Akgun, E. Auffray, G. Auzinger, J. Baechler, P.
Baillon, A.H. Ball, D. Barney, J. Bendavid, N. Beni, M. Bianco, A. Bocci, P.
Bortignon, E. Bossini, E. Brondolin, T. Camporesi, G. Cerminara, L. Cristella,
D. d’Enterria, A. Dabrowski, N. Daci, V. Daponte, A. David, A. De Roeck, M.
Deile, R. Di Maria, M. Dobson, M. Dünser, N. Dupont, A. Elliott-Peisert, N.
Emriskova, F. Fallavollita56, D. Fasanella, S. Fiorendi, G. Franzoni, J.
Fulcher, W. Funk, S. Giani, D. Gigi, K. Gill, F. Glege, L. Gouskos, M.
Guilbaud, D. Gulhan, M. Haranko, J. Hegeman, Y. Iiyama, V. Innocente, T.
James, P. Janot, J. Kaspar, J. Kieseler, M. Komm, N. Kratochwil, C. Lange, P.
Lecoq, K. Long, C. Lourenço, L. Malgeri, M. Mannelli, A. Massironi, F.
Meijers, S. Mersi, E. Meschi, F. Moortgat, M. Mulders, J. Ngadiuba, J.
Niedziela, S. Orfanelli, L. Orsini, F. Pantaleo20, L. Pape, E. Perez, M.
Peruzzi, A. Petrilli, G. Petrucciani, A. Pfeiffer, M. Pierini, D. Rabady, A.
Racz, M. Rieger, M. Rovere, H. Sakulin, J. Salfeld-Nebgen, S. Scarfi, C.
Schäfer, C. Schwick, M. Selvaggi, A. Sharma, P. Silva, W. Snoeys, P.
Sphicas57, J. Steggemann, S. Summers, V.R. Tavolaro, D. Treille, A. Tsirou,
G.P. Van Onsem, A. Vartak, M. Verzetti, K.A. Wozniak, W.D. Zeuner Paul
Scherrer Institut, Villigen, Switzerland
L. Caminada58, W. Erdmann, R. Horisberger, Q. Ingram, H.C. Kaestli, D.
Kotlinski, U. Langenegger, T. Rohe ETH Zurich - Institute for Particle Physics
and Astrophysics (IPA), Zurich, Switzerland
M. Backhaus, P. Berger, A. Calandri, N. Chernyavskaya, G. Dissertori, M.
Dittmar, M. Donegà, C. Dorfer, T. Gadek, T.A. Gómez Espinosa, C. Grab, D.
Hits, W. Lustermann, A.-M. Lyon, R.A. Manzoni, M.T. Meinhard, F. Micheli, F.
Nessi-Tedaldi, F. Pauss, V. Perovic, G. Perrin, L. Perrozzi, S. Pigazzini,
M.G. Ratti, M. Reichmann, C. Reissel, T. Reitenspiess, B. Ristic, D. Ruini,
D.A. Sanz Becerra, M. Schönenberger, L. Shchutska, V. Stampf, M.L. Vesterbacka
Olsson, R. Wallny, D.H. Zhu Universität Zürich, Zurich, Switzerland
C. Amsler59, C. Botta, D. Brzhechko, M.F. Canelli, A. De Cosa, R. Del Burgo,
J.K. Heikkilä, M. Huwiler, A. Jofrehei, B. Kilminster, S. Leontsinis, A.
Macchiolo, P. Meiring, V.M. Mikuni, U. Molinatti, I. Neutelings, G. Rauco, A.
Reimers, P. Robmann, K. Schweiger, Y. Takahashi, S. Wertz National Central
University, Chung-Li, Taiwan
C. Adloff60, C.M. Kuo, W. Lin, A. Roy, T. Sarkar35, S.S. Yu National Taiwan
University (NTU), Taipei, Taiwan
L. Ceard, 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, E. Yazgan Chulalongkorn University,
Faculty of Science, Department of Physics, Bangkok, Thailand
B. Asavapibhop, C. Asawatangtrakuldee, N. Srimanobhas Çukurova University,
Physics Department, Science and Art Faculty, Adana, Turkey
F. Boran, S. Damarseckin61, Z.S. Demiroglu, F. Dolek, C. Dozen62, I.
Dumanoglu63, E. Eskut, G. Gokbulut, Y. Guler, E. Gurpinar Guler64, I. Hos65,
C. Isik, E.E. Kangal66, O. Kara, A. Kayis Topaksu, U. Kiminsu, G. Onengut, K.
Ozdemir67, A. Polatoz, A.E. Simsek, B. Tali68, U.G. Tok, S. Turkcapar, I.S.
Zorbakir, C. Zorbilmez Middle East Technical University, Physics Department,
Ankara, Turkey
B. Isildak69, G. Karapinar70, K. Ocalan71, M. Yalvac72 Bogazici University,
Istanbul, Turkey
I.O. Atakisi, E. Gülmez, M. Kaya73, O. Kaya74, Ö. Özçelik, S. Tekten75, E.A.
Yetkin76 Istanbul Technical University, Istanbul, Turkey
A. Cakir, K. Cankocak63, Y. Komurcu, S. Sen77 Istanbul University, Istanbul,
Turkey
F. Aydogmus Sen, S. Cerci68, B. Kaynak, S. Ozkorucuklu, D. Sunar Cerci68
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
E. Bhal, S. Bologna, J.J. Brooke, E. Clement, D. Cussans, H. Flacher, J.
Goldstein, G.P. Heath, H.F. Heath, L. Kreczko, B. Krikler, S. Paramesvaran, T.
Sakuma, S. Seif El Nasr-Storey, V.J. Smith, J. Taylor, A. Titterton Rutherford
Appleton Laboratory, Didcot, United Kingdom
K.W. Bell, A. Belyaev78, C. Brew, R.M. Brown, D.J.A. Cockerill, K.V. Ellis, 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 Imperial College, London, United Kingdom
R. Bainbridge, P. Bloch, S. Bonomally, J. Borg, S. Breeze, O. Buchmuller, A.
Bundock, V. Cepaitis, G.S. Chahal79, D. Colling, P. Dauncey, G. Davies, M.
Della Negra, P. Everaerts, G. Fedi, G. Hall, G. Iles, J. Langford, L. Lyons,
A.-M. Magnan, S. Malik, A. Martelli, V. Milosevic, J. Nash80, V. Palladino, M.
Pesaresi, D.M. Raymond, A. Richards, A. Rose, E. Scott, C. Seez, A.
Shtipliyski, M. Stoye, A. Tapper, K. Uchida, T. Virdee20, N. Wardle, S.N.
Webb, D. Winterbottom, A.G. Zecchinelli, S.C. Zenz Brunel University,
Uxbridge, United Kingdom
J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, C.K. Mackay, I.D. Reid, L.
Teodorescu, S. Zahid Baylor University, Waco, USA
A. Brinkerhoff, K. Call, B. Caraway, J. Dittmann, K. Hatakeyama, A.R.
Kanuganti, C. Madrid, B. McMaster, N. Pastika, S. Sawant, C. Smith Catholic
University of America, Washington, DC, USA
R. Bartek, A. Dominguez, R. Uniyal, A.M. Vargas Hernandez The University of
Alabama, Tuscaloosa, USA
A. Buccilli, O. Charaf, S.I. Cooper, S.V. Gleyzer, C. Henderson, P. Rumerio,
C. West Boston University, Boston, USA
A. Akpinar, A. Albert, D. Arcaro, C. Cosby, Z. Demiragli, D. Gastler, C.
Richardson, J. Rohlf, K. Salyer, D. Sperka, D. Spitzbart, I. Suarez, S. Yuan,
D. Zou Brown University, Providence, USA
G. Benelli, B. Burkle, X. Coubez21, D. Cutts, Y.t. Duh, M. Hadley, U. Heintz,
J.M. Hogan81, K.H.M. Kwok, E. Laird, G. Landsberg, K.T. Lau, J. Lee, M.
Narain, S. Sagir82, R. Syarif, E. Usai, W.Y. Wong, 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${}^{\textrm{\textdagger}}$, O. Kukral, R. Lander, M. Mulhearn, D. Pellett,
J. Pilot, M. Shi, D. Taylor, K. Tos, M. Tripathi, Y. Yao, F. Zhang University
of California, Los Angeles, USA
M. Bachtis, R. Cousins, A. Dasgupta, A. Florent, D. Hamilton, J. Hauser, M.
Ignatenko, T. Lam, 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, O.R. Long, N. Manganelli, M. Olmedo Negrete, M.I. Paneva, W. Si,
S. Wimpenny, Y. Zhang University of California, San Diego, La Jolla, USA
J.G. Branson, P. Chang, S. Cittolin, S. Cooperstein, N. Deelen, M. Derdzinski,
J. Duarte, 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 University of California, Santa Barbara - Department of
Physics, Santa Barbara, USA
N. Amin, C. Campagnari, M. Citron, A. Dorsett, V. Dutta, J. Incandela, B.
Marsh, H. Mei, A. Ovcharova, H. Qu, M. Quinnan, 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
J. Alison, M.B. Andrews, T. Ferguson, T. Mudholkar, M. Paulini, M. Sun, I.
Vorobiev University of Colorado Boulder, Boulder, USA
J.P. Cumalat, W.T. Ford, E. MacDonald, T. Mulholland, R. Patel, A. Perloff, K.
Stenson, K.A. Ulmer, S.R. Wagner Cornell University, Ithaca, USA
J. Alexander, Y. Cheng, J. Chu, D.J. Cranshaw, A. Datta, A. Frankenthal, K.
Mcdermott, J. Monroy, J.R. Patterson, D. Quach, A. Ryd, W. Sun, S.M. Tan, Z.
Tao, J. Thom, P. Wittich, M. Zientek Fermi National Accelerator Laboratory,
Batavia, USA
S. Abdullin, M. Albrow, M. Alyari, G. Apollinari, A. Apresyan, A. Apyan, S.
Banerjee, L.A.T. Bauerdick, A. Beretvas, D. Berry, J. Berryhill, P.C. Bhat, K.
Burkett, J.N. Butler, A. Canepa, G.B. Cerati, H.W.K. Cheung, F. Chlebana, M.
Cremonesi, V.D. Elvira, J. Freeman, Z. Gecse, E. Gottschalk, L. Gray, D.
Green, S. Grünendahl, O. Gutsche, R.M. Harris, S. Hasegawa, R. Heller, T.C.
Herwig, J. Hirschauer, B. Jayatilaka, S. Jindariani, M. Johnson, U. Joshi, P.
Klabbers, T. Klijnsma, B. Klima, M.J. Kortelainen, S. Lammel, D. Lincoln, R.
Lipton, M. Liu, T. Liu, J. Lykken, K. Maeshima, D. Mason, P. McBride, P.
Merkel, S. Mrenna, S. Nahn, V. O’Dell, V. Papadimitriou, K. Pedro, C. Pena83,
O. Prokofyev, F. Ravera, A. Reinsvold Hall, L. Ristori, B. Schneider, E.
Sexton-Kennedy, N. Smith, A. Soha, W.J. Spalding, L. Spiegel, S. Stoynev, J.
Strait, L. Taylor, S. Tkaczyk, N.V. Tran, L. Uplegger, E.W. Vaandering, M.
Wang, H.A. Weber, A. Woodard University of Florida, Gainesville, USA
D. Acosta, P. Avery, D. Bourilkov, L. Cadamuro, V. Cherepanov, F. Errico, R.D.
Field, D. Guerrero, B.M. Joshi, M. Kim, J. Konigsberg, A. Korytov, K.H. Lo, 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, D. Diaz, R. Habibullah, S. Hagopian, V. Hagopian, K.F.
Johnson, R. Khurana, T. Kolberg, G. Martinez, H. Prosper, C. Schiber, R.
Yohay, J. Zhang Florida Institute of Technology, Melbourne, USA
M.M. Baarmand, S. Butalla, T. Elkafrawy15, M. Hohlmann, D. Noonan, M. Rahmani,
M. Saunders, F. Yumiceva University of Illinois at Chicago (UIC), Chicago, USA
M.R. Adams, L. Apanasevich, H. Becerril Gonzalez, R. Cavanaugh, X. Chen, S.
Dittmer, O. Evdokimov, C.E. Gerber, D.A. Hangal, D.J. Hofman, C. Mills, G. Oh,
T. Roy, M.B. Tonjes, N. Varelas, J. Viinikainen, X. Wang, Z. Wu The University
of Iowa, Iowa City, USA
M. Alhusseini, B. Bilki64, K. Dilsiz84, S. Durgut, R.P. Gandrajula, M.
Haytmyradov, V. Khristenko, O.K. Köseyan, J.-P. Merlo, A. Mestvirishvili85, A.
Moeller, J. Nachtman, H. Ogul86, Y. Onel, F. Ozok87, A. Penzo, C. Snyder, E.
Tiras, J. Wetzel, K. Yi88 Johns Hopkins University, Baltimore, USA
O. Amram, B. Blumenfeld, L. Corcodilos, M. Eminizer, A.V. Gritsan, S.
Kyriacou, P. Maksimovic, C. Mantilla, J. Roskes, M. Swartz, T.Á. Vámi The
University of Kansas, Lawrence, USA
C. Baldenegro Barrera, P. Baringer, A. Bean, A. Bylinkin, T. Isidori, S.
Khalil, J. King, G. Krintiras, A. Kropivnitskaya, C. Lindsey, 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
E. Adams, A. Baden, O. Baron, A. Belloni, S.C. Eno, Y. Feng, N.J. Hadley, S.
Jabeen, G.Y. Jeng, R.G. Kellogg, T. Koeth, A.C. Mignerey, S. Nabili, M.
Seidel, A. Skuja, S.C. Tonwar, L. Wang, K. Wong Massachusetts Institute of
Technology, Cambridge, USA
D. Abercrombie, B. Allen, R. Bi, S. Brandt, W. Busza, I.A. Cali, Y. Chen, M.
D’Alfonso, G. Gomez Ceballos, M. Goncharov, P. Harris, D. Hsu, M. Hu, M.
Klute, D. Kovalskyi, J. Krupa, Y.-J. Lee, P.D. Luckey, B. Maier, A.C. Marini,
C. Mcginn, C. Mironov, S. Narayanan, X. Niu, C. Paus, D. Rankin, C. Roland, G.
Roland, Z. Shi, G.S.F. Stephans, K. Sumorok, K. Tatar, D. Velicanu, J. Wang,
T.W. Wang, Z. Wang, B. Wyslouch University of Minnesota, Minneapolis, USA
R.M. Chatterjee, A. Evans, S. Guts${}^{\textrm{\textdagger}}$, P. Hansen, J.
Hiltbrand, Sh. Jain, M. Krohn, Y. Kubota, Z. Lesko, J. Mans, M. Revering, R.
Rusack, R. Saradhy, N. Schroeder, N. Strobbe, M.A. Wadud University of
Mississippi, Oxford, USA
J.G. Acosta, S. Oliveros University of Nebraska-Lincoln, Lincoln, USA
K. Bloom, S. Chauhan, D.R. Claes, C. Fangmeier, L. Finco, F. Golf, J.R.
González Fernández, I. Kravchenko, J.E. Siado, G.R.
Snow${}^{\textrm{\textdagger}}$, B. Stieger, W. Tabb State University of New
York at Buffalo, Buffalo, USA
G. Agarwal, C. Harrington, L. Hay, 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, B.
Marzocchi, D.M. Morse, V. Nguyen, T. Orimoto, L. Skinnari, A. Tishelman-
Charny, T. Wamorkar, B. Wang, A. Wisecarver, D. Wood Northwestern University,
Evanston, USA
S. Bhattacharya, J. Bueghly, Z. Chen, A. Gilbert, T. Gunter, K.A. Hahn, N.
Odell, M.H. Schmitt, K. Sung, M. Velasco University of Notre Dame, Notre Dame,
USA
R. Bucci, N. Dev, R. Goldouzian, M. Hildreth, K. Hurtado Anampa, C. Jessop,
D.J. Karmgard, K. Lannon, W. Li, N. Loukas, N. Marinelli, I. Mcalister, F.
Meng, K. Mohrman, Y. Musienko45, R. Ruchti, P. Siddireddy, S. Taroni, M.
Wayne, A. Wightman, M. Wolf, L. Zygala The Ohio State University, Columbus,
USA
J. Alimena, B. Bylsma, B. Cardwell, L.S. Durkin, B. Francis, C. Hill, A.
Lefeld, B.L. Winer, B.R. Yates Princeton University, Princeton, USA
G. Dezoort, P. Elmer, B. Greenberg, N. Haubrich, S. Higginbotham, A.
Kalogeropoulos, G. Kopp, S. Kwan, D. Lange, M.T. Lucchini, J. Luo, D. Marlow,
K. Mei, I. Ojalvo, J. Olsen, C. Palmer, P. Piroué, D. Stickland, C. Tully
University of Puerto Rico, Mayaguez, USA
S. Malik, S. Norberg Purdue University, West Lafayette, USA
V.E. Barnes, R. Chawla, S. Das, L. Gutay, M. Jones, A.W. Jung, B. Mahakud, G.
Negro, N. Neumeister, C.C. Peng, S. Piperov, H. Qiu, J.F. Schulte, N.
Trevisani, F. Wang, R. Xiao, W. Xie Purdue University Northwest, Hammond, USA
T. Cheng, J. Dolen, N. Parashar, M. Stojanovic Rice University, Houston, USA
A. Baty, S. Dildick, K.M. Ecklund, S. Freed, F.J.M. Geurts, M. Kilpatrick, A.
Kumar, W. Li, B.P. Padley, R. Redjimi, J. Roberts${}^{\textrm{\textdagger}}$,
J. Rorie, W. Shi, A.G. Stahl Leiton, A. Zhang University of Rochester,
Rochester, USA
A. Bodek, P. de Barbaro, R. Demina, J.L. Dulemba, C. Fallon, T. Ferbel, M.
Galanti, A. Garcia-Bellido, O. Hindrichs, A. Khukhunaishvili, E. Ranken, R.
Taus Rutgers, The State University of New Jersey, Piscataway, USA
B. Chiarito, J.P. Chou, A. Gandrakota, Y. Gershtein, E. Halkiadakis, A. Hart,
M. Heindl, E. Hughes, S. Kaplan, O. Karacheban24, I. Laflotte, A. Lath, R.
Montalvo, K. Nash, M. Osherson, S. Salur, S. Schnetzer, S. Somalwar, R. Stone,
S.A. Thayil, S. Thomas, H. Wang University of Tennessee, Knoxville, USA
H. Acharya, A.G. Delannoy, S. Spanier Texas A&M University, College Station,
USA
O. Bouhali89, M. Dalchenko, A. Delgado, R. Eusebi, J. Gilmore, T. Huang, T.
Kamon90, H. Kim, S. Luo, S. Malhotra, R. Mueller, D. Overton, L. Perniè, D.
Rathjens, A. Safonov, J. Sturdy Texas Tech University, Lubbock, USA
N. Akchurin, J. Damgov, V. Hegde, 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
E. Appelt, 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
L. Ang, M.W. Arenton, B. Cox, G. Cummings, J. Hakala, R. Hirosky, M. Joyce, A.
Ledovskoy, C. Neu, B. Tannenwald, Y. Wang, E. Wolfe, F. Xia Wayne State
University, Detroit, USA
P.E. Karchin, N. Poudyal, P. Thapa University of Wisconsin - Madison, Madison,
WI, USA
K. Black, T. Bose, J. Buchanan, C. Caillol, S. Dasu, I. De Bruyn, C. Galloni,
H. He, M. Herndon, A. Hervé, U. Hussain, A. Lanaro, A. Loeliger, R. Loveless,
J. Madhusudanan Sreekala, A. Mallampalli, D. Pinna, T. Ruggles, A. Savin, V.
Shang, V. Sharma, W.H. Smith, D. Teague, S. Trembath-reichert, W. Vetens †:
Deceased
1: Also at Vienna University of Technology, Vienna, Austria
2: Also at Department of Basic and Applied Sciences, Faculty of Engineering,
Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt
3: Also at Université Libre de Bruxelles, Bruxelles, Belgium
4: Also at IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
5: Also at Universidade Estadual de Campinas, Campinas, Brazil
6: Also at Federal University of Rio Grande do Sul, Porto Alegre, Brazil
7: Also at UFMS, Nova Andradina, Brazil
8: Also at Universidade Federal de Pelotas, Pelotas, Brazil
9: Also at University of Chinese Academy of Sciences, Beijing, China
10: Also at Institute for Theoretical and Experimental Physics named by A.I.
Alikhanov of NRC ‘Kurchatov Institute’, Moscow, Russia
11: Also at Joint Institute for Nuclear Research, Dubna, Russia
12: Also at Suez University, Suez, Egypt
13: Now at British University in Egypt, Cairo, Egypt
14: Also at Zewail City of Science and Technology, Zewail, Egypt
15: Now at Ain Shams University, Cairo, Egypt
16: Now at Fayoum University, El-Fayoum, Egypt
17: Also at Purdue University, West Lafayette, USA
18: Also at Université de Haute Alsace, Mulhouse, France
19: Also at Erzincan Binali Yildirim University, Erzincan, Turkey
20: Also at CERN, European Organization for Nuclear Research, Geneva,
Switzerland
21: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen,
Germany
22: Also at University of Hamburg, Hamburg, Germany
23: Also at Department of Physics, Isfahan University of Technology, Isfahan,
Iran, Isfahan, Iran
24: Also at Brandenburg University of Technology, Cottbus, Germany
25: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State
University, Moscow, Russia
26: Also at Institute of Physics, University of Debrecen, Debrecen, Hungary,
Debrecen, Hungary
27: Also at Physics Department, Faculty of Science, Assiut University, Assiut,
Egypt
28: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös
Loránd University, Budapest, Hungary, Budapest, Hungary
29: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary
30: Also at IIT Bhubaneswar, Bhubaneswar, India, Bhubaneswar, India
31: Also at Institute of Physics, Bhubaneswar, India
32: Also at G.H.G. Khalsa College, Punjab, India
33: Also at Shoolini University, Solan, India
34: Also at University of Hyderabad, Hyderabad, India
35: Also at University of Visva-Bharati, Santiniketan, India
36: Also at Indian Institute of Technology (IIT), Mumbai, India
37: Also at Deutsches Elektronen-Synchrotron, Hamburg, Germany
38: Also at Department of Physics, University of Science and Technology of
Mazandaran, Behshahr, Iran
39: Now at INFN Sezione di Bari a, Università di Bari b, Politecnico di Bari
c, Bari, Italy
40: Also at Italian National Agency for New Technologies, Energy and
Sustainable Economic Development, Bologna, Italy
41: Also at Centro Siciliano di Fisica Nucleare e di Struttura Della Materia,
Catania, Italy
42: Also at Riga Technical University, Riga, Latvia, Riga, Latvia
43: Also at Consejo Nacional de Ciencia y Tecnología, Mexico City, Mexico
44: Also at Warsaw University of Technology, Institute of Electronic Systems,
Warsaw, Poland
45: Also at Institute for Nuclear Research, Moscow, Russia
46: Now at National Research Nuclear University ’Moscow Engineering Physics
Institute’ (MEPhI), Moscow, Russia
47: Also at St. Petersburg State Polytechnical University, St. Petersburg,
Russia
48: Also at University of Florida, Gainesville, USA
49: Also at Imperial College, London, United Kingdom
50: Also at P.N. Lebedev Physical Institute, Moscow, Russia
51: Also at INFN Sezione di Padova a, Università di Padova b, Padova, Italy,
Università di Trento c, Trento, Italy, Padova, Italy
52: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia
53: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia
54: Also at Università degli Studi di Siena, Siena, Italy
55: Also at Trincomalee Campus, Eastern University, Sri Lanka, Nilaveli, Sri
Lanka
56: Also at INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy,
Pavia, Italy
57: Also at National and Kapodistrian University of Athens, Athens, Greece
58: Also at Universität Zürich, Zurich, Switzerland
59: Also at Stefan Meyer Institute for Subatomic Physics, Vienna, Austria,
Vienna, Austria
60: Also at Laboratoire d’Annecy-le-Vieux de Physique des Particules,
IN2P3-CNRS, Annecy-le-Vieux, France
61: Also at Şırnak University, Sirnak, Turkey
62: Also at Department of Physics, Tsinghua University, Beijing, China,
Beijing, China
63: Also at Near East University, Research Center of Experimental Health
Science, Nicosia, Turkey
64: Also at Beykent University, Istanbul, Turkey, Istanbul, Turkey
65: Also at Istanbul Aydin University, Application and Research Center for
Advanced Studies (App. & Res. Cent. for Advanced Studies), Istanbul, Turkey
66: Also at Mersin University, Mersin, Turkey
67: Also at Piri Reis University, Istanbul, Turkey
68: Also at Adiyaman University, Adiyaman, Turkey
69: Also at Ozyegin University, Istanbul, Turkey
70: Also at Izmir Institute of Technology, Izmir, Turkey
71: Also at Necmettin Erbakan University, Konya, Turkey
72: Also at Bozok Universitetesi Rektörlügü, Yozgat, Turkey
73: Also at Marmara University, Istanbul, Turkey
74: Also at Milli Savunma University, Istanbul, Turkey
75: Also at Kafkas University, Kars, Turkey
76: Also at Istanbul Bilgi University, Istanbul, Turkey
77: Also at Hacettepe University, Ankara, Turkey
78: Also at School of Physics and Astronomy, University of Southampton,
Southampton, United Kingdom
79: Also at IPPP Durham University, Durham, United Kingdom
80: Also at Monash University, Faculty of Science, Clayton, Australia
81: Also at Bethel University, St. Paul, Minneapolis, USA, St. Paul, USA
82: Also at Karamanoğlu Mehmetbey University, Karaman, Turkey
83: Also at California Institute of Technology, Pasadena, USA
84: Also at Bingol University, Bingol, Turkey
85: Also at Georgian Technical University, Tbilisi, Georgia
86: Also at Sinop University, Sinop, Turkey
87: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey
88: Also at Nanjing Normal University Department of Physics, Nanjing, China
89: Also at Texas A&M University at Qatar, Doha, Qatar
90: Also at Kyungpook National University, Daegu, Korea, Daegu, Korea