PRF-18-002

PRF-18-002

# The very forward CASTOR calorimeter
of the CMS experiment

###### Abstract

The physics motivation, detector design, triggers, calibration, alignment,
simulation, and overall performance of the very forward CASTOR calorimeter of
the CMS experiment are reviewed. The CASTOR Cherenkov sampling calorimeter is
located very close to the LHC beam line, at a radial distance of about 1cm
from the beam pipe, and at 14.4m from the CMS interaction point, covering the
pseudorapidity range of $-6.6<\eta<-5.2$. It was designed to withstand high
ambient radiation and strong magnetic fields. The performance of the detector
in measurements of forward energy density, jets, and processes characterized
by rapidity gaps, is reviewed using data collected in proton and nuclear
collisions at the LHC.

###### Contents

  1. 0.1 Introduction
  2. 0.2 Physics motivation
    1. 0.2.1 Forward physics in proton-proton collisions
    2. 0.2.2 Ultrahigh-energy cosmic ray air showers
    3. 0.2.3 Proton-nucleus and nucleus-nucleus collisions
  3. 0.3 Detector design
  4. 0.4 Triggers and operation
  5. 0.5 Event reconstruction and calibration
    1. 0.5.1 Noise and baseline
    2. 0.5.2 Gain correction factors
    3. 0.5.3 Channel-by-channel intercalibration
    4. 0.5.4 Absolute energy scale
  6. 0.6 Geometry and alignment
  7. 0.7 Detector simulation and validation
    1. 0.7.1 Simulation
    2. 0.7.2 Validation
    3. 0.7.3 Analysis backgrounds and noise levels
  8. 0.8 Summary
  9. .9 The CMS Collaboration

##  0.1 Introduction

The CASTOR (Centauro And STrange Object Research) calorimeter was proposed [1,
2], built [3, 4, 5, 6, 7], and installed in the CMS experiment [8] with the
purpose of studying very forward particle production in heavy ion (HI) and
proton-proton () collisions at the CERN LHC. The calorimeter extends the CMS
acceptance to the very forward pseudorapidity range, $-6.6<\eta<-5.2$.

The location and design of CASTOR are optimized for the study of the
longitudinal development of electromagnetic and hadronic showers produced by
particles emitted at very small polar angles (between 0.16∘ and 0.64∘) with
respect to the beam direction. This prime detector motivation—focused on
searches for deeply penetrating particles, such as strangelets [1, 2],
connected to exotic events observed in high-energy cosmic ray (CR)
interactions [9, 10, 11]—was extended also to measurements of generic
properties of particle production at forward rapidities in inelastic proton
and nuclear collisions, as well as to identify rapidity gaps (regions in the
detector devoid of any particle production) in diffractive and exclusive
processes [12, 13]. The physics program and reach of the multipurpose CASTOR
calorimeter are broad and unique, because no comparable instrumentation exists
in this $\eta$ range at any other interaction point (IP) of the LHC.

The installation of a calorimeter at the forward rapidities covered by CASTOR
involves significant challenges. The beam pipe is just $\approx$1cm away from
the detector and must be carefully protected. The calorimeter is immersed in
the forward shielding of CMS, which channels the strong magnetic field from
the central region of the apparatus. Small gaps in the shielding produce
strong local variations in the magnetic field structure. The field has a
magnitude of about $B=0.2\unit{T}$ in the vicinity of the detector and has an
impact on the shower development inside the calorimeter and also, more
importantly, on the gain of the photomultiplier tubes (PMTs) used to record
the signals. Because of the enormous Lorentz boost of the particles produced
at rapidities approaching the beam direction, the energy absorbed by the
calorimeter reaches hundreds of per collision event [14], and thus the
radioactive activation of the calorimeter itself, as well as of the
surrounding shielding, is one of the largest at the LHC per unit of integrated
luminosity. Because of the lack of precise vertex-pointing capabilities of the
calorimeter, it is not possible to distinguish particles produced in different
proton or nucleus collisions occurring simultaneously at the IP (pileup),
within the default 25ns readout integration time of the detector. Thus,
operation of the CASTOR calorimeter is most useful for LHC luminosities
corresponding to a maximum average number of collisions around one per bunch
crossing. Lower luminosities are often required for physics analyses that are
very sensitive to pileup backgrounds.

Figure 1 shows the CASTOR calorimeter installed around the beam pipe in front
of the central part of the CMS detector, and Fig. 2 (left) displays a closeup
view of the calorimeter surrounded by the open collar shielding. A
visualization of the calorimeter integration around the beam pipe is shown in
Fig. 2 (right).

Figure 1: Picture of the forward region of the CMS experiment with the two
half-cylinders of the CASTOR calorimeter (indicated by an arrow) visible in
the zone where the collar shielding (orange) structures are open. The green
cylindrical structure in the foreground belongs to the target absorber region.
The massive shielding around the calorimeter is in the open position. The two
half-cylinders of CASTOR are not yet closed around the LHC beam pipe that is
visible emerging from the target absorber region in the foreground and
disappearing towards the IP inside the CMS detector.

Figure 2: Left: Closeup image of the CASTOR calorimeter surrounded by the CMS
collar shielding in the open position during an integration test, about 5m
below the beam pipe. Right: Visualization of the CASTOR detector integrated
around the beam pipe. The CMS interaction point is at 14.4m upstream (towards
the upper right). The TOTEM T2 tracking station, and several pieces of LHC/CMS
infrastructure, are visible along this direction.

Many of the results discussed in this work are based on the various CASTOR
measurements carried out so far in [15, 16, 17, 18, 19, 20, 21, 22], proton-
lead (Pb) [23, 24, 25], and lead-lead (PbPb) [26] collisions at the LHC. The
calorimeter took data during LHC Runs 1 (2009–13) and 2 (2015–2018). It was
decommissioned in 2019–20, since a redesign of the beam pipe renders the
detector mechanically incompatible with LHC Run 3 operation (expected to start
in 2022). The CASTOR data are being made publicly available via the CERN open
data initiative [27, 28]. The data are accompanied with all tools,
calibration, and running conditions as described and summarized here.

The paper is organized as follows. Section 0.2 reviews the main physics
motivations of the CASTOR calorimeter project. The detector design is
summarized in Section 0.3, whereas the CASTOR operation and triggers are
described in Section 0.4. The event reconstruction, including the calorimeter
calibration, and the detector geometry and alignment are discussed in Sections
0.5 and 0.6, respectively. The CASTOR simulation and its validation are
presented in Section 0.7. The summary of the paper is provided in Section 0.8.

##  0.2 Physics motivation

The CASTOR detector closes to a large extent the gap in the calorimetric
coverage between the central detector ($\abs{\eta}\lesssim 5.2$) and the zero-
degree calorimeter ($\abs{\eta}>8.4$ for neutral particles) [29] on one side
of the CMS experiment. The addition of the CASTOR calorimeter provides the CMS
detector with the most complete geometric coverage a the LHC (Fig. 3, left),
thereby opening up unique possibilities for physics measurements in , Pb, and
PbPb collisions. This section summarizes the main research topics accessible
with the CASTOR detector.

Figure 3: Left: Comparison of the pseudorapidity acceptances of all the LHC
experiments [30, 31, 32, 33, 34]. The ATLAS ALFA and CMS TOTEM proton
spectrometers (“Roman pots”) installed inside the beam tunnel at $\approx$200m
around IPs 1 and 5 are not plotted. The TOTEM forward tracking T1 and T2
telescopes [34] are individually identified. Right: Kinematic coverage in the
parton fractional momentum $x$ and momentum transfer $Q^{2}$ plane
corresponding to the CASTOR detector, the central CMS/ATLAS [30, 31], the LHCb
[33], and the DESY HERA [35, 36] experiments.

###  0.2.1 Forward physics in proton-proton collisions

The physics program of the CASTOR calorimeter includes typical “forward
physics” studies [12, 13, 37], such as those connected with low-$x$ parton
dynamics, underlying event (UE), multiparton interactions (MPI), as well as
with diffractive, photon-induced, and central exclusive processes. Key
measurements in the investigation of these different topics are very forward
energy densities, single-particle (muon, electron) spectra, very forward jets,
as well as soft- and hard-diffractive and exclusive processes with a large
rapidity gap. Measurements of diffractive, photon-induced, and central
exclusive production benefit from the possibility to tag events with large
gaps devoid of particle production that are typical of processes with color-
singlet (pomeron and/or photon) exchanges, over a broad range of rapidities in
the forward acceptance.

By measuring the very forward energy density deposited in the CASTOR
acceptance, the characteristics of the UE can be studied in a rapidity region
far away from the central hard scattering processes. The impact of CASTOR has
been particularly important for underlying event studies [16, 19, 22], and has
helped to constrain and tune models used to describe MPI, which are
responsible of a large fraction of the UE activity, in Monte Carlo event
generators [21].

For the study of processes characterized by rapidity gaps, CASTOR further
extends the accessible range in terms of $\xi$, the fractional momentum loss
of the proton, from about $\xi>10^{-6}$ for the central part of the CMS
experiment alone, down to $\xi>10^{-7}$ [20]. The low noise level of the
CASTOR calorimeter, equivalent to a few hundred of energy, allows the
reduction in the rate of misidentified rapidity gaps, and improves the
rapidity gap tagging efficiency. The use of the CASTOR detector not only
extends the kinematic range in which diffraction can be observed, but also
helps to disentangle single- and double-diffractive dissociation processes
[17].

Measurements of very forward jets in the CASTOR acceptance [38, 23] have
opened up the possibility to study parton dynamics in a region of very small
parton fractional momenta, $x\approx\pt\exp{(\pm\eta)}/{\sqrt{s}}\approx
10^{-6}$ (Fig. 3, right), which has never been accessible before. In this
low-$x$ regime, where the standard Dokshitzer–Gribov–Lipatov–Altarelli–Parisi
(DGLAP) parton evolution equations [39, 40, 41] are expected to fail,
alternative evolutions described by the Balitsky–Fadeev–Kuraev–Lipatov (BFKL)
[42, 43, 44, 45] or gluon saturation [46] dynamics should become important.
Forward (di)jets, as proxies of the underlying low-$x$ parton scatterings [47,
48], have long been identified as useful probes of beyond-DGLAP phenomena.
When one jet is measured in CASTOR and the other in the central CMS region,
unique dijet rapidity separations of up to $\Delta\eta\approx 10$ can be
reached. Such Mueller–Navelet dijet topologies [49] are sensitive probes of
the BFKL parton dynamics [50].

###  0.2.2 Ultrahigh-energy cosmic ray air showers

Improvements in our understanding of particle production in collisions of
ultrahigh-energy cosmic rays (mostly protons, with energies in the range
${E_{\mathrm{CR}}}\approx 10^{17}$–$10^{20}\unit{eV}$) with air nuclei in the
upper atmosphere, are among the top motivations of the CASTOR research
program. Such CRs lead to collisions at nucleon-nucleon center-of-mass
energies of $\sqrtsNN\approx 14$–450, which are around or well above those
reachable at the LHC. The subsequent shower of secondary particles generated
in the atmosphere, called extensive air shower (EAS), needs to be well-
reproduced by the hadronic Monte Carlo (MC) models to determine the nature and
energy of the incoming cosmic particle. The description of semihard and
diffractive processes, which dominate multiparticle production in hadronic
collisions within the EAS, is based on phenomenological approaches whose
parameters are tuned to particle accelerator data [51, 52]. Since in hadronic
collisions most of the primary energy is transported along the forward
direction, collider data at the highest accessible energies, and in particular
in the poorly known forward region, are needed for tuning and refinement of
the MC models used in CR physics [51, 52]. The importance of CASTOR to cosmic
ray research is driven by its very forward location that allows detailed
studies of particle production in a region not covered so far by collider
experiments. The highest nucleon-nucleon center-of-mass energy of 13reached at
the LHC corresponds to that of a fixed-target collision of a primary cosmic
ray proton of ${E_{\mathrm{CR}}}=10^{16.9}\unit{eV}$ lab energy against a
nucleus at rest in the upper atmosphere.

The highest energy particles of an EAS drive the peak of the shower activity
deeper into the atmosphere and determine the most important features of EAS,
such as the position of its shower maximum, used to identify the CR energy and
identity. In this context, the CASTOR measurement of the forward-directed
energy flow up to 13center-of-mass energies [15, 53] provides a powerful
benchmark for air-shower modeling. In addition, the muon component in air
showers remains poorly described by the current MC models [54], clearly
pointing to an insufficient understanding of the underlying hadronic processes
in the core of the shower. The fraction of collision energy going into neutral
pions does not contribute to the production of muons in the downstream air
shower processes and, thus, by independently measuring hadronic (mostly
charged pions) and electromagnetic (mostly neutral pions) energy densities in
the CASTOR detector, the production mechanisms that influence muon production
in air showers can be directly studied [18]. The understanding of the
development of extensive air showers also depends on nuclear effects, since
EAS develop via hadronic collisions with nitrogen and oxygen nuclei in the
atmosphere. Thus, measurements in proton-nucleus collisions are also of high
interest (in particular with lighter nuclei in the near future [55]), as
discussed next.

###  0.2.3 Proton-nucleus and nucleus-nucleus collisions

The menu of heavy ion physics studies in the very forward region is very rich
and mostly theoretically and experimentally unexplored [14]. The original
motivation for the CASTOR calorimeter construction was the search for new
phenomena in HI collisions [56, 1], such as strangelets [57] and disoriented
chiral condensates (DCCs) [58]. The exotic Centauro events observed in CR
collisions in the upper atmosphere [10, 11] have been interpreted in terms of
deconfined quark matter in the forward fragmentation region [9], where the net
baryon number (baryochemical potential) is very high due to nuclear
transparency. Strangelets require a similar environment for strangeness
distillation. The DCCs are theoretical states of low and can therefore be best
found in the forward region. The CASTOR calorimeter occupies the peak of the
net baryon distribution in HI collisions and is thus well suited for all these
searches. To identify irregular longitudinal shower developments in the
calorimeter, CASTOR is equipped with a 14-fold longitudinal segmentation.

Besides its potential for the discovery of exotic phenomena, CASTOR is
particularly well suited to distinguish hadronic, photon-nucleus, and purely
electromagnetic (photon-photon) processes in HI collisions [59]. Because of
the large charge of the colliding nuclei, photons are directly involved as an
exchange particle in a significant fraction of the ion-ion interactions. The
absence of activity in CASTOR leading to large rapidity gaps, spanning over an
increasingly larger fraction of the central detectors, can be combined with
signals from the zero-degree calorimeters, to separate hadronic and
electromagnetic interactions. This is a powerful tool in particular to study
photon-pomeron scattering, as for example, in exclusive vector meson
production that is sensitive to the nuclear parton distribution functions
[25]. As aforementioned, the distribution of energy emitted in the forward
direction in Pb and PbPb collisions provides constraints on CR physics models
[26, 24]. The energy transport in the forward phase space [60], as well as
forward jets in Pb collisions [23], can be used to study saturation effects in
the nuclear parton densities. Last but not least, the fine segmentation of the
calorimeter allows the measurement of elliptic flow in HI collisions, a signal
of final-state parton collective effects, far away in rapidity from the hard
scattering [14].

##  0.3 Detector design

Figure 4: Schematics of one CASTOR readout module, illustrating the signal
production mechanism. Each module consists of five quartz and five tungsten
absorber plates, an air-core light guide, and a PMT photosensor. Incident
particles develop a shower in the tungsten absorber, and Cherenkov light is
produced in the interleaved quartz plates. Total internal reflection
transports very efficiently the Cherenkov photons through the quartz and the
air light-guide to the PMT. One tower consists of 14 such modules. Note that
six tungsten plates are visible here since the air light-guides are attached
on top of the tungsten plates between neighboring modules.

The CASTOR detector is a Cherenkov sampling calorimeter constructed from two
half-cylinders with an outer radius of 40cm and a length of 160cm, placed
around the beam pipe at a distance of 14.4m in the negative $z$ direction from
the CMS interaction point. The acceptance of the calorimeter is
$-6.6<\eta<-5.2$, and $2\pi$ in azimuth $\phi$. The whole calorimeter is made
of nonmagnetic materials, where the bulk of the mass is tungsten used as
absorber, and the active material is quartz in which Cherenkov photons are
produced. The integration in CMS is described in Ref. [8].

The CASTOR calorimeter has an electromagnetic section of 20 radiation lengths
($20\mathrm{X}_{0}$) and a total depth of 10 interaction lengths
($10\lambda_{\mathrm{I}}$). The segmentation is 16-fold in $\phi$ and 14-fold
in depth, resulting in 224 individual channels. The sum of all channels in one
$\phi$-segment form one calorimeter tower that is, thus, constructed from 14
layers in depth, which are called modules. The first two modules of a tower
correspond to the electromagnetic section, where the tungsten (quartz) plates
have a thickness of 5(2), while the remaining 12 modules form the hadronic
section, where the thickness of tungsten (quartz) plates is 10(4). Thus, the
material depth of a hadronic module is twice that of the electromagnetic one.

Figure 5: Observed loss of sensitivity of the CASTOR photocathodes, attributed
to the aging of the borosilicate PMT windows, during the 2011 high-intensity
proton-proton LHC operation. The relative loss of the signal (normalized to
its original strength) is plotted as a function of integrated luminosity. The
uncertainties indicate the RMS of the respective 48 PMTs involved in the
measurement (3 modules times 16 towers).

The schematics of one readout module is depicted in Fig. 4. The signal is
generated in radiation-hard quartz plates in which Cherenkov photons are
produced. Interleaved with the quartz plates are tungsten plates as absorber
material to generate and contain the electromagnetic and hadronic particle
showers. The quartz plates are tilted at 45∘ to coincide with the Cherenkov
light emission angle in quartz, and to obtain the best photon transport
efficiency. The photosensors are mechanically coupled to the body of the
calorimeter, and photons are transported from the quartz to the photocathodes
via air-core light guides. Five tungsten and five quartz plates are grouped
together and read out by a single PMT to form one readout module. The inner
surface of the light guides is covered with Dupont polyester film reflector
coated with AlO and a reflection enhancing SiO2+TiO2 dielectric layer stack,
which is both very hard against exposure to radiation and an extremely good
specular reflector of ultraviolet light [61]. The reflectivity of the foil has
a low-wavelength cutoff of 400nm that suppresses the wavelength region where
the Cherenkov yield from quartz becomes significantly dependent on radiation
exposure.

Because of severe space restrictions, as well as stringent demands on
radiation hardness and magnetic field resistance, the photosensors must meet
special requirements. Photomultiplier tubes with fine-mesh dynodes were
selected after extensive test-beam studies [3, 4, 7]. The original set of
Hamamatsu R5505 [62] photosensors was provided from the decommissioned H1
calorimeter SpaCal [63] at the DESY HERA collider. However, their photocathode
window, made of borosilicate glass, was observed to degrade noticeably under
exposure to intense radiation during the higher luminosity LHC data-taking
period of 2011, as shown in Fig. 5. To prepare for the LHC Run 2 and data
taking at 13, these original PMTs were replaced by R7494 PMTs from Hamamatsu
during 2012. These new PMTs have a fused silica entrance window, which is very
radiation tolerant. The PMTs are driven by a passive base. The last dynode
with the largest current is powered by a dedicated low-voltage power supply of
typically 100V. The rest of the dynodes and the cathode are powered by a high-
voltage (HV) supply in the range 800 to 1800V, depending on the physics
measurement. The separation of the last dynode from the HV supply allows the
safe powering of four PMTs by a single HV channel. The response of all new and
exchanged PMTs has been characterized in a dedicated external calibration
setup as a function of the HV for several light-emitting diode (LED)
wavelengths.

Figure 6: Impact of the CMS magnet on the signals observed in the CASTOR
calorimeter in the $(z,\phi)$ plane for 2010 minimum-bias (left) and 2016 LED
pulser (right) data. The numerical values at each bin correspond to the ratio
of signals measured at 3.8T over those at 0T. The channels in gray have
normalized signals below 0.2, or are excluded for technical reasons.

An in-situ monitoring system is available for the evaluation of the
performance of all channels of the detector at all times during data taking.
This is done using an LED pulser system that can illuminate the entrance
window of the PMTs when CASTOR is installed inside CMS. The light pulse of
wavelength 470nm and pulse duration 20ns is generated by a dedicated pulser in
the electronics crate and is transported through an optical fiber to the
calorimeter, where it is distributed via optical splitters to all 224
channels. The fibers are positioned so that they illuminate roughly the entire
photocathode area. The LED signals are used to study the response of the
electronics while there are no collisions at the IP. In this way, dead
channels are identified, and the PMT gain is measured. Gain correction factors
are obtained for various high-voltage operation modes, as well as magnetic
field environment situations. Since the LED signals are neither absolutely
calibrated, nor are identical during different installation periods, they are
mainly useful for characterizing the time-dependence and stability of single
channels. However, assuming small PMT gain variances, the gain $g$ can be
estimated approximately from the signal $S$ and variance $\sigma^{2}_{S}$,
according to $g\approx\sigma^{2}_{S}/S$ (Section 0.5.2).

The PMTs are read out by fast charge integrating circuits (QIEs) [64] in time
samples of 25ns duration. Before digitization, the analog signals are stored
in a cyclic buffer consisting of four capacitors. This is also relevant for
the calibration and event reconstruction, since each capacitor has slightly
different properties. One recorded event consists of ten (before 2012) or six
(after 2012) time samples. The pulse shape of CASTOR is distributed over more
than one time slice, which is essentially a consequence of the dispersion in
the long cables from the PMTs to the digitizers. For this reason, CASTOR is
not optimized to operate at LHC bunch spacings smaller than 50ns (two time
slices). CASTOR only recorded data with bunch spacings of 50ns, with typically
separations being much larger. Typical running conditions, , during HI runs,
have much larger bunch spacings. However, the finite pulse width is exploited
during the reconstruction to retrieve the signal size for events where the
signal in one of the time slices is saturated, as explained below.

While the Hamamatsu fine-mesh PMTs are very tolerant to ambient magnetic
fields of the magnitude observed in the vicinity of CASTOR, there is a well
known dependence of the PMT performance on the relative field direction with
respect to the PMT axis. Unfortunately, because of the massive shielding
around the calorimeter, the direction of the magnetic field varies
considerably. This has, for some channels, a very strong and complex impact on
the PMTs, as shown in Fig. 6. The left panel illustrates the impact of the
field on the PMT response measured with data in 2010, and the right plot is
the result from LED data taken in 2016. Some general features, as the
insensitivity in the range of the modules 6–9, are visible in both years, but
there are also important differences clearly asking for a careful time-
dependent calibration. There were major modifications to CMS between 2010 and
2018, , the installation of an additional 125-ton yoke endcap shielding disc.
Details of the shieldings directly around CASTOR (rotating and collar shields)
were also changed, and finally the exact positioning and geometry of CASTOR is
slightly different in each installation period. For data taking with the
nominal CMS magnetic field, the recordings by most channels in modules 7 and 8
are significantly less useful, and modules 6, 9 and 10 are also compromised.
Furthermore, in 2016 there appears to be a general larger impact on the first
towers (numbers 1 to 5), which are located in the top part of the calorimeter,
where displacements of the PMTs caused by the ramp-up of the CMS magnet are
the largest. These observations clearly show the importance of properly
calibrating the CASTOR channels during physics data taking with a stable
magnetic field.

The insensitivity in the central parts of CASTOR, induced by the magnetic
field channeling through the massive radiation shielding of CMS, was
discovered after the first data taking in 2009, and could not be mitigated
without a major redesign of the shielding and/or the detector. However, the
impact on almost all typical physics applications remains limited: the front
modules 1 to 5 are sufficient to detect $\approx$100% of the electromagnetic
showers, as well as $\approx$75% of the hadronic showers in the calorimeter.
Furthermore, also the tails of hadronic showers (and through-going muons) are
seen in the back modules 10 to 14.

Figure 7: Validation of the electron/gamma (EG) tag (left) and the hadron veto
(right) triggers in Pb collisions at $\sqrtsNN=5.02\TeV$. Shown are density
distributions of the number of events with digitized signals (in fC) in pairs
of towers in one octant. A trigger is issued for each pair of towers when the
logical condition “$\text{EG tag}\wedge\text{Hadron veto}$” is met. The number
of events that (do not) produce a trigger signal are in blue (red). At very
small signals, one can see additional quantization effects from the nonlinear
behavior of the QIE digitizers.

##  0.4 Triggers and operation

Overview of the running periods of the CASTOR detector, with indication of the
year, colliding system, nucleon-nucleon center-of-mass energy, and the
triggers provided to the CMS L1 global trigger system.  Year $\sqrtsNN$
Colliding system CASTOR trigger(s) 2009 0.9 proton-proton 2010 0.9 proton-
proton halo muon 2.76 proton-proton halo muon 7 proton-proton halo muon 2.76
lead-lead halo muon 2011 7 proton-proton halo muon 2.76 lead-lead halo muon
2013 5.02 proton-lead halo muon & e.m. cluster 2.76 proton-proton halo muon &
e.m. cluster 2015 13 proton-proton halo muon & jet 5.02 proton-proton halo
muon & jet 5.02 lead-lead halo muon & jet 2016 5.02 proton-lead halo muon &
jet 8.16 proton-lead halo muon & jet 2018 5.02 lead-lead halo muon & jet

Together with the data recorded during the various run periods, CASTOR
delivered signals to the CMS level-1 (L1) trigger system [65]. These triggers,
listed in Table 0.4, were able to calibrate the detector (halo muon trigger),
as well as to carry out dedicated physics analyses (jet trigger,
electromagnetic (e.m.) cluster trigger). Depending on the physics goals, the
triggers varied during the various running periods. During all listed years,
CASTOR recorded a total luminosity of about 5of data with a large variety of
colliding systems and center-of-mass energies. These samples have been partly
analysed for various physics measurements, and further studies are still
ongoing or planned in the future with the collected data [27, 28].

The CASTOR electronics chain provides four simultaneous hardware trigger
outputs using the standard CMS hadron calorimeter trigger readout (HTR)
boards. There are eight HTR boards; each analyzing the data of one octant (two
towers) of the calorimeter. Since one HTR board can handle 24 front-end
channels, the last two modules per tower are not included in the trigger
system. The output of all octants is further processed in the “trigger timing
control” (TTC) board to form four trigger output signals for the whole CASTOR
calorimeter. These are sent to the CMS L1 global trigger system.

Figure 8: Detector-level distribution of isolated electron candidates recorded
in proton-lead collisions in 2013 with a dedicated trigger [66]. It is a
unique feature that the of these electrons can be precisely determined since
the angles of their associated tracks are measured with the TOTEM T2 tracker.
The uncertainties are purely statistical.

In terms of operation, the CASTOR detector was installed in CMS prior to the
first LHC beams and took data continuously during years 2009–2011. Over this
period, collisions were recorded at ${\sqrt{s}}=0.9$, 2.76, and 7. The CASTOR
calorimeter was removed at the end of 2011, before the high-intensity LHC
operations in 2012, because data recorded at high luminosities are
significantly less useful for physics analyses since the calorimeter is unable
to distinguish particles originating from different pileup vertices because of
its location, geometry, timing capabilities, and granularity. The CASTOR
detector was then reinstalled in 2013 for the Pb run at $\sqrtsNN=5.02\TeV$,
and for the run at ${\sqrt{s}}=2.76\TeV$. During LHC Run 2, CASTOR was never
installed in high-luminosity data taking to avoid needless radiation damage,
but it was installed in all HI runs and in a few special low-luminosity runs.
In 2015, after the first long shutdown of the LHC, the detector was operated
during the low-luminosity phase of the first weeks of collisions at 13and
during the PbPb run at the end of the year. In 2016, the Pb runs at
$\sqrtsNN=5.02$ and 8.16were recorded, as well as the PbPb runs at
$\sqrtsNN=5.02\TeV$ in 2018. A detailed summary of all running periods is
shown in Table 0.4.

In 2013, the CASTOR calorimeter had an active trigger for isolated
electron/photon objects. The calorimeter trigger was combined during part of
the data taking with a TOTEM T2 tracker low-multiplicity tag at the L1
trigger. The resulting data set contains a very large and clean sample of
isolated electron candidates. The validation of the calorimeter trigger for
each octant is shown in Fig. 7. Since the trigger is generated from the
combination of two adjacent towers of one octant, the trigger validation
corresponds to a two-dimensional analysis. The trigger requires a single tower
in one octant in which the first two (electromagnetic) modules are above a
charge threshold of 40fC (corresponding to $\approx$0.6energy), and applies an
additional veto on any hadronic activity in the modules at depths 4 to 6 in
this octant. No more than 100fC (corresponding to $\approx$1.6) of hadronic
energy is allowed.

The data recorded with this trigger during the Pb run leads to the
distribution of candidate electrons shown in Fig. 8. Events are only shown if
they have an isolated electromagnetic energy cluster in CASTOR that was
associated to one isolated track in T2 during offline analysis. Thus, the
energy of the electron candidate is measured with CASTOR and its trajectory
with T2 (no acceptance corrections are applied in the plot). This combination
of the two detectors provides a unique case where individual charged particles
can be fully reconstructed in the very forward direction at the LHC. It is
interesting to note that the lower threshold for the observation of electron
candidates, $\pt\approx 100\MeV$, is similar to the performance of the CMS
pixel tracking detector at central pseudorapidities [67].

Figure 9: Performance of the CASTOR jet trigger in collisions during the LHC
Run 2. Left: Trigger efficiency turn-on curve in data and simulations
generated with the 8 [68] with the MBR tune [69]. Right: Number of jets as a
function of ${E_{\text{jet}}}$ collected with the CMS zero-bias and CASTOR jet
triggers. In both figures the uncertainties are statistical only.

During the 2015, 2016, and 2018 runs, CASTOR provided a jet trigger with the
simple requirement of at least one tower with energy above a given threshold.
The jet trigger has a well defined turn-on curve, high efficiency and is
sufficiently well reproduced by the detector simulations as shown in Fig. 9
(left). The slightly slower rise of the turn-on seen below 2in the data with
respect to the simulations is a consequence of the nominal MC reconstructed
jet resolution that is better than the actual one, since not all detector
misalignment and miscalibration uncertainties are propagated into the
simulation. The trigger is used only in the range of ${E_{\text{jet}}}$ above
2, where the efficiency reaches a plateau in both data and simulations. This
assures that the efficiency corrections remain minimal and that the
predictions of the detector simulation in this region are well compatible with
the data. In Fig. 9 (right), the impact on the size of the event sample is
illustrated by comparing the jet spectra collected with the CMS zero-bias and
CASTOR jet triggers.

##  0.5 Event reconstruction and calibration

The fundamental quantity measured by the CASTOR calorimeter is the charge per
channel. Here, the procedure for obtaining a measurement of the energy per
channel from the signals digitized by the detector is described. One signal
pulse is distributed over several time slices (TSs) of 25ns duration, as shown
in Fig. 10 (left). The timing is adjusted so that the time sample with the
maximum energy sits at the TS number 4. The channel energy is estimated from
the sum of the energies in time samples 4 and 5\. Although the fourth time
sample saturates at around 400of energy per channel, for the high-voltage
settings applied during collisions, particle energies of up to almost a factor
of 10 higher can be reconstructed from the energy in the TS number 5, since
the ratio between the energies in these time slices is constant at high
channel energies, as shown in Fig. 10 (right). Saturation is not a common
problem in CASTOR, but can affect particularly interesting events, such as
those with high-energy electrons, high-energy jets, or from central PbPb
collisions. Considering the normal HV settings during PbPb collisions, where
the PMT gain is reduced by a factor of $\approx$20 with respect to , and using
the fifth time slice to estimate the energy when saturation occurs, signals
can be measured up to a maximum of around $400\GeV\times 10\times 20=80\TeV$
per single channel.

Figure 10: Left: Typical example of a pulse shape in CASTOR showing the energy
(in nC) distributed over several time slices, in data recorded in March 2011
using minimum-bias collisions at ${\sqrt{s}}=2.76\TeV$ (the shaded area of the
pulse is used to derive the signal energy in the event reconstruction). Right:
Ratio of the second-largest (TS5) to largest (TS4) signal time bin for one
typical CASTOR channel. The small scale structure in this ratio of neighboring
bins apparent for TS4+TS5 larger than $\approx$100is a feature of the QIE
digitizer. Saturation is visible at very high signals, but before saturation
sets in, the signal ratio has a stable region (in red) that is used to
estimate the channel signal at higher energies.

The fourteen channels belonging to one $\phi$-segment of CASTOR are grouped
into one tower during offline event reconstruction. The energy of a tower is
the sum of its two electromagnetic and twelve hadronic channels. Channels are
nonactive only if they have been flagged as bad channels during detector
commissioning. The signals from the towers are zero suppressed, to remove
noise, using a typical threshold of ($650\sqrt{{N_{\text{channel}}}})\MeV$,
where ${N_{\text{channel}}}$ is the number of active channels in this tower.
This threshold has shown to yield very consistent results for data and
simulations in many different data analysis applications.

Energy-momentum vectors are constructed from the towers using the measured
energies and the geometry of the detector. Those four-vectors are then
clustered into jet objects using the anti-clustering algorithm [70] with a
distance parameter of $R=0.5$, a value optimized taking into account the given
detector segmentation and matching the typical size of jets at very forward
rapidities [23]. The distribution of azimuthal separations of the towers to
the reconstructed jet axis for data and MC simulations, generated with 8 (MBR
and CUETP8M1 [71] tunes) and Epos-LHC [72] models, are compared in Fig. 11.

Figure 11: Average CASTOR tower energies, as a function of their azimuthal
separation from the reconstructed jet axis (reconstructed with the anti-
algorithm with $R=0.5$) measured in data (squares) and in minimum-bias 8 and
Epos-LHC MC simulations (histograms) in collisions at ${\sqrt{s}}=13\TeV$.

###  0.5.1 Noise and baseline

The baseline (pedestal) and noise levels are estimated for each channel by
analyzing a large set of events recorded in-situ in the absence of beams in
the LHC. These data samples typically comprise around one million events, and
take less than one hour to be collected. The charge spectrum for one of the
noisiest analog buffer capacitors is shown in Fig. 12. This noise spectrum is
depicted for HV off (blue), 1500V (magenta), and 1800V (black). During data
taking, the high voltage typically corresponds to 1500V where, even for this
noisy capacitor, the random noise probability per event is much below 1% for
signals in the high tail of the charge distribution ($\approx$20fC in this
example).

The measurement without high voltage represents the purely electronic noise
from the cables, the amplification, and the digitization chain. After applying
a voltage to the PMT, additional noise components start to contribute. The
first shoulder in Fig. 12 corresponds to the thermal emission of single
electrons from the PMT cathode. For CASTOR’s fine-mesh PMTs, single
photoelectrons produce a smooth spectrum instead of a clear peak. Signals of
ion feedback (also known as afterpulses) start to become visible at very high
voltages, but with a tiny noise rate of $<10^{-4}$ per event. The maximum
noise signal observed here is $\approx$2000fC corresponding to about 10 to
100of energy depending on the gain setting.

Figure 12: Charge spectrum for the noisiest capacitor of a typical CASTOR
channel (tower 12, module 12) for various cathode/last dynode voltage settings
(1800/100, 1500/80, and 0/0V). A Gaussian fit to the data recorded with 0V is
also shown.

The mean of the Gaussian fit of the charge distribution for zero HV is used as
an estimate of the pedestal value, and subtracted from all subsequent
measurements. The width of the Gaussian fit is used to monitor the response of
a given channel. In Fig. 13 (upper left), the distribution of the fitted
charge value of the pedestal is shown, which is 11fC on average. The fitted
width of the Gaussian is displayed in the upper right plot, and the root-mean-
square (RMS) value of the data in the lower left plot. On average, the RMS is
larger by 10% than the width obtained from the Gaussian fit, because of the
aforementioned rare non-Gaussian tails. Comparing the RMS to the Gaussian
width has proven to be a useful method for identifying noisy or malfunctioning
channels. In the lower right plot, the distribution of the difference between
the RMS and the Gaussian width per channel is displayed. A channel with a
difference in excess of 2fC is considered noisy, and is subject to additional
inspection. Channels that cannot be reliably reconstructed are masked in the
online trigger and excluded from physics analyses. The number of such channels
is kept to a minimum by repairing PMTs, cables, and HV supplies, where needed.
This was the case, , for eleven CASTOR channels in 2015, of which none were
located in the first three modules, where most of the energy is deposited.

###  0.5.2 Gain correction factors

The CASTOR high-voltage settings are adapted to the varying LHC operation
configurations, depending on the instantaneous luminosity and bunch spacing,
and physics goals, with the heavy ion running being a particular case. In
central lead-lead collisions, the energy that is deposited within the CASTOR
acceptance can be enormous—the total energy stored in a Pb nucleus at the LHC
can be up to $5.5\TeV\times 208\approx 1\PeV$ ($\approx$0.2mJ). Since the PMTs
are always operated at gains in the range with an optimal signal-to-noise
ratio, the difference in gain settings between and PbPb collisions is about a
factor of twenty. Furthermore, in physics data taking, the PMTs close to the
shower maximum are configured with lower gains compared to the ones measuring
the shower tails in the back of the calorimeter. On the other hand, the
signals from LHC beam halo muons are close to the noise levels and very high
amplification can be advantageous.

Figure 13: Distributions of the baseline (upper left) and variances (width
$\sigma$, upper right; RMS, lower left; and $\Delta\sigma=$RMS-$\sigma$, lower
right) of the CASTOR pedestals, with 1500V supplied to the PMTs, from data
recorded in 2015\.

Correction factors are used to adjust the signals for these different high-
voltage settings. Three different methods are used, and they are cross-checked
against each other. In this section, we compare and contrast the different
methods and their results. These methods have different performances depending
on the presence of magnetic fields. Thus, we compare the results obtained with
the CMS magnet on and off.

One option is to rely on the PMT characterization measurements performed in
the laboratory prior to their installation in the experiment. In Fig. 14
(left), the measured dependence of the gain on the high-voltage value is
depicted for one channel. Very precise data exist for almost all PMTs.
However, potential aging effects are not included. The use of the results is
also limited since they are not performed under exact data-taking conditions,
and this restricts its usage to data sets recorded without magnetic field.

A second option is to use LHC collision data to perform a direct in-situ
measurement of the dependence of the PMT gain on the high voltage (Fig. 14,
right). This is restricted to special situations where stable collisions are
recorded that are not useful for most physics analyses. The ideal scenario is
to use higher-pileup data that generate very strong signals and, at the same
time, are not used for physics analyses with CASTOR. In 2016, a dedicated
voltage scan was performed in such optimal conditions. The average pileup in
this data sample was $\approx$5, and a CMS minimum-bias trigger that used
coincidences of towers in the hadron-forward (HF) calorimeters within
$3.15<\abs{\eta}<4.90$ was employed to select the events. To account for the
slowly changing LHC beam intensity, the CASTOR data were also normalized by
the measured average energy in the HF detector.

Figure 14: Response of the CASTOR PMT in tower 1, module 3 versus the high-
voltage setting prior to data taking exposed to LED pulses (left) and in
physics events (right). The (statistical) uncertainties are too small to be
seen. For the latter, no absolute gain measurement was performed, and the
ratio with respect to the gain at the voltage of 950V is shown. The model fit
is of the form $g(V)={p_{0}}V^{p_{1}}$ with two free parameters $p_{0}$ and
$p_{1}$.

Figure 15: Distributions of the weighed difference of gains, $\Delta
g=(g_{1}-g_{2})/\sqrt{\sigma_{g_{1}}^{2}+\sigma_{g_{2}}^{2}}$, for two sets of
correction factors obtained with two different methods, fitted to a Gaussian
function. The uncertainties are statistical only. Left: Comparison of LED and
PMT characterization methods at $B=0\unit{T}$ for muon halo and proton physics
HV settings in 2016. Right: Comparison of LED and physics events methods for
muon halo and heavy ion physics HV settings at $B=3.8\unit{T}$. Note that in
both distributions the number of entries differs from 224 since only a subset
of all channels has valid data available in both measurements. The boxes show
the parameters of the red Gaussian fit.

Finally, a third method relies on a statistical gain estimate performed using
LED pulses after the PMTs are installed in CMS. Such a statistical method
evaluates the fluctuations observed in each channel as a response to the
illumination of the cathode with LED pulses. The signal seen by a PMT for a
LED pulse, ${S_{\text{LED}}}$, is related to the PMT gain, $g$, and the number
of photoelectrons, ${N_{\text{p.e.}}}$, according to
${S_{\text{LED}}}=g{N_{\text{p.e.}}}$. Thus, the relative variance of
${S_{\text{LED}}}$ consists of a contribution from the gain fluctuations
($\sigma_{g}$) and from the Poisson fluctuations of the conversion of photons
to photoelectrons ($\sigma_{{N_{\text{p.e.}}}}$), and is given by

$\left(\frac{\sigma_{{S_{\text{LED}}}}}{{S_{\text{LED}}}}\right)^{2}=\left(\frac{\sigma_{{N_{\text{p.e.}}}}}{{N_{\text{p.e.}}}}\right)^{2}+\left(\frac{\sigma_{g}}{g}\right)^{2}\approx\left(\frac{\sigma_{{N_{\text{p.e.}}}}}{{N_{\text{p.e.}}}}\right)^{2}=\frac{1}{{N_{\text{p.e.}}}}=\frac{g}{{S_{\text{LED}}}}\,,$
(1)

where it is assumed that the relative fluctuations of $g$ are much smaller
than the relative conversion fluctuations, and all effects of bandwidth
limitations on the fluctuations are neglected. The gain is then estimated
using $g=\sigma_{{S_{\text{LED}}}}^{2}/{S_{\text{LED}}}$.

For a final set of high-precision gain correction factors, usually the results
of at least two of the methods described above are combined. In Fig. 15
(left), the distribution of weighted differences between the gain correction
factors obtained with the PMT characterization and LED methods during
operation at $B=0\unit{T}$ is displayed. The width of this pull distribution
is consistent with unity only at the level of 2 standard deviations
($2\,\sigma$). The corresponding distribution for heavy ion collisions at
$B=3.8\unit{T}$ is shown in Fig. 15 (right) comparing correction factors
obtained with physics events and LED measurements. This latter pull
distribution reveals a better statistical consistency between the two methods.
From those results, it seems that the uncertainties obtained during the PMT
characterization setup are slightly underestimated, although not in a
significant way. Furthermore, since all these measurements have in general
very high statistical precision, of the order of 1%, the absolute impact on
calibration and data analyses are negligible. We conclude that the statistical
method using LED data, which has the broadest range of applicability, is well
suited for gain correction factors whether or not a magnetic field is present.

###  0.5.3 Channel-by-channel intercalibration

The relative channel-by-channel intercalibration is carried out with recorded
beam halo muons originating from the LHC beam. These muons traverse the CASTOR
towers longitudinally, and lose only about 10of energy traversing the
calorimeter. For the high-energy muons of the beam halo, with energies in the
hundreds of or above, such energy loss is to a very good approximation
insignificant. Radiative muon energy losses above the critical energy are an
important contribution at such high energies, but on average they contribute
equally to all channels of the calorimeter. Almost all halo muons used for the
calibration were taken during the period when the LHC is filled with new
protons at a constant beam energy of 450. Halo muons are an excellent and
stable probe for the relative calibration over the entire lifetime of CASTOR.

Figure 16: Schematic front view of CASTOR indicating its 16 towers. In this
example event a muon candidate is identified in the blue tower. The
calorimeter noise is then updated from the same event in the red towers.

Beam halo muons are recorded during regular CMS data taking in the periods
when the LHC is in interfill and circulating beam modes. A dedicated CASTOR
hardware trigger is activated for these runs to record events that are sent to
the L1 CMS trigger. For this trigger, each tower is split into four groups of
three consecutive modules. Because of the hardware design of the trigger, only
the front 12 channels in each tower are included in the trigger logic,
excluding the two channels in the rear. This may lead to a small bias for
these last rear channels that must be addressed by the subsequent event
reconstruction and data analysis. The trigger requires one channel (two
channels in 2015/2016) above noise level in at least three of these groups,
and that there be no other channel with energy above the noise level anywhere
else in the calorimeter. The typical trigger rate of this configuration is
around 10 to 100Hz, depending on the number of protons in the LHC. The
separation of signal from noise during triggering is challenging since the
signal corresponds to just about one observed photoelectron per channel,
requiring very precise channel-by-channel estimates of the noise level and
baseline. To reliably detect muons, the high voltage is typically increased to
1800V during the dedicated halo muon runs. However, during LHC Run 2, muon
data were partly taken at the specific physics HV setting for cross-checks,
and to avoid any additional corrections due to changing PMT gains and detector
noise.

Figure 17: Visualization of the main parameters of the iterative offline muon
event selection process: Average muon signal (black circles), average noise
(turquoise squares), number of muon candidates (blue triangles), and number of
bad channels (red inverted triangles) versus number of iterations. The
progress is mainly steered by first adjusting the number of bad (noisy)
channels. The muon and noise responses quickly converge to stable levels. In
the last four iterations, global parameters of the procedure are fine tuned.
The shown uncertainties are statistical only, however, they are highly
correlated in each graph.

A detailed offline event analysis is required to prepare the collected data
for intercalibration. For this purpose, for each of the 224 channels, the no-
beam noise thresholds must be accurately determined. Using this information,
an offline zero suppression of the data is applied, using a threshold of
$2\,\sigma$ above the noise. A high-quality exclusive muon candidate is
defined as a single tower with at least 4 non-zero-suppressed channels
(${n_{\mu,\text{min}}}$) in an event with at most 6 additional non-zero-
suppressed channels (${n_{\text{extra,max}}}$) in the rest of CASTOR.
Furthermore, the tower containing the muon candidate must have at least two
non-zero-suppressed channels in three of these four longitudinal regions:
modules 1 to 3, modules 4 to 7, modules 8 to 11, and modules 12 to 14. This
requirement is sensitive to the penetrating nature of muons and rejects low-
energy pions that do not reach the back of the calorimeter.

Since the muon event selection uses energies very close to the pedestals, a
precise determination of the noise levels is of paramount importance. We found
that only an iterative procedure, where the noise is measured from the same
events as those from the actual muon candidates, provides the required
accuracy. For this purpose, when a muon candidate is found, the calorimeter
noise levels for the channels in the five towers most distant from the muon
candidate (opposite in the transverse direction) are updated. Thus, the data
are used simultaneously to measure the muon response and the calorimeter noise
levels. This procedure is illustrated in Fig. 16. The threshold levels for
zero suppression are determined from Gaussian fits to these data. If no
statistically significant improvement of the noise levels can be determined in
any of the channels of the calorimeter, the procedure has converged and is
stopped. Convergence typically occurs after 10 to 20 iterations, as
demonstrated in Fig. 17 for data recorded in June 2015. For this run, the
noise levels quickly converge after about five iterations. It is crucial to
closely supervise this process and to identify noisy channels that have a big
impact on the selection process. The list of bad channels is revised by
excluding the most noisy channels in several steps until no further impact on
the muon response is found. In the last four points shown in Fig. 17, the
average noise and the number of muon candidates converges to slightly
different values, because of a fine tuning of the parameters
${n_{\mu,\text{min}}}$ and ${n_{\text{extra,max}}}$. The fine tuning is
performed considering all data from 2011 to 2016 simultaneously. This yields a
slightly better global noise level, increased muon data samples, and no
significant change in the measured muon response.

In Fig. 18, an example of the result of the online and offline muon selections
can be seen. These data were taken with the CMS magnet at 3.8T, which is why
many of the modules at depth 7 to 9 yield less signal. In Fig. 19, the signal
and noise spectrum after offline selection are displayed for a typical channel
together with a prediction based on a simplified model of a fine-mesh PMT,
tuned to the data. The model assumes constant amplification per dynode
including Poissonian fluctuations. For the used fine-mesh PMT, it is important
to consider the probability of electrons missing a particular dynode,
$p_{\textrm{miss}}$. This leads to a nonideal low-energy resolution that is of
concern to understand the recorded muon data: while the muons are clearly seen
above noise level, there is no obvious muon peak produced.

Figure 18: Example of a reconstructed halo muon event in the tower ($\phi$)
vs. module ($z$) plane recorded during the proton-lead run period in 2016;
channels are zero suppressed at the $2\,\sigma$ noise level.  Figure 19:
Signal spectrum for a typical CASTOR channel after an offline isolated muon
event selection. The data were recorded in June 2015 with proton beams and CMS
magnet at 0T. The dedicated muon high-voltage setting is used. The
uncertainties on the measured data are statistical only. The overlaid noise
distribution is measured from noncolliding bunch data. The model line
corresponds to a simplified mesh-type PMT with 15 dynodes,
amplification/dynode of 2.65, and dynode-miss probability of 0.21, for an
average number of photoelectrons $\langle{N_{\text{p.e.}}}\rangle=0.58$.
Figure 20: Relative muon response averaged over all channels versus time.
Shown is the average of the channel-by-channel ratios relative to the 2013
data. The vertical bars indicate the RMS of the distributions. All of the
distributions are found to be consistent with a Gaussian shape.

Since there is a large overlap of muon signal with noise levels, the average
recorded signal is not an ideal estimator of the muon response. We found that
the RMS is a much better estimator instead. Since there are very rare spurious
high-signal noise hits also present in the recorded muon data, we exclude the
2% highest energy deposits for each channel. In most channels this has no
visible effect, but it helps to remove abnormal fluctuations in a few
channels. Thus, the muon response is defined to be the truncated RMS per
channel after rejecting the 2% highest energy events.

These muon data provide a very powerful probe of the stability of the
calorimeter over time. The time dependence of the gain for each channel is
studied using the average from muons collected during 2013 (Run 1) as a
reference. This is also of paramount importance for maintaining a stable
energy scale over the time span of the various runs, from 2011 to 2018. In
Fig. 20, the relative change of the muon response during all data-taking
periods is shown. This is done by normalizing the average channel-by-channel
ratio of the muon response to that from the 2013 data. In the 2011 data, the
loss of sensitivity due to radiation damage is visible. The step between 2011
and 2013 coincides with the upgrade of the calorimeter with new PMTs, which
obviously had a positive effect on the sensitivity. June 2015 is the only
period where the halo muon data were recorded with 0T magnetic field in CMS.
It is particularly interesting to compare the data collected in 2013 with
those in 2016, since they were all recorded with nominal magnetic field and
almost the same (physics) HV settings. The stability is better than 10% over
three years. The change in response is thought to be related to residual
differences in the magnetic field structure, detector aging, geometry, and HV
settings. Contrary to other components of the CMS experiment, the CASTOR
calorimeter did not experience any significant aging effects in Run 2. The
spread of the channel gains can be well described by Gaussian distributions.
The observed differences are corrected with the described calibration
procedure. The response to muons is used as an absolute reference scale. Using
a bootstrapping method (case resampling) to estimate the statistical
uncertainties of the intercalibration constants indicates an uncertainty of
$\approx$15% in each channel.

###  0.5.4 Absolute energy scale

Figure 21: Pseudorapidity dependence of the forward energy flow in collisions
at ${\sqrt{s}}=7\TeV$ measured in HF [15] and CASTOR, compared to MC
simulations extrapolated to CASTOR. The uncertainties on the data are
dominated by systematic effects. For the CASTOR datum the horizontal error bar
indicates the $\eta$ range of the calorimeter.

The initial fundamental calibration of CASTOR was done with test-beam
measurements at the CERN SPS using electrons, charged pions, and muons of well
defined energies ranging from 10 to 300\. The corresponding results are
published in Ref. [7]. Good energy linearity as well as resolution were found
for electron beams, and the ratio between charged pion and electron responses
at the same particle energy was measured. The latter is a fundamental property
of any calorimeter and is linked to noncompensation of energy losses in
hadronic showers due to nuclear breakup, and production of neutrons, muons,
etc.

However, it is not sufficient to determine the energy scale in the test-beam
environment. The conditions when installed in CMS are quite different from
those of the test-beam measurement. For example, the presence of magnetic
fields changes the PMT gains in a nontrivial way. Correction factors have been
determined for the difference in gain, yielding an estimated energy scale in
Run 1 of 0.016/fC for the proton physics high-voltage settings used in 2010.

An independent and more realistic calibration is performed from collision data
recorded when CASTOR was installed in CMS by cross-calibrating its response to
that of the HF calorimeter ($3.15<\abs{\eta}<4.90$). The pseudorapidity
dependence of the energy deposited in the HF calorimeter in minimum-bias
events (defined as collisions at generator level with at least one stable
particle produced in both HF sides over $3.9<\abs{\eta}<4.4$) studied in Ref.
[15], has been used for this task. In Fig. 21, we show the measured HF data
extrapolated to the CASTOR region together with different MC predictions from
6.424 [73] (D6T [74] and ProQ20 [75] tunes), 8.145 MBR, ++ [76], and commonly
used models in cosmic ray physics: QGSJet01 [77], QGSJetII [78], Sibyll2.1
[79], and Epos-LHC. The 6 D6T tune is derived from charged particle
multiplicities measured by the UA5 experiment at the CERN
S$\Pp\PAp$S-collider. The ProQ20 tune describes the CDF data at
${\sqrt{s}}=0.63$ and 1.8, using LEP results to tune the parton-to-hadron
fragmentation. The 8 generator includes a new MPI model interleaved with
parton showering. In the 8 generator, the treatment of diffraction is improved
compared to 6. The ++ generator uses a different parton fragmentation model
than . All cosmic ray MC models share the underlying Gribov’s Reggeon field
theory framework [80], but different implementations of perturbative parton
scatterings and diffraction [52]. The full set of models chosen provides
thereby a very comprehensive range of predictions for minimum-bias collisions
at the LHC.

The measured $\eta$ dependence of energy deposits in HF is extrapolated to the
CASTOR $\eta$ range. The model predictions are scaled to best reproduce the
measured $\eta$ dependence in HF for this purpose. All model predictions are
calculated with the generator-level definition using the online and offline
event selections defined by the HF measurement: at least one charged particle
on both sides of CMS over $3.9<\abs{\eta}<4.4$, and a minimal lifetime of
particles of c$\tau>10\mm$, excluding muons and neutrinos. The spread of the
model predictions in the CASTOR acceptance provides an estimate of the model
dependence of this extrapolation. The extrapolated average energy deposit in
the CASTOR acceptance found by this procedure is
$E_{{\Pp}{\Pp}}(7\TeV)\equiv\rd E/\rd\eta_{\mathrm{CASTOR}}=665\GeV$ with an
RMS of 60(10%) for the 7minimum-bias proton-proton data. The uncertainties in
the absolute calibration of the HF as well as in the HF generator-level
correction are 10%, and the total uncertainty in the energy estimate in CASTOR
is thus $\Delta(E_{{\Pp}{\Pp}}(7\TeV))=\sqrt{(10\%)^{2}+(10\%)^{2}}\approx
14\%$. The average charge signal recorded in the five frontmost modules for
this minimum-bias data is ${S_{{\Pp}{\Pp}}}(7\TeV)=21\,000\unit{fC}$. Since
only these front modules of the calorimeter are used, about 75% of the energy
of hadronic showers are contained. Furthermore, two channels of these front
modules (module 1 in towers 5 and 6) are not included in the measurement due
to stringent quality criteria on noise. The calorimeter response factor
${C_{{\Pp}{\Pp}}}(7\TeV)={E_{\text{}{gen}}}/{E_{\text{det}}}=1.978$ is
obtained from a -based [81] simulation of CASTOR using minimum-bias events
generated using 6 (tune Z2), where the simulated detector configuration and
conditions were matched to those used during data taking. Here,
${E_{\text{}{gen}}}$ is the total energy from in the acceptance range and
${E_{\text{det}}}$ is the corresponding energy from the simulation. The
calibrated energy ${E_{\text{cal}}}$ measured by CASTOR for any type of
collisions, with either protons and/or lead ions, at any center-of-mass
energy, is then given by

${E_{\text{cal}}}={S_{\text{det}}}\;\frac{E_{{\Pp}{\Pp}}(7\TeV)}{{S_{{\Pp}{\Pp}}}(7\TeV)}\;\frac{C}{{C_{{\Pp}{\Pp}}}(7\TeV)}\;{g_{\text{HV}}}\;,$
(2)

where ${S_{\text{det}}}$ is the measured signal of CASTOR, $C$ is the
corresponding calorimeter response factor, and ${g_{\text{HV}}}$ are the gain
changes due to any difference in high-voltage settings. Regarding the
uncertainties in this assignment: the measurements of ${S_{\text{det}}}$,
${S_{{\Pp}{\Pp}}}(7\TeV)$, and ${g_{\text{HV}}}$ have negligible statistical
uncertainties, the estimation of $E_{{\Pp}{\Pp}}(7\TeV)$ is based on HF and
has a 14% uncertainty. It is important to note that the numerator and
denominator of the $C$/${C_{{\Pp}{\Pp}}}(7\TeV)$ ratio have uncertainties that
are highly correlated. The individual absolute model-dependent uncertainties
of $C$ and ${C_{{\Pp}{\Pp}}}(7\TeV)$ are not relevant, but rather the
uncertainty in the ratio of the two. Since these uncertainties are not
dominant, and their concrete evaluation requires detailed studies for any new
data-taking scenario, we assume that the fractional uncertainty in
${E_{\text{cal}}}$ is equal to that of the fully uncorrelated uncertainties in
$C$. This neglects the existing correlations and, thus, the resulting
uncertainty is slightly conservative. From extensive detector simulation
studies, the CASTOR response $C$ has an uncertainty of $\approx$5% arising
from the noncompensation in the energy response of the detector and the
predicted relative composition of hadrons and electromagnetic particles in
minimum-bias events for all colliding systems at the LHC (combination of
protons and lead nuclei) and different nucleon-nucleon center-of-mass energies
(2.76, 5.02, 7, and 13). The effect of the geometry and alignment of CASTOR
with respect to the LHC luminous region can have an important impact on $C$,
as discussed also in the next section. This is the largest source of
uncertainty for the determination of $C$. For example, the uncertainty due to
imperfections in the detector alignment during the 13proton-proton collisions
in June 2015 is 6.5% (Fig. 22, right). Also the number and location of dead
channels in the calorimeter can, in principle, have a large effect on $C$.
However, once they are incorporated into the simulation of the detector, their
effect is negligible.

Thus, the absolute energy scale in CASTOR is determined by the energy measured
in the HF detector, combined with a model-dependent extrapolation using the
shape of event generator predictions and assumptions on the stability of the
determination of $C$, with a total precision of about
$\sqrt{(14\%)^{2}+(5\%)^{2}+(6.5\%)^{2}}\approx 17\%$. The approach leading to
Eq. (2) can also be used for the absolute calibration factor corresponding to
the scenario of a pure electron beam

$k_{\mathrm{cal,e}}=\frac{E_{{\Pp}{\Pp}}(7\TeV)}{{S_{{\Pp}{\Pp}}}(7\TeV)\;{C_{{\Pp}{\Pp}}}(7\TeV)}=\frac{665\GeV}{21\,000\unit{fC}\;\;1.978}=(0.0160\pm
0.0027)\GeV/\unit{fC},$ (3)

which is consistent with the corresponding test-beam measurement.

Figure 22: Left: Impact of a pseudorapidity shift $\Delta\eta$ (due to a
transverse CASTOR detector displacement $\Delta R$) on the energy response for
simulated collisions at ${\sqrt{s}}=13\TeV$. Right: Distribution of the ratio
between reconstructed and true energy measured in minimum-bias simulations
generated with 8 (CUETP8M1 tune). The average of about 0.6 is due to the
noncompensating properties of CASTOR. The yellow error band indicates the
uncertainty due to the alignment.

##  0.6 Geometry and alignment

Because of the very forward location of CASTOR, even small movements of the
calorimeter can significantly affect its $\eta$ acceptance. A $\Delta R=1\mm$
(10) radial shift affects the inner acceptance of $\eta=-6.6$ by
$\Delta\eta=0.025$ (0.3) and the outer acceptance of $\eta=-5.2$ by
$\Delta\eta=0.01$ (0.07). Since CASTOR is located in a region where the energy
deposition per collision depends strongly on $\eta$, even small changes in the
acceptance can have a large effect on the expected energy deposit (Fig. 22,
left). The position of the calorimeter must be known with a precision of a few
mm for a precise interpretation of the measurements. Fig. 22 (right)
illustrates the impact on the energy response of the measured position
uncertainty during the data taking in June 2015. This leads to uncertainties
for reconstructed energies on hadron level of up to 6.5%.

The precise determination of the location of CASTOR is difficult since the
whole structure of the forward region of CMS moves by several centimeters when
the CMS magnet is ramped up. This also leads to nonnegligible movements of the
otherwise nonmagnetic calorimeter, which are probed with different infrared
(Excelitas VTR24F1H) and potentiometer (Active Sensors CLS1313) positioning
sensors. Such probes are particularly critical to keep a safe distance between
the CASTOR and the fragile beam pipe.

Figure 23: Infrared distance sensor calibration data in the $(x,y)$ plane.
Shown is the measured response during a bidimensional position scan in front
of a beam pipe mockup. The used sensors do not work for very close proximities
to the beam pipe due to self-shadowing effects, however, this is well outside
of the range where the sensors are used in CMS. This effect can be seen in the
calibration data at $x=0$ and close to $y=30\mm$.

The position of CASTOR is measured after final installation, when there is
negligible magnetic field, by a survey team with laser targets to a precision
of $<$1\. Movements of the calorimeter during the magnetic field ramp-up are
observed with the positioning sensors. An absolute position at full magnetic
field is therefore determined with information from the positioning sensors
and the laser measurements.

On the side facing the interaction point, two infrared sensors are mounted on
each half that measure the distance to the beam pipe. Two potentiometer
sensors measure the opening between the two halves of CASTOR at $y=\pm 19\mm$.
On the other side, there are three infrared sensors and three potentiometer
sensors that measure relative $x$, $y$, and $z$ movements. All sensor
measurements are combined in a global goodness-of-fit $\chi^{2}$ minimization
for which the $x$\- and $y$-positions of the two halves are free parameters.
The output voltage of the sensors is calibrated to absolute distances with a
custom setup, which yields an uncertainty of about 1for both sensor types.
This is achieved with an automated procedure on a two-dimensional (2D)
positioning table for the infrared sensors, and by measuring the distance with
the fully contracted potentiometer sensor. In Fig. 23, the measured response
of one infrared sensor in the 2D scan is shown. The surface of the beam pipe
mockup is shown as dashed line. The uncertainties propagated to the position
found with the minimization procedure are lower when the infrared sensors are
pointing perpendicular to the beam pipe. Figure 24 shows the result of this
procedure for the position of CASTOR after a magnet cycle in April 2015. A
position with an uncertainty in $x$ and $y$ of about 2is found.

Figure 24: Illustration of the global fit result of the position of both
CASTOR halves in the $(x,y)$ plane. The beam pipe is indicated as a dashed
circle. The measured distances of the sensors are shown as lines attached to
the round markers that indicate the reconstructed sensor positions. The
potentiometer sensors are not shown here but are also used as constraints for
the alignment.

At the same time, other independent methods to determine the position of the
calorimeter with LHC collision data have been implemented and cross-checked
with the above procedure. Firstly, during 2013 a special trigger was developed
to record single electrons in CASTOR during the Pb data taking. The trigger
made use of the TOTEM T2 tracking station [82] in front of CASTOR (see Fig.
2). The trigger decision requires a very small track multiplicity in T2 and an
isolated electromagnetic cluster in the calorimeter. The offline event
selection requires a single e.m. cluster above 10with all other towers being
below 5in total energy, representing a very strong isolation criterion as well
as a very efficient hadron veto. The analysis of these data yielded a very
accurate picture of the alignment relative to the T2 detector. The measured
geometric correlation of T2 tracks with electromagnetic energy clusters in
CASTOR is shown in Fig. 25 (right). The ring with reduced track density
corresponds to the shadowing induced by the conical beam pipe in front of T2
and CASTOR. The number of background events in this plot becomes visually
enhanced since the background is uniformly distributed, whereas the signal is
highly clustered on top of each of the CASTOR towers. The main source of this
background is calorimeter noise, since the energy threshold to select these
electron events is 10per tower, whereas the calorimeter noise tails contribute
at the level of $<$10-4 per event (Fig. 25, left). It can also be seen that
the acceptance of T2 is slightly smaller than that of CASTOR. The geometric
correlation between track and energy cluster is then used for an alignment fit
of CASTOR. From this alignment fit, we find that both detector halves are
separated by 22, a situation that was not exactly known prior to this
measurement.

Furthermore, minimum-bias collision data at ${\sqrt{s}}=0.9$, 2.76, and 7have
been compared to corresponding detailed detector simulations. The energy
distributions in depth and azimuth depend on the position and tilts of the two
calorimeter halves. The position of CASTOR in simulations was therefore
optimized to best describe the distribution found in data. There is agreement
within the experimental uncertainties among the different approaches.

Figure 25: Left: Noise distribution of e.m. clusters in data used for the
alignment of CASTOR with TOTEM T2 tracks, measured with a random trigger in Pb
events with no collisions [66]. The RMS of the distribution is $<$1; the
uncertainties are statistical only. Right: Projected (x,y) coordinates of
single isolated T2 tracks onto the CASTOR front surface [66]. Only tracks
(electron candidates) that coincide with a single isolated e.m. cluster in
CASTOR are plotted. The points are colored alternatively depending on which
calorimeter tower has registered the deposited energy. The black contours
indicate the optimal positions of the two halves of CASTOR as derived from the
analysis of these data. The empty ring at a radius of about 110is caused by
the shadow of a conical beam pipe section.

##  0.7 Detector simulation and validation

Typical particle physics measurements need detector simulations to calculate
the acceptance, correction factors, and bin migration effects. Having at hand
a precise simulation and validation of the CASTOR detector response is thereby
of fundamental importance for physics analyses. Both topics are discussed
next.

###  0.7.1 Simulation

The detector simulation is fully integrated into the offline software package
of the CMS experiment CMSSW [83]. The particles produced by the event
generator are transported to the CASTOR detector using the framework [81].
Hereby, interactions with detector materials and the beam pipe in front of the
calorimeter, as well as the magnetic field produced by the CMS solenoid that
extends into the very forward region, are simulated. The geometry
implementation of CASTOR is shown in Fig. 26. The two halves of the
calorimeter are shown in the geometry corresponding to the 2013 Pb data-taking
period. Electromagnetic and/or hadronic particle showers are detected in the
sensitive volumes of the detector. The quartz and tungsten plates that define
the volume of the CASTOR detector are accurately and separately modeled for
each detector-half to allow the description of tilts and movements of the
individual halves. Various physics settings have been compared to SPS beam
data [7]. For the test-beam data, the best agreement with full simulations of
the detector is obtained with the “QGSP_FTFP_BERT_EML” physics setting [84],
which gives a good description of the longitudinal shower profiles. However,
it underestimates the effect of noncompensation for hadronic particles. Thus,
a further residual scaling factor of about 0.8, applied solely to the response
generated by incoming hadrons, is implemented in the simulation to match the
measured test-beam data.

Figure 26: Visualization of the CASTOR detector geometry in simulations. The
inner trapezoids in darker blue are the active volumes, while the outer
volumes describe the light guides and the PMTs. The near and far halves are
placed at the measured position corresponding to the data taking in 2013\. An
opening of 2.2cm along the $x$-axis is visible between the two halves.

The number of Cherenkov photons, ${N^{\gamma}_{\text{Ch}}}$, produced by
charged particles traversing the quartz plates is estimated by considering
Poissonian fluctuations and a conversion of $\rd E/\rd
x=1.24\unit{eV}/\unit{cm}$ of emitted energy per unit distance, in the
relevant wavelength interval from 400 to 700nm. The photons are converted into
charge by a simplified analytical model of the photon transport in the quartz,
light guides, and PMTs. The first energy emission is described by an incident-
angle-dependent probability to reach the light guides, ${p_{\mathrm{dqz}}}$.
Combined with the quantum efficiency of the PMT,
${\epsilon_{\text{quant}}}=19\%$, and the PMT response factor,
${f_{\text{electrons}}}=0.307$, necessary to obtain a simulated signal for
electrons in agreement with the SPS beam measurement, one obtains the number
of photoelectrons as

${N_{\text{p.e.}}}={N^{\gamma}_{\text{Ch}}}\,{\epsilon_{\text{quant}}}\,{p_{\mathrm{dqz}}}\,{f_{\text{electrons}}}~{}.$
(4)

The number of photoelectrons is then converted to charge according to

$Q={N_{\text{p.e.}}}\,{g_{\text{PMT}}}\,,$ (5)

with ${g_{\text{PMT}}}=4.01\unit{fC}/\mathrm{p.e.}$ assuming an instantaneous
signal processing (no pulse shape) and, thus, only detected over one 25ns
readout bin. Since data taking with the CASTOR detector is performed with low-
intensity beams and large spacing between filled bunches, this assumption is
sufficient. With these values for the factors ${g_{\text{PMT}}}$ and
${f_{\text{electrons}}}$ the simulated response of CASTOR is consistent with
the measured energy of incoming individual electrons in beam tests.

Figure 27: Comparison of detector simulations (circles) to test-beam
measurements (triangles). Resolution of electron energy reconstruction (left),
and observed charged pion/electron response ratio (right) as a function of
test-beam energy. The uncertainties plotted are statistical only, and the
fitted curves are shown to guide the eye.

Figure 28: Comparison of the full (blue histogram) and fast (red histogram)
simulation responses (number of photoelectrons as a function of detector
depth) for charged pion (left) and electron (right) showers penetrating CASTOR
with energies from 50 to 100. Error bars around data points indicate
statistical uncertainties.

In Fig. 27, the simulation of the CASTOR standalone detector is compared to
test-beam data for electrons (left) and charged pions (right), illustrating
the good agreement between data and simulations. In CMS detector simulations,
the required time per generated event is an important factor, since the
available computing resources are limited. For CASTOR, such computing
constraints are important since particles and jets with very high energies,
close to that of the beam, need to be simulated. The subsequent large and
complex hadronic showers developing in the calorimeter require significant
computing time to be simulated in full detail. To increase the performance, a
fast version of the simulations has been implemented using a tabulated shower
library. Instead of tracking each individual particle, the energy deposit of
particles in each readout channel is tabulated in 37 inequally spaced bins in
energy ($1\leq E(\GeV)\leq 1500$), five equally spaced bins in azimuth angle
($0\leq\phi\leq\pi/4$), and seven equally spaced bins in pseudorapidity. About
50 events are tabulated per phase space bin. In Fig. 28 the validation of the
shower library with respect to full simulations for the energy bins from 50 to
100is shown. When a particle that is tracked in is found to enter the CASTOR
volume and fulfills basic requirements, such as a minimum energy of 1or a
limited incident angle, it is replaced by a randomly chosen tabulated shower
of its corresponding phase space bin. This results in a faster simulation time
by factors of 3 to 25 (2 to 16) for charged pions (electrons) at energies from
1.0 to 1.5. A disadvantage of the shower library is that the geometry of
CASTOR cannot be changed or shifted for systematic studies, since this would
require new shower libraries. Thus, for dedicated systematic studies the full
simulations are required.

###  0.7.2 Validation

The quality of the simulations of proton and nuclear collisions in the very
forward direction at the LHC, dominated by semihard and nonperturbative
dynamics (MPI, beam remnants, ), cannot be cross-checked with first-principles
calculations. The event generator predictions in this phase space region are
purely phenomenological in nature, and the description of the measurements
depends on MC parameter tuning, which in the very forward direction
essentially corresponds to data extrapolations. Different simulated event
characteristics need to be consistently compared to the measured data to
cross-check their validity. The simulations can be obtained from a variety of
MC generator predictions to determine which ones provide a better description
of the data in the relevant phase space. The observed discrepancies in
detailed comparisons between simulated and real detector responses can be used
to estimate the uncertainty related to the understanding of the detector
response. Also, the level of model dependence of a specific process can be
used to estimate the related systematic uncertainties in physics measurements.
The integration of CASTOR inside CMS has a further impact on the response of
the calorimeter, due to the additional scattering of particles in the
materials of the CMS detector around it. The simulated response to single
electrons as a function of pseudorapidity, is shown in Fig. 29. The dip around
$\eta\approx-5.5$ is due to the projection of the conical beam pipe towards
the interaction point.

Figure 29: Pseudorapidity dependence of reconstructed energies in CASTOR for
50electrons emitted from the nominal CMS interaction point. The CASTOR
simulation includes all CMS passive and active materials and the beam pipe.
The dip around $\eta=-5.5$ corresponds to the projection of the conical beam
pipe.

(a) Proton-lead collisions at $\sqrtsNN=5.02\TeV$ recorded in 2013 and 2016 at
$B=3.8\unit{T}$.

(b) Proton-proton collisions at ${\sqrt{s}}=13\TeV$ from 2015 at
$B=0\unit{T}$.

Figure 30: CASTOR signal response in calibrated (filled symbols) and
uncalibrated (open symbols) data compared to the detector simulations
(histograms), projected into the azimuthal (left) and longitudinal (right)
direction. Upper: Comparison of minimum-bias Pb collision data recorded in
2013 and 2016 with the Epos-LHC simulations (with the nominal MC geometry).
The impact of the magnetic field around module 7 is clearly visible. Lower:
Comparison of the minimum-bias collision data at ${\sqrt{s}}=13\TeV$ to the
QGSJetII and Epos-LHC (with geometry tuned to the 2015 data alignment)
simulations. The CMS magnet was off while taking those data. The uncertainties
of the calibrated data in all plots are statistical only, and originate almost
entirely from the limited beam halo statistics for intercalibration. There is
an additional scaling factor of 1.25 on the uncertainties of the calibrated
data, as discussed in the text.

Figure 30 presents several comparisons of minimum-bias data, collected in Pb
(upper panels) and (lower panels) collisions at the LHC, to detector
simulations. Minimum-bias events are selected that have at least one HF tower
with more than 10of energy on both sides of CMS, and exactly one reconstructed
collision vertex. The open symbols show the data before calibration, while the
filled symbols show them after normalization to the beam halo muon response.
Bad channels are removed in data as well as in simulations. The data and MC
distributions are absolutely normalized in order to perform a shape-sensitive
comparison with the aim to study the performance of the intercalibration
procedure. This is done since the average energy per collision is a highly
model-dependent quantity and is not necessary for the understanding of the
intercalibration. Furthermore, we exploit that the underlying physics at the
LHC is, on average, symmetric in the azimuth angle. However, a small sinus-
shaped modulation in $\phi$ is seen in the simulations in Fig. 30 (left) that
is due to the known relative alignment of the LHC luminous region (“beam
spot”) to the CASTOR calorimeter. Some parts of the detector are a bit closer
in the transverse plane to the luminous region than others. In the very
forward region, this represents a sizable effect on the detector acceptance.
Such a modulation is also observed in the data. After the full calibration of
collision data with beam halo muons, the statistical agreement of the
azimuthal distributions between data and realistic simulations of the detector
response is only fair. This suggests that the point-to-point uncertainties are
underestimated, possibly because of additional unspecified sources of
statistical and systematic uncertainties. This is corrected by scaling the
error bars to yield $\chi^{2}/N_{\textrm{d.f.}}=1$ (where $N_{\textrm{d.f.}}$
are the number of degrees of freedom) in the azimuthal shape distributions
with respect to the realistic full detector simulations (with events generated
with the Epos-LHC model). We determine a residual additional calibration
uncertainty at the tower level of about 25%, corresponding to the shown
vertical bars. This uncertainty on the tower level translates into a possible
impact on the energy scale normalization of the full CASTOR of the order of
25%/$\sqrt{16}\approx 6.5\%$, consistent with alignment-related uncertainties
in the absolute energy scale. Reasons for such residual mismatches can be
numerous and cannot be disentangled from the available data. Possible reasons
include differences in the signal generation between beam halo muons (as used
for the calibration) and minimum-bias collisions, statistical fluctuations,
geometrical as well as mechanical effects, etc. For any specific physics
analysis of the data, these effects must be studied and quantified explicitly.

The projection in the longitudinal direction indicates a very good calibration
from the front towards the rear of the calorimeter over almost two orders of
magnitude in signal strength, showing that the calibration with the beam halo
muon response yields a good measurement of the shower shape. The uncertainties
shown in the shape of the longitudinal projected data are scaled with the same
factor as described in the previous paragraph.

In the proton-lead data, there is some data-MC discrepancy in the rear part of
the calorimeter, which is due to known alignment issues in 2013 relative to
the LHC beam pipe, affecting the rear part of CASTOR. Thus, the data in
modules 8 to 14 recorded in 2013 must be treated with special care. Dedicated
detector simulation studies are needed to yield the required accuracy for
physics analysis.

It is also important to realize that the detailed shower development in the
CASTOR calorimeter is very sensitive to the underlying physics processes under
study and cannot be assumed to be well known at such high energies. Thus, the
comparisons in depth are only indicative and a $\chi^{2}$ test would not be a
good measure of the detector performance. For physics measurements that use
CASTOR data, the uncertainties in the calibration procedure must be
specifically studied and quantified.

###  0.7.3 Analysis backgrounds and noise levels

There are two typical applications where the CASTOR detector is used for event
classification measurements. First, tag events with forward particle
production to provide the best possible acceptance for inelastic collisions
[20]. Second, tag events with no activity that are related to processes with
large rapidity gaps [17]. The ability to identify these conditions are both
related to the noise levels in the calorimeter. Noise levels were already
discussed at the level of single readout capacitors per channel as a tool of
data quality control in Section 0.5.1. For physics analysis, this study must
be extended (in units of energy) to account for noise effects in the
reconstruction of calibrated physics objects.

Noise studies are best performed with data taken in periods without LHC
collisions, or alternatively with dedicated triggers for event selection. In
Fig. 31, we show the noise levels per channel (upper left), per tower (upper
right), and total in CASTOR (lower left), as well as the rate of energy
deposits misidentified as jets (lower right). These are examples obtained from
data recorded in 2015 in proton-proton collisions at ${\sqrt{s}}=13\TeV$ with
a CMS trigger requiring the absence of at least one of the two LHC beams per
recorded bunch crossing. Because of the length of the pulse shape, particles
from immediately preceding collisions can produce signals in the bunch
crossing of interest. Hence, to avoid noise from out-of-time pileup, the data
from bunch crossings that are closer in time than 250ns to any bunch of the
nominal LHC filling scheme are excluded. The leakage of signal just outside
the nominally colliding bunches is important to consider for LHC operations
with small bunch separations. Some CASTOR data were recorded with bunch
separation as small as 50ns. Extra care must be taken in such scenarios,
although the overall performance does not change in a significant way. Readout
noise is seen in the no-beam data, while beam-related backgrounds can be
studied with one of the beams present. Since CASTOR is installed in the
negative-$\eta$ side of the CMS experiment, the noise and timing of
backgrounds from each LHC beam is different. Only the LHC beam 1 has a
direction that can contribute to backgrounds for physics analyses, since the
beam background will arrive timed-in with the secondaries from actual
collisions at the IP. Recording and studying such data is very important for
many physics analyses that are sensitive to noise and background levels. The
noise level in units of energy mainly depends on the PMT gain settings, the
channel intercalibration, and the absolute energy scale calibration. However,
the main contribution is electronics noise from the cables and the digitizers,
and is very stable.

Figure 31: Measured CASTOR noise distributions for reconstructed physics
objects at the channel (upper left), tower (upper right), full-CASTOR (lower
left), and jet (lower right) levels. The data are from June 2015 during
proton-proton collisions at ${\sqrt{s}}=13\TeV$. Towers are normally zero
suppressed although, here, towers both with (solid) and without (dashed) zero
suppression are shown. Since jets are zero suppressed, the distribution is
non-Gaussian, and no RMS values are reported in this case.

For collision HV settings, the typical analysis energy thresholds for physics
measurements are about 0.4per channel, 1.5per tower, around 5for the full
CASTOR detector, and a few hundred for jets. These values correspond roughly
to the measured $3\,\sigma$ noise levels. The noise probability per event
drops significantly faster towards the higher energy thresholds than observed
in the jet rates from particle collision data (as seen by comparing Fig. 31,
lower right, with Fig. 9, right). Even at the lowest energy thresholds, the
fraction of noise in the reconstructed jet sample never exceeds $10^{-3}$. The
typical of jets at the lower threshold of about 500is about
$500\GeV/\cosh(-6.0)\approx 2.5\GeV$. All these results confirm that the small
noise levels in CASTOR make it a suitable detector for full cross section
measurements, for event type classification, as well as for jet reconstruction
studies.

##  0.8 Summary

The physics motivation, detector design, triggers, calibration, alignment,
simulation, and overall performance of the CASTOR calorimeter project have
been described. The detector, located in an extremely challenging environment
at very forward rapidities in the CMS experiment, has proven to be a valuable
apparatus for multiple physics measurements in proton and nuclear collisions
at the CERN LHC. The physics motivations for the detector construction, as
well as measurements carried out during the Run 1 and 2 operations at the LHC,
have been outlined. The overall detector design has been reviewed, emphasizing
the features that allow its operation under a significant radiation exposure,
required fast optical response, high occupancy, magnetic fields, and severe
space constraints.

The calibration of CASTOR is a major challenge. Three different methods
(offbeam, onbeam, and LED pulsers) used to set the gain correction factors of
the CASTOR photosensors have been reviewed. The absolute energy scale is
determined with a total precision of about 17%, which represents the dominant
source of systematic uncertainty for many measurements. Despite the
difficulties to precisely determine the absolute energy scale of the
calorimeter, beam halo muons have proven a valuable tool to intercalibrate all
channels, to monitor the stability of the detector, and to determine the noise
levels. The alignment of CASTOR, which has an important impact on the
calorimeter energy scale, data-simulation agreement, and on the actual
detector acceptance, has been surveyed with two types of sensors, and cross-
checked with measurements taken concurrently with the TOTEM T2 tracker in
front of CASTOR. An alignment precision of the order of 1was achieved.

Different comparisons of the data to Monte Carlo simulations confirm that the
reconstructed objects can be used for various physics analyses, from event-
type classification to more advanced measurements (jets, single-particle
spectra, etc.). Since CASTOR covers a unique phase space at the LHC, and since
there are no future plans for the installation of a detector with comparable
performance, the exploitation of the recorded data can provide many
opportunities for interesting measurements in proton and nuclear collisions at
the LHC.

###### 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. Moreover, the CASTOR detector project received important support and
explicit funding from the Helmholtz Association (Germany) via grants HN-NG-733
and the joint Helmholtz-Russian grant HRJRG-002, as well from the “A. G.
Leventis” Foundation (Greece). We are also grateful to our Colleagues from the
TOTEM Collaboration for the combined data taking and the fantastic work on the
T2+CASTOR combined data analysis. Finally, we acknowledge the enduring support
for the construction and operation of the LHC and the CMS detector provided by
the following funding agencies: the Austrian Federal Ministry of Education,
Science and Research and the Austrian Science Fund; the Belgian Fonds de la
Recherche Scientifique, and Fonds voor Wetenschappelijk Onderzoek; the
Brazilian Funding Agencies (CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP); the
Bulgarian Ministry of Education and Science; CERN; the Chinese Academy of
Sciences, Ministry of Science and Technology, and National Natural Science
Foundation of China; the Colombian Funding Agency (COLCIENCIAS); the Croatian
Ministry of Science, Education and Sport, and the Croatian Science Foundation;
the Research and Innovation Foundation, Cyprus; the Secretariat for Higher
Education, Science, Technology and Innovation, Ecuador; the Ministry of
Education and Research, Estonian Research Council via PRG780, PRG803 and
PRG445 and European Regional Development Fund, Estonia; the Academy of
Finland, Finnish Ministry of Education and Culture, and Helsinki Institute of
Physics; the Institut National de Physique Nucléaire et de Physique des
Particules / CNRS, and Commissariat à l’Énergie Atomique et aux Énergies
Alternatives / CEA, France; the Bundesministerium für Bildung und Forschung,
the Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy
– EXC 2121 “Quantum Universe” – 390833306, and Helmholtz-Gemeinschaft
Deutscher Forschungszentren, Germany; the General Secretariat for Research and
Technology, Greece; the National Research, Development and Innovation Fund,
Hungary; the Department of Atomic Energy and the Department of Science and
Technology, India; the Institute for Studies in Theoretical Physics and
Mathematics, Iran; the Science Foundation, Ireland; the Istituto Nazionale di
Fisica Nucleare, Italy; the Ministry of Science, ICT and Future Planning, and
National Research Foundation (NRF), Republic of Korea; the Ministry of
Education and Science of the Republic of Latvia; the Lithuanian Academy of
Sciences; the Ministry of Education, and University of Malaya (Malaysia); the
Ministry of Science of Montenegro; the Mexican Funding Agencies (BUAP,
CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI); the Ministry of Business,
Innovation and Employment, New Zealand; the Pakistan Atomic Energy Commission;
the Ministry of Science and Higher Education and the National Science Centre,
Poland; the Fundação para a Ciência e a Tecnologia, Portugal; JINR, Dubna; the
Ministry of Education and Science of the Russian Federation, the Federal
Agency of Atomic Energy of the Russian Federation, Russian Academy of
Sciences, the Russian Foundation for Basic Research, and the National Research
Center “Kurchatov Institute”; the Ministry of Education, Science and
Technological Development of Serbia; the Secretaría de Estado de
Investigación, Desarrollo e Innovación, Programa Consolider-Ingenio 2010, Plan
Estatal de Investigación Científica y Técnica y de Innovación 2017–2020,
research project IDI-2018-000174 del Principado de Asturias, and Fondo Europeo
de Desarrollo Regional, Spain; the Ministry of Science, Technology and
Research, Sri Lanka; the Swiss Funding Agencies (ETH Board, ETH Zurich, PSI,
SNF, UniZH, Canton Zurich, and SER); the Ministry of Science and Technology,
Taipei; the Thailand Center of Excellence in Physics, the Institute for the
Promotion of Teaching Science and Technology of Thailand, Special Task Force
for Activating Research and the National Science and Technology Development
Agency of Thailand; the Scientific and Technical Research Council of Turkey,
and Turkish Atomic Energy Authority; the National Academy of Sciences of
Ukraine; the Science and Technology Facilities Council, UK; the US Department
of Energy, and the US National Science Foundation. Individuals have received
support from the Marie-Curie program and the European Research Council and
Horizon 2020 Grant, contract Nos. 675440, 724704, 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 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
Scientific 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; the Programa de Excelencia
María de Maeztu, 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
V. Khachatryan, 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
W. Beaumont, 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, 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, W. Carvalho, J. Chinellato5,
E. Coelho, E.M. Da Costa, G.G. Da Silveira6, D. De Jesus Damiao, S. Fonseca De
Souza, H. Malbouisson, 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, C.H. Jiang, 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, G. Chen, A. Levin, L. Li, Q. Li, M. Lu, X. Lyu,
Y. Mao, S.J. Qian, D. Wang, Q. Wang, J. Xiao, D. Yang 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. 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
H. Abdalla12, A. Mohamed13, E. Salama14,15 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
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, Palaiseau, France
S. Ahuja, C. Amendola, F. Beaudette, M. Bonanomi, P. Busson, C. Charlot, O.
Davignon, B. Diab, G. Falmagne, R. Granier de Cassagnac, I. Kucher, A.
Lobanov, C. Martin Perez, M. Nguyen, C. Ochando, P. Paganini, J. Rembser, R.
Salerno, J.B. Sauvan, Y. Sirois, A. Zabi, A. Zghiche Université de Strasbourg,
CNRS, IPHC UMR 7178, Strasbourg, France
J.-L. 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 Tbilisi State University, Tbilisi, Georgia
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, P. Goettlicher, A. Grohsjean, M. Guthoff, M. Haranko,
A. Harb, A. Jafari23, N.Z. Jomhari, H. Jung, A. Kasem21, M. Kasemann, P.
Katsas, H. Kaveh, J. Keaveney, C. Kleinwort, J. Knolle, D. Krücker, W. Lange,
T. Lenz, J. Lidrych, K. Lipka, W. Lohmann24, R. Mankel, H. Maser, I.-A.
Melzer-Pellmann, J. Metwally, A.B. Meyer, M. Meyer, M. Missiroli, J. Mnich, C.
Muhl, A. Mussgiller, V. Myronenko, Y. Otarid, D. Pérez Adán, S.K. Pflitsch, D.
Pitzl, A. Raspereza, B. Roland, 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, A. Zuber 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, D. Gonzalez, P.
Gunnellini, J. Haller, A. Hinzmann, A. Karavdina, G. Kasieczka, R. Klanner, R.
Kogler, S. Kurz, 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. Akbiyik, M. Baselga, S. Baur, C. Baus, 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. Kudella, 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, T.J. Mertzimekis, 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