Federico de Guio

Abstract As part of its HL-LHC upgrade program, the CMS collaboration is developing a High Granularity Calorimeter (CE) to replace the existing endcap calorimeters. The CE is a sampling calorimeter with unprecedented transverse and longitudinal readout for both electromagnetic (CE-E) and hadronic (CE-H) compartments. The calorimeter will be built with ∼30,000 hexagonal silicon modules. Prototype modules have been constructed with 6-inch hexagonal silicon sensors with cell areas of 1.1 cm 2 , and the SKIROC2-CMS readout ASIC. Beam tests of different sampling configurations were conducted with the prototype modules at DESY and CERN in 2017 and 2018. This paper describes the construction and commissioning of the CE calorimeter prototype, the silicon modules used in the construction, their basic performance, and the methods used for their calibration.

Abstract The Compact Muon Solenoid collaboration is designing a new high-granularity endcap calorimeter, HGCAL, to be installed later this decade. As part of this development work, a prototype system was built, with an electromagnetic section consisting of 14 double-sided structures, providing 28 sampling layers. Each sampling layer has an hexagonal module, where a multipad large-area silicon sensor is glued between an electronics circuit board and a metal baseplate. The sensor pads of approximately 1.1 cm 2 are wire-bonded to the circuit board and are readout by custom integrated circuits. The prototype was extensively tested with beams at CERN's Super Proton Synchrotron in 2018. Based on the data collected with beams of positrons, with energies ranging from 20 to 300 GeV, measurements of the energy resolution and linearity, the position and angular resolutions, and the shower shapes are presented and compared to a detailed Geant4 simulation.

Abstract The barrel section of the novel MIP Timing Detector (MTD) will be constructed as part of the upgrade of the CMS experiment to provide a time resolution for single charged tracks in the range of 30–60 ps using LYSO:Ce crystal arrays read out with Silicon Photomultipliers (SiPMs). A major challenge for the operation of such a detector is the extremely high radiation level, of about 2 × 10 14 1 MeV(Si) Eqv. n/cm 2 , that will be integrated over a decade of operation of the High Luminosity Large Hadron Collider (HL-LHC). Silicon Photomultipliers exposed to this level of radiation have shown a strong increase in dark count rate and radiation damage effects that also impact their gain and photon detection efficiency. For this reason during operations the whole detector is cooled down to about -35°C. In this paper we illustrate an innovative and cost-effective solution to mitigate the impact of radiation damage on the timing performance of the detector, by integrating small thermo-electric coolers (TECs) on the back of the SiPM package. This additional feature, fully integrated as part of the SiPM array, enables a further decrease in operating temperature down to about -45°C. This leads to a reduction by a factor of about two in the dark count rate without requiring additional power budget, since the power required by the TEC is almost entirely offset by a decrease in the power required for the SiPM operation due to leakage current. In addition, the operation of the TECs with reversed polarity during technical stops of the accelerator can raise the temperature of the SiPMs up to 60°C (about 50°C higher than the rest of the detector), thus accelerating the annealing of radiation damage effects and partly recovering the SiPM performance.

The CMS detector will be upgraded for the HL-LHC to include a MIP Timing Detector (MTD). The MTD will consist of barrel and endcap timing layers, BTL and ETL respectively, providing precision timing of charged particles. The BTL sensors are based on LYSO:Ce scintillation crystals coupled to SiPMs with TOFHIR2 ASICs for the front-end readout. A resolution of 30-60 ps for MIP signals at a rate of 2.5 Mhit/s per channel is expected along the HL-LHC lifetime. We present an overview of the TOFHIR2 requirements and design, simulation results and measurements with TOFHIR2 ASICs. The measurements of TOFHIR2 associated to sensor modules were performed in different test setups using internal test pulses or blue and UV laser pulses emulating the signals expected in the experiment. The measurements show a time resolution of 24 ps initially during Beginning of Operation (BoO) and 58 ps at End of Operation (EoO) conditions, matching well the BTL requirements. We also showed that the time resolution is stable up to the highest expected MIP rate. Extensive radiation tests were performed, both with x-rays and heavy ions, showing that TOFHIR2 is not affected by the radiation environment during the experiment lifetime.

We describe our R&D effort to develop potentially radiation-hard scintillating and wavelength shifting fibers by doping fused-silica with cerium. The cerium-doped optical fibers with different core structures and concentrations were exposed to gamma radiation (60Co) at different dose rates up to 100 kGy. We evaluated the radiation-induced degradation in photoluminescence, optical transmission, and recovery phenomena in the wavelength range from 300 to 700 nm. We were able to model the experimental data based on second-order rate equations where the fit parameters that govern the damage profile were utilized to predict recovery. We also measured the influence of radiation on the numerical aperture. Finally, we offer some thoughts on the use of these types of fibers in particle and nuclear physics detectors.

Abstract The CMS experiment at the CERN LHC will be upgraded to accommodate the 5-fold increase in the instantaneous luminosity expected at the High-Luminosity LHC (HL-LHC) [1]. Concomitant with this increase will be an increase in the number of interactions in each bunch crossing and a significant increase in the total ionising dose and fluence. One part of this upgrade is the replacement of the current endcap calorimeters with a high granularity sampling calorimeter equipped with silicon sensors, designed to manage the high collision rates [2]. As part of the development of this calorimeter, a series of beam tests have been conducted with different sampling configurations using prototype segmented silicon detectors. In the most recent of these tests, conducted in late 2018 at the CERN SPS, the performance of a prototype calorimeter equipped with ≈12,000 channels of silicon sensors was studied with beams of high-energy electrons, pions and muons. This paper describes the custom-built scalable data acquisition system that was built with readily available FPGA mezzanines and low-cost Raspberry Pi computers.

The certification of the CMS experiment data as usable for physics analysis is a crucial task to ensure the quality of all physics results published by the collaboration. Currently, the certification conducted by human experts is labor intensive and based on the scrutiny of distributions integrated on several hours of data taking. This contribution focuses on the design and prototype of an automated certification system assessing data quality on a per-luminosity section (i.e. 23 seconds of data taking) basis. Anomalies caused by detector malfunctioning or sub-optimal reconstruction are difficult to enumerate a priori and occur rarely, making it difficult to use classical supervised classification methods such as feedforward neural networks. We base our prototype on a semi-supervised approach which employs deep autoencoders. This approach has been qualified successfully on CMS data collected during the 2016 LHC run: we demonstrate its ability to detect anomalies with high accuracy and low false positive rate, when compared against the outcome of the manual certification by experts. A key advantage of this approach over other machine learning technologies is the great interpretability of the results, which can be further used to ascribe the origin of the problems in the data to a specific sub-detector or physics objects.

The performance of electromagnetic calorimeter modules made of proton-irradiated PbWO4 crystals has been studied in beam tests. The modules, similar to those used in the Endcaps of the CMS electromagnetic calorimeter (ECAL), were formed from 5×5 matrices of PbWO4 crystals, which had previously been exposed to 24 GeV protons up to integrated fluences between 2.1× 1013 and 1.3× 1014 cm−2. These correspond to the predicted charged-hadron fluences in the ECAL Endcaps at pseudorapidity η = 2.6 after about 500 fb−1 and 3000 fb−1 respectively, corresponding to the end of the LHC and High Luminosity LHC operation periods. The irradiated crystals have a lower light transmission for wavelengths corresponding to the scintillation light, and a correspondingly reduced light output. A comparison with four crystals irradiated in situ in CMS showed no significant rate dependence of hadron-induced damage. A degradation of the energy resolution and a non-linear response to electron showers are observed in damaged crystals. Direct measurements of the light output from the crystals show the amplitude decreasing and pulse becoming faster as the fluence increases. The latter is interpreted, through comparison with simulation, as a side-effect of the degradation in light transmission. The experimental results obtained can be used to estimate the long term performance of the CMS ECAL.

The Data Quality Monitoring (DQM) Software proved to be a central tool in the CMS experiment. Its flexibility allowed its integration in several environments: Online, for real-time detector monitoring; Offline, for the final, fine-grained Data Certification; Release-Validation, to constantly validate the functionality and the performance of the reconstruction software; in Monte Carlo productions. The central tool to deliver Data Quality information is a web site for browsing data quality histograms (DQM GUI). In this contribution the structure of the DQM framework is described and the usage of the DQM software in the different environments and the performance of the system after the first years of data taking are presented.

The CMS hadronic calorimeter employs a plastic-scintillator-based endcap detector. In early 2017, a 20° wedge of the endcap was upgraded with silicon photomultipliers (SiPMs) and readout electronics based on the QIE11 digitizer. Based on the excellent experience with this 20° pilot system in 2017, the entire endcap detector was upgraded with SiPMs in early 2018. We report on the first ever operation of SiPMs in a high-rate collider detector.

We describe our R&D effort to develop radiation-hard scintillating and wavelength shifting fibers by doping fused-silica with cerium. This new type of cerium-doped fiber potentially offers myriad new applications in calorimeters for high-energy physics, tracking systems, and profiling of charged particle beams.

The MIP Timing Detector will provide additional timing capabilities for detection of minimum ionizing particles (MIPs) at CMS during the High Luminosity LHC era, improving event reconstruction and pileup rejection. The central portion of the detector, the Barrel Timing Layer (BTL), will be instrumented with LYSO:Ce crystals and Silicon Photomultipliers (SiPMs) providing a time resolution of about 30 ps at the beginning of operation, and degrading to 50-60 ps at the end of the detector lifetime as a result of radiation damage. In this work, we present the results obtained using a 120 GeV proton beam at the Fermilab Test Beam Facility to measure the time resolution of unirradiated sensors. A proof-of-concept of the sensor layout proposed for the barrel region of the MTD, consisting of elongated crystal bars with dimensions of about 3 x 3 x 57 mm$^3$ and with double-ended SiPM readout, is demonstrated. This design provides a robust time measurement independent of the impact point of the MIP along the crystal bar. We tested LYSO:Ce bars of different thickness (2, 3, 4 mm) with a geometry close to the reference design and coupled to SiPMs manufactured by Hamamatsu and Fondazione Bruno Kessler. The various aspects influencing the timing performance such as the crystal thickness, properties of the SiPMs (e.g. photon detection efficiency), and impact angle of the MIP are studied. A time resolution of about 28 ps is measured for MIPs crossing a 3 mm thick crystal bar, corresponding to an MPV energy deposition of 2.6 MeV, and of 22 ps for the 4.2 MeV MPV energy deposition expected in the BTL, matching the detector performance target for unirradiated devices.

The CMS Detector will be upgraded for the High-Luminosity LHC to include a MIP Timing Detector (MTD). The MTD will consist of barrel and endcap timing layers, BTL and ETL, respectively, providing precision timing of charged particles. The BTL sensors are based on LYSO:Ce scintillating crystals coupled to SiPMs that are read out by TOFHIR2 ASICs in the front-end system. A resolution of 30 ps for MIP signals is expected at the beginning of HL-LHC operation degrading to 60 ps at the end of operation due to the SiPMs radiation damage. Relative to the first version of the front-end ASIC, TOFHIR2X implements improved circuitry for mitigation of the SiPM dark current noise as well as a new current mode discriminator. We present an overview of the TOFHIR2 requirements and design, simulation results and the first measurements with TOFHIR2X silicon samples coupled to LYSO/SiPM prototype sensors.

We have evaluated the performance of a Ce-doped fused-silica fiber as wavelength shifter coupled to a CeF3 crystal using electron beams at CERN . The pulse shape and collection efficiency were measured using irradiated (100 kGy) and un-irradiated fibers. In addition, we evaluated the light yield of various Ce-doped fibers and explored the possibility of using them in the future, including for precision timing applications in a high-luminosity collider environment.

The barrel section of the novel MIP Timing Detector (MTD) will be constructed as part of the upgrade of the CMS experiment to provide a time resolution for single charged tracks in the range of $30-60$ ps using LYSO:Ce crystal arrays read out with Silicon Photomultipliers (SiPMs). A major challenge for the operation of such a detector is the extremely high radiation level, of about $2\times10^{14}$ 1 MeV(Si) Eqv. n/cm$^2$, that will be integrated over a decade of operation of the High Luminosity Large Hadron Collider (HL-LHC). Silicon Photomultipliers exposed to this level of radiation have shown a strong increase in dark count rate and radiation damage effects that also impact their gain and photon detection efficiency. For this reason during operations the whole detector is cooled down to about $-35^{\circ}$C. In this paper we illustrate an innovative and cost-effective solution to mitigate the impact of radiation damage on the timing performance of the detector, by integrating small thermo-electric coolers (TECs) on the back of the SiPM package. This additional feature, fully integrated as part of the SiPM array, enables a further decrease in operating temperature down to about $-45^{\circ}$C. This leads to a reduction by a factor of about two in the dark count rate without requiring additional power budget, since the power required by the TEC is almost entirely offset by a decrease in the power required for the SiPM operation due to leakage current. In addition, the operation of the TECs with reversed polarity during technical stops of the accelerator can raise the temperature of the SiPMs up to $60^{\circ}$C (about $50^{\circ}$C higher than the rest of the detector), thus accelerating the annealing of radiation damage effects and partly recovering the SiPM performance.

Daily operation of a large-scale experiment is a resource consuming task, particularly from perspectives of routine data quality monitoring. Typically, data comes from different sub-detectors and the global quality of data depends on the combinatorial performance of each of them. In this paper, the problem of identifying channels in which anomalies occurred is considered. We introduce a generic deep learning model and prove that, under reasonable assumptions, the model learns to identify 'channels' which are affected by an anomaly. Such model could be used for data quality manager cross-check and assistance and identifying good channels in anomalous data samples. The main novelty of the method is that the model does not require ground truth labels for each channel, only global flag is used. This effectively distinguishes the model from classical classification methods. Being applied to CMS data collected in the year 2010, this approach proves its ability to decompose anomaly by separate channels.

The Data Quality Monitoring (DQM) Software is a central tool in the CMS experiment. Its robustness and flexibility is critical for monitoring detector performance and providing fast and comprehensive feedback centrally for the experiment in real-time (Online DQM), after a full event processing with fine-grained analysis (Offline DQM), and as a validation tool to validate both the CMS software (CMSSW), calibration and alignment scenarios and extensive simulations. The entire DQM framework has undergone fundamental changes, and the first performance results of this newly updated system will be presented in the context of the first proton-proton collisions for CERNs Large Hadron Collider at a center of mass energy of 13 TeV. These results will encapsulate the performance of the CMS detector in the context of the upgraded DQM system that makes available more sophisticated methods for evaluating data quality, as well as a dedicated review of the technical challenges and improvements specific to the DQM framework itself. Presented at IEEE-NSS-MIC-2015 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference The data quality monitoring challenge at CMS: experience from first collisions and future plans Federico De Guio (CERN) on behalf of the CMS collaboration Abstract—The Data Quality Monitoring (DQM) Software is a central tool in the CMS experiment. Its robustness and flexibility is critical for monitoring detector performance and providing fast and comprehensive feedback centrally for the experiment in realtime (Online DQM), after a full event processing with fine-grained analysis (Offline DQM), and as a validation tool to validate both the CMS software (CMSSW), calibration and alignment scenarios and extensive simulations. The entire DQM framework has undergone fundamental changes, and the first performance results of this newly updated system will be presented in the context of the first proton-proton collisions for CERN’s Large Hadron Collider at a center of mass energy of 13 TeV. These results will encapsulate the performance of the CMS detector in the context of the upgraded DQM system that makes available more sophisticated methods for evaluating data quality, as well as a dedicated review of the technical challenges and improvements specific to the DQM framework itself.The Data Quality Monitoring (DQM) Software is a central tool in the CMS experiment. Its robustness and flexibility is critical for monitoring detector performance and providing fast and comprehensive feedback centrally for the experiment in realtime (Online DQM), after a full event processing with fine-grained analysis (Offline DQM), and as a validation tool to validate both the CMS software (CMSSW), calibration and alignment scenarios and extensive simulations. The entire DQM framework has undergone fundamental changes, and the first performance results of this newly updated system will be presented in the context of the first proton-proton collisions for CERN’s Large Hadron Collider at a center of mass energy of 13 TeV. These results will encapsulate the performance of the CMS detector in the context of the upgraded DQM system that makes available more sophisticated methods for evaluating data quality, as well as a dedicated review of the technical challenges and improvements specific to the DQM framework itself.

We review the online and offline workflows designed to align and calibrate the CMS detector. Starting from the gained experience during the first LHC run, we discuss the expected developments for Run II. In particular, we describe the envisioned different stages, from the alignment using cosmic rays data to the detector alignment and calibration using the first proton-proton collisions data (O(100 pb−1)) and a larger dataset (O(1 fb−1) ) to reach the target precision. The automatisation of the workflow and the integration in the online and offline activity (dedicated triggers and datasets, data skims, workflows to compute the calibration and alignment constants) are discussed.

The electromagnetic calorimeter of CMS (ECAL) is a hermetic, fine grained and homogeneous calorimeter containing 75848 lead-tungstate (PbWO4) crystals, completed by a silicon preshower installed in front of the endcaps. The ECAL sensitivity to decay modes with electromagnetic objects in the final state, such as narrow resonances decaying into two photons, is achieved through its excellent energy and position resolution. The ECAL performance from 2010–2012 is presented in detail and its role in the hunt for the Higgs boson, through the 2-photon decay mode, is discussed.

A large sample of cosmic ray events collected by the CMS detector is exploited to measure the specific energy loss of muons in the lead tungstate (PbWO4) of the electromagnetic calorimeter. The measurement spans a momentum range from 5GeV/c to 1TeV/c. The results are consistent with the expectations over the entire range. The calorimeter energy scale, set with 120GeV/c electrons, is validated down to the sub-GeV region using energy deposits, of order 100 MeV, associated with low-momentum muons. The muon critical energy in PbWO4 is measured to be 160−6+5±8GeV, in agreement with the expectations.

Daily operation of a large-scale experiment is a resource consuming task, particularly from perspectives of routine data quality monitoring. Typically, data comes from different sub-detectors and the global quality of data depends on the combinatorial performance of each of them. In this paper, the problem of identifying channels in which anomalies occurred is considered. We introduce a generic deep learning model and prove that, under reasonable assumptions, the model learns to identify 'channels' which are affected by an anomaly. Such model could be used for data quality manager cross-check and assistance and identifying good channels in anomalous data samples. The main novelty of the method is that the model does not require ground truth labels for each channel, only global flag is used. This effectively distinguishes the model from classical classification methods. Being applied to CMS data collected in the year 2010, this approach proves its ability to decompose anomaly by separate channels.

Abstract Certifying the data recorded by the Compact Muon Solenoid (CMS) experiment at CERN is a crucial and demanding task as the data is used for publication of physics results. Anomalies caused by detector malfunctioning or sub-optimal data processing are difficult to enumerate a priori and occur rarely, making it difficult to use classical supervised classification. We base out prototype towards the automation of such procedure on a semi-supervised approach using deep autoencoders. We demonstrate the ability of the model to detect anomalies with high accuracy, when compared against the outcome of the fully supervised methods. We show that the model has great interpretability of the results, ascribing the origin of the problems in the data to a specific sub-detector or physics object. Finally, we address the issue of feature dependency on the LHC beam intensity.

As part of its HL-LHC upgrade program, the CMS Collaboration is developing a High Granularity Calorimeter (CE) to replace the existing endcap calorimeters. The CE is a sampling calorimeter with unprecedented transverse and longitudinal readout for both electromagnetic (CE-E) and hadronic (CE-H) compartments. The calorimeter will be built with $\sim$30,000 hexagonal silicon modules. Prototype modules have been constructed with 6-inch hexagonal silicon sensors with cell areas of 1.1~$cm^2$, and the SKIROC2-CMS readout ASIC. Beam tests of different sampling configurations were conducted with the prototype modules at DESY and CERN in 2017 and 2018. This paper describes the construction and commissioning of the CE calorimeter prototype, the silicon modules used in the construction, their basic performance, and the methods used for their calibration.