High Granularity Calorimeter Research Articles

Application of machine learning method for energy reconstruction on space based high granularity calorimeter

Application of machine learning method for energy reconstruction on space based high granularity calorimeter

The optimal use of segmentation for sampling calorimeters

One of the key design choices of any sampling calorimeter is how fine to make the longitudinal and transverse segmentation. To inform this choice, we study the impact of calorimeter segmentation on energy reconstruction. To ensure that the trends are due entirely to hardware and not to a sub-optimal use of segmentation, we deploy deep neural networks to perform the reconstruction. These networks make use of all available information by representing the calorimeter as a point cloud. To demonstrate our approach, we simulate a detector similar to the forward calorimeter system intended for use in the ePIC detector, which will operate at the upcoming Electron Ion Collider. We find that for the energy estimation of isolated charged pion showers, relatively fine longitudinal segmentation is key to achieving an energy resolution that is better than 10% across the full phase space. These results provide a valuable benchmark for ongoing EIC detector optimizations and may also inform future studies involving high-granularity calorimeters in other experiments at various facilities.

Studies on a hadronic calorimeter with MPGD technology for a future Muon Collider experiment

In the context of the European strategy for particle physics, the Multi-TeV Muon Collider has emerged as a compelling alternative for advancing our understanding of the Standard Model, after the full exploitation of the High-Luminosity LHC. The physics programme at the Muon Collider includes precise measurements in the Higgs boson sector and the search for new physics at the TeV scale. Achieving these goals relies on accurate full event reconstruction, including the identification and precise four-momentum estimation of various particles. The Particle Flow (PF) algorithm is one of the most suitable approach for this task, exploiting information from tracking, calorimeter, and muon detectors for particle identification and measurements of their momenta/energies. Tracking detectors measure charged particle momenta, while calorimeters provide energy measurements for photons and neutral hadrons. Therefore, a combination of an exceptional tracking system and high-granularity calorimeters is necessary. However, one of the biggest challenges for a future experiment at the Muon Collider is to discriminate the product of the μμ collisions from the intense beam-induced-background (BIB), due to the unstable nature of muons, whose decay products interact with the detector material. To address this, an innovative hadronic calorimeter (HCAL) based on Micro Pattern Gas Detectors (MPGDs) is proposed. MPGDs offer robust technology for high radiation environments and a high granularity for precise spatial measurements. Dedicated studies are needed to assess and optimize the performance of an MPGD-based HCAL, including the development of medium-scale prototypes for performance measurements. The response of HCAL to incoming particles is examined through Monte Carlo simulations using Geant4, comparing the performance of digital and semi-digital readouts, with energy resolution as the figure of merit. The simulated geometry will be integrated into the Muon Collider software to study its impact on jet reconstruction within the full apparatus and in the presence of BIB. The simulation will be also validated through the test of a small-size calorimeter cell equipped with advanced resistive MPGD technologies, namely resistive MicroMegas, resistive μRWELL and RP-WELL.

Timing performance of the CMS High Granularity Calorimeter prototype

This paper describes the experience with the calibration, reconstruction and evaluation of the timing capabilities of the CMS HGCAL prototype in the beam tests in 2018. The calibration procedure includes multiple steps and corrections ranging from tens of nanoseconds to a few hundred picoseconds. The timing performance is studied using signals from positron beam particles with energies between 20 GeV and 300 GeV. The performance is studied as a function of particle energy against an external timing reference as well as standalone by comparing the two different halves of the prototype. The timing resolution is found to be 60 ps for single-channel measurements and better than 20 ps for full showers at the highest energies, setting excellent perspectives for the HGCAL calorimeter performance at the HL-LHC.

Performance of H2GCROC3, the readout ASIC of SiPMs for the back hadronic sections of the CMS High Granularity Calorimeter

H2GCROC is a 130 nm CMOS ASIC designed to read out the SiPMs coupled to the scintillating tiles of the back hadronic sections of CMS HGCAL. Each of its 72 channels comprises a current conveyor, a high-gain preamplifier, a shaper, an ADC for energy measurement, and two discriminators linked to TDCs for capturing time-of-arrival and time-over-threshold information, respectively. This work presents the ASIC architecture and its characterization in the laboratory and test beam environments. The results demonstrate its adaptability in calibration, proving its capability to measure the SiPM single-photon spectrum and MIP's energy with high resolution under the expected radiation conditions during the entire operation of HGCAL.

Study of residual artificial neural network for particle identification in the CEPC high-granularity calorimeter prototype

Particle Identification (PID) plays a central role in associating the energy depositions in calorimeter cells with the type of primary particle in a particle flow oriented detector system. In this paper, we propose novel PID methods based on the Residual Network (ResNet) architecture which enable the training of very deep networks, bypass the need to reconstruct feature variables, and ensure the generalization ability among various geometries of detectors, to classify electromagnetic showers and hadronic showers. Using Geant4 simulation samples with energy ranging from 5 GeV to 120 GeV, the efficacy of Residual Connections is validated and the performance of our model is compared with Boosted Decision Trees (BDT) and other pioneering Artificial Neural Network (ANN) approaches. In shower classification, we observe an improvement in background rejection over a wide range of high signal efficiency (> 95%). These findings highlight the prospects of ANN with Residual Blocks for imaging detectors in the PID task of particle physics experiments.

First results on MPGD prototypes in test beam for MPGD-based HCAL at a future Muon Collider experiment

The Multi-TeV Muon Collider will allow significant advancement in particle physics and in the understanding of its Standard Model for the era after the High-Luminosity LHC. The Muon Collider physics program involves precise Higgs boson sector measurements and TeV-scale new physics exploration. These goals demand accurate full-event reconstruction. The Particle Flow algorithm, which utilizes tracking, calorimeter, and muon detectors, is ideal for identifying and precisely estimating particle momenta/energies and can accomplish this task. Tracking detectors measure charged particle momenta, while calorimeters provide energy measurements for photons and neutral hadrons. Therefore, combining an exceptional tracking system with high-granularity calorimeters is essential. A major challenge is discerning μμ collision products from beam-induced-background, due to muon decay. To address this, an innovative hadronic calorimeter (HCAL) using Micro Pattern Gas Detectors (MPGDs) is proposed. MPGDs provide robust technology for high radiation and ensure precise spatial measurements. Dedicated studies are needed to assess and optimize the performance of an MPGD-based HCAL, including the development of medium-scale prototypes for performance measurements.This article describes the studies for a hadronic calorimeter based on MPGDs, relying on advanced technologies such as μ-rwell, resistive MicroMegas, and RPWELL. To assess the performance of MPGD detectors, a test beam was conducted in July 2023 at the Super Proton Synchrotron at CERN. This test beam aimed to evaluate the performance of MPGD detectors with 1×1 cm2 pad readout under beam irradiation. Preliminary results from this test are presented.

First test results of the HGCAL concentrator ASICs: ECON-T and ECON-D

With over 6 million channels, the High Granularity Calorimeter for the CMS HL-LHC upgrade presents a unique data transmission challenge. The ECON ASICs provide a critical stage of on-detector data compression and selection for the trigger path (ECON-T) and data acquisition path (ECON-D) of the HGCAL. The ASICs, fabricated in 65 nm CMOS, are radiation tolerant up to 200 Mrad and require low power consumption: < 2.5 mW/sensor-channel per chip. We report on the first functionality and radiation tests for the ECON-D-P1 full-functionality prototype. We present a comparison of single event effect (SEE) cross sections measured for different methods of triple modular redundancy using test results from the ECON-T-P1 full-functionality prototype.

Design of the CMS High Granularity Calorimeter trigger primitive generator system

The High Luminosity phase of the LHC (HL-LHC) is planned to begin data taking in 2029, with the instantaneous luminosity expected to increase to 4 times its current value and the average number of particle interactions per bunch crossing increasing by approximately 3.5 times its current value. The current endcap calorimeters in the CMS detector will be replaced by a novel high granularity calorimeter (HGCAL) that will have fine segmentation in both the transverse and longitudinal directions and will be the first such calorimeter specifically optimised for particle flow reconstruction to operate at a colliding-beam experiment. The Level 1 trigger of the CMS experiment will combine the calorimeter data with tracking information to allow particle-flow techniques to be used. The high granularity of the calorimeter leads to about six million readout channels in total, concentrated in one million trigger cells, sampled for the L1 trigger at a rate of 40 MHz. The trigger data volumes will be an order of magnitude above those currently handled in CMS, posing significant challenges for data manipulation and processing. In addition, the trigger algorithms must be able to efficiently reject the huge background rate in the forward region of the detector due to the high pileup environment. The status of the HGCAL trigger architecture and design, as well as the algorithms developed in order to tackled these major issues, is presented here. The data flow is described from the formation and preselection of trigger cells on detector to the generation of two types of trigger primitives off detector, projective towers of energy in the (η, ϕ) plane and three-dimensional clusters of particles. Particle flow will use these 3D clusters, and their formation is described and followed by recent developments in the optimization of their transverse size.

CMS HGCAL electronics vertical integration system tests

In preparation for the High-Luminosity phase of the CERN Large Hadron Collider, the Compact Muon Solenoid collaboration will replace the existing endcap calorimeters with the High Granularity Calorimeter. Considering both endcaps, this novel sub-detector will have around six million readout channels, pushing approximately 108 Tbit s-1 of usable data between the detector front-end (on-detector) electronics and the back-end (off-detector) electronics. Given the scale and complexity of the endcap calorimeter upgrade project, the electronics testing must be carefully planned. The strategy has been to split the efforts between vertical (start-to-end) and horizontal (parallelisation) test systems wherever possible. An important milestone was the development and operation of a test system that prototypes a complete vertical slice of the future endcap electronics system. For the first time, a version of a test system consisting of the full vertical electronics readout chain was successfully operated in a beam test, where it was used to acquire real physics data.

The k4Clue package: Empowering future collider experiments with the CLUE algorithm

High granularity calorimeters have become increasingly crucial in modern particle physics experiments, and their importance is set to grow even further in the future. The CLUstering of Energy (CLUE) algorithm has shown excellent performance in clustering calorimeter hits in the High Granularity Calorimeter (HGCAL) developed for the Phase-2 upgrade of the CMS experiment. In this paper, the suitability of the CLUE algorithm for future collider experiments has been investigated and its capabilities tested outside the HGCAL reconstruction software. To this end, a new package, k4Clue, was developed which is now fully integrated into the Gaudi software framework and supports the EDM4hep data format for inputs and outputs. The performance of CLUE was demonstrated in three detectors for future colliders: CLICdet for the CLIC accelerator, CLD for the FCC-ee collider and a second calorimeter based on Noble Liquid technology also proposed for FCC-ee. Excellent reconstruction performance was observed for single photon events, even in the presence of noise, and the results are compatible with the performance of the algorithms used currently as the baseline for shower reconstruction in future e+e−-colliders. Moreover, CLUE demonstrates impressive timing capabilities, outperforming the current baseline algorithms and this advantage remains consistent regardless of the number of input hits. This work highlights the adaptability and versatility of the CLUE algorithm for a wide range of experiments and detectors and the algorithm’s potential for future high-energy physics experiments beyond CMS.

Overview of the HL-LHC Upgrade for the CMS Level-1 Trigger

The High-Luminosity LHC will open an unprecedented window on the weak-scale nature of the universe, providing high-precision measurements of the standard model as well as searches for new physics beyond the standard model. Such precision measurements and searches require informationrich datasets with a statistical power that matches the high-luminosity provided by the Phase-2 upgrade of the LHC. Efficiently collecting those datasets will be a challenging task, given the harsh environment of 200 proton-proton interactions per LHC bunch crossing. For this purpose, CMS is designing an efficient data-processing hardware trigger (Level-1) that will include tracking information and high-granularity calorimeter information. Trigger data analysis will be performed through sophisticated algorithms such as particle flow reconstruction, including widespread use of Machine Learning. The current conceptual system design is expected to take full advantage of advances in FPGA and link technologies over the coming years, providing a high-performance, lowlatency computing platform for large throughput and sophisticated data correlation across diverse sources.

Leveraging staggered tessellation for enhanced spatial resolution in high-granularity calorimeters

We advance the concept of high-granularity calorimeters with staggered tessellations, underscoring the effectiveness of a design incorporating multifold staggering cycles based on hexagonal cells to enhance position resolution. Moreover, we introduce HEXPLIT, a sub-cell re-weighting algorithm tailored to harness staggered designs, resulting in additional performance improvements. By combining our proposed staggered design with HEXPLIT, we achieve an approximately twofold enhancement in position resolution for neutrons across a wide energy range, as compared to unstaggered designs. These findings hold the potential to improve particle-flow performance across various forthcoming facilities.

Reduction of light output of plastic scintillator tiles during irradiation at cold temperatures and in low-oxygen environments

The advent of the silicon photomultiplier has allowed the development of highly segmented calorimeters using plastic scintillator as the active media, with photodetectors embedded in the calorimeter, in dimples in the plastic. To reduce the photodetector’s dark current and radiation damage, the high granularity calorimeter designed for the high luminosity upgrade of the CMS detector at CERN’s Large Hadron Collider will be operated at a temperature of about −30 °C. Due to flammability considerations, a low oxygen environment is being considered. However, the radiation damage to the plastic scintillator during irradiation in this operating environment needs to be considered. In this paper, we present measurements of the relative decrease of light output during irradiation of small plastic scintillator tiles read out by silicon photomultipliers. The irradiations were performed using a 60Co source both to produce the tiles’ light and as a source of ionizing irradiation at dose rates of 0.3, 1.3, and 1.6 Gy/h, temperatures of −30, −15, −5, and 0 °C, and with several different oxygen concentrations in the surrounding atmosphere. The effect of the material used to wrap the tile was also studied. Substantial temporary damage, which annealed when the sample was warmed, was seen during the low-temperature irradiations, regardless of the oxygen concentration and wrapping material. The relative light loss was largest with 3MTM Enhanced Specular Reflector Film wrapping and smallest with no wrapping, although due to the substantially higher light yield with wrapping, the final light output is largest with wrapping. The light loss was less at warmer temperatures. Damage with 3% oxygen was similar to that in standard atmosphere. Evidence of a plateau in the radical density was seen for the 0 °C data.

Beam Test of the First Prototype of SiPM-on-Tile Calorimeter Insert for the EIC Using 4 GeV Positrons at Jefferson Laboratory

We recently proposed a high-granularity calorimeter insert for the Electron-Ion Collider (EIC) that uses plastic scintillator tiles read out by SiPMs. Among its features are an ASIC-away-from-SiPM strategy for reducing cooling requirements and minimizing space use, along with employing 3D-printed frames to reduce optical crosstalk and dead areas. To evaluate these features, we built a 40-channel prototype and tested it using a 4 GeV positron beam at Jefferson Laboratory. The measured energy spectra and 3D shower shapes are well described by simulations, confirming the effectiveness of the design, construction techniques, and calibration strategy. This constitutes the first use of SiPM-on-tile technology in an EIC detector design.

Calculation of Forces to the High Granularity Calorimeter Stainless Steel Absorber Plates in the CMS Magnetic Field

The general-purpose Compact Muon Solenoid (CMS) detector at the Large Hadron Collider (LHC) at CERN incorporates a hadronic calorimeter to register the energies of the charged and neutral hadrons produced in proton–proton collisions at the LHC at a center-of-mass energy of 13.6 TeV. This calorimeter is located inside a superconducting solenoid that is 6 m in diameter and 12.5 m in length, generating a central magnetic flux density of 3.8 T. For operating optimally in the high pileup and high radiation environment of the High-Luminosity LHC, the existing CMS endcap calorimeters will be replaced with a new high granularity calorimeter (HGCal) with an electromagnetic section and a hadronic section in each of the two endcaps. The hadronic section of the HGCal will include 44 stainless-steel absorber plates with a relative permeability value well below 1.05. The volume occupied by 22 plates in each endcap is about 21 m3. The calculation of the axial electromagnetic forces acting on the absorber plates is a crucial element in designing the mechanical construction of the device. With a three-dimensional computer model of the CMS magnet, the axial forces on each absorber plate were calculated, and the dependence of forces on the central magnetic flux density value is presented. The method of calculation and the obtained results are discussed.

Automated visual inspection of CMS HGCAL silicon sensor surface using an ensemble of a deep convolutional autoencoder and classifier

More than a thousand 8″ silicon sensors will be visually inspected to look for anomalies on their surface during the quality control preceding assembly into the High-Granularity Calorimeter for the CMS experiment at CERN. A deep learning-based algorithm that pre-selects potentially anomalous images of the sensor surface in real time was developed to automate the visual inspection. The anomaly detection is done by an ensemble of independent deep convolutional neural networks: an autoencoder and a classifier. The algorithm was deployed and has been continuously running in production, and data gathered were used to evaluate its performance. The pre-selection reduces the number of images requiring human inspection by 85%, with recall of 97%, and saves 15 person-hours per a batch of a hundred sensors. Data gathered in production can be used for continuous learning to improve the accuracy incrementally.

Neutron irradiation and electrical characterisation of the first 8” silicon pad sensor prototypes for the CMS calorimeter endcap upgrade

As part of its HL-LHC upgrade program, the CMS collaboration is replacing its existing endcap calorimeters with a high-granularity calorimeter (CE). The new calorimeter is a sampling calorimeter with unprecedented transverse and longitudinal readout for both electromagnetic and hadronic compartments. Due to its compactness, intrinsic time resolution, and radiation hardness, silicon has been chosen as active material for the regions exposed to higher radiation levels. The silicon sensors are fabricated as 20 cm (8”) wide hexagonal wafers and are segmented into several hundred pads which are read out individually. As part of the sensor qualification strategy, 8” sensor irradiation with neutrons has been conducted at the Rhode Island Nuclear Science Center (RINSC) and followed by their electrical characterisation in 2020-21. The completion of this important milestone in the CE's R&D program is documented in this paper and it provides detailed account of the associated infrastructure and procedures. The results on the electrical properties of the irradiated CE silicon sensors are presented.

Performance of the CMS High Granularity Calorimeter prototype to charged pion beams of 20–300 GeV/c

The upgrade of the CMS experiment for the high luminosity operation of the LHC comprises the replacement of the current endcap calorimeter by a high granularity sampling calorimeter (HGCAL). The electromagnetic section of the HGCAL is based on silicon sensors interspersed between lead and copper (or copper tungsten) absorbers. The hadronic section uses layers of stainless steel as an absorbing medium and silicon sensors as an active medium in the regions of high radiation exposure, and scintillator tiles directly read out by silicon photomultipliers in the remaining regions. As part of the development of the detector and its readout electronic components, a section of a silicon-based HGCAL prototype detector along with a section of the CALICE AHCAL prototype was exposed to muons, electrons and charged pions in beam test experiments at the H2 beamline at the CERN SPS in October 2018. The AHCAL uses the same technology as foreseen for the HGCAL but with much finer longitudinal segmentation. The performance of the calorimeters in terms of energy response and resolution, longitudinal and transverse shower profiles is studied using negatively charged pions, and is compared to GEANT4 predictions. This is the first report summarizing results of hadronic showers measured by the HGCAL prototype using beam test data.

Operational radiation protection challenges for the LHC experiments.

Radiation protection physicists at CERN are often required to assess residual activation for the Large Hadron Collider (LHC) experiments during stop periods in order to ensure adequate optimization during planned exposure situations and to establish proper procedures for the radiological control of materials. Given the complexity of the facilities and of the high-energy and mixed fields inducing the activation, Monte Carlo transport codes are an essential tool to simulate both prompt and residual radiation. The present work highlights the challenges of assessing residual dose rates for the LHC experiments in shutdown configurations and of establishing residual activation zonings. For the latter, a method based on fluence conversion coefficients was developed and is efficiently employed. The practical example of the assessment of the activation of the 600 tons of austenitic stainless steel in the future Compact Muon Solenoid (CMS) High Granularity Calorimeter will be used to demonstrate how these challenges are dealt with and the capabilities of the method developed.