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1. FASER’s Electromagnetic Calorimeter Test Beam Studies

   https://doi.org/10.3390/instruments6030031  

   Cavanagh, Charlotte (September 2022, Instruments)

   FASER, or the Forward Search Experiment, is a new experiment at CERN designed to complement the LHC’s ongoing physics program, extending its discovery potential to light and weakly interacting particles that may be produced copiously at the LHC in the far-forward region. New particles targeted by FASER, such as long-lived dark photons or axion-like particles, are characterised by a signature with two oppositely charged tracks or two photons in the multi-TeV range that emanate from a common vertex inside the detector. The full detector was successfully installed in March 2021 in an LHC side tunnel 480 m downstream from the interaction point in the ATLAS detector. FASER is planned to be operational for LHC Run 3. The experiment is composed of a silicon-strip tracking-based spectrometer using three dipole magnets with a 20 cm aperture, supplemented by four scintillator stations and an electromagnetic calorimeter. The FASER electromagnetic calorimeter is constructed from four spare LHCb calorimeter modules. The modules are of the Shashlik type with interleaved scintillator and lead plates that result in 25 radiation lengths and 1% energy resolution for TeV electromagnetic showers. In 2021, a test beam campaign was carried out using one of the CERN SPS beam lines to set up the calibration of the FASER calorimeter system in preparation for physics data taking. The relative calorimeter response to electrons with energies between 10 and 300 GeV, as well as high energy muons and pions, has been measured under various high voltage settings and beam positions. The measured calorimeter resolution, energy calibration, and particle identification capabilities are presented. 

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2. Performance of the CMS electromagnetic calorimeter in pp collisions at √ _s = 13 TeV

   https://doi.org/10.1088/1748-0221/19/09/P09004  

   Hayrapetyan, A; Tumasyan, A; Adam, W; Andrejkovic, JW; Bergauer, T; Chatterjee, S; Damanakis, K; Dragicevic, M; Escalante_Del_Valle, A; Hussain, PS; et al (September 2024, Journal of Instrumentation)

   Abstract The operation and performance of the Compact Muon Solenoid (CMS) electromagnetic calorimeter (ECAL) are presented, based on data collected in pp collisions at √_s=13 TeV at the CERN LHC, in the years from 2015 to 2018 (LHC Run 2), corresponding to an integrated luminosity of 151 fb^-1. The CMS ECAL is a scintillating lead-tungstate crystal calorimeter, with a silicon strip preshower detector in the forward region that provides precise measurements of the energy and the time-of-arrival of electrons and photons. The successful operation of the ECAL is crucial for a broad range of physics goals, ranging from observing the Higgs boson and measuring its properties, to other standard model measurements and searches for new phenomena. Precise calibration, alignment, and monitoring of the ECAL response are important ingredients to achieve these goals. To face the challenges posed by the higher luminosity, which characterized the operation of the LHC in Run 2, the procedures established during the 2011–2012 run of the LHC have been revisited and new methods have been developed for the energy measurement and for the ECAL calibration. The energy resolution of the calorimeter, for electrons from Z boson decays reaching the ECAL without significant loss of energy by bremsstrahlung, was better than 1.8%, 3.0%, and 4.5% in the |η| intervals [0.0,0.8], [0.8,1.5], [1.5, 2.5], respectively. This resulting performance is similar to that achieved during Run 1 in 2011–2012, in spite of the more severe running conditions. 

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3. Novel position reconstruction methods for highly granular electromagnetic calorimeters

   https://doi.org/10.1051/epjconf/202431503007  

   Madison, Brendon; Wilson, Graham (December 2024, EPJ Web of Conferences)

   Jeans, D; Tian, J (Ed.)

   We present work on design and reconstruction methods for sampling electromagnetic calorimeters with emphasis on highly granular designs. We use the clustered logarithmically weighted center-of-gravity estimator (lwk-means) for initial benchmarking of position resolution. We find that the θ and φ resolution for high energy photons in Si-W designs improves when increasing both sampling frequency and sampling thickness. Augmenting only one is found to have mixed results. We find that lwk-means is unable to effectively use calorimeter transverse cell sizes smaller than 2 mm. New reconstruction methods for highly granular designs are developed. We find that methods that only measure the initial particle shower and disregard the remaining shower can take advantage of cell sizes down to at least 10 µm, significantly outperforming the benchmark method. Of these, the best method and design is the initial particle shower “single hit” method using the calorimeter design with the highest sampling frequency and sampling fraction. 

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4. GPT-Based Fast Simulation of CLAS12 Detector Hits via Conditional Autoregressive Generation

   Granger, C; Giroux, J; Tyson, R; Ungaro, M; Fanelli, C (June 2026, https://arxiv.org/pdf/2606.16035)

   Modern particles physics experiments have demonstrated an increasing need for fast, high-fidelity detector simulation as detector components have improved and subsequent computational requirements approach the limits of available resources. Recently, deep generative models have emerged as a promising alternative to traditional Monte-Carlo methods, with recent works drawing inspiration from large language models (LLMs) and self-supervised next- token prediction methods. In this work, we present an application of a GPT-style autoregressive transformer as a fast surrogate model for the calorimeter inside the CLAS12 experiment at the Thomas Jefferson National Accelerator Facility. The model is conditioned on incident momentum and generates realistic detector hits autoregressively across all nine calorimeter layers as sequences of strip, ADC, and TDC tokens. We demonstrate that the model faithfully reproduces hit multiplicity, spatial distributions, energy deposits,and the energy-momentum response of the electromagnetic calorimeter. The generator achievesinference rates exceeding 700 events per second on a single GPU, providing a substantial speedup over traditional Geant4-based simulations while maintaining physics fidelity essential for high-luminosity experimental programs 

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5. Autoencoder-Based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter

   https://doi.org/10.1007/s41781-024-00118-z  

   Abadjiev, D; Adams, T; Adzic, P; Ahmad, M; Amendola, C; Andrews, M B; Arcidiacono, R; Argiro, S; Askew, A; Auffray, E; et al (June 2024, Computing and Software for Big Science)

   Abstract The CMS detector is a general-purpose apparatus that detects high-energy collisions produced at the LHC. Online data quality monitoring of the CMS electromagnetic calorimeter is a vital operational tool that allows detector experts to quickly identify, localize, and diagnose a broad range of detector issues that could affect the quality of physics data. A real-time autoencoder-based anomaly detection system using semi-supervised machine learning is presented enabling the detection of anomalies in the CMS electromagnetic calorimeter data. A novel method is introduced which maximizes the anomaly detection performance by exploiting the time-dependent evolution of anomalies as well as spatial variations in the detector response. The autoencoder-based system is able to efficiently detect anomalies, while maintaining a very low false discovery rate. The performance of the system is validated with anomalies found in 2018 and 2022 LHC collision data. In addition, the first results from deploying the autoencoder-based system in the CMS online data quality monitoring workflow during the beginning of Run 3 of the LHC are presented, showing its ability to detect issues missed by the existing system. 

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