Electromagnetic Calorimeter Research Articles

Correction to: Production and test of sPHENIX W/SciFiber electromagnetic calorimeter blocks in China

Correction to: Production and test of sPHENIX W/SciFiber electromagnetic calorimeter blocks in China

A comparison of deep learning models for proton background rejection with the AMS electromagnetic calorimeter

Abstract The alpha magnetic spectrometer (AMS) is a high-precision particle detector onboard the International Space Station containing six different subdetectors. The transition radiation detector and electromagnetic calorimeter (ECAL) are used to separate electrons/positrons from the abundant cosmic-ray proton background. The positron flux measured in space by AMS falls with a power law which unexpectedly softens above 25 GeV and then hardens above 280 GeV. Several theoretical models try to explain these phenomena, and a more accurate measurement of positrons at higher energies is needed to help test them. The currently used methods to reject the proton background at high energies involve extrapolating shower features from the ECAL to use as inputs for boosted decision tree and likelihood classifiers. We present a new approach for particle identification with the AMS ECAL using deep learning (DL). By taking the energy deposition within all the ECAL cells as an input and treating them as pixels in an image-like format, we train an MLP, a CNN, and multiple ResNets and convolutional vision transformers (CvTs) as shower classifiers. Proton rejection performance is evaluated using Monte Carlo (MC) events and ISS data separately. For MC, using events with a reconstructed energy between 0.2–2 TeV, at 90% electron accuracy, the proton rejection power of our CvT model is more than five times that of the other DL models. Similarly, for ISS data with a reconstructed energy between 50–70 GeV, the proton rejection power of our CvT model is more than 2.5 times that of the other DL models.

Identification of positrons and electrons at TeV energy scale using the AMS tracker, time-of-flight counters, and electromagnetic calorimeter

The Alpha Magnetic Spectrometer, AMS, is successfully collecting data since its installation on the International Space Station on May 19, 2011. One of the main AMS objectives is the measurement of cosmic ray positrons and electrons at the highest energies. We present a new technique for the identification of electrons and positrons, which is used in the AMS data analysis in the energy range up to a few TeV. The focus of this article is on the rejection of the proton background using the AMS silicon tracker, the time-of-flight counters, and the electromagnetic calorimeter.

Developing an alternative calorimeter solution for the future Muon Collider: The Crilin design

The Crilin calorimeter represents a novel approach in the development of electromagnetic calorimeters for future colliders, especially for a Muon Collider. This paper details the design and performance of the innovative semi-homogeneous Crilin calorimeter, highlighting its capabilities in mitigating beam-induced backgrounds (BIB) while maintaining excellent time resolution (less than 50 ps), longitudinal segmentation, and fine granularity. These performances are achieved throughout a series of stackable and interchangeable lead fluoride (PbF2) crystal matrices readout by surface-mount UV-extended Silicon Photo-multipliers. Simulated and experimental results demonstrate the Crilin design’s potential to work as an efficient and cost-effective alternative to traditional electromagnetic calorimeters. Crilin radiation tolerance is discussed, as measured in several irradiation campaigns, as well as timing performances during a beam test at CERN-H2 with 120 GeV electrons for the latest prototype, Proto-1. Additionally, a description of the results from a recent beam test conducted at the LNF Beam Test Facility with 450 MeV electrons is provided, aiming to measure light yield losses due to irradiation.

PICOSEC-Micromegas Detector, an innovative solution for Lepton Time Tagging

The PICOSEC-Micromegas (PICOSEC-MM) detector is a novel gaseous detector designed for precise timing resolution in experimental measurements. It eliminates time jitter from charged particles in ionization gaps by using extreme UV Cherenkov light emitted in a crystal, detected by a Micromegas photodetector with an appropriate photocathode. The first single-channel prototype tested in 150 GeV/c muon beams achieved a timing resolution below 25 ps, a significant improvement compared to standard Micropattern Gaseous Detectors (MPGDs). This work explores the specifications for applying these detectors in monitored neutrino beams for the ENUBET Project. Key aspects include exploring resistive technologies, resilient photocathodes, and scalable electronics. New 7-pad resistive detectors are designed to handle the particle flux. In this paper, two potential scenarios are briefly considered: tagging electromagnetic showers with a timing resolution below 30ps in an electromagnetic calorimeter as well as individual particles (mainly muons) with about 20ps respectively.

High-precision alignment techniques for realizing an ultracompact electromagnetic calorimeters using oriented high-z scintillator crystals

Electromagnetic calorimeters used in high-energy physics and astrophysics rely heavily on high-Z inorganic scintillators, such as lead tungstate (PbWO4 or PWO). The crystalline structure and lattice orientation of inorganic scintillators are frequently underestimated in detector design, even though it is known that the crystalline lattice strongly modifies the features of the electromagnetic processes inside the crystal. A novel method has been developed for precisely bonding PWO crystals with aligned atomic planes within 100 μrad, exploiting X-ray diffraction (XRD) to accurately measure miscut angles. This method demonstrates the possibility to align a layer of crystals along the same crystallographic direction, opening a new technological path towards the development of next-generation electromagnetic calorimeters.

The SHADOWS calorimeter — A pointing calorimeter for future beam dump experiments

This contribution presents advancements in the design and testing of an electromagnetic calorimeter intended for the detection of feebly interacting particles in future beam dump experiments. The calorimeter is designed to accurately reconstruct particle decays into photons, focusing on achieving precise energy and direction measurements. We propose a plastic scintillator-based calorimeter capable of providing the necessary resolution for these measurements. The pointing capability has been validated through GEANT4 simulations, optimizing scintillator granularity for effective shower direction reconstruction. Additionally, we detail the prototyping efforts and the first results of test beam measurements conducted at DESY.

Cosmic ray test of shashlik electromagnetic calorimeter modules for NICA-MPD

The electromagnetic calorimeter (ECal) plays a critical role in high-energy particle colliders, by measuring the energy of particles that interact primarily via the electromagnetic interaction such as electrons, positrons and photons. Fudan University has undertaken part of the mass production of ECal modules for the Multi-Purpose Detector (MPD) project of the Nuclotron-based Ion Collider fAcility (NICA). Preliminary tests on the ECal modules is an essential step in detector construction. This article introduces the cosmic ray testing of the ECal modules produced by Fudan University. We tested the cosmic ray under the conditions of horizontal and vertical placement of the module, calculated the average number of generated photons, and estimated the module’s energy resolution. Moreover we analyzed the time resolution performance of the device. The test results indicate that when cosmic rays vertically traverse the entire ECal, the number of photons produced can reach 834, with a potential energy resolution less than 5%/E. The time resolution is 189ps. Based on the comprehensive test results, the production of the ECal meets the design requirements.

ALLEGRO FCC-ee detector concept & Noble liquid calorimetry

ALLEGRO is a proposed FCC-ee general-purpose detector concept with a noble liquid (NL) electromagnetic calorimeter (ECAL) as a central feature. Calorimetry based on liquified noble gases is a well-proven technology that has been successfully applied in numerous high-energy physics (HEP) experiments. This technology provides excellent energy resolution, linearity, stability, uniformity and timing properties at a reasonable cost, making it a strong candidate for future HEP experiments. By using multi-layer printed circuit boards as readout electrodes, we can build a calorimeter with almost arbitrarily high granularity. This in turn allows for four-dimensional imaging, machine learning algorithms and particle-flow reconstruction to be fully exploited. In this proceedings contribution we give an overview to the ALLEGRO concept and present the ongoing R&D work for adapting NL calorimetry to an ECAL of a lepton collider experiment. In addition to simulation studies and expected performance, we show results on signal extraction and noise mitigation studies made with readout electrode prototypes and compare the measurements to simulations, as well as discuss test results of absorber prototypes. In addition we present progress of the mechanical design project and status of the barrel ECAL test-beam prototype module development.

The Mu2e Digitizer ReAdout Controller (DiRAC): Characterization and radiation hardness

The Mu2e experiment at Fermilab will search for the neutrino-less coherent conversion of a muon into an electron in the field of a nucleus. Mu2e detectors comprise a straw tracker, an electromagnetic calorimeter and a veto for cosmic rays. The calorimeter employs 1348 Cesium Iodide crystals readout by silicon photo-multipliers and fast front-end, and digitization electronics. The digitization board is named DiRAC (Digitizer ReAdout Controller) and 140 cards are needed for the readout of the full calorimeter. The DiRACs are hosted in crates located on the external surface of calorimeter disks, inside the detector solenoid cryostat and must sustain very high radiation and magnetic field so it was necessary to fully qualify it. Several version of prototypes were validated for operation in a high-vacuum (10−4 Torr) and under a 1T magnetic field. An extensive radiation hardness qualification campaign, carried out with photons, 14 MeV neutron beams, and 200 MeV protons, certified the DiRAC design to sustain doses up to 12 krad, neutron fluences up to ∼10111MeV neq/cm2, and very low occurrences of single-event effects. The qualification campaigns and quality assurance procedures will be reviewed.

Readout studies for the HIKE main electromagnetic calorimeter

The High-Intensity Kaon Experiment (HIKE) is a proposed future kaon experiment designed for the CERN SPS accelerator. To cope with a planned increase of beam intensity by a factor of 4 with respect to the current NA62 experiment, the new Main Electromagnetic Calorimeter (MEC) has to provide a similar improvement in time resolution over the current Liquid Krypton calorimeter, down to ≈100ps for a 40GeV photon, while maintaining a comparable energy and spatial resolution. To achieve these goals, the planned detector consists of a shashlik calorimeter paired with high-resolution analog to digital converter for both energy deposit and arrival time estimates, operating at fast sample rates (∼1GHz).This contribution describes a prototype of the readout system using commercial “off-the-shelf” boards. In particular, we focused on feature extraction and tested it using a pulse generator. We included a data readout logic based on the TCP/IP protocol allows for easy deployment in beam tests.

Electron Identification with the Electromagnetic Calorimeter and Its Application for Charmonia Studies in the Experiment ALICE3 at the LHC

Electron Identification with the Electromagnetic Calorimeter and Its Application for Charmonia Studies in the Experiment ALICE3 at the LHC

Scintillating sampling ECAL technology for the LHCb PicoCal

The aim of the LHCb Upgrade II is to operate at a luminosity of up to 1.5×1034 cm2s−1 to collect a data set of 300 fb−1. The required substantial modifications of the current LHCb electromagnetic calorimeter due to high radiation doses in the central region and increased particle densities are referred to as PicoCal. Modification of the ECAL already during Long Shutdown 3 (LS3) will reduce the occupancy and mitigate substantial ageing effects in the central region after Run 3. Several scintillating sampling ECAL technologies are currently being investigated in an ongoing R&D campaign: Spaghetti Calorimeter (SpaCal) with garnet scintillating crystals and tungsten absorber, SpaCal with scintillating plastic fibres and tungsten or lead absorber, and Shashlik with polystyrene tiles, lead absorber and fast WLS-fibres. Timing capabilities with tens of picoseconds precision and increased granularity with denser absorber in the central region are needed for pile-up mitigation. Time resolutions of better than 20 ps at high energy were observed in test beam measurements of SpaCal and Shashlik prototype modules.

Soft Margin Spectral Normalization for GANs

In this paper, we explore the use of Generative Adversarial Networks (GANs) to speed up the simulation process while ensuring that the generated results are consistent in terms of physics metrics. Our main focus is the application of spectral normalization for GANs to generate electromagnetic calorimeter (ECAL) response data, which is a crucial component of the LHCb. We propose an approach that allows to balance between model’s capacity and stability during training procedure, compare it with previously published ones and study the relationship between proposed method’s hyperparameters and quality of generated objects. We show that the tuning of normalization method’s hyperparameters boosts the quality of generative model.

Performance of the electromagnetic and hadronic prototype segments of the ALICE Forward Calorimeter

We present the performance of a full-length prototype of the ALICE Forward Calorimeter (FoCal). The detector is composed of a silicon-tungsten electromagnetic sampling calorimeter with longitudinal and transverse segmentation (FoCal-E) of about 20X 0 and a hadronic copper-scintillating-fiber calorimeter (FoCal-H) of about 5λ int. The data were taken in various test beam campaigns between 2021 and 2023 at the CERN PS and SPS beam lines with hadron beams up to energies of 350 GeV, and electron beams up to 300 GeV. Regarding FoCal-E, we report a comprehensive analysis of its response to minimum ionizing particles across all pad layers, employing various operational modes including different pre-amplifier and bias voltage settings. The longitudinal shower profile of electromagnetic showers is measured with a layer-wise segmentation of 1X 0. As a projection to the performance of the final detector in electromagnetic showers, we demonstrate linearity in the full energy range, and show that the energy resolution fulfills the requirements for the physics needs. Additionally, the performance to separate two-showers events was studied by quantifying the transverse shower width. Regarding FoCal-H, we report a detailed analysis of the response to hadron beams between 60 and 350 GeV. The results are compared to simulations obtained with a Geant4 model of the test beam setup, which in particular for FoCal-E are in good agreement with the data. The energy resolution of FoCal-E was found to be lower than 3% at energies larger than 100 GeV. The response of FoCal-H to hadron beams was found to be linear, albeit with a significant intercept that is about factor 2 larger than in simulations. Its resolution, which is non-Gaussian and generally larger than in simulations, was quantified using the FWHM, and decreases from about 16% at 100 GeV to about 11% at 350 GeV. The discrepancy to simulations, which is particularly evident at low hadron energies, needs to be further investigated.

Machine learning assisted reconstruction of positron-on-target annihilation events in the PADME experiment

Abstract The PADME Experiment at the Laboratori Nationali di Frascati, INFN is used in the search for a Dark photon, produced with an ordinary photon in electron-positron annihilation events. The energy of the photons, emitted in the annihilation is measured using a segmented electromagnetic calorimeter. Machine learning methods consisting of various convolutional neural networks are used for the reconstruction of close-in-time signals with high resolution. These algorithms were used on two-photon annihilation events e + e − → γγ to calibrate the photon energy values. In order to calibrate the neural network output from signal amplitude to energy, the machine learning based results were compared to the conventional methods used for reconstructing the signals. The use of machine learning models for reconstructing real data and the process of calibrating the machine learning method output are presented and discussed.

Improvement in the electron/positron proton separation based on the Electromagnetic Calorimeter of the Alpha Magnetic Spectrometer

Improvement in the electron/positron proton separation based on the Electromagnetic Calorimeter of the Alpha Magnetic Spectrometer

Reconstruction of electromagnetic showers in calorimeters using Deep Learning

The precise reconstruction of properties of photons and electrons in modern high energy physics detectors, such as the CMS or ATLAS experiments, plays a crucial role in numerous physics results. Conventional geometrical algorithms are used to reconstruct the energy and position of these particles from the showers they induce in the electromagnetic calorimeter. Despite their accuracy and efficiency, these methods still suffer from several limitations, such as low-energy background and limited capacity to reconstruct close-by particles. This paper introduces an innovative machine-learning technique to measure the energy and position of photons and electrons based on convolutional and graph neural networks, taking the geometry of the CMS electromagnetic calorimeter as an example. The developed network demonstrates a significant improvement in resolution both for photon energy and position predictions compared to the algorithm used in CMS. Notably, one of the main advantages of this new approach is its ability to better distinguish between multiple close-by electromagnetic showers.

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

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.

A SoC-FPGA based readout platform for the free-running AMBER data acquisition system

Owing to a change in the scope of physics research, the AMBER spectrometer at CERN is undergoing an update on its instrumentation and trigger strategy for the data acquisition system. One of the key updates is the adoption of a free-running and trigger-less operation, which differs from the predecessor of AMBER, COMPASS, by working in a continuous mode but in an event-based triggered way.In this article, we present a multichannel data-acquisition platform developed in line with the next generation of trigger-less and free-running data acquisition systems. The platform is based on a pre-existing Mezzanine Sampling ADC board for the analog-to-digital conversion and a Xilinx Zynq Ultrascale+ System on Module for online real-time data processing. We also present the evaluation of the system operating in continuous mode, taking data from a 25-element electromagnetic calorimeter prototype with a muon beam, during the first AMBER pilot run. The acquired data were used for noise and pulse shape studies required for the design of the algorithms for lossless compression and data feature extraction needed for trigger-less operation.