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In the context of a gas-sampling Digital Hadronic Calorimeter (DHCAL), we explore the potential of using Graph Neural Networks (GNN) for hadron energy reconstruction and Particle Identification (PID) in future collider experiments. For PID,…
The electromagnetic calorimeter (ECAL) of the AMS-02 experiment is a 3-dimensional sampling calorimeter, made of lead and scintillating fibers. The detector allows for a high granularity, with 18 samplings in the longitudinal direction, and…
In the present work, accurate determination of single-particle ignition is focused on using high-speed optical diagnostics combined with machine learning approaches. Ignition of individual particles in a laminar flow reactor are visualized…
A high-energy e+e- collider, such as the ILC or CLIC, is arguably the best option to complement and extend the LHC physics programme. A lepton collider will allow for exploration of Standard Model Physics, such as precise measurements of…
Recent advancements in machine learning have led to an exponential increase in computational demands, driving the need for innovative computing platforms. Quantum computing, with its Hilbert space scaling exponentially with the number of…
Particle identification is one of the core tasks in the data analysis pipeline at the Large Hadron Collider (LHC). Statistically, this entails the identification of rare signal events buried in immense backgrounds that mimic the properties…
This paper discusses the hadronic energy reconstruction of two combined electromagnetic and hadronic calorimeter systems using physics prototypes of the CALICE collaboration: the silicon-tungsten electromagnetic calorimeter (Si-W ECAL) and…
Low-loss electron energy loss spectroscopy (EELS) has emerged as a technique of choice for exploring the localization of plasmonic phenomena at the nanometer level, necessitating analysis of physical behaviors from 3D spectral data sets.…
We present a first proof of concept to directly use neural network based pattern recognition to trigger on distinct calorimeter signatures from displaced particles, such as those that arise from the decays of exotic long-lived particles.…
Axion-like particles (ALPs) appear in various new physics models with spontaneous global symmetry breaking. When the ALP mass is in the range of MeV to GeV, the cosmology and astrophysics bounds are so far quite weak. In this work, we…
Photon reconstruction in calorimeters represents a crucial challenge in particle physics experiments, especially in high-density environments where shower overlapping probabilities become significant. We present an energy-core-based photon…
The MicroBooNE liquid argon time projection chamber located at Fermilab is a neutrino experiment dedicated to the study of short-baseline oscillations, the measurements of neutrino cross sections in liquid argon, and to the research and…
Most measurements in particle and nuclear physics use matrix-based unfolding algorithms to correct for detector effects. In nearly all cases, the observable is defined analogously at the particle and detector level. We point out that while…
Based on a novel shower reconstruction algorithm, the study of two particle separation with tile hadron calorimeter (HCAL) is performed. The separability of two close-by particles is found to be strongly dependent on transverse and…
Precision measurement of hadronic final states presents complex experimental challenges. The study explores the concept of a gaseous Digital Hadronic Calorimeter (DHCAL) and discusses the potential benefits of employing Graph Neural Network…
The DArk Matter Particle Explorer (DAMPE) is a space experiment designed to search for dark matter indirectly by measuring the spectra of photons, electrons, and positrons up to 10 TeV. The BGO electromagnetic calorimeter (ECAL) is its main…
The precise modeling of subatomic particle interactions and propagation through matter is paramount for the advancement of nuclear and particle physics searches and precision measurements. The most computationally expensive step in the…
The online Data Quality Monitoring system (DQM) of the CMS electromagnetic calorimeter (ECAL) is a crucial operational tool that allows ECAL experts to quickly identify, localize, and diagnose a broad range of detector issues that would…
This paper presents a method to identify electrons using the Cherenkov light emitted when a charged particle travels in air and photons are detected with a Silicon PhotoMultiplier (SiPM). The analysis is based on a photon-counting approach…
Machine learning methods have revolutionized the discovery process of new molecules and materials. However, the intensive training process of neural networks for molecules with ever-increasing complexity has resulted in exponential growth…