Hadron Identification Prospects with Granular Calorimeters

Hadron Identification Prospects with Granular Calorimeters

In this work we consider the problem of determining the identity of hadrons at high energies based on the topology of their energy depositions in dense matter, along with the time of the interactions. Using GEANT4 simulations of a homogeneous lead tungstate calorimeter with high transverse and longi...

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Bibliographic Details
Main Authors: Andrea De Vita, Abhishek, Max Aehle, Muhammad Awais, Alessandro Breccia, Riccardo Carroccio, Long Chen, Tommaso Dorigo, Nicolas R. Gauger, Ralf Keidel, Jan Kieseler, Enrico Lupi, Federico Nardi, Xuan Tung Nguyen, Fredrik Sandin, Kylian Schmidt, Pietro Vischia, Joseph Willmore
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Particles
Subjects:
particle detectors
calorimetry
particle identification
physics
machine learning
Online Access:https://www.mdpi.com/2571-712X/8/2/58
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