Shower Identification in Calorimeter using Deep Learning

TL;DR

This paper explores the application of deep learning to identify and reconstruct particle showers in calorimeters, improving accuracy and efficiency for high-energy collision experiments.

Contribution

It introduces a deep learning approach for calorimeter shower identification, demonstrating enhanced performance over traditional methods.

Findings

Deep learning models improve shower identification accuracy.

The approach is computationally efficient for real-time analysis.

Enhanced reconstruction capabilities for high-energy physics experiments.

Abstract

Pions constitute nearly $70\%$ of final state particles in ultra high energy collisions. They act as a probe to understand the statistical properties of Quantum Chromodynamics (QCD) matter i.e. Quark Gluon Plasma (QGP) created in such relativistic heavy ion collisions (HIC). Apart from this, direct photons are the most versatile tools to study relativistic HIC. They are produced, by various mechanisms, during the entire space-time history of the strongly interacting system. Direct photons provide measure of jet-quenching when compared with other quark or gluon jets. The $\pi^{0}$ decay into two photons make the identification of non-correlated gamma coming from another process cumbersome in the Electromagnetic Calorimeter. We investigate the use of deep learning architecture for reconstruction and identification of single as well as multi particles showers produced in calorimeter by particles created in high energy collisions. We utilize the data of electromagnetic shower at calorimeter cell-level to train the network and show improvements for identification and characterization. These networks are fast and computationally inexpensive for particle shower identification and reconstruction for current and future experiments at particle colliders.