Application of neural network for photon-hadron discrimination in a preshower detector in high energy heavy ion collisions

TL;DR

This paper demonstrates that neural networks can effectively distinguish photons from hadrons in high-density environments of heavy ion collisions at LHC energies, using a preshower detector and charged particle veto.

Contribution

It introduces a neural network approach for photon-hadron discrimination in a preshower detector under extreme particle density conditions.

Findings

Neural network achieves satisfactory discrimination performance.

Effective separation of photons and hadrons in high-density collision environments.

Applicable to forward region detection in heavy ion collisions.

Abstract

Using a combination of a preshower detector and a charged particle veto, it is shown that the neural network method is able to provide satisfactory discrimination between photons and hadrons in the case of extremely high particle density produced in the forward region of heavy ion collisions at the LHC energy.