Simulation of particle identification with the cluster counting technique

TL;DR

This paper demonstrates through simulations that the cluster counting technique enhances particle identification capabilities over traditional dE/dx methods, and discusses algorithms for reproducing cluster distributions using Geant4.

Contribution

It introduces the potential of the cluster counting technique for particle identification and compares it with traditional methods, including algorithms for simulating cluster distributions.

Findings

Cluster counting improves particle separation over dE/dx.

Simulations using Garfield++ validate the technique's effectiveness.

Algorithms for reproducing cluster distributions are discussed.

Abstract

In this paper we show the potential of the cluster counting technique for particle identification. Simulations based on Garfield++ software prove that this technique improves the particle separation capabilities with respect to the ones obtained with the traditional method of dE/dx. Moreover three different algorithms to reproduce the clusters number and the cluster size distribution with Geant4 software are discussed.