Charged particle identification with the liquid xenon calorimeter of the CMD-3 detector

TL;DR

This paper presents a machine learning-based method using boosted decision trees for charged particle identification in the CMD-3 detector's liquid xenon calorimeter, improving separation of electrons, muons, pions, and kaons across a broad momentum range.

Contribution

It introduces a novel particle identification technique utilizing boosted decision trees trained on calorimeter data, with detailed calibration and simulation tuning for the CMD-3 detector.

Findings

Effective separation of particle types demonstrated in example analyses.

Calibration and simulation tuning improve classifier performance.

Method applicable to high-energy particle physics experiments.

Abstract

The paper describes a method of the charged particle identification, developed for the \mbox{CMD-3} detector, installed at the VEPP-2000 $e^{+}e^{-}$ collider. The method is based on the application of the boosted decision trees classifiers, trained for the optimal separation of electrons, muons, pions and kaons in the momentum range from 100 to $1200~{\rm MeV}/c$. The input variables for the classifiers are linear combinations of the energy depositions of charged particles in 12 layers of the liquid xenon calorimeter of the \mbox{CMD-3}. The event samples for training of the classifiers are taken from the simulation. Various issues of the detector response tuning in simulation and calibration of the calorimeter strip channels are considered. Application of the method is illustrated by the examples of separation of the $e^+e^-(\gamma)$ and $\pi^+\pi^-(\gamma)$ final states and of selection of the $K^+K^-$ final state at high energies.