Performance of the FARICH-based particle identification at charm superfactories using machine learning

TL;DR

This paper evaluates the particle identification performance of the FARICH subsystem at future charm superfactories, using machine learning classifiers and detailed simulations.

Contribution

It introduces Boosted Decision Trees classifiers for particle ID and validates their effectiveness with decay analysis in a simulated environment.

Findings

High efficiency in pion/muon separation reduces systematic uncertainties.

Machine learning classifiers improve particle identification accuracy.

Simulation results demonstrate the potential of the FARICH system for future experiments.

Abstract

A detailed study of the particle identification by the Focusing Aerogel Ring Imaging CHerenkov subsystem at the future charm superfactory detector is presented. The dedicated signal ring reconstruction algorithm is implemented in the detector simulation, the algorithm performance is tested with single particles generated within the Aurora framework. Two Boosted Decision Trees-based classifiers for the particle identification have been developed for various assumptions about photosensor noise levels. The approach is validated with the analysis of the D0->Kmunu decays, for which the systematic uncertainty and background contribution related to the pion/muon separation performance can be minimised due to high efficiency of the particle identification algorithm.