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

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.