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

A detailed study of particle identification by the Focusing Aerogel Ring-Imaging Cherenkov subsystem at the future charm superfactory detector is presented. A dedicated signal ring reconstruction algorithm was implemented in the detector simulation, considering realistic operating conditions. The algorithm performance was tested using single particles generated within the Aurora framework. Two boosted decision trees-based classifiers for particle identification were developed for moderate and the most conservative assumptions about photosensor noise levels. The approach is validated with the analysis of the D0→Kμνμ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$D^0\rightarrow K\mu \nu _\mu$$\end{document} decays, for which the systematic uncertainty and background contribution related to the π/μ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pi /\mu$$\end{document} separation performance can be minimized owing to the high efficiency of the particle identification algorithm.