Machine Learning for FARICH Reconstruction at NICA SPD

Abstract

In the end-cap region of the SPD detector complex, particle identification will be provided by a Focusing Aerogel RICH detector (FARICH). FARICH’s primary function is to separate pions and kaons in final open charmonia states (momenta below 5 GeV/\(c\)). The optimization of detector parameters, as well as a free-running (triggerless) data acquisition pipeline to be employed in the SPD necessitate a fast and robust method of event reconstruction. In this work, we employ a Convolutional Neural Network (CNN) for particle identification in FARICH. The CNN model achieves a superior separation between pions and kaons compared with traditional approaches. Unlike algorithmic methods, an end-to-end CNN model is able to process a full 2-dimensional detector response and skip the intermediate step of computing particle velocity, solving the particle classification task directly.