Muon identification with Deep Neural Network in the Belle II K-Long and Muon detector
Abstract
Muon identification is crucial for elementary particle physics experiments. At the Belle II experiment, muons and pions with momenta greater than 0.7 GeV/c are distinguished by their penetration ability through the and Muon (KLM) sub-detector, which is the outermost sub-detector of Belle II. In this paper, we first discuss the possible room for identification performance improvement and then present a new method based on Deep Neural Network (DNN). This DNN model utilizes the KLM hit pattern variables as the input and thus can digest the penetration information better than the current algorithm. We test the new method in simulation and find that the pion fake rate (specificity) is reduced from 4.1% to 1.6% at a muon efficiency (recall) of 90%.
Keywords
Cite
@article{arxiv.2503.11351,
title = {Muon identification with Deep Neural Network in the Belle II K-Long and Muon detector},
author = {Zihan Wang and Yo Sato and Akimasa Ishikawa and Yutaka Ushiroda and Kenta Uno and Kazutaka Sumisawa and Naveen Kumar Baghel and Seema Choudhury and Giacomo De Pietro and Christopher Ketter and Haruki Kindo and Tommy Lam and Frank Meier and Soeren Prell},
journal= {arXiv preprint arXiv:2503.11351},
year = {2025}
}