Muon identification with Deep Neural Network in the Belle II K-Long and Muon detector

2025/03/14 by Wang, Zihan, Sato, Yo, Ishikawa, Akimasa +11
#FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #Instrumentation and Detectors (physics.ins-det)

paper · doi:10.48550/arxiv.2503.11351

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 K_L and Muon (KLM) sub-detector, which is the outermost sub-detector of Belle II. In this paper, we first discuss the possible room for μ/π 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%.

Related

• Improved calorimetric particle identification in NA62 using machine learning techniques
• Identification of light leptons and pions in the electromagnetic calorimeter of Belle II
• ML-based muon identification using a FNAL-NICADD scintillator chamber for the MID subsystem of ALICE 3
• Design and deployment of a fast neural network for measuring the properties of muons originating from displaced vertices in the CMS Endcap Muon Track Finder
• Estimation of Temporal Muon Signals in Water-Cherenkov Detectors of the Surface Detector of the Pierre Auger Observatory