The High Energy cosmic-Radiation Detection (HERD) facility is a dedicated high energy astronomy and particle physics experiment planned to be installed on the Chinese space station, aiming to detect high-energy cosmic rays (GeV ∼ PeV) and high-energy gamma rays (> 500 MeV). The Plastic Scintillator Detector (PSD) is one of the sub-detectors of HERD, with its main function of providing real-time anti-conincidence signals for gamma-ray detection and the secondary function of measuring the charge of cosmic-rays. In 2023, a prototype of PSD was developed and tested at CERN PS&SPS. In this paper, we investigate the position response of the PSD using two reconstruction algorithms: the classic dual-readout ratio and the deep learning method (KAN & MLP neural network). With the latter, we achieved a position resolution of 2 mm (1σ), which is significantly better than the classic method.
@article{arxiv.2412.18301,
title = {Position reconstruction using deep learning for the HERD PSD beam test},
author = {Longkun Yu and Chenxing Zhang and Dongya Guo and Yaqing Liu and Wenxi Peng and Zhigang Wang and Bing Lu and Rui Qiao and Ke Gong and Jing Wang and Shuai Yang and Yongye Li},
journal= {arXiv preprint arXiv:2412.18301},
year = {2024}
}