The particle identification (PID) of hadrons plays a crucial role in particle physics experiments, especially in flavor physics and jet tagging. The cluster-counting method, which measures the number of primary ionizations in gaseous detectors, is a promising breakthrough in PID. However, developing an effective reconstruction algorithm for cluster counting remains challenging. To address this challenge, we propose a cluster-counting algorithm based on long short-term memory and dynamic graph convolutional neural networks for the CEPC drift chamber. Experiments on Monte Carlo simulated samples demonstrate that our machine-learning-based algorithm surpasses traditional methods. It improves the K/π separation of PID by 10\%, meeting the PID requirements of CEPC.
@article{arxiv.2402.16493,
title = {Cluster Counting Algorithm for the CEPC Drift Chamber using LSTM and DGCNN},
author = {Zhefei Tian and Guang Zhao and Linghui Wu and Zhenyu Zhang and Xiang Zhou and Shuiting Xin and Shuaiyi Liu and Gang Li and Mingyi Dong and Shengsen Sun},
journal= {arXiv preprint arXiv:2402.16493},
year = {2025}
}