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arXiv · 2402.16493

Cluster Counting Algorithm for the CEPC Drift Chamber using LSTM and DGCNN

Zhefei Tian·Guang Zhao·Linghui Wu·Zhenyu Zhang·Xiang Zhou·Shuiting Xin·Shuaiyi Liu·Gang Li·Mingyi Dong·Shengsen Sun
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Abstract

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 $K/\pi$ separation of PID by 10\%, meeting the PID requirements of CEPC.

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Zhefei Tian, Guang Zhao, Linghui Wu, Zhenyu Zhang, Xiang Zhou, Shuiting Xin, Shuaiyi Liu, Gang Li, Mingyi Dong, Shengsen Sun. 2024-02-26. Cluster Counting Algorithm for the CEPC Drift Chamber using LSTM and DGCNN. https://doi.org/10.1007/s41365-025-01670-y

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