Cluster Counting Algorithm for the CEPC Drift Chamber using LSTM and DGCNN
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915284440317952 |
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| author | Tian, Zhefei Zhao, Guang Wu, Linghui Zhang, Zhenyu Zhou, Xiang Xin, Shuiting Liu, Shuaiyi Li, Gang Dong, Mingyi Sun, Shengsen |
| author_facet | Tian, Zhefei Zhao, Guang Wu, Linghui Zhang, Zhenyu Zhou, Xiang Xin, Shuiting Liu, Shuaiyi Li, Gang Dong, Mingyi Sun, Shengsen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_16493 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cluster Counting Algorithm for the CEPC Drift Chamber using LSTM and DGCNN Tian, Zhefei Zhao, Guang Wu, Linghui Zhang, Zhenyu Zhou, Xiang Xin, Shuiting Liu, Shuaiyi Li, Gang Dong, Mingyi Sun, Shengsen High Energy Physics - Experiment Instrumentation and Detectors 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. |
| title | Cluster Counting Algorithm for the CEPC Drift Chamber using LSTM and DGCNN |
| topic | High Energy Physics - Experiment Instrumentation and Detectors |
| url | https://arxiv.org/abs/2402.16493 |