A Domain Adaptive Position Reconstruction Method for Time Projection Chamber based on Deep Neural Network
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912673985200128 |
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| author | Guo, Xiaoran Gao, Fei Li, Kaihang Lin, Qing Liu, Jiajun Tong, Lijun Xiao, Xiang Xie, Lingfeng Zhao, Yifei |
| author_facet | Guo, Xiaoran Gao, Fei Li, Kaihang Lin, Qing Liu, Jiajun Tong, Lijun Xiao, Xiang Xie, Lingfeng Zhao, Yifei |
| contents | Transverse position reconstruction in a Time Projection Chamber (TPC) is crucial for accurate particle tracking and classification, and is typically accomplished using machine learning techniques. However, these methods often exhibit biases and limited resolution due to incompatibility between real experimental data and simulated training samples. To mitigate this issue, we present a domain-adaptive reconstruction approach based on a cycle-consistent generative adversarial network. In the prototype detector, the application of this method led to a 60.6% increase in the reconstructed radial boundary. Scaling this method to a simulated 50-kg TPC, by evaluating the resolution of simulated events, an additional improvement of at least 27% is achieved. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_24329 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Domain Adaptive Position Reconstruction Method for Time Projection Chamber based on Deep Neural Network Guo, Xiaoran Gao, Fei Li, Kaihang Lin, Qing Liu, Jiajun Tong, Lijun Xiao, Xiang Xie, Lingfeng Zhao, Yifei High Energy Physics - Experiment Transverse position reconstruction in a Time Projection Chamber (TPC) is crucial for accurate particle tracking and classification, and is typically accomplished using machine learning techniques. However, these methods often exhibit biases and limited resolution due to incompatibility between real experimental data and simulated training samples. To mitigate this issue, we present a domain-adaptive reconstruction approach based on a cycle-consistent generative adversarial network. In the prototype detector, the application of this method led to a 60.6% increase in the reconstructed radial boundary. Scaling this method to a simulated 50-kg TPC, by evaluating the resolution of simulated events, an additional improvement of at least 27% is achieved. |
| title | A Domain Adaptive Position Reconstruction Method for Time Projection Chamber based on Deep Neural Network |
| topic | High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2510.24329 |