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arXiv · 2510.24329

A Domain Adaptive Position Reconstruction Method for Time Projection Chamber based on Deep Neural Network

Xiaoran Guo·Fei Gao·Kaihang Li·Qing Lin·Jiajun Liu·Lijun Tong·Xiang Xiao·Lingfeng Xie·Yifei Zhao
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Abstract

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.

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Xiaoran Guo, Fei Gao, Kaihang Li, Qing Lin, Jiajun Liu, Lijun Tong, Xiang Xiao, Lingfeng Xie, Yifei Zhao. 2025-10-28. A Domain Adaptive Position Reconstruction Method for Time Projection Chamber based on Deep Neural Network. https://arxiv.org/abs/2510.24329

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