DCTracks: An Open Dataset for Machine Learning-Based Drift Chamber Track Reconstruction
Fuente:
arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866912906985078784 |
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| author | Liyan, Qian Yao, Zhang Ye, Yuan Zhaoke, Zhang Jin, Fang Shimiao, Jiang Jin, Zhang Ke, Li Beijiang, Liu Chenglin, Xu Yifan, Zhang Xiaoqian, Jia Xiaoshuai, Qin Xingtao, Huang |
| author_facet | Liyan, Qian Yao, Zhang Ye, Yuan Zhaoke, Zhang Jin, Fang Shimiao, Jiang Jin, Zhang Ke, Li Beijiang, Liu Chenglin, Xu Yifan, Zhang Xiaoqian, Jia Xiaoshuai, Qin Xingtao, Huang |
| contents | We introduce a Monte Carlo (MC) dataset of single- and two-track drift chamber events to advance Machine Learning (ML)-based track reconstruction. To enable standardized and comparable evaluation, we define track reconstruction specific metrics and report results for traditional track reconstruction algorithms and a Graph Neural Networks (GNNs) method, facilitating rigorous, reproducible validation for future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_14571 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DCTracks: An Open Dataset for Machine Learning-Based Drift Chamber Track Reconstruction Liyan, Qian Yao, Zhang Ye, Yuan Zhaoke, Zhang Jin, Fang Shimiao, Jiang Jin, Zhang Ke, Li Beijiang, Liu Chenglin, Xu Yifan, Zhang Xiaoqian, Jia Xiaoshuai, Qin Xingtao, Huang Machine Learning High Energy Physics - Experiment We introduce a Monte Carlo (MC) dataset of single- and two-track drift chamber events to advance Machine Learning (ML)-based track reconstruction. To enable standardized and comparable evaluation, we define track reconstruction specific metrics and report results for traditional track reconstruction algorithms and a Graph Neural Networks (GNNs) method, facilitating rigorous, reproducible validation for future research. |
| title | DCTracks: An Open Dataset for Machine Learning-Based Drift Chamber Track Reconstruction |
| topic | Machine Learning High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2602.14571 |