DCTracks: An Open Dataset for Machine Learning-Based Drift Chamber Track Reconstruction

Fuente: arXiv
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Auteurs principaux: 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
Format: Preprint
Publié: 2026
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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