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Bibliographic Details
Main Authors:	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
Published:	2026
Subjects:	Machine Learning High Energy Physics - Experiment
Online Access:	https://arxiv.org/abs/2602.14571
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Table of 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.