BESIII Drift Chamber Tracking with Machine Learning

被引：0
|
作者：
Zhang, Yao [1 ]
Yuan, Ye [1 ]
Ma, Qiumei [1 ]
机构：
[1] Inst High Energy Phys, Beijng Yuquan Rd 19B, Beijing, Peoples R China
来源：
24TH INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY AND NUCLEAR PHYSICS (CHEP 2019) | 2020年 / 245卷
关键词：
D O I：
10.1051/epjconf/202024502033
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
The tracking efficiency and the quality for the drift chamber of the BESIII experiment is essential to the physics analysis. The tracking efficiency of the drift chamber of BESIII is high for the high momentum tracks but still have room to improve for the low momentum tracks, especially for the tracks with multiple turn. A novel way to use a convolutional network called U-Net network is represented to solve the identification of the first turn's hits for the multiple-turn tracks.
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页数：6
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