Improving the time resolution of the MRPC detector using deep-learning algorithms

被引：4
|
作者：
Wang, F. [1 ]
Han, D. [1 ]
Wang, Y. [1 ]
机构：
[1] Tsinghua Univ, Dept Engn Phys, Key Lab Particle & Radiat Imaging, Minist Educ, Beijing 100084, Peoples R China
来源：
JOURNAL OF INSTRUMENTATION | 2020年 / 15卷 / 09期
基金：
中国国家自然科学基金;
关键词：
Data processing methods; Gaseous detectors; Performance of High Energy Physics Detectors;
D O I：
10.1088/1748-0221/15/09/C09033
中图分类号：
TH7 [仪器、仪表];
学科分类号：
0804 ; 080401 ; 081102 ;
摘要：
The multi-gap resistive plate chambers (MRPCs) will be used as the Time-of-Flight (ToF) system in the Solenoidal Large Intensity Device (SoLID). The time resolution required by the experiment for the MRPC system is 20 ps in order to make a 3 sigma separation of the pi/K created in the collisions. To achieve this goal, the whole system including the MRPC detector, the front-end electronics and the readout system will be upgraded. Based on the new system, a time reconstruction algorithm using a combined LSTM (ComLSTM) neural network is proposed. The best time resolution achieved with this algorithm in a cosmic ray test is 16.8 ps, which largely improves the timing ability of the MRPC detector and well satisfies the requirement of the SoLID.
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收藏
页数：9
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