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

2020/05/08 by Fuyue Wang, Dong Han, Wang, Fuyue +3
Physics and Astronomy · #Advanced Frequency and Time Standards #Atomic and Subatomic Physics Research #FOS: Physical sciences #Instrumentation and Detectors (physics.ins-det) #Particle Detector Development and Performance

paper · pdf · doi:10.48550/arxiv.2005.03903

openalex publication_date 2020/05/08 · openalex created_date 2020/05/13 · openalex updated_date 2026/07/28

Abstract

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 σ separation of the π/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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