The study of a new time reconstruction method for MRPC read out by waveform digitizer

TL;DR

This paper introduces a neural network-based waveform analysis method that significantly improves the time resolution of MRPC detectors, achieving around 36 ps in cosmic ray tests, which is promising for particle identification in high-energy physics experiments.

Contribution

The paper presents a novel neural network approach to analyze MRPC waveforms, enhancing time resolution beyond traditional methods for particle detection.

Findings

Achieved 36 ps time resolution with a 6-gap MRPC in cosmic ray tests.

Demonstrated potential for further improvement with thinner MRPCs.

Showed neural network analysis outperforms conventional waveform processing.

Abstract

The measurement of the $K^{\pm}$ production in the Semi-Inclusive Deep Inelastic Scattering (SIDIS) can provide further knowledge about the structure of nucleon, and thus it is purposed in the Solenoidal Large Intensity Device(SoLID) at Jefferson Lab(JLab). In this experiment, the identification of the kaons is planed to be accomplished with the Multi-gap Resistive Plate Chambers(MRPC), and the requirement for the time resolution is around 20 $\rm ps$. This is very challenging for the present MRPC systems (typical resolution 60 $\rm ps$), while in this paper, it is proved that the performance can be improved largely if the signal waveform is obtained and analyzed with a neural network method. In a cosmic ray experiment, the time resolution of a 6-gap 0.25$\rm mm$-thick MRPC reaches 36 $\rm ps$ with this method, and a even better performance is expected with a thinner MRPC.