A neural network based algorithm for MRPC time reconstruction

TL;DR

This paper introduces a deep neural network-based algorithm for MRPC time reconstruction, significantly enhancing timing resolution and potentially surpassing current methods for high-precision timing in physics experiments.

Contribution

The paper presents a novel neural network algorithm that improves MRPC time resolution by about 10 ps, offering a new approach beyond traditional geometric modifications.

Findings

Achieved approximately 10 ps improvement in MRPC time resolution.

Potential to reach below 30 ps resolution with neural network method.

Provides an alternative to the ToT method for high-precision timing.

Abstract

Multi-gap Resistive Plate Chamber(MRPC) is a widely used timing detector with a typical time resolution of about 60 ps. This makes MRPC an optimal choice for the time of flight(ToF) system in many large physics experiments. The prior work on improving the time resolution is mainly focused on altering the detector geometry, and therefore the improvement of the data analysis algorithm has not been fully explored. This paper proposes a new time reconstruction algorithm based on the deep neural networks(NN) and improves the MRPC time resolution by about 10 ps. Since the development of the high energy physics experiments has pushed the timing requirements for the MRPC to a higher level, this algorithm could become a potential substitution of the time over threshold(ToT) method to achieve a time resolution below 30 ps.