A neural network based algorithm for MRPC time reconstruction

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.

arXiv comment6 pages, 3 figures

References in corpus (4)

• A neural network clustering algorithm for the ATLAS silicon pixel detector
• Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets
• Ultimate position resolution of pixel clusters with binary readout for particle tracking
• A standalone simulation framework of the MRPC detector read out in waveforms

Cited by in corpus (2)

• Next Frontiers in Particle Physics Detectors: INSTR2020 Summary and a Look into the Future
• The study of a new time reconstruction method for MRPC read out by waveform digitizer