A data-based machine learning approach for RPC time resolution study based on ToF reconstruction

Abstract

Abstract A data-based machine learning approach is proposed to study the properties of time resolution of RPC detectors by measuring the time of flight of cosmic muons. This method utilises a multi-layer perceptron and a type of recurrent neural network called long short-term memory. The neural network is trained with the waveforms of RPC signals digitized by an oscilloscope at a sampling frequency of 10 GHz and a 2 GHz bandwidth. A data augmentation approach is implemented for labelling. Compared to the results from conventional waveform analysis, this approach achieves a better time resolution of 1-mm gap RPCs. Based on the data, the approach has a generalisation capacity for performance studies of other timing detectors.