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A neural network based algorithm for MRPC position reconstruction

Over the last ten years, muon imaging has attracted much attention due to its possible applications to detect high-Z materials in shipping containers or image very large objects such as volcanoes and large buildings. Precise measurements of the incident and outgoing angles of the cosmic muons are mandatory in this application. Large size (M)RPC detector with sub-millimetre position resolution should be an ideal candidate for the detector system. Prior work on improving the position resolution of MRPC mainly focuses on adjusting read-out panel and the detector geometry, while little work has been done on improving the position reconstruction algorithm. This paper proposes a new position reconstruction algorithm based on the neural networks which has no systematic error and gives better results than the center of gravity algorithm.