Application of CNN to a fine segmented scintillator detector for a single particle and neutrino-nucleon event

Abstract

This paper presents studies on application of convolutional neural network (CNN) to GEANT4 optical simulation data generated with a scintillator detector subdivided into 1 cubic cm, which is designed for the long-baseline neutrino experiment. Classification of interaction, regression of momentum, and segmentation of hits are demonstrated for single particle and neutrino-nucleon interaction events with well established CNN architectures by feeding reconstructed 2D projection images. In the study it is shown that the application of CNN to the 1 cm subdivided scintillator detector can provide a factor about 2 better momentum resolution compared to a standard method, as well as a classification capability of about 94% for the single particle and 70% for the neutrino-nucleon interaction events. Cross-section analyses with CNN is also shown to be feasible.