  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.
