A Case Study on Deep Learning applied to Capture Cross Section Data Analysis

Abstract

A good data analysis of neutron cross section measurements is necessary for generating high quality and reliable nuclear databases. Artificial intelligence techniques, and in particular deep learning, have proven to be very useful for pattern recognition and data analysis, and thus may be used in the field of experimental nuclear physics. In this publication, we train a neural network in order to improve the capture-to-background ratio of neutron capture data of measurements performed in the time-of-flight facility n_TOF at CERN with the so-called Total Absorption Calorimeter. The evaluation of this deep learning-based method on accurate Monte Carlo simulated measurements with 197Au and 239Pu samples suggests that the capture-to-background ratio can be increased 5 times above the standard method.