Time-of-flight discrimination between gamma-rays and neutrons by neural networks

TL;DR

This paper demonstrates that neural networks can effectively discriminate between gamma-ray and neutron events using time-of-flight data, improving spectral analysis in gamma-ray spectroscopy.

Contribution

The study introduces a neural network approach for classifying gamma-ray and neutron events based on time-of-flight distributions, showing improved discrimination capabilities.

Findings

Neural networks accurately classify gamma-ray and neutron events.

ANN-based method enhances spectral analysis in gamma-ray spectroscopy.

Experimental data confirms effective TOF discrimination.

Abstract

In gamma-ray spectroscopy, a number of neutrons are emitted from the nuclei together with the gamma-rays and these neutrons influence gamma-ray spectra. An obvious method of separating between neutrons and gamma-rays is based on the time-of-flight (tof) technique. This work aims obtaining tof distributions of gamma-rays and neutrons by using feed-forward artificial neural network (ANN). It was shown that, ANN can correctly classify gamma-ray and neutron events. Testing of trained networks on experimental data clearly shows up tof discrimination of gamma-rays and neutrons.