Consistent empirical physical formula construction for recoil energy distribution in HPGe detectors using artificial neural networks

TL;DR

This paper develops neural network-based empirical formulas to accurately model recoil energy distributions in HPGe detectors, aiding neutron interaction analysis in gamma-ray tracking for nuclear physics experiments.

Contribution

It introduces layered feed-forward neural network-based empirical physical formulas for modeling recoil energy distributions, enhancing neutron interaction understanding in gamma-ray detectors.

Findings

Neural networks effectively model nonlinear detector responses.

Constructed empirical formulas match experimental recoil energy data.

Method improves neutron interaction analysis in gamma-ray tracking.

Abstract

The gamma-ray tracking technique is one of the highly efficient detection method in experimental nuclear structure physics. On the basis of this method, two gamma-ray tracking arrays, AGATA in Europe and GRETA in the USA, are currently being developed. The interactions of neutrons in these detectors lead to an unwanted background in the gamma-ray spectra. Thus, the interaction points of neutrons in these detectors have to be determined in the gamma-ray tracking process in order to improve photo-peak efficiencies and peak-to-total ratios of the gamma-ray peaks. Therefore, the recoil energy distributions of germanium nuclei due to inelastic scatterings of 1-5 MeV neutrons were obtained both experimentally and using artificial neural networks. Also, for highly nonlinear detector response for recoiling germanium nuclei, we have constructed consistent empirical physical formulas (EPFs) by appropriate layered feed-forward neural networks (LFNNs). These LFNN-EPFs can be used to derive further physical functions which could be relevant to determination of neutron interactions in gamma-ray tracking process.