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

• Published in: Radiation measurements Vol. 47; no. 8; pp. 571 - 576
• Main Authors: Akkoyun, Serkan, Yildiz, Nihat
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.08.2012; Elsevier
• Subjects: AGATA; Artificial neural network; Earth sciences; Earth, ocean, space; Empirical physical formula; Exact sciences and technology; Gamma-ray tracking; Geochronology; HPGe; Isotope geochemistry. Geochronology; Recoiling nucleus; Europe; Artificial neural network; Empirical physical formula; AGATA; Gamma-ray tracking; Recoiling nucleus; HPGe; experimental studies; detection; efficiency; gamma rays; simulation; gamma-ray spectra; neural networks; germanium; neutrons; lead; energy
• ISSN: 1350-4487; 1879-0925
• DOI: 10.1016/j.radmeas.2012.06.018

The gamma-ray tracking technique is a 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 tested. 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. In this paper, the recoil energy distributions of germanium nuclei due to inelastic scatterings of 1–5 MeV neutrons were first obtained by simulation experiments. Secondly, as a novel approach, for these highly nonlinear detector responses of recoiling germanium nuclei, consistent empirical physical formulas (EPFs) were constructed by appropriate feedforward neural networks (LFNNs). The LFNN-EPFs are of explicit mathematical functional form. Therefore, the LFNN-EPFs can be used to derive further physical functions which could be potentially relevant for the determination of neutron interactions in gamma-ray tracking process. ► We generated germanium recoil energy distribution by using artificial neural networks. ► Determination of recoil energy points is important for gamma-ray tracking. ► We obtained empirical physical formulas for germanium recoil energies for several neutron energies.