dc.contributor.author | Akkoyun, Serkan | |
dc.contributor.author | Yildiz, Nihat | |
dc.date.accessioned | 2019-07-27T12:10:23Z | |
dc.date.accessioned | 2019-07-28T10:03:29Z | |
dc.date.available | 2019-07-27T12:10:23Z | |
dc.date.available | 2019-07-28T10:03:29Z | |
dc.date.issued | 2012 | |
dc.identifier.issn | 1350-4487 | |
dc.identifier.uri | https://dx.doi.org/10.1016/j.radmeas.2012.06.018 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12418/9048 | |
dc.description | WOS: 000309635100002 | en_US |
dc.description.abstract | 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. (c) 2012 Elsevier Ltd. All rights reserved. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | en_US |
dc.relation.isversionof | 10.1016/j.radmeas.2012.06.018 | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Artificial neural network | en_US |
dc.subject | Empirical physical formula | en_US |
dc.subject | HPGe | en_US |
dc.subject | Gamma-ray tracking | en_US |
dc.subject | Recoiling nucleus | en_US |
dc.subject | AGATA | en_US |
dc.title | Consistent empirical physical formula construction for recoil energy distribution in HPGe detectors by using artificial neural networks | en_US |
dc.type | article | en_US |
dc.relation.journal | RADIATION MEASUREMENTS | en_US |
dc.contributor.department | [Akkoyun, Serkan -- Yildiz, Nihat] Cumhuriyet Univ, Dept Phys, Fac Sci, TR-58140 Sivas, Turkey | en_US |
dc.identifier.volume | 47 | en_US |
dc.identifier.issue | 8 | en_US |
dc.identifier.endpage | 576 | en_US |
dc.identifier.startpage | 571 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |