کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1884063 | 1043323 | 2012 | 6 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Consistent empirical physical formula construction for recoil energy distribution in HPGe detectors by using artificial neural networks Consistent empirical physical formula construction for recoil energy distribution in HPGe detectors by using artificial neural networks](/preview/png/1884063.png)
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.
Journal: Radiation Measurements - Volume 47, Issue 8, August 2012, Pages 571–576