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

被引：11

作者：

Akkoyun, S. ^{[1
]}

机构：

[1] Cumhuriyet Univ, Dept Phys, Fac Sci, Sivas, Turkey

来源：

ANNALS OF NUCLEAR ENERGY | 2013年 / 55卷

关键词：

Artificial neural network; Time-of-flight; Monte Carlo simulation; HPGe detector; PHYSICAL FORMULA CONSTRUCTION; AGATA;

D O I：

10.1016/j.anucene.2013.01.006

中图分类号：

TL [原子能技术]; O571 [原子核物理学];

学科分类号：

0827 ; 082701 ;

摘要：

In gamma-ray spectroscopy, a number of neutrons are emitted from the nuclei together with the gamma-rays. These neutrons influence gamma-ray spectra. An obvious method for discrimination between neutrons and gamma-rays is based on the time-of-flight (tot) technique. In this work, the tof distributions of gamma-rays and neutrons were obtained both experimentally and by using artificial neural networks (ANNs). It was shown that, ANN can correctly classify gamma-ray and neutron events. Also, for highly nonlinear detector response for tof, we have constructed consistent empirical physical formulas (EPFs) by appropriate ANNs. These ANN-EPFs can be used to derive further physical functions which could be relevant to discrimination between gamma-rays and neutrons. (C) 2013 Elsevier Ltd. All rights reserved.

引用

页码：297 / 301

页数：5

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