Antalya Bilim University, Vocational School of Health Services, Department of Medical Imaging Techniques, 07190, Antalya, Turkey.
Akdeniz University, Vocational School of Technical Sciences, Department of Biomedical Equipment Technology, 07070, Antalya, Turkey.
Appl Radiat Isot. 2021 Mar;169:109583. doi: 10.1016/j.apradiso.2020.109583. Epub 2021 Jan 7.
The main aim of this study is to develop accurate artificial neural network (ANN) algorithms to estimate level density parameters. An efficient Bayesian-based algorithm is presented for classification algorithms. Unknown model parameters are estimated using the observed data, from which the Bayesian-based algorithm is predicted. This paper focuses on the Bayesian method for parameter estimations of Gilbert Cameron Model (GCM), Back Shifted Fermi Gas Model (BSFGM) and Generalised Super Fluid Model (GSM), which are known as the phonemological level density models. Obtained level density parameters have been compared with the Reference Input Parameter Library for Calculation of Nuclear Reactions and Nuclear Data Evaluations (RIPL) data. R values of the Bayesian method have been found as 0.9946, 0.9981 and 0.9824 for BSFGM, GCM and GSM, respectively. In order to validate our results, default level density parameters of TALYS 1.95 code have been changed with our newly obtained results and photo-neutron cross-section calculations of the Sn(γ,n)Sn, Sn(γ,n)Sn, Sn(γ,n)Sn and Sn(γ,n)Sn reactions have been calculated by using these newly obtained level density parameters.
本研究的主要目的是开发精确的人工神经网络（ANN）算法来估计能级密度参数。本文提出了一种有效的基于贝叶斯的分类算法。未知模型参数使用观测数据进行估计，然后基于这些数据对贝叶斯算法进行预测。本文重点介绍了贝叶斯方法在 Gilbert Cameron 模型（GCM）、后移费米气体模型（BSFGM）和广义超流模型（GSM）的参数估计中的应用，这些模型是已知的声子能级密度模型。获得的能级密度参数与核反应和核数据评估的参考输入参数库（RIPL）数据进行了比较。BSFGM、GCM 和 GSM 的贝叶斯方法的 R 值分别为 0.9946、0.9981 和 0.9824。为了验证我们的结果，使用我们新获得的结果修改了 TALYS 1.95 代码的默认能级密度参数，并使用这些新获得的能级密度参数计算了 Sn(γ,n)Sn、Sn(γ,n)Sn、Sn(γ,n)Sn 和 Sn(γ,n)Sn 反应的光中子截面。
