Iterative Bayesian Monte Carlo for nuclear data evaluation

• DOI: 10.1007/s41365-022-01034-w
• Publication Date: 2022-05-02T15:03:52Z
• Authors: Erwin Alhassan; Dimitri Rochman; Alexander Vasiliev; Mathieu Hursin; Arjan J. Koning; Hakim Ferroukhi
• Keywords: Data set; Nuclear data

Abstract In this work, we explore the use of an iterative Bayesian Monte Carlo (iBMC) method for nuclear data evaluation within a TALYS Evaluated Nuclear Data Library (TENDL) framework. The goal is to probe model and parameter space code system find optimal sets that reproduces selected experimental data. involves simultaneous variation many reaction models as well their parameters included in code. ‘best’ set with its was obtained by comparing calculations Three types were used: (1) cross sections, (2) residual production (3) elastic angular distributions. To improve our fit data, update set—the file maximizes likelihood function—in fashion. Convergence determined monitoring evolution maximum estimate (MLE) values considered reached when relative change MLE last two iterations 5%. Once final identified, infer uncertainties covariance information varying around file. way, ensured distributions are centered on evaluation. proposed applied p+ $$^{59}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mn>59</mml:mn> </mml:msup> </mml:math> Co between 1 100 MeV. Finally, adjusted files compared from EXFOR database evaluations TENDL-2019, JENDL/He-2007 JENDL-4.0/HE libraries.