Applicability evaluation of Akaike's Bayesian information criterion to covariance modeling in the cross-section adjustment method

Maruyama, Shuhei   ; Endo, Tomohiro*; Yamamoto, Akio*

The applicability of Akaike's Bayesian Information Criterion (ABIC) to covariance modeling in the cross-section adjustment method was investigated. One of the most important things for a reliable cross-section adjustment method is giving a suitable covariance matrix. However, since we cannot know the true covariance matrix in advance, we usually estimate and assume it. To judge the goodness of the covariance matrix modeling, a metric is desirable. As a candidate for this metric, we focus on ABIC which is one of the information criteria in Bayesian inference, because the cross-section adjustment method is often discussed within the framework of Bayesian inference. In the conventional cross-section adjustment method, incorporation of the analysis model uncertainty in a covariance matrix still requires ad hoc treatment. In JAEA, the integral experimental database for fast reactors has been developed and the adjusted cross-section set ADJ2017 has been created based on this database. Many of the core characteristics in the database have been analyzed by a deterministic method. Therefore, the predicted core characteristics have non-negligible uncertainties with correlations due to some numerical approximations. However, the evaluations of the uncertainties and their correlations are still challenging issues. In addition, there would be unknown uncertainties that experimenters and analysts of reactor physics experiments could not recognize. To judge the goodness of the covariance matrix related to these uncertainties, the applicability of ABIC to the cross-section adjustment method was investigated.