2009/01/31 by Bertrand Bouriquet, Jean‐Philippe Argaud, Jean-Philippe Argaud · 4 citations
Engineering · Environmental Science · Mathematics · Physics and Astronomy · #Algorithm #Artificial intelligence #Atmospheric and Environmental Gas Dynamics #Best linear unbiased prediction #Climate variability and models #Computer science #Data assimilation #Machine learning #Mathematical optimization #Mathematics #Meteorology #Nuclear data #Nuclear physics #Nuclear reactor physics and engineering #Physics #Statistics #Unbiased Estimation #Value (mathematics) #nucl-ex #nucl-th #physics.data-an
paper · pdf · doi:10.1016/j.anucene.2011.05.014
published in Annals of Nuclear Energy 38(9), 1863-1866 (Elsevier BV)
arxiv created 2011/05/24 · openalex publication_date 2011/06/24 · arxiv updated 2015/03/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
This paper presents methods to provide an optimal evaluation of the nuclear masses. The techniques used for this purpose come from data assimilation that allows combining, in an optimal and consistent way, information coming from experiment and from numerical model. Using all the available information, it leads to improve not only masses evaluations, but also to decrease uncertainties. Each newly evaluated mass value is associated with some accuracy that is sensibly reduced with respect to the values given in tables, especially in the case of the less well-known masses. In this paper, we first introduce a useful tool of data assimilation, the Best Linear Unbiased Estimation (BLUE). This BLUE method is applied to nuclear mass tables and some results of improvement are shown.