Reducing parametric uncertainties through information geometry methods

2025/08/26 by M. Imbrišak, Imbrišak, M., A. E. Lovell +3
Computer Science · #Explainable Artificial Intelligence (XAI) #FOS: Physical sciences #Neural Networks and Applications #Nuclear Theory (nucl-th)

paper · pdf · doi:10.48550/arxiv.2508.19474

openalex publication_date 2025/08/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Information geometry is a study of applying differential geometry methods to challenging statistical problems, such as uncertainty quantification. In this work, we use information geometry to study how measurement uncertainties in pre-neutron emission mass distributions affect the parameter estimation in the Hauser-Feshbach fission fragment decay code, CGMF. We quantify the impact of reduced uncertainties on the pre-neutron mass yield of specific masses to these parameters, for spontaneous fission of ²⁵²Cf, first using a toy model assuming Poissonian uncertainties, then an experimental measurement taken from Göök et al., 2014 in EXFOR. We achieved a reduction of up to ∼15% in CGMF parameter errors, predominantly in w₀⁽¹⁾ and w₁⁽⁰⁾.

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