Reducing parametric uncertainties through information geometry methods

TL;DR

This paper applies information geometry techniques to quantify how measurement uncertainties influence parameter estimation in a fission fragment decay model, demonstrating a significant reduction in parameter errors with improved data.

Contribution

It introduces an information geometry approach to assess and reduce uncertainties in nuclear fission model parameters based on experimental data.

Findings

Up to 15% reduction in parameter errors achieved.

Impact of measurement uncertainties on model parameters quantified.

Method demonstrated on spontaneous fission of Cf-252.

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 ${}^{252}$Cf, first using a toy model assuming Poissonian uncertainties, then an experimental measurement taken from G\"o\"ok et al., 2014 in EXFOR. We achieved a reduction of up to $\sim15\%$ in CGMF parameter errors, predominantly in $w_0^{(1)}$ and $w_1^{(0)}$.