Post by Frédéric Barbaresco

THALES "QUANTUM ALGORITHMS/COMPUTING" AND "AI/ALGO FOR SENSORS" SEGMENT LEADER

INFORMATION GEOMETRY FOR FISSION by Los Alamos National Laboratory Reducing parametric uncertainties through information geometry methods https://lnkd.in/eqqe34kw 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 w (1) 0 and w (0) 1 .

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