Nuclear Mass Systematics With Neural Nets And Astrophysical Nucleosynthesis

Details Nuclear Mass Systematics With Neural Nets And Astrophysical Nucleosynthesis Nuclear Mass Systematics With Neural Nets And Astrophysical Nucleosynthesis

Apr 2006

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2006aipc..831..397a&link_type=abstract

FRONTIERS IN NUCLEAR STRUCTURE, ASTROPHYSICS, AND REACTIONS - FINUSTAR. AIP Conference Proceedings, Volume 831, pp. 397-399 (20

Mathematics

Logic

Binding Energies And Masses, Collective Models, Neural Networks, Fuzzy Logic, Artificial Intelligence

Scientific paper

We construct a neural network model that predicts the differences between the experimental mass-excess values ΔMexp and the theoretical values ΔMFRDM given by the Finite Range Droplet Model of Möller et al. This difficult study reveals that subtle regularities of nuclear structure not yet embodied in the best microscopic/phenomenological models of atomic-mass systematics do actually exist. By combining the FRDM and the above neural network model we construct a Hybrid Model with improved predictive performance in the majority of the calculations of the systematics of nuclear mass excess and of related quantities. Such systematics is of current interest among others in such astrophysical problems as nucleosynthesis processes and the justification of the present abundances.

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