Nuclear mass systematics using neural networks

Simple item page
dc.creatorAthanassopoulos, S.
dc.creatorMavrommatis, E.
dc.creatorGernoth, K. A.
dc.creatorClark, J. W.
dc.date2003-07-31
dc.date2004-09-10
dc.date.accessioned2026-07-07T10:50:55Z
dc.date.available2026-07-07T10:50:55Z
dc.descriptionNew global statistical models of nuclidic (atomic) masses based on multilayered feedforward networks are developed. One goal of such studies is to determine how well the existing data, and only the data, determines the mapping from the proton and neutron numbers to the mass of the nuclear ground state. Another is to provide reliable predictive models that can be used to forecast mass values away from the valley of stability. Our study focuses mainly on the former goal and achieves substantial improvement over previous neural-network models of the mass table by using improved schemes for coding and training. The results suggest that with further development this approach may provide a valuable complement to conventional global models.
dc.description17 pages, 4 figures, revised version, accepted for publication at Nuclear Physics A
dc.identifierhttps://arxiv.org/abs/nucl-th/0307117
dc.identifierhttp://arxiv.org/abs/nucl-th/0307117
dc.identifierNucl.Phys.A743:222-235,2004
dc.identifierdoi:10.1016/j.nuclphysa.2004.08.006
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/184690
dc.subjectNuclear Theory
dc.titleNuclear mass systematics using neural networks
dc.typetext

Files

Collections

arXiv