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Title: Nuclear Mass Systematics With Neural Nets And Astrophysical Nucleosynthesis

Abstract

We construct a neural network model that predicts the differences between the experimental mass-excess values {delta}Mexp and the theoretical values {delta}MFRDM given by the Finite Range Droplet Model of Moeller 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.

Authors:
;  [1];  [2];  [3];  [4]
  1. Physics Department, Division of Nuclear and Particle Physics, University of Athens, GR-15771 Athens (Greece)
  2. Department of Physics, UMIST, P.O. Box 88, Manchester M60 1QD (United Kingdom)
  3. McDonnell Center for the Space Sciences, Washington University, St. Louis, Missouri 63130 (United States)
  4. (United States)
Publication Date:
OSTI Identifier:
20798570
Resource Type:
Journal Article
Resource Relation:
Journal Name: AIP Conference Proceedings; Journal Volume: 831; Journal Issue: 1; Conference: International conference on frontiers in nuclear structure, astrophysics, and reactions - FINUSTAR, Isle of Kos (Greece), 12-17 Sep 2005; Other Information: DOI: 10.1063/1.2200961; (c) 2006 American Institute of Physics; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
73 NUCLEAR PHYSICS AND RADIATION PHYSICS; DROPLET MODEL; ELEMENT ABUNDANCE; LIQUID DROP MODEL; MASS; NEURAL NETWORKS; NUCLEAR STRUCTURE; NUCLEOSYNTHESIS

Citation Formats

Athanassopoulos, S., Mavrommatis, E., Gernoth, K. A., Clark, J. W., and Department of Physics, Washington University, St. Louis, Missouri 63130. Nuclear Mass Systematics With Neural Nets And Astrophysical Nucleosynthesis. United States: N. p., 2006. Web. doi:10.1063/1.2200961.
Athanassopoulos, S., Mavrommatis, E., Gernoth, K. A., Clark, J. W., & Department of Physics, Washington University, St. Louis, Missouri 63130. Nuclear Mass Systematics With Neural Nets And Astrophysical Nucleosynthesis. United States. doi:10.1063/1.2200961.
Athanassopoulos, S., Mavrommatis, E., Gernoth, K. A., Clark, J. W., and Department of Physics, Washington University, St. Louis, Missouri 63130. Wed . "Nuclear Mass Systematics With Neural Nets And Astrophysical Nucleosynthesis". United States. doi:10.1063/1.2200961.
@article{osti_20798570,
title = {Nuclear Mass Systematics With Neural Nets And Astrophysical Nucleosynthesis},
author = {Athanassopoulos, S. and Mavrommatis, E. and Gernoth, K. A. and Clark, J. W. and Department of Physics, Washington University, St. Louis, Missouri 63130},
abstractNote = {We construct a neural network model that predicts the differences between the experimental mass-excess values {delta}Mexp and the theoretical values {delta}MFRDM given by the Finite Range Droplet Model of Moeller 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.},
doi = {10.1063/1.2200961},
journal = {AIP Conference Proceedings},
number = 1,
volume = 831,
place = {United States},
year = {Wed Apr 26 00:00:00 EDT 2006},
month = {Wed Apr 26 00:00:00 EDT 2006}
}