HNPS Advances in Nuclear Physics Banner

Nuclear mass systematics by complementing the Finite Range Droplet Model with neural networks

PDF
Published: Dec 5, 2019
DOI: https://doi.org/10.12681/hnps.2250
S. Athanassopoulos
Physics Department, Division of Nuclear & Particle Physics, University of Athens
E. Mavrommatis
Physics Department, Division of Nuclear & Particle Physics, University of Athens
K. A. Gernoth
School of Physics & Astronomy, University of Manchester
J. W. Clark
McDonnell Center for the Space Sciences and Department of Physics, Washington University
Abstract

A neural-network model is developed to reproduce the differences between experimental nuclear mass-excess values and the theoretical values given by the Finite Range Droplet Model. The results point to the existence of subtle regularities of nuclear structure not yet contained in the best microscopic/phenomenological models of atomic masses. Combining the FRDM and the neural-network model, we create a hybrid model with improved predictive performance on nuclear-mass systematics and related quantities.

Article Details
  • Section
  • Oral contributions (deprecated)
References
D. Lunney, J. M. Pearson, C. Thibault, Rev. Mod. Phys. 75 (2003) 1021.
Opportunities in Nuclear Science: "A Long Range Plan in the Next Decade" (DOE/NSF, 2002); NUPECC Long Range Plan 2004: Perspectives for Nuclear Physics Research in Europe in the Coming Decade and Beyond" (April 2004).
P. Möller, J. R. Nix, W. D. Myers, W. J. Swiatecki, At. Data Nucl. Data Tables 59 (1995)185; P. Möller, J. R. Nix, K. L. Kratz, At. Data Nucl. Data Tables 66 (1997) 131.
S. Goriely, M. Samyn, P.-H. Heenen, J. M. Pearson, F. Tondeur, Phys. Rev. C66 (2002) 024326.
S. Athanassopoulos, E. Mavrommatis, K. A. Gernoth, J. W. Clark, Nucl. Phys. A743 (2004) 222, and references therein.
S. Athanassopoulos, E. Mavrommatis, K. A. Gernoth, J. W. Clark, to be submitted.
A. H. Wapstra, G. Audi, C. Thibault, Nucl. Phys. A729 (2003) 337.