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Nuclear mass predictions for the crustal composition of neutron stars: A Bayesian neural network approach

R. Utama, J. Piekarewicz, and H. B. Prosper
Phys. Rev. C 93, 014311 – Published 20 January 2016

Abstract

Background: Besides their intrinsic nuclear-structure value, nuclear mass models are essential for astrophysical applications, such as r-process nucleosynthesis and neutron-star structure.

Purpose: To overcome the intrinsic limitations of existing “state-of-the-art” mass models through a refinement based on a Bayesian neural network (BNN) formalism.

Methods: A novel BNN approach is implemented with the goal of optimizing mass residuals between theory and experiment.

Results: A significant improvement (of about 40%) in the mass predictions of existing models is obtained after BNN refinement. Moreover, these improved results are now accompanied by proper statistical errors. Finally, by constructing a “world average” of these predictions, a mass model is obtained that is used to predict the composition of the outer crust of a neutron star.

Conclusions: The power of the Bayesian neural network method has been successfully demonstrated by a systematic improvement in the accuracy of the predictions of nuclear masses. Extension to other nuclear observables is a natural next step that is currently under investigation.

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  • Received 25 August 2015
  • Revised 14 December 2015

DOI:https://doi.org/10.1103/PhysRevC.93.014311

©2016 American Physical Society

Physics Subject Headings (PhySH)

Nuclear Physics

Authors & Affiliations

R. Utama*, J. Piekarewicz, and H. B. Prosper

  • Department of Physics, Florida State University, Tallahassee, Florida 32306, USA

  • *ru11@my.fsu.edu
  • jpiekarewicz@fsu.edu
  • harry@hep.fsu.edu

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Issue

Vol. 93, Iss. 1 — January 2016

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