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

By R. Utama, J. Piekarewicz, Harrison B. Prosper. 2015

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Besides their intrinsic nuclear-structure value, nuclear mass models are essential for astrophysical applications, such as r-process nucleosynthesis and neutron-star structure. To overcome the intrinsic limitations of existing "state-of-the-art" mass models, we propose a refinement based on a Bayesian Neural Network (BNN) formalism. A novel BNN approach is implemented with the goal of optimizing mass residuals between theory and experiment. 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. 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.

Subfields: Astronomy and Astrophysics, Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/75e12253c200f1da5dd8f90967859a583ec3d41f

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In Astronomy and Astrophysics

In Nuclear and High Energy Physics

Citations in the corpus

Unified equations of state for cold non-accreting neutron stars with Brussels-Montreal functionals. I. Role of symmetry energy (2018, methodology): “These include the RMF model using the TMA parametrization (see Rüster et al. 2006), the model of Duflo & Zucker (1995) (see Roca-Maza & Piekarewicz 2008), the finite-range droplet model of Möller et al. (1995) (see Kreim et al. 2013), and the recent BNN model of Utama et al. (2016).”