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Nuclear mass predictions for the crustal composition of neutron stars

Title: Nuclear mass predictions for the crustal composition of neutron stars: A Bayesian neural network approach.
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Name(s): Utama, R., author
Piekarewicz, J., author
Prosper, H. B., author
Type of Resource: text
Genre: Text
Date Issued: 2016-01-20
Physical Form: computer
online resource
Extent: 1 online resource
Language(s): English
Abstract/Description: 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.
Identifier: FSU_libsubv1_wos_000368514400002 (IID), 10.1103/PhysRevC.93.014311 (DOI)
Keywords: energies, equation, ground-state, matter, models, stability, Systematics
Publication Note: The publisher’s version of record is available at http://www.dx.doi.org/10.1103/PhysRevC.93.014311
Persistent Link to This Record: http://purl.flvc.org/fsu/fd/FSU_libsubv1_wos_000368514400002
Owner Institution: FSU
Is Part Of: Physical Review C.
0556-2813
Issue: iss. 1, vol. 93

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Utama, R., Piekarewicz, J., & Prosper, H. B. (2016). Nuclear mass predictions for the crustal composition of neutron stars: A Bayesian neural network approach. Physical Review C. Retrieved from http://purl.flvc.org/fsu/fd/FSU_libsubv1_wos_000368514400002