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Title: Beyond the proton drip line: Bayesian analysis of proton-emitting nuclei

Abstract

The limits of the nuclear landscape are determined by nuclear binding energies. Beyond the proton drip lines, where the separation energy becomes negative, there is not enough binding energy to prevent protons from escaping the nucleus. Predicting properties of unstable nuclear states in the vast territory of proton emitters poses an appreciable challenge for nuclear theory as it often involves far extrapolations. In addition, significant discrepancies between nuclear models in the proton-rich territory call for quantified predictions. With the help of Bayesian methodology, we mix a family of nuclear mass models corrected with statistical emulators trained on the experimental mass measurements, in the proton-rich region of the nuclear chart. Separation energies were computed within nuclear density functional theory using several Skyrme and Gogny energy density functionals. We also considered mass predictions based on two models used in astrophysical studies. Quantified predictions were obtained for each model using Bayesian Gaussian processes trained on separation-energy residuals and combined via Bayesian model averaging. Here, we obtained a good agreement between averaged predictions of statistically corrected models and experiment. In particular, we quantified model results for one- and two-proton separation energies and derived probabilities of proton emission. This information enabled us to produce amore » quantified landscape of proton-rich nuclei. The most promising candidates for two-proton decay studies have been identified. Finally, the methodology used in this work has broad applications to model-based extrapolations of various nuclear observables. It also provides a reliable uncertainty quantification of theoretical predictions.« less

Authors:
 [1];  [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [1]
  1. Michigan State Univ., East Lansing, MI (United States)
  2. Univ. Libre de Bruxelles, Brussels (Belgium)
Publication Date:
Research Org.:
Michigan State Univ., East Lansing, MI (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Nuclear Physics (NP)
OSTI Identifier:
1615735
Grant/Contract Number:  
SC0013365; SC0008511; NA0002847; SC0018083; NA0003885
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Physical Review C
Additional Journal Information:
Journal Volume: 101; Journal Issue: 1; Journal ID: ISSN 2469-9985
Publisher:
American Physical Society (APS)
Country of Publication:
United States
Language:
English
Subject:
73 NUCLEAR PHYSICS AND RADIATION PHYSICS; nuclear binding; nuclear density functional theory; nuclear structure & decays; proton emission; bayesian methods; Monte Carlo methods

Citation Formats

Neufcourt, Léo, Cao, Yuchen, Giuliani, Samuel, Nazarewicz, Witold, Olsen, Erik, and Tarasov, Oleg B. Beyond the proton drip line: Bayesian analysis of proton-emitting nuclei. United States: N. p., 2020. Web. doi:10.1103/PhysRevC.101.014319.
Neufcourt, Léo, Cao, Yuchen, Giuliani, Samuel, Nazarewicz, Witold, Olsen, Erik, & Tarasov, Oleg B. Beyond the proton drip line: Bayesian analysis of proton-emitting nuclei. United States. doi:10.1103/PhysRevC.101.014319.
Neufcourt, Léo, Cao, Yuchen, Giuliani, Samuel, Nazarewicz, Witold, Olsen, Erik, and Tarasov, Oleg B. Wed . "Beyond the proton drip line: Bayesian analysis of proton-emitting nuclei". United States. doi:10.1103/PhysRevC.101.014319.
@article{osti_1615735,
title = {Beyond the proton drip line: Bayesian analysis of proton-emitting nuclei},
author = {Neufcourt, Léo and Cao, Yuchen and Giuliani, Samuel and Nazarewicz, Witold and Olsen, Erik and Tarasov, Oleg B.},
abstractNote = {The limits of the nuclear landscape are determined by nuclear binding energies. Beyond the proton drip lines, where the separation energy becomes negative, there is not enough binding energy to prevent protons from escaping the nucleus. Predicting properties of unstable nuclear states in the vast territory of proton emitters poses an appreciable challenge for nuclear theory as it often involves far extrapolations. In addition, significant discrepancies between nuclear models in the proton-rich territory call for quantified predictions. With the help of Bayesian methodology, we mix a family of nuclear mass models corrected with statistical emulators trained on the experimental mass measurements, in the proton-rich region of the nuclear chart. Separation energies were computed within nuclear density functional theory using several Skyrme and Gogny energy density functionals. We also considered mass predictions based on two models used in astrophysical studies. Quantified predictions were obtained for each model using Bayesian Gaussian processes trained on separation-energy residuals and combined via Bayesian model averaging. Here, we obtained a good agreement between averaged predictions of statistically corrected models and experiment. In particular, we quantified model results for one- and two-proton separation energies and derived probabilities of proton emission. This information enabled us to produce a quantified landscape of proton-rich nuclei. The most promising candidates for two-proton decay studies have been identified. Finally, the methodology used in this work has broad applications to model-based extrapolations of various nuclear observables. It also provides a reliable uncertainty quantification of theoretical predictions.},
doi = {10.1103/PhysRevC.101.014319},
journal = {Physical Review C},
issn = {2469-9985},
number = 1,
volume = 101,
place = {United States},
year = {2020},
month = {1}
}

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