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

Journal Article · Wed Jan 22 00:00:00 UTC 2020 · Physical Review C

DOI: https://doi.org/10.1103/PhysRevC.101.014319 · OSTI ID:1615735

Neufcourt, Léo ^[1]; Cao, Yuchen ^[2]; Giuliani, Samuel  ^[2]; Nazarewicz, Witold  ^[2]; Olsen, Erik  ^[3]; Tarasov, Oleg B.  ^[2]

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1. Michigan State Univ., East Lansing, MI (United States); Michigan State University
2. Michigan State Univ., East Lansing, MI (United States)
3. Univ. Libre de Bruxelles, Brussels (Belgium)

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.

• Research Organization: Michigan State Univ., East Lansing, MI (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Nuclear Physics (NP) (SC-26)
• Grant/Contract Number: NA0002847; NA0003885; SC0008511; SC0013365; SC0018083
• OSTI ID: 1615735
• Journal Information: Physical Review C, Journal Name: Physical Review C Journal Issue: 1 Vol. 101; ISSN PRVCAN; ISSN 2469-9985
• Publisher: American Physical Society (APS)Copyright Statement
• Country of Publication: United States
• Language: English