Beyond the proton drip line: Bayesian analysis of proton-emitting nuclei
Autor: | Witold Nazarewicz, Yuchen Cao, Leo Neufcourt, Samuel A. Giuliani, Oleg B. Tarasov, E. Olsen |
---|---|
Rok vydání: | 2020 |
Předmět: |
FOS: Computer and information sciences
Nuclear Theory Proton Binding energy FOS: Physical sciences Machine Learning (stat.ML) Bayesian inference 01 natural sciences Nuclear Theory (nucl-th) Nuclear physics symbols.namesake Statistics - Machine Learning 0103 physical sciences Nuclear drip line Nuclear Experiment (nucl-ex) Proton emission Uncertainty quantification Nuclear Experiment 010306 general physics Gaussian process Physics 010308 nuclear & particles physics 62F15 62P35 60G15 symbols Nuclear density |
Zdroj: | Physical Review C. 101 |
ISSN: | 2469-9993 2469-9985 |
Popis: | Background: 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.Purpose: With the help of Bayesian methodology, we mix a family of nuclear mass models corrected with statistical emulators trained on the experimental mass measurements. We study the impact of such model mixing in the proton-rich region of the nuclear chart.Methods: 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.Results: 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.Conclusions: 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. |
Databáze: | OpenAIRE |
Externí odkaz: |