Quantified limits of the nuclear landscape

Abstract

Background: The chart of the nuclides is limited by particle drip lines beyond which nuclear stability to proton or neutron emission is lost. Predicting the range of particle-bound isotopes poses an appreciable challenge for nuclear theory as it involves extreme extrapolations of nuclear masses well beyond the regions where experimental information is available. Still, quantified extrapolations are crucial for a wide variety of applications, including the modeling of stellar nucleosynthesis.Purpose: We use microscopic nuclear global mass models, current mass data, and Bayesian methodology to provide quantified predictions of proton and neutron separation energies as well as Bayesian probabilities of existence throughout the nuclear landscape all the way to the particle drip lines. Methods:We apply nuclear density functional theory with several energy density functionals. We also consider two global mass models often used in astrophysical nucleosynthesis simulations. To account for uncertainties, Bayesian Gaussian processes are trained on the separation-energy residuals for each individual model, and the resulting predictions are combined via Bayesian model averaging. This framework allows to account for systematic and statistical uncertainties and propagate them to extrapolative predictions. Results:We establish and characterize the drip-line regions where the probability that the nucleus is particlebound decreases from 1 to 0. In these regions, we provide quantified predictions for one-and two-nucleon separation energies. According to our Bayesian model averaging analysis, 7759 nuclei with Z ≤ 119 have a probability of existence ≥ 0.5. Conclusions:The extrapolation results obtained in this study will be put through stringent tests when new experimental information on existence and masses of exotic nuclei becomes available. In this respect, the quantified landscape of nuclear existence obtained in this study should be viewed as a dynamical prediction that will be fine-tuned when new experimental information and improved global mass models become available.

A Guided Tour of ab initio Nuclear Many-Body Theory

“…In such an effort, the generation of sufficient training data poses a significant challenge, because it would require calculations at several truncation levels (see section 4.1). A possible strategy for mitigating this issue is to combine non-perturbative methods with cheaper high-order MBPT in Bayesian mixed models (see references [212,292,293] for applications in nuclear physics). The successful application of factorization methods to the nuclear many-body problem could likely resolve the issue once and for all by reducing the computational scaling of high-order truncations, at the cost of introducing an additional uncertainty from the factorization procedure.…”

Ab Initio Limits of Atomic Nuclei

“…work in a similar spirit has applied Bayesian machine learning algorithms to global mass models [10,41,42].…”

Skyrme-Hartree-Fock-Bogoliubov mass models on a 3D mesh: effect of triaxial shape

“…The energy density is given by (5) in terms of three local, time-even densities ρ t (r), τ t (r) and J t (r) that characterize the auxiliary state |Φ [56]. The ten coupling constants {C} are determined in terms of the model parameters t 0−3 , x 0−3 , W 0 , and W 0 (see Appendix A for more details).…”

“…Despite significant efforts over several decades, experimental information only covers a fraction of the entire data set required for nuclear applications. Neutron-rich nuclei are of particular interest, especially for understanding how heavy elements are made through the rapid neutron capture process, or r-process, [1,2] and for exploring the limits of nuclear stability [3][4][5]. A major challenge for nuclear theory is to make reliable extrapolations into regions beyond current experimental reach.…”

“…[29,30]. Such techniques are becoming ubiquitous in nuclear physics [30][31][32][33][34][35][36][37][38][39][40].…”