# Quantified limits of the nuclear landscape

arXiv: https://arxiv.org/abs/2001.05924 · HTML: https://mlsanity.com/paper/2001.05924/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2020.

## Built on by (most cited first, 5 of 5)

- [Machine Learning in Nuclear Physics](https://mlsanity.com/paper/2112.02309/) (2021)
- [Predicting nuclear masses with product-unit networks](https://mlsanity.com/paper/2305.04675/) (2023)
- [Local Bayesian Dirichlet mixing of imperfect models](https://mlsanity.com/paper/2311.01596/) (2023)
- [Model orthogonalization and Bayesian forecast mixing via Principal Component Analysis](https://mlsanity.com/paper/2405.10839/) (2024)
- [Bridging Theory and Data: Correcting Nuclear Mass Models with Interpretable Machine Learning](https://mlsanity.com/paper/2603.15203/) (2026)

## Abstract (arXiv)

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 beyond the regions where experimental information is available. Still, quantified extrapolations are crucial for a variety of applications, including the modeling of stellar nucleosynthesis. We use microscopic nuclear mass models 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. We apply nuclear density functional theory with several energy density functionals. 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. We characterize the drip-line regions where the probability that the nucleus is particle-bound 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. The extrapolations obtained in this study will be put through stringent tests when new experimental information on 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.