Bayesian approach to model-based extrapolation of nuclear observables

Abstract

Background: The mass, or binding energy, is the basis property of the atomic nucleus. It determines its stability, and reaction and decay rates. Quantifying the nuclear binding is important for understanding the origin of elements in the universe. The astrophysical processes responsible for the nucleosynthesis in stars often take place far from the valley of stability, where experimental masses are not known. In such cases, missing nuclear information must be provided by theoretical predictions using extreme extrapolations. In order to take full advantage of the information contained in mass model residuals, i.e., deviations between experimental and calculated masses, one can utilize Bayesian machine learning techniques to improve predictions.Purpose: In order to improve the quality of model-based predictions of nuclear properties of rare isotopes far from stability, we consider the information contained in the residuals in the regions where the experimental information exist. As a case in point, we discuss two-neutron separation energies S2n of even-even nuclei. Through this observable, we assess the predictive power of global mass models towards more unstable neutron-rich nuclei and provide uncertainty quantification of predictions.Methods: We consider 10 global models based on nuclear Density Functional Theory with realistic energy density functionals as well as two more phenomenological mass models. The emulators of S2n residuals and credibility intervals (Bayesian confidence intervals) defining theoretical error bars are constructed using Bayesian Gaussian processes and Bayesian neural networks. We consider a large training dataset pertaining to nuclei whose masses were measured before 2003. For the testing datasets, we considered those exotic nuclei whose masses have been determined after 2003. By establishing statistical methodology and parameters, we carried out extrapolations towards the 2n dripline.Results: While both Gaussian processes and Bayesian neural networks reduce the rms deviation from experiment significantly, GP offers a better and much more stable performance. The increase in the predictive power of microscopic models aided by the statistical treatment is quite astonishing: the resulting rms deviations from experiment on the testing dataset are similar to those of more phenomenological models. We found that Bayesian neural networks results are prone to instabilities caused by the large number of parameters in this method. Moreover, since the classical sigmoid activation function used in this approach has linear tails that do not vanish, it is poorly suited for a bounded extrapolation. The empirical coverage probability curves we obtain match very well the reference values, in a slightly conservative way in most cases, which is highly desirable to ensure honesty of uncertainty quantification. The estimated credibility intervals on predictions make it possible to evaluate predictive power of individual models, and also make quantified predictions using groups of models. Conclusions:The propose...

Quantified limits of the nuclear landscape

“…We have found in a previous study [26] that Gaussian processes overall outperform Bayesian neural networks, achieving similar rms deviations with a more faithful uncertainty quantification and considerably fewer parameters. We have also demonstrated [30,31] that the parameters θ are well constrained and fairly uncorrelated.…”

“…Our methodology follows closely our previous work [26,30,31] in which we combined the current theoretical and experimental information using Bayesian simulations to arrive at informed predictions.…”

“…For each model, the mass table of even-even nuclei was computed self-consistently by solving the Hartree-Fock-Bogoliubov (HFB) equations as described in Refs. [5,26,30,31]; masses of odd-Z and odd-N systems were then extracted using computed pairing gaps [30,41].…”

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

“…Our Bayesian methodology for building Gaussian process (GP) emulators to produce quantified extrapolations of theoretical nuclear model predictions beyond the experimental data range has been extensively developed in our previous work [40,41]. Here, we incorporate two statistical innovations, a non-zero GP mean parameter and a new Bayesian calculation of model mixing weights.…”

“…In this respect, this work follows closely the methodology described in Refs. [1,40,41,43]. For D1M and BCPM, the binding energies of odd-A and odd-odd nuclei are computed by solving the HFB equations for one-and two-quasiparticle configurations with the appropriate constraint on particle number [54].…”