Quantifying uncertainty in physics-based predictions of rare-isotope production cross sections via Bayesian-inspired model averaging across nuclear mass tables

TL;DR

This paper introduces a Bayesian-inspired model averaging framework that combines multiple nuclear mass table-based predictions to improve the accuracy and uncertainty quantification of rare-isotope production cross sections, aiding experimental planning.

Contribution

The work develops a novel Bayesian-inspired model averaging method that reduces systematic bias in nuclear reaction predictions by empirically weighting models based on fit quality, incorporating multiple nuclear mass tables.

Findings

Improved cross-section predictions with quantified uncertainties.

Enhanced interpolation and limited extrapolation for rare-isotope production.

Assessment of candidate new isotopes in proton-rich fragmentation.

Abstract

Accurate prediction of fragmentation cross sections is essential for rare-isotope beam production, planning new-isotope searches, and designing experiments to study the most exotic regions of the nuclear chart. However, existing reaction models and phenomenological cross-section parameterizations often exhibit significant deviations over broad regions of mass and charge. In this work, a Bayesian-inspired model-averaging framework is developed to combine abrasion--ablation (AA) calculations based on multiple nuclear mass tables into a single statistically weighted estimate. For the calibrated systems, the model weights are assigned empirically according to the relative quality of fit to measured cross sections, thereby reducing systematic model bias while preserving the underlying physics content of the AA description. The weights are constrained using proton-rich fragmentation data for the $^{78}$Kr and $^{124}$Xe projectiles. The resulting parameter trends are then propagated to the $^{92}$Mo and $^{144}$Sm systems through a controlled scaling procedure. In the present implementation, the excitation-energy prescription is fixed, while the averaging is performed across nuclear-mass inputs; the framework provides both weighted cross sections and associated uncertainty estimates. Applied to proton-rich fragmentation, the present approach provides a practical basis for interpolation and limited extrapolation in regions relevant to rare-isotope production. The resulting predictions are used to assess the production of very proton-rich nuclei, and candidate new isotopes are discussed.