Quantifying uncertainty in physics-based predictions of rare-isotope production cross sections via Bayesian-inspired model averaging across nuclear mass tables
This paper introduces a Bayesian-inspired model-averaging framework that combines abrasion-ablation calculations from multiple nuclear mass tables to generate statistically weighted predictions and uncertainty estimates for rare-isotope production cross sections, thereby improving accuracy for both interpolation and limited extrapolation in proton-rich fragmentation regimes.