Uncertainty-quantified phenomenological optical potentials for single-nucleon scattering

TL;DR

This paper develops uncertainty-quantified optical potentials for single-nucleon scattering, improving their reliability and predictive power by addressing biases and providing covariance information, with practical tools included.

Contribution

The study introduces well-calibrated uncertainties into two global optical-model potentials using Markov-Chain Monte Carlo, enhancing their accuracy and applicability in nuclear reaction calculations.

Findings

Uncertainty-quantified OMPs show improved data fit and predictive performance.

Bias and overconfidence in original OMPs are identified and corrected.

Updated OMPs include covariance information and are validated against multiple data sets.

Abstract

Optical-model potentials (OMPs) continue to play a key role in nuclear reaction calculations. However, the uncertainty of phenomenological OMPs in widespread use -- inherent to any parametric model trained on data -- has not been fully characterized, and its impact on downstream users of OMPs remains unclear. Here we assign well-calibrated uncertainties for two representative global OMPs, those of Koning-Delaroche and Chapel Hill '89, using Markov-Chain Monte Carlo for parameter inference. By comparing the canonical versions of these OMPs against the experimental data originally used to constrain them, we show how a lack of outlier rejection and a systematic underestimation of experimental uncertainties contributes to bias of, and overconfidence in, best-fit parameter values. Our updated, uncertainty-quantified versions of these OMPs address these issues and yield complete covariance information for potential parameters. Scattering predictions generated from our ensembles show improved performance both against the original training corpora of experimental data and against a new "test" corpus comprising many of the experimental single-nucleon scattering data collected over the last twenty years. Finally, we apply our uncertainty-quantified OMPs to two case studies of application-relevant cross sections. We conclude that, for many common applications of OMPs, including OMP uncertainty should become standard practice. To facilitate their immediate use, digital versions of our updated OMPs and related tools for forward uncertainty propagation are included as Supplementary Material.