Estimating model bias over the complete nuclide chart with sparse Gaussian processes at the example of INCL/ABLA and double-differential neutron spectra

TL;DR

This paper demonstrates how sparse Gaussian processes can estimate systematic biases in nuclear models across the entire nuclide chart, improving predictions of neutron spectra where experimental data are sparse or missing.

Contribution

It introduces a novel application of sparse Gaussian process regression to quantify model bias globally in nuclear physics models, specifically for neutron spectra predictions.

Findings

Effective bias estimation over the complete nuclide chart

Ability to predict biases at unmeasured energies, angles, and isotopes

Sparse Gaussian processes handle large datasets efficiently

Abstract

Predictions of nuclear models guide the design of nuclear facilities to ensure their safe and efficient operation. Because nuclear models often do not perfectly reproduce available experimental data, decisions based on their predictions may not be optimal. Awareness about systematic deviations between models and experimental data helps to alleviate this problem. This paper shows how a sparse approximation to Gaussian processes can be used to estimate the model bias over the complete nuclide chart at the example of inclusive double-differential neutron spectra for incident protons above 100\,MeV. A powerful feature of the presented approach is the ability to predict the model bias for energies, angles, and isotopes where data are missing. The number of experimental data points that can be taken into account is at least in the order of magnitude of~$10^4$ thanks to the sparse approximation. The approach is applied to the Li\`ege Intranuclear Cascade Model (INCL) coupled to the evaporation code ABLA. The results suggest that sparse Gaussian process regression is a viable candidate to perform global and quantitative assessments of models. Limitations of a philosophical nature of this (and any other) approach are also discussed.