An optical-lensing inspired data thinning method for nuclear cross section data

TL;DR

This paper introduces an optical-lensing inspired data thinning method that reduces large nuclear cross section datasets efficiently, enhancing computational performance while maintaining data integrity for better analysis.

Contribution

A novel data thinning algorithm inspired by optical lensing principles, tailored for nuclear cross section data, improving efficiency without losing critical information.

Findings

Improved fitting accuracy in toy and real data scenarios

Significant reduction in computational time

Maintained quality of uncertainty quantification

Abstract

In the study of nuclear cross sections, the computational demands of data assimilation methods can become prohibitive when dealing with large data sets. We have developed a novel variant of the data thinning algorithm, inspired by the principles of optical lensing, which effectively reduces data volume while preserving critical information. We show how it improves fitting through a toy problem and for several examples of total cross sections for neutron-induced reactions on rare-earth isotopes. We demonstrate how this method can be applied as an efficient pre-processing step prior to smoothing, significantly improving computational efficiency without compromising the quality of uncertainty quantification.