Optimizing Nuclear Reaction Analysis (NRA) using Bayesian Experimental Design

TL;DR

This paper demonstrates how Bayesian Experimental Design can optimize Nuclear Reaction Analysis measurements, significantly improving the extraction of Deuterium depth profiles by maximizing information gain and enabling adaptive measurement strategies.

Contribution

It introduces a Bayesian optimization approach for NRA, enhancing measurement efficiency and accuracy compared to standard methods.

Findings

Optimized measurement settings increase information gain substantially.

Bayesian design reduces the number of measurements needed.

Adaptive measurement strategies improve inversion quality.

Abstract

Nuclear Reaction Analysis with ${}^{3}$He holds the promise to measure Deuterium depth profiles up to large depths. However, the extraction of the depth profile from the measured data is an ill-posed inversion problem. Here we demonstrate how Bayesian Experimental Design can be used to optimize the number of measurements as well as the measurement energies to maximize the information gain. Comparison of the inversion properties of the optimized design with standard settings reveals huge possible gains. Application of the posterior sampling method allows to optimize the experimental settings interactively during the measurement process.