Optimizing Nuclear Reaction Analysis (NRA) using Bayesian Experimental Design
Accelerator Physics
2009-11-13 v1 Data Analysis, Statistics and Probability
Abstract
Nuclear Reaction Analysis with 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.
Cite
@article{arxiv.0812.3789,
title = {Optimizing Nuclear Reaction Analysis (NRA) using Bayesian Experimental Design},
author = {U. von Toussaint and T. Schwarz-Selinger and S. Gori},
journal= {arXiv preprint arXiv:0812.3789},
year = {2009}
}
Comments
Bayesian Inference and Maximum Entropy Conference 2008, AIP Conference proceedings 1073, p. 348-358, 4 figures