Optimizing NRA depth profiling using Bayesian experimental design

Nuclear reaction analysis with 3He holds the promise to derive Deuterium depth profiles up to large depths from a set of measurements with different energies. Unfortunately, the extraction of the depth profile from the measured data is very sensitive to the quality of the data and the commonly chosen set of equispaced energies is in hindsight often noticeably inferior to other choices. However, recent advances in Bayesian experimental design together with increased computing power allow to optimize the measurement strategy 'on-the-fly', taking into account the results of previous measurements to select the best energy for the next measurement and to compute the expected information gain from further measurements. As example depth profiling of Deuterium in tungsten is presented and the results of the different measurement strategies are compared.