用贝叶斯实验设计优化核反应分析（NRA）
Optimizing Nuclear Reaction Analysis (NRA) using Bayesian Experimental Design
摘要：
使用${}^{3}$进行核反应分析他承诺测量大深度的氘深度剖面。然而，从实测数据中提取深度剖面是一个不适定的反演问题。在这里，我们演示了如何使用贝叶斯实验设计来优化测量的数量以及测量能量，以最大限度地提高信息增益。优化设计的反演特性与标准设置的比较揭示了巨大的可能收益。后验抽样方法的应用允许在测量过程中交互地优化实验设置。
英文摘要：
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.
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