https://www.selleckchem.com/pr....oducts/pf-06882961.h
We present a novel application of machine learning techniques to optimize the design of a radiation detection system. A decision tree-based algorithm is described which greedily optimizes partitioning of energy depositions based on a minimum detectable concentration metric - appropriate for radiation measurement. We apply this method to the task of optimizing sensitivity to radioxenon decays in the presence of a high rate of radon-progeny backgrounds (i.e., assuming no physical radon removal by traditional gas separation techniques)
Like
Comment
Share
