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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). A
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