Decision trees for optimizing the minimum detectable concentration of radioxenon detectors. (April 2021) - Hagen, A.; Loer, B.; Orrell, J.L.; Saldanha, R.
Abstract: 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). Assuming other backgrounds are negligible, and considering sensitivity to each xenon isotope separately (neglecting interference between isotopes), we find that, in general, high resolution readout and high spatial segmentation yield little additional capability to discriminate against radon backgrounds compared to simpler detector designs. Highlights: Decision Trees provide interpretable results to guide radiation detector design. Decision Trees to minimize MDC outperforms the standard method. The tool identifies regions of interest similar to human-driven analyses. Higher-order coincidences do not improve radioXe sensitivity vs radon background. Energy resolution has a small effect on radioxenon sensitivity vs radon background.