Classifying scintillation and ionization signals in xenon detectors with machine learning

Liquid-xenon time projection chambers (TPCs) have set leading limits on WIMP dark matter. These detectors also can probe a number of electrophilic and nucleophilic processes beyond the Standard Model. The XENONnT experiment will be sensitive to new low-threshold signals, such as the neutrino magnetic moment and solar axions. These searches require ultra-low energy thresholds and backgrounds. Interactions in xenon TPCs are characterized by two signals: prompt scintillation and delayed ionization. At high energies, these signals are trivial to classify. However, at threshold-scale energies, classifying scintillation and ionization signals is nontrivial. Misclassification can result in increased accidental coincidence background rates or reduced signal detection efficiency. We present a machine learning approach for low-energy signal classification in XENONnT called a Naive Bayes classifier. Compared with standard techniques, our method infers signal probabilities, allowing experiments to improve upon current deterministic decision boundaries for signal classification and improve detection efficiency.