Episodic reinforcement learning for 0νββ decay signal discrimination in HPGe detectors

Neutrinoless double beta (0νββ) decay is a Beyond the Standard Model process that, if discovered, could prove the Majorana nature of neutrinos—that they are their own antiparticles. In their search for this process, 0νββ decay experiments rely on signal/background discrimination, which is traditionally approached as a supervised learning problem. However, the experiment data are by nature unlabeled, and producing ground-truth labels for each data point is an involved process if using traditional methods. As such, we reformulate the task of classifying 0νββ decay experiment data as a weakly-supervised learning task and develop an episodic reinforcement learning (RL) algorithm with Randomized Return Decomposition to address it, training and validating our algorithm on real data produced by the Majorana Demonstrator experiment. With significantly fewer labels, the RL-trained weakly-supervised classifier slightly outperforms a fully-supervised classifier under the same energy cut. Our classifier serves as a proof of concept and shows potential for training classifiers to reject background in future 0νββ decay experiments like LEGEND.