Speaker
Description
Neutrinoless double beta ($0 \nu \beta \beta$) 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, $0 \nu \beta \beta$ 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 $0 \nu \beta \beta$ 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. We find that the RL-trained classifier slightly outperforms a standard supervised learning model trained under the same conditions. Our classifier serves as a proof of concept and shows potential for application in future $0 \nu \beta \beta$ decay experiments like LEGEND.
| Focus areas | HEP |
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