A semi-supervised approach to dark matter searches in direct detection data with machine learning

The dark matter sector remains completely unknown. It is therefore crucial to keep an open mind regarding its nature and possible interactions. Focusing on the case of Weakly Interacting Massive Particles, in this work we make this general philosophy more concrete by applying modern machine learning techniques to dark matter direct detection. We do this by encoding and decoding the graphical representation of background events in the XENONnT experiment with a convolutional variational autoencoder. We describe a methodology that utilizes the 'anomaly score' derived from the reconstruction loss of the convolutional variational autoencoder as well as a pre-trained standard convolutional neural network, in a semi-supervised fashion. Indeed, we observe that optimum results are obtained only when both unsupervised and supervised anomaly scores are considered together. A data set that has a higher proportion of anomaly score is deemed anomalous and deserves further investigation. Contrary to classical analyses, in principle all information about the events is used, preventing unnecessary information loss. Lastly, we demonstrate the reach of learning-focused anomaly detection in this context by comparing results with classical inference, observing that, if tuned properly, these techniques have the potential to outperform likelihood-based methods.

Automated summary

The paper applies modern machine learning techniques to dark matter direct detection, utilizing a convolutional variational autoencoder and a convolutional neural network in a semi-supervised manner for anomaly detection. The results show that the optimum performance is achieved when both unsupervised and supervised anomaly scores are considered together, and datasets with higher anomaly scores are deemed anomalous and require further investigation. These learning-focused anomaly detection methods have the potential to outperform likelihood-based methods.