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Herrero-Garcia, J., Schwetz, T., & Zupan, J. (2012). On the annual modulation signal in dark matter direct detection. J. Cosmol. Astropart. Phys., 03(3), 005–28pp.
Abstract: We derive constraints on the annual modulation signal in Dark Matter (DM) direct detection experiments in terms of the unmodulated event rate. A general bound independent of the details of the DM distribution follows from the assumption that the motion of the earth around the sun is the only source of time variation. The bound is valid for a very general class of particle physics models and also holds in the presence of an unknown unmodulated background. More stringent bounds are obtained, if modest assumptions on symmetry properties of the DM halo are adopted. We illustrate the bounds by applying them to the annual modulation signals reported by the DAMA and CoGeNT experiments in the framework of spin-independent elastic scattering. While the DAMA signal satisfies our bounds, severe restrictions on the DM mass can be set for CoGeNT.
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Herrero-Garcia, J., Patrick, R., & Scaffidi, A. (2022). A semi-supervised approach to dark matter searches in direct detection data with machine learning. J. Cosmol. Astropart. Phys., 02, 039–19pp.
Abstract: 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.
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