Decoding Dark Matter Substructure without Supervision

Abstract

The identity of dark matter remains one of the most pressing questions in physics today. While many promising dark matter candidates have been put forth over the last half-century, to date the true identity of dark matter remains elusive. While it is possible that one of the many proposed candidates may turn out to be dark matter, it is at least equally likely that the correct physical description has yet to be proposed. To address this challenge, novel applications of machine learning can help physicists gain insight into the dark sector from a theory agnostic perspective. In this work we demonstrate the use of unsupervised machine learning techniques to infer the presence of substructure in dark matter halos using galaxy-galaxy strong lensing simulations.

18 pages, 9 figures, 6 tables

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• Online-compatible Unsupervised Non-resonant Anomaly Detection
• Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection
• Extracting the Subhalo Mass Function from Strong Lens Images with Image Segmentation
• Machine Learning and Cosmology
• Reconstructing Cosmic Polarization Rotation with ResUNet-CMB
• An Accurate Comprehensive Approach to Substructure: I. Accreted Subhaloes
• An Accurate Comprehensive Approach to Substructure: II. Stripped Subhaloes
• Detecting dark matter subhalos with the Nancy Grace Roman Space Telescope
• Preserving New Physics while Simultaneously Unfolding All Observables