Deep Learning the Morphology of Dark Matter Substructure
Stephon Alexander, Sergei Gleyzer, Evan McDonough, Michael W. Toomey, Emanuele Usai
Abstract
Abstract Strong gravitational lensing is a promising probe of the substructure of dark matter halos. Deep-learning methods have the potential to accurately identify images containing substructure, and differentiate weakly interacting massive particle dark matter from other well motivated models, including vortex substructure of dark matter condensates and superfluids. This is crucial in future efforts to identify the true nature of dark matter. We implement, for the first time, a classification approach to identifying dark matter based on simulated strong lensing images with different substructure. Utilizing convolutional neural networks trained on sets of simulated images, we demonstrate the feasibility of deep neural networks to reliably distinguish among different types of dark matter substructure. With thousands of strong lensing images anticipated with the coming launch of Vera C. Rubin Observatory, we expect that supervised and unsupervised deep-learning models will play a crucial role in determining the nature of dark matter.