Direct Detection of Dark Matter Substructure in Strong Lens Images with Convolutional Neural Networks

Understand

Strong gravitational lensing is a promising way of uncovering the nature of dark matter, by finding perturbations to images that cannot be well accounted for by modeling the lens galaxy without additional structure, be it subhalos (smaller halos within the smooth lens) or line-of-sight (LOS) halos.

• We present results attempting to infer the presence of substructure from images without requiring an intermediate step in which a smooth model has to be subtracted, using a simple convolutional neural network (CNN).
• We find that the network is only able to infer the presence of subhalos with $>75\%$ accuracy when they have masses of $\geq 5 \times 10^9$M$_{\odot}$ if they lie within the main lens galaxy.
• Since less massive foreground LOS halos can have the same effect as higher mass subhalos, the CNN can probe lower masses in the halo mass function.