Direct detection of dark matter substructure in strong lens images with convolutional neural networks

Strong gravitational lensing is a promising way of uncovering the nature of dark matter, by finding perturbations to images that cannot be accounted for well 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 greater than 75% accuracy when they have masses of greater than or equal to 510^9M_ 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. The accuracy does not improve significantly if we add a population of less massive subhalos. With the expectation of experiments such as Hubble Space Telescope and Euclid yielding thousands of high-quality strong lensing images in the next years, having a way of analyzing images quickly to identify candidates that merit further analysis to determine individual subhalo properties while preventing extensive resources being used for images that would yield null detections could be very useful. By understanding the sensitivity as a function of substructure mass, nondetections could be combined with the information from images with substructure to constrain the cold dark matter scenario, in particular if the sensitivity can be pushed to lower masses.