Deep learning pipelines for Astrophysics

Over 100,000 galaxy-scale strong lenses are estimated to be detected from large sky surveys such as the Large Synoptic Survey Telescope (LSST), Euclid, and the Wide-Field Infrared Survey Telescope (WFIRST). Population statistics of the strong gravitational lenses could provide insights about matter density profiles, probe the evolution of lensing media, and, constrain cosmological parameters. This necessitates robust, automated pipelines for detection and analysis of gravitational lenses.

During my summer fellowship at the Cosmological Physics and Advanced Computing (CPAC) at the Argonne National Laboratories, Chicago, I explored the possibility of utilizing Deep Learning algorithms for high energy physics applications - specifically for the proton-proton collisions at the Large Hadron Collider, and mock LSST strong lensing images. I implemented a Convolutional Neural Network (CNNs) which was trained using simulated galaxy-galaxy lensing images.

We reached about 90 percent accuracy in classification of images as lensing or non-lensing with a relatively small training set of 8,000 images. These codes were run on some of the largest supercomputers in the world – including Cori and Edison at the National Energy Research Scientific Computing Center, Cooley, Theta, and other state-of-the-art GPU facilities at Argonne. Highlights of this research are featured here. The codes are publicly made available here