DeepGraviLens: a Multi-Modal Architecture for Classifying Gravitational Lensing Data

In astrophysics, a gravitational lens is a matter distribution (e.g., a black hole) able to bend the trajectory of transiting light, similar to an optical lens. Such apparent distortion is caused by the curvature of the geometry of space-time around the massive body acting as a lens, a phenomenon that forces the light to travel along the geodesics (i.e., the shortest paths in the curved space-time). Strong and weak gravitational lensing focus on the effects produced by particularly massive bodies (e.g., galaxies and black holes), while microlensing addresses the consequences produced by lighter entities (e.g., stars). This research proposes an approach to automatically classify strong gravitational lenses with respect to the lensed objects and to their evolution through time. Automatically finding and classifying gravitational lenses is a major challenge in astrophysics. As [103, 91, 39, 44] show, gravitational lensing systems can be complex, ubiquitous and hard to detect without computer-aided data processing. The volumes of data gathered by contemporary instruments make manual inspection unfeasible. As an example, the Vera C. Rubin Observatory is expected to collect petabytes of data [108]. Moreover, strong lensing is involved in major astrophysical problems: studying massive bodies that are too faint to be analyzed with current instrumentation; characterizing the geometry, content and kinematics of the universe; and investigating mass distribution in the galaxy formation process [103].