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LensQuery: A Catalog of Roman Strong Gravitational Lenses and an Event Broker to Enable Cosmology and Time-Domain Science
Program ID 19074
Science Category Large Scale Structure of the Universe
Program Type Analysis
Category Medium
Principal Investigator Patrick Kelly
PI Institution University of Minnesota
Co-Investigators
  • Bharath Chowdhary Nagam (University of Minnesota)
  • Benjamin Border (University of Minnesota)
Abstract The Roman Space Telescope’s wide field, high resolution, and sensitivity will make it an exceptionally effective tool for identifying strong gravitational lenses. Indeed, simulations forecast that more than one-hundred thousand galaxy-scale strong gravitational lenses should be detectable in the High-Latitude Wide-Area Survey and the High-Latitude Time-Domain Survey. Transients in these strong lensing systems detected by Roman or the Rubin Observatory will include hundreds of multiply imaged supernovae, and these should enable independent and competitive constraints on the value of the Hubble constant. Likewise, magnified SNe Ia at redshift z>1 will provide excellent targets for high signal-to-noise (S/N) spectroscopy to evaluate whether the low- and high-redshift SNe Ia cosmology samples differ, a key systematic. Finally, strong lenses can briefly magnify individual stars by factors of many thousands, which should enable spectroscopy of individual stars. Strong gravitational lenses themselves will enable a wide range of science. Hundreds of arcs will have sufficient S/N to identify dark-matter substructures. Exotic configurations including double source-plane lenses offer the opportunity for novel constraints on cosmological parameters. The Einstein radii of galaxy-scale strong lenses measure the enclosed mass, and yield constraints on the inner mass slope and stellar initial mass function. To enable this broad set of investigations, we propose to assemble a public, searchable catalog of galaxy- and group-scale lenses in the Roman imaging data acquired in its first two years. We will use machine learning to identify gravitational lenses and characterize the properties of each (with uncertainties), as well as identify the pixel footprint of lensing arcs. We will construct a real-time broker that ingests transient alerts from Roman RAPID PIT and Rubin and share candidate lensed events.