Grainger College of Engineering, All Events

Abstract: Describing the current expansion rate of the universe, the Hubble constant, H₀, is one of the most important yet contested parameters in modern cosmology. Despite many efforts to resolve it, a persistent discrepancy exists between values for H₀ derived using early-universe measurements, such as the Cosmic Microwave Background (CMB) under the standard ΛCDM model and those obtained using late-universe measurements like the local distance ladder. This discrepancy, called the “Hubble tension” has motivated the search for probes of cosmic expansion independent of the local distance ladder and early-universe physics to further constrain H₀. Strongly-lensed quasars, or active galactic nuclei (SL-AGN), offer one such probe through time-delay cosmography, wherein multiply lensed images of a source experience a relative time delay when observed due to variations in path length and gravitational potentials the photons comprising different images travel through. These time delays can then be used to determine information about absolute cosmic distances and expansion. This presentation presents a review of the theoretical basis, source injection pipeline, and machine learning classification methods developed to detect simulated SL-AGN in expected data from Year 1 of the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST). We discuss the role and potential of Rubin to dramatically increase the known population of lensed quasars, outline our source injection and image subtraction methods, and evaluate the performance of a Random Forest classifier in distinguishing injected SL-AGN systems from non-lensed sources.

Join via Zoom: https://illinois.zoom.us/j/84701225497?pwd=Xln6ZCKraIhgRswrUDdn1NfpTlOK4A