Event

By analyzing the gravitational lensing effects of stars and planets in our galaxy, and other massive distant galaxies, microlensing provides unique insights into phenomena that are otherwise difficult to observe. In the era of large surveys like the Vera C. Rubin Observatory and the Roman Space Telescope, we will encounter large volumes of light curves that we have to classify and model or prioritize for follow-up observations fast and efficiently. In the first part of my talk, I will focus on galactic microlensing that aims at studying populations of distant planetary systems with properties different than the ones found by other methods like transit. I will introduce algorithms I have developed to improve detection and classification of microlensing events and extract preliminary planetary parameters for candidates with planetary system lenses. In the second part of my talk, I will discuss how better modeling the microlensing variability in light curves of lensed quasars and supernovae enhances accurate measurements of time delays and the Hubble constant, along with improving our understanding of quasars structure and the stellar mass distributions in distant galaxies. In the era of Rubin, there will be thousands of events that need microlensing modeling. Traditional modeling approaches use computationally intensive ray-tracing methods to generate microlensing magnification maps. I will introduce an Autoencoder (a type of deep-learning model) we have trained on pre-computed magnification maps to reduce their dimension and form a latent space representation while optimizing for acceptable reconstruction of the maps. Used to generate large samples of magnification maps, this model can enable fast modeling of microlensing variability in lensed quasars and supernovae.