AI-enabled Insights into Galaxy Evolution

The next generation of observatories such as the Vera C. Rubin Observatory and Euclid are posing a massive data challenge. An obstacle we need to overcome is the inference of accurate redshifts from photometric observations that can be limited to a handful of bands. We addressed this challenge with a forward modeling framework, pop-COSMOS, calibrated by fitting a population model to observations on the photometry space. This high-dimensional fitting, complete with data-driven noise modeling and flexible selection effects, is achieved via a novel use of simulation-based inference. Sampling from our fitted model provides the full spectral energy densities (SEDs) of galaxies through stellar population synthesis. pop-COSMOS therefore unlocks a medium for the study of galaxy evolution science that was not possible before, as it far surpasses the scope of current spectroscopic catalogs and their wavelength coverage. This talk will feature population-level results, specifically a comprehensive look at the star formation histories (SFHs) of galaxy populations over cosmic time inferred from our model. I will first present our findings on the stellar mass assembly of star-forming and quiescent galaxy populations. I will then demonstrate how the cosmic star formation rate density from our model compares with well-known results from the literature.