DeCAF Seminar Series:

Abstract: Galaxy surveys of the next decade will observe hundreds of millions of galaxies over unprecedented cosmic volumes and produce detailed 3D maps of galaxies. These maps encode the growth and expansion histories of the Universe that can be used to precisely test the standard “Lambda-CDM” cosmological model and probe the nature of dark energy. While, current analyses extract some of this cosmological information by summarizing the galaxy maps into 2-point clustering statistics, much more information still remain in the data. In my talk, I will present how we can leverage high-fidelity cosmological simulations with generative AI to extract the full cosmological information of galaxy surveys. I will present SimBIG, a galaxy clustering analysis framework using simulation-based inference with normalizing flows. I will show the latest results from applying SimBIG to data from current galaxy surveys and showcase the improvements we find over the current baseline analyses. Lastly, I will discuss how SimBIG will be extended to the next-generation galaxy surveys to produce even more precise tests of the Lambda-CDM model and probe dark energy across cosmic history.

Speaker Bio: Changhoon Hahn is an assistant professor in the University of Texas at Austin, Department of Astronomy. He is also a member of the NSF-Simons AI Institute for Cosmic Origins and the Cosmic Frontier Center. He works at the intersection of cosmology and galaxy evolution, bringing together observations, simulations, and machine learning techniques. His research addresses fundamental questions about the nature of dark energy, neutrinos, dark matter, and the evolution of galaxies. He is a key contributor to the Dark Energy Spectroscopic Instrument and a co-leader of the Subaru Prime Focus Spectrograph (PFS) Cosmology Survey. He holds a PhD in Physics from NYU and previously held positions at Lawrence Berkeley National Lab, Princeton, and the University of Arizona.