Date: Wednesday 21-Feb-2024
Time: 11:30-12:30 pm
Location: PSC 1136
Speaker: Lucia Perez (Flatiron Institute, Princeton University)
Title: CAMELS-SAM and beyond: cracking open the galaxy-halo connection with machine learning
Abstract: While many advanced methods for the statistical analysis of cosmological data have been developed to handle the looming breadth of new astronomical observations, a common constraint is the availability of training cosmological simulation data, especially that which includes realistic galaxy formation physics and the volume and/or resolution necessary to match observations. CAMELS-SAM and its newest updates offer crucial and unique training data sets of realistic galaxies across an enormous range of cosmologies and galaxy physics formulations. As the larger-volume ‘hump’ of the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, CAMELS-SAM uses semi-analytic models (SAMs) of galaxy formation to flexibly and quickly generate galaxies over 1000 dark-matter only simulations of L=100 h^-1 cMpc and N=640^3. We discuss new or upcoming expansions to CAMELS-SAM: the updated galaxy catalogs using the Santa Cruz SAM with 1) up to 9 varied astrophysical parameters, with 2) complete photometry generated for all galaxies; and, 3) new galaxy catalogs with the L-Galaxies SAM. These CAMELS-SAM datasets together will offer many possibilities for the next generation of simulation-based inference on observed galaxies.
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