October 08, 2026 3:45 pm - 5:00 pm ET
FCAD Colloquia
LGRT 1033

Our understanding of dwarf galaxies suffers from a streetlight effect: we have looked mostly where the light is, at satellites of the Milky Way and its analogs, biasing models of galaxy evolution and small-scale structure toward dense environments. I present my dissertation work that uncovered how dwarfs are quenched beyond massive halos' virial radii, using low-surface-brightness dwarfs in the Dark Energy Survey and simulated field dwarfs from Illustris-TNG50. Internal processes also matter: feedback from star formation shapes dwarf morphologies, and my study of SAGAbg dwarfs links the growth of central bulges to the specific star formation rate.  With Rubin data on the horizon, there is a need to develop a volume-complete, environment-classified dwarf catalog in the low-redshift universe. I discuss the deep learning-based framework that I am developing for this purpose. This catalog will enable us to further disentangle environmental quenching from stellar feedback behind the evolution of dwarfs and thereby constrain models of dark matter and baryonic physics in the lowest-mass halos.