Galaxy formation is currently faced with immense datasets, both observational and simulated, with much more on the way. As the simulations continue to improve, I argue it is time to start thinking about how to use these realistic-looking galaxies to develop a predictive theory of galaxy formation. A new generation of semi-analytic models and statistical techniques are needed to simultaneously address the strong degeneracies that persist in the problem of galaxy evolution, and to coherently comprehend the vast quantities of extant and forthcoming data. I will focus on a particular set of puzzles around the turbulent driving in galactic disks - is stellar feedback, local gravitational instabilities, the direct impact of cosmological accretion, or something else responsible?
BIO
John Forbes a theoretical and computational astrophysicist at the Flatiron Institute, a division of the Simons Foundation, in New York City. He studies how galaxies and stars form using supercomputer simulations, statistical modeling, and machine learning. He earned his undergraduate degree at Caltech, a PhD at the University of California, Santa Cruz, and was an Institute for Theory and Computation fellow at Harvard before joining the Flatiron Institute.
Host: Cheng Li
