HEP-Astro Seminar | Rapid Inference of Galaxy Properties in the Age of Deep and Large-Scale Surveys of the Universe

The inference of the physical properties of galaxies at cosmological distance requires modeling a wide range of physics, including e.g. stellar evolution and atmospheres; dust attenuation and re-emission; nebular physics; and AGN emission. Bayesian inference is often used to map the inevitable degeneracies, and the large amount of physics and wide parameter space means these codes are typically not fast. Yet current and near-future surveys of the universe will yield spectra for millions of galaxies and imaging for billions. I will discuss the tactics employed to speed up these codes, ranging from neural net emulators of key physics (photoionization modeling; stellar spectra) to efficient gradient enhanced GPU-accelerated high-dimensional sampling to rapid simulation-based inference. These yield speed-ups of somewhere between 100x and 100,000x, with unavoidable trade-offs in flexibility and accuracy. I will discuss applications of these techniques to model modern astronomical data, including both industrial-scale modeling of galaxy SEDs and newly-possible directions such as spatially resolved galaxy modeling. Finally, time permitting, I will discuss some of the exciting new discoveries made with these techniques in the very distant universe seen by JWST.