Title: 

Again but faster, better and with more physics: ML-accelerated inference of galaxy properties in deep and wide surveys of the universe

 

Abstract: 

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 introduce new 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 tactics yield speed-ups of somewhere between 100x and 100,000x with different trade-offs in flexibility and accuracy. In addition to unlocking industrial-scale modeling of galaxy surveys, I will discuss qualitatively new science directions enabled by these breakthroughs, such as modeling entire galaxy populations rather than one-at-a-time approaches and extremely high dimensional modeling of individual systems, e.g. spatially resolved modeling.