Princeton University Star Formation/ISM Rendezvous (SFIR)

Machine learning has found increasing use in astronomy over the last few years. In this talk, instead of discussing machine learning itself, I will discuss how a mathematical tool borrowed from machine learning - auto-differentiation - can be put to use in astronomy. I will focus on applications of auto-differentiation to modeling the three-dimensional distribution of interstellar dust, stellar spectral energy distributions and gravitational potentials. The ability to construct highly flexible yet differentiable models allows elegant and straightforward approaches to solving each of these three problems.