Differentiable Physical Simulations for Astronomy.

The technology that underpins machine learning - differentiable programming - is poised to revolutionise astronomy, making it possible for the first time to fit very high dimensional models: hierarchical models describing every star in a survey; the sensitivity of millions of pixels in a detector; models of images or spectra with very many free parameters; or neural networks that represent physics we cannot easily solve in closed form. It also enables fundamental information-theoretic quantities like the Fisher information to be calculated, allowing for determination and optimisation of the information content of an experiment. 
 

I will give an overview of recent work applying this to astronomy and in particular exoplanet science, giving examples from imaging and time series data.