Keynote speaker

Title: Physics-Informed Astrophysical Machine Learning

Abstract: While “off-the-shelf” ML has become pervasively used throughout astronomy inference workflows, there is an exciting new space emerging where novel learning architectures and computational approaches are demanded and developed to address specific domain questions. After describing such efforts—in the search for Planet 9 and new classes of variable sources—I turn addition to new practical implementations and uses for generative models in astronomy. One application arises in the need to optimize telescope observing cadences, requiring the generation of physically plausible astronomical time-series. I present our approach to this using semi-supervised variational autoencoders where physical inputs are mapped to the (generative) latent space. I also present our recent work on a successful fast imaging artifact (cosmic rays) discovery and inpainting framework; improving the efficiency for how future data can be taking, this presents clear cost implications for major upcoming space and ground base missions.