Thursday, April 18, 2019
From streaming, repeated, noisy, and distorted images of the sky, time-domain astronomers are tasked with extracting novel science as quickly as possible with limited and imperfect information. Employing algorithms developed in other fields, we have has already reached important milestones demonstrating the speed and efficacy of using ML in data and inference workflows. Now we look to innovations in learning architectures and computational approaches that are purpose-built alongside the specific domain questions. I will describe such efforts—developed in the search for Planet 9, new classes of variable sources, and for data-driven emulators—and discuss on-going efforts to imbue physical understanding into the learning process itself.
