The IACS seminar series is free and open to the public but registration is required.

Talk Abstract: While “off-the-shelf” ML has become pervasively used throughout astronomy inference workflows, there is an exciting new space emerging where novel learning algorithms and computational approaches are demanded and developed to address specific domain questions. After describing such efforts—in the search for Planet 9 new classes of variable sources—Dr. Bloom will turn his attention 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. Bloom will present his team's approach to this using semi-supervised variational autoencoders where physical inputs are mapped to the (generative) latent space. He will also present a new architecture to exploit known symmetries in periodic variable star observations that yield in state-of-the-art classification results. Last, he will highlight work on a successful fast imaging artifact (cosmic rays) discovery and inpainting framework.