IN-PERSON SCHEDULE

Led by Dan Kessler, PhD Candidate.

Meeting ID: 92672037881

Password: 994258

In-person and virtual prospective students are invited to discuss with each other and with current students.

...then walk to West Hall with current students for your visit day!

Learn about the PhD program from the program’s director.

Bayesian statistics provides an elegant formalism for interpreting astronomical images: scientifically interesting but unknown properties of imaged stars and galaxies are treated as latent random variables and pixel intensities are treated as observed random variables. It is challenging, however, to efficiently infer the posterior distribution for realistic Bayesian models of astronomical images. In this talk, I present the Bayesian Light Source Separator (BLISS), which overcomes this challenge through the judicious use of modern variational inference techniques. BLISS embeds a flexible galaxy model that is encoded by a neural network within an interpretable generative model of astronomical images, and employs neural networks, stochastic optimization, and a novel variational bound to facilitate posterior inference. In comparison to a recently published method based on MCMC for detecting astronomical objects in crowded fields, BLISS performs posterior inference 10,000 times faster while achieving substantially higher accuracy.

Learn about current PhD students' research in a casual poster session; don't forget to vote for your favorite presentation!