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Seminar: Jeffrey Regier

Statistics Seminar Series
March 3, 2016
All Day
209 W. Eighteenth Ave. (EA), Room 170

Title

Approximate Inference for Generative Models of Astronomical Images

Speaker

Jeffrey Regier, University of California, Berkeley

Abstract

A central problem in astronomy is to infer locations, colors, and other properties of stars and galaxies appearing in astronomical images. In this talk, I present a hierarchical probabilistic model for the problem: the number of photons arriving at each pixel during an exposure is Poisson distributed, with a rate parameter that is a deterministic function of the latent properties of the imaged stars and galaxies. I then propose a procedure based on variational inference to approximate the posterior distribution of the latent properties. The procedure attains state-of-the-art results. It demonstrates the scaling characteristics necessary to construct an astronomical catalog from the full 20-terabyte Sloan Digital Sky Survey dataset, with a computational budget of 1 million CPU hours. Finally, I present an enhanced model for galaxies that uses a neural network, also trained with variational inference, to encode the conditional distribution of the data given the latent random variables. The proposed model assigns higher probability to held-out data than the current standard practice and provides a rare example of successful application of a neural network to unsupervised learning.