Abstract
We present a new, fully generative model of op- tical telescope image sets, along with a varia- tional procedure for inference. Each pixel inten- sity is treated as a Poisson random variable, with a rate parameter dependent on latent properties of stars and galaxies. Key latent properties are themselves random, with scientific prior distribu- tions constructed from large ancillary data sets. We check our approach on synthetic images. We also run it on images from a major sky survey, where it exceeds the performance of the current state-of-the-art method for locating celestial bod- ies and measuring their colors.
Author
Jeffrey Regier, Andrew Miller, Jon McAuliffe, Ryan Adams, Matt Hoffman, Dustin Lang, David Schlegel, and Prabhat
Journal
International Conference on Machine Learning (ICML)
Paper Publication Date
2015
Paper Type
Astroinformatics