@inproceedings{e369ffcdacfb4a64b34a00d2b72574da,
title = "Celeste: Variational inference for a generative model of astronomical images",
abstract = "We present a new, fully generative model of optical telescope image sets, along with a variational procedure for inference. Each pixel intensity 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 distributions 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 bodies and measuring their colors.",
author = "Jeffrey Regier and Andrew Miller and Jon McAuliffe and Ryan Adams and Matt Hoffman and Dustin Lang and David Schlegel and Prabhat",
note = "Publisher Copyright: {\textcopyright} Copyright 2015 by International Machine Learning Society (IMLS). All rights reserved.; 32nd International Conference on Machine Learning, ICML 2015 ; Conference date: 06-07-2015 Through 11-07-2015",
year = "2015",
language = "English (US)",
series = "32nd International Conference on Machine Learning, ICML 2015",
publisher = "International Machine Learning Society (IMLS)",
pages = "2095--2103",
editor = "Francis Bach and David Blei",
booktitle = "32nd International Conference on Machine Learning, ICML 2015",
}