Celeste: Variational inference for a generative model of astronomical images

Jeffrey Regier, Andrew Miller, Jon McAuliffe, Ryan Adams, Matt Hoffman, Dustin Lang, David Schlegel, Prabhat

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

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.

Original languageEnglish (US)
Title of host publication32nd International Conference on Machine Learning, ICML 2015
EditorsFrancis Bach, David Blei
PublisherInternational Machine Learning Society (IMLS)
Pages2095-2103
Number of pages9
ISBN (Electronic)9781510810587
StatePublished - Jan 1 2015
Externally publishedYes
Event32nd International Conference on Machine Learning, ICML 2015 - Lile, France
Duration: Jul 6 2015Jul 11 2015

Publication series

Name32nd International Conference on Machine Learning, ICML 2015
Volume3

Other

Other32nd International Conference on Machine Learning, ICML 2015
CountryFrance
CityLile
Period7/6/157/11/15

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Computer Science Applications

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  • Cite this

    Regier, J., Miller, A., McAuliffe, J., Adams, R., Hoffman, M., Lang, D., Schlegel, D., & Prabhat (2015). Celeste: Variational inference for a generative model of astronomical images. In F. Bach, & D. Blei (Eds.), 32nd International Conference on Machine Learning, ICML 2015 (pp. 2095-2103). (32nd International Conference on Machine Learning, ICML 2015; Vol. 3). International Machine Learning Society (IMLS).