Celeste: Variational inference for a generative model of astronomical images
By Jeffrey C. Regier, Andrew C. Miller, Jon D. McAuliffe, Ryan P. Adams, Matthew W. Hoffman, Adobe Research, Dustin Lang, David Schlegel. 2015
40 citations recorded by the source corpus.
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.
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- Jeff Schneider
- Barnab'as P'oczos
- Siamak Ravanbakhsh
- François Lanusse
- Rachel Mandelbaum
- P. K. Mogensen
- A. N. Riseth
- Kirthevasan Kandasamy
Citations in the corpus
- Enabling Dark Energy Science with Deep Generative Models of Galaxy Images (2016, background): “In related works in machine learning literature Regier et al. (2015) use a convex combination of smooth and spiral templates in an (unconditioned) generative model of galaxy images and Regier, McAuliffe, and Prabhat (2015) propose using VAE for this task.”
- Optim: A mathematical optimization package for Julia (2018, methodology): “…2017; Rackauckas and Nie 2017; and Dony, He, and Stumpf 2018), quantum physics (Damle, Levitt, and Lin 2018), crystalline modelling (Chen and Ortner 2017; Braun, Buze, and Ortner 2017), and the large-scale astronomical cataloguing project Ce-leste (Regier et al. 2015; Regier et al. 2016).”