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    Rights statement: This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Monthly Notices of the Royal Astronomical Society following peer review. The definitive publisher-authenticated version Mike Walmsley, Lewis Smith, Chris Lintott, Yarin Gal, Steven Bamford, Hugh Dickinson, Lucy Fortson, Sandor Kruk, Karen Masters, Claudia Scarlata, Brooke Simmons, Rebecca Smethurst, Darryl Wright, Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning, Monthly Notices of the Royal Astronomical Society, 491 (2), https://doi.org/10.1093/mnras/stz2816 is available online at: https://academic.oup.com/mnras/article/491/2/1554/5583078

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Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published
  • Mike Walmsley
  • Lewis Smith
  • Chris Lintott
  • Yarin Gal
  • Steven Bamford
  • Hugh Dickinson
  • Lucy Fortson
  • Sandor Kruk
  • Karen Masters
  • Claudia Scarlata
  • Brooke Simmons
  • Rebecca Smethurst
  • Darryl Wright
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<mark>Journal publication date</mark>1/01/2020
<mark>Journal</mark>Monthly Notices of the Royal Astronomical Society
Issue number2
Volume491
Number of pages21
Pages (from-to)1554–1574
Publication StatusPublished
Early online date7/10/19
<mark>Original language</mark>English

Abstract

We use Bayesian convolutional neural networks and a novel generative model of Galaxy Zoo volunteer responses to infer posteriors for the visual morphology of galaxies. Bayesian CNN can learn from galaxy images with uncertain labels and then, for previously unlabelled galaxies, predict the probability of each possible label. Our posteriors are well-calibrated (e.g. for predicting bars, we achieve coverage errors of 10.6% within 5 responses and 2.9% within 10 responses) and hence are reliable for practical use. Further, using our posteriors, we apply the active learning strategy BALD to request volunteer responses for the subset of galaxies which, if labelled, would be most informative for training our network. We show that training our Bayesian CNNs using active learning requires up to 35-60% fewer labelled galaxies, depending on the morphological feature being classified. By combining human and machine intelligence, Galaxy Zoo will be able to classify surveys of any conceivable scale on a timescale of weeks, providing massive and detailed morphology catalogues to support research into galaxy evolution....

Bibliographic note

This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Monthly Notices of the Royal Astronomical Society following peer review. The definitive publisher-authenticated version Mike Walmsley, Lewis Smith, Chris Lintott, Yarin Gal, Steven Bamford, Hugh Dickinson, Lucy Fortson, Sandor Kruk, Karen Masters, Claudia Scarlata, Brooke Simmons, Rebecca Smethurst, Darryl Wright, Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning, Monthly Notices of the Royal Astronomical Society, 491 (2), https://doi.org/10.1093/mnras/stz2816 is available online at: https://academic.oup.com/mnras/article/491/2/1554/5583078