Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning

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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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.
Original languageEnglish
Pages (from-to)1554-1574
Number of pages21
JournalMonthly Notices of the Royal Astronomical Society
Volume491
Issue number2
Early online date7 Oct 2019
DOIs
Publication statusPublished - 1 Jan 2020

Keywords

  • astro-ph.GA
  • cs.CV
  • methods: data analysis
  • methods: statistical
  • galaxies: evolution
  • galaxies: statistics
  • galaxies: structure
  • RCUK
  • STFC
  • ST/R505006/1
  • ST/N003179/1

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