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

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

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 11.8 per cent within a vote fraction deviation of 0.2) 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 per cent 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 time-scale of weeks, providing massive and detailed morphology catalogues to support research into galaxy evolution.

Original languageEnglish (US)
Pages (from-to)1554-1574
Number of pages21
JournalMonthly Notices of the Royal Astronomical Society
Volume491
Issue number2
DOIs
StatePublished - Jan 1 2020

Bibliographical note

Funding Information:
MW would like to thank H. Dom?nguez Sanchez and M. Huertas-Company for helpful discussions. MW acknowledges funding from the Science and Technology Funding Council (STFC) Grant Code ST/R505006/1. We also acknowledge support from STFC under grant ST/N003179/1. LF, CS, HD, and DW acknowledge partial support from one or more of the US National Science Foundation grants IIS-1619177, OAC-1835530, and AST-1716602. This research made use of the open-source PYTHON scientific computing ecosystem, including SCIPY (Jones et al. 2001), MAT-PLOTLIB (Hunter 2007), SCIKIT-LEARN (Pedregosa et al. 2011), SCIKIT-IMAGE (van der Walt et al. 2014), and PANDAS (McKinney 2010). This research made use of ASTROPY, a community-developed core PYTHON package for Astronomy (The Astropy Collaboration 2013, 2018). This research made use of TENSORFLOW (Abadi et al. 2015). All code is publicly available on GitHub at www.github.com/m walmsley/galaxy-zoo-bayesian-cnn (Walmsley 2019).

Funding Information:
MW acknowledges funding from the Science and Technology Funding Council (STFC) Grant Code ST/R505006/1. We also acknowledge support from STFC under grant ST/N003179/1. LF, CS, HD, and DW acknowledge partial support from one or more of the US National Science Foundation grants IIS-1619177, OAC-1835530, and AST-1716602.

Publisher Copyright:
© 2019 The Author(s).

Keywords

  • Galaxies: evolution
  • Galaxies: statistics
  • Galaxies: structure
  • Methods: data analysis
  • Methods: statistical

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