TY - JOUR

T1 - Galaxy Zoo

T2 - Probabilistic Morphology through Bayesian CNNs and Active Learning

AU - Walmsley, Mike

AU - Smith, Lewis

AU - Lintott, Chris

AU - Gal, Yarin

AU - Bamford, Steven

AU - Dickinson, Hugh

AU - Fortson, Lucy

AU - Kruk, Sandor

AU - Masters, Karen

AU - Scarlata, Claudia

AU - Simmons, Brooke

AU - Smethurst, Rebecca

AU - Wright, Darryl

N1 - 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

PY - 2020/1/1

Y1 - 2020/1/1

N2 - 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....

AB - 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....

U2 - 10.1093/mnras/stz2816

DO - 10.1093/mnras/stz2816

M3 - Journal article

VL - 491

SP - 1554

EP - 1574

JO - Monthly Notices of the Royal Astronomical Society

JF - Monthly Notices of the Royal Astronomical Society

SN - 0035-8711

IS - 2

ER -