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Title: Galaxy morphological classification in deep-wide surveys via unsupervised machine learning

Abstract

ABSTRACT Galaxy morphology is a fundamental quantity, which is essential not only for the full spectrum of galaxy-evolution studies, but also for a plethora of science in observational cosmology (e.g. as a prior for photometric-redshift measurements and as contextual data for transient light-curve classifications). While a rich literature exists on morphological-classification techniques, the unprecedented data volumes, coupled, in some cases, with the short cadences of forthcoming ‘Big-Data’ surveys (e.g. from the LSST), present novel challenges for this field. Large data volumes make such data sets intractable for visual inspection (even via massively distributed platforms like Galaxy Zoo), while short cadences make it difficult to employ techniques like supervised machine learning, since it may be impractical to repeatedly produce training sets on short time-scales. Unsupervised machine learning, which does not require training sets, is ideally suited to the morphological analysis of new and forthcoming surveys. Here, we employ an algorithm that performs clustering of graph representations, in order to group image patches with similar visual properties and objects constructed from those patches, like galaxies. We implement the algorithm on the Hyper-Suprime-Cam Subaru-Strategic-Program Ultra-Deep survey, to autonomously reduce the galaxy population to a small number (160) of ‘morphological clusters’, populated by galaxiesmore » with similar morphologies, which are then benchmarked using visual inspection. The morphological classifications (which we release publicly) exhibit a high level of purity, and reproduce known trends in key galaxy properties as a function of morphological type at z < 1 (e.g. stellar-mass functions, rest-frame colours, and the position of galaxies on the star-formation main sequence). Our study demonstrates the power of unsupervised machine learning in performing accurate morphological analysis, which will become indispensable in this new era of deep-wide surveys.« less

Authors:
ORCiD logo [1]; ORCiD logo [2];  [3]; ORCiD logo [4]; ORCiD logo [2]
  1. Centre for Astrophysics Research, School of Physics, Astronomy and Mathematics, University of Hertfordshire, College Lane, Hatfield AL10 9AB, UK, Steward Observatory, University of Arizona, 933 N. Cherry Ave, Tucson, AZ 85719, USA, Korea Astronomy and Space Science Institute, 776 Daedeokdae-ro, Yuseong-gu, Daejeon 34055, Korea
  2. Centre for Astrophysics Research, School of Physics, Astronomy and Mathematics, University of Hertfordshire, College Lane, Hatfield AL10 9AB, UK
  3. Centre for Computer Science and Informatics Research, University of Hertfordshire, Hatfield AL10 9AB, UK
  4. Centre for Astrophysics Research, School of Physics, Astronomy and Mathematics, University of Hertfordshire, College Lane, Hatfield AL10 9AB, UK, I.N.A.F-Osservatotio Astronomico di Roma, via Frascati 33, I-00040 Monte Porzio Catone (Roma), Italy
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1575933
Resource Type:
Published Article
Journal Name:
Monthly Notices of the Royal Astronomical Society
Additional Journal Information:
Journal Name: Monthly Notices of the Royal Astronomical Society Journal Volume: 491 Journal Issue: 1; Journal ID: ISSN 0035-8711
Publisher:
Oxford University Press
Country of Publication:
United Kingdom
Language:
English

Citation Formats

Martin, G., Kaviraj, S., Hocking, A., Read, S. C., and Geach, J. E. Galaxy morphological classification in deep-wide surveys via unsupervised machine learning. United Kingdom: N. p., 2019. Web. doi:10.1093/mnras/stz3006.
Martin, G., Kaviraj, S., Hocking, A., Read, S. C., & Geach, J. E. Galaxy morphological classification in deep-wide surveys via unsupervised machine learning. United Kingdom. doi:10.1093/mnras/stz3006.
Martin, G., Kaviraj, S., Hocking, A., Read, S. C., and Geach, J. E. Sat . "Galaxy morphological classification in deep-wide surveys via unsupervised machine learning". United Kingdom. doi:10.1093/mnras/stz3006.
@article{osti_1575933,
title = {Galaxy morphological classification in deep-wide surveys via unsupervised machine learning},
author = {Martin, G. and Kaviraj, S. and Hocking, A. and Read, S. C. and Geach, J. E.},
abstractNote = {ABSTRACT Galaxy morphology is a fundamental quantity, which is essential not only for the full spectrum of galaxy-evolution studies, but also for a plethora of science in observational cosmology (e.g. as a prior for photometric-redshift measurements and as contextual data for transient light-curve classifications). While a rich literature exists on morphological-classification techniques, the unprecedented data volumes, coupled, in some cases, with the short cadences of forthcoming ‘Big-Data’ surveys (e.g. from the LSST), present novel challenges for this field. Large data volumes make such data sets intractable for visual inspection (even via massively distributed platforms like Galaxy Zoo), while short cadences make it difficult to employ techniques like supervised machine learning, since it may be impractical to repeatedly produce training sets on short time-scales. Unsupervised machine learning, which does not require training sets, is ideally suited to the morphological analysis of new and forthcoming surveys. Here, we employ an algorithm that performs clustering of graph representations, in order to group image patches with similar visual properties and objects constructed from those patches, like galaxies. We implement the algorithm on the Hyper-Suprime-Cam Subaru-Strategic-Program Ultra-Deep survey, to autonomously reduce the galaxy population to a small number (160) of ‘morphological clusters’, populated by galaxies with similar morphologies, which are then benchmarked using visual inspection. The morphological classifications (which we release publicly) exhibit a high level of purity, and reproduce known trends in key galaxy properties as a function of morphological type at z < 1 (e.g. stellar-mass functions, rest-frame colours, and the position of galaxies on the star-formation main sequence). Our study demonstrates the power of unsupervised machine learning in performing accurate morphological analysis, which will become indispensable in this new era of deep-wide surveys.},
doi = {10.1093/mnras/stz3006},
journal = {Monthly Notices of the Royal Astronomical Society},
number = 1,
volume = 491,
place = {United Kingdom},
year = {2019},
month = {10}
}

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  • DOI: 10.1109/TPAMI.2005.151