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dc.contributor.authorHocking, Alex
dc.contributor.authorGeach, James E.
dc.contributor.authorSun, Yi
dc.contributor.authorDavey, Neil
dc.date.accessioned2018-04-11T16:21:07Z
dc.date.available2018-04-11T16:21:07Z
dc.date.issued2017-09-15
dc.identifier.citationHocking , A , Geach , J E , Sun , Y & Davey , N 2017 , ' An automatic taxonomy of galaxy morphology using unsupervised machine learning ' , Monthly Notices of the Royal Astronomical Society , vol. 473 , no. 1 , pp. 1108-1129 . https://doi.org/10.1093/mnras/stx2351
dc.identifier.issn0035-8711
dc.identifier.otherPURE: 13307004
dc.identifier.otherPURE UUID: f1bc4eb1-9c47-44bd-bf5a-25bb77263595
dc.identifier.otherScopus: 85032578083
dc.identifier.urihttp://hdl.handle.net/2299/19972
dc.descriptionThis article has been accepted for publication in Monthly Notices of the Royal Astronomical Society ©: 2017 the Author (s). Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved
dc.description.abstractWe present an unsupervised machine learning technique that automatically segments and labels galaxies in astronomical imaging surveys using only pixel data. Distinct from previous unsupervised machine learning approaches used in astronomy we use no pre-selection or pre-filtering of target galaxy type to identify galaxies that are similar. We demonstrate the technique on the Hubble Space Telescope (HST) Frontier Fields. By training the algorithm using galaxies from one field (Abell 2744) and applying the result to another (MACS 0416.1-2403), we show how the algorithm can cleanly separate early and late type galaxies without any form of pre-directed training for what an 'early' or 'late' type galaxy is. We then apply the technique to the HST Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) fields, creating a catalogue of approximately 60 000 classifications. We show how the automatic classification groups galaxies of similar morphological (and photometric) type and make the classifications public via a catalogue, a visual catalogue and galaxy similarity search. We compare the CANDELS machine-based classifications to human-classifications from the Galaxy Zoo: CANDELS project. Although there is not a direct mapping between Galaxy Zoo and our hierarchical labelling, we demonstrate a good level of concordance between human and machine classifications. Finally, we show how the technique can be used to identify rarer objects and present lensed galaxy candidates from the CANDELS imaging.en
dc.format.extent22
dc.language.isoeng
dc.relation.ispartofMonthly Notices of the Royal Astronomical Society
dc.subjectMethods: data analysis
dc.subjectMethods: observational
dc.subjectMethods: statistical
dc.subjectAstronomy and Astrophysics
dc.subjectSpace and Planetary Science
dc.titleAn automatic taxonomy of galaxy morphology using unsupervised machine learningen
dc.contributor.institutionCentre for Astrophysics Research
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.description.statusPeer reviewed
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85032578083&partnerID=8YFLogxK
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.1093/mnras/stx2351
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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