Pushing automated morphological classifications to their limits with the Dark Energy Survey
dc.contributor.author
Vega-Ferrero, Jesús
dc.contributor.author
Domínguez-Sánchez, Helena
dc.contributor.author
Bernardi, Mariangela
dc.contributor.author
Huertas-Company, Marc
dc.contributor.author
Morgan, Robert
dc.contributor.author
Margalef-Bentabol, Berta
dc.contributor.author
Aguena, Michel
dc.contributor.author
Allam, Sahar
dc.contributor.author
Annis, James
dc.contributor.author
Avila, Santiago
dc.contributor.author
Bacon, David
dc.contributor.author
Bertin, Emmanuel
dc.contributor.author
Brooks, David
dc.contributor.author
Carnero Rosell, Aurelio
dc.contributor.author
Carrasco Kind, Matias
dc.contributor.author
Carretero, Jorge
dc.contributor.author
Choi, Ami
dc.contributor.author
Conselice, Christopher
dc.contributor.author
Costanzi, Matteo
dc.contributor.author
da Costa, Luiz N.
dc.contributor.author
Hartley, William G.
dc.contributor.author
Tarsitano, Federica
dc.contributor.author
et al.
dc.date.accessioned
2021-08-25T14:59:51Z
dc.date.available
2021-08-19T02:47:19Z
dc.date.available
2021-08-19T07:34:29Z
dc.date.available
2021-08-25T14:59:51Z
dc.date.issued
2021-09
dc.identifier.issn
0035-8711
dc.identifier.issn
1365-2966
dc.identifier.other
10.1093/mnras/stab594
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/501373
dc.description.abstract
We present morphological classifications of ~27 million galaxies from the Dark Energy Survey (DES) Data Release 1 (DR1) using a supervised deep learning algorithm. The classification scheme separates: (a) early-type galaxies (ETGs) from late-type galaxies (LTGs); and (b) face-on galaxies from edge-on. Our convolutional neural networks (CNNs) are trained on a small subset of DES objects with previously known classifications. These typically have mr ≤17.7 mag; we model fainter objects to mr < 21.5 mag by simulating what the brighter objects with well-determined classifications would look like if they were at higher redshifts. The CNNs reach 97 per cent accuracy to mr < 21.5 on their training sets, suggesting that they are able to recover features more accurately than the human eye. We then used the trained CNNs to classify the vast majority of the other DES images. The final catalogue comprises five independent CNN predictions for each classification scheme, helping to determine if the CNN predictions are robust or not. We obtain secure classifications for ~87 per cent and 73 per cent of the catalogue for the ETG versus LTG and edge-on versus face-on models, respectively. Combining the two classifications (a) and (b) helps to increase the purity of the ETG sample and to identify edge-on lenticular galaxies (as ETGs with high ellipticity). Where a comparison is possible, our classifications correlate very well with Sérsic index (n), ellipticity (ϵ), and spectral type, even for the fainter galaxies. This is the largest multiband catalogue of automated galaxy morphologies to date.
en_US
dc.language.iso
en
en_US
dc.publisher
Oxford University Press
en_US
dc.subject
methods: observational
en_US
dc.subject
catalogues
en_US
dc.subject
galaxies: structure
en_US
dc.title
Pushing automated morphological classifications to their limits with the Dark Energy Survey
en_US
dc.type
Journal Article
dc.date.published
2021-03-02
ethz.journal.title
Monthly Notices of the Royal Astronomical Society
ethz.journal.volume
506
en_US
ethz.journal.issue
2
en_US
ethz.journal.abbreviated
Mon. Not. R. Astron. Soc.
ethz.pages.start
1927
en_US
ethz.pages.end
1943
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Oxford
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02010 - Dep. Physik / Dep. of Physics::02532 - Institut für Teilchen- und Astrophysik / Inst. Particle Physics and Astrophysics::03928 - Refregier, Alexandre / Refregier, Alexandre
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02010 - Dep. Physik / Dep. of Physics::02532 - Institut für Teilchen- und Astrophysik / Inst. Particle Physics and Astrophysics::03928 - Refregier, Alexandre / Refregier, Alexandre
en_US
ethz.date.deposited
2021-08-19T02:47:21Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-08-25T15:00:00Z
ethz.rosetta.lastUpdated
2024-02-02T14:34:02Z
ethz.rosetta.versionExported
true
ethz.COinS
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