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  Pushing automated morphological classifications to their limits with the Dark Energy Survey

Vega-Ferrero, J., Sanchez, H. D., Bernardi, M., Huertas-Company, M., Morgan, R., Margalef, B., et al. (2021). Pushing automated morphological classifications to their limits with the Dark Energy Survey. Monthly Notices of the Royal Astronomical Society, 506(2), 1927-1943. doi:10.1093/mnras/stab594.

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Vega-Ferrero, J., Author
Sanchez, H. Domınguez, Author
Bernardi, M., Author
Huertas-Company, M., Author
Morgan, R., Author
Margalef, B., Author
Aguena, M., Author
Allam, S., Author
Annis, J., Author
Avila, S., Author
Bacon, D., Author
Bertin, E., Author
Brooks, D., Author
Rosell, A. Carnero, Author
Kind, M. Carrasco, Author
Carretero, J., Author
Choi, A., Author
Conselice, C., Author
Costanzi, M., Author
da Costa, L. N., Author
Pereira, M. E. S., AuthorVicente, J. De, AuthorDesai, S., AuthorFerrero, I., AuthorFosalba, P., AuthorFrieman, J., AuthorGarcıa-Bellido, J., AuthorGruen, D., AuthorGruendl, R. A., AuthorGschwend, J., AuthorGutierrez, G., AuthorHartley, W. G., AuthorHinton, S. R., AuthorHollowood, D. L., AuthorHonscheid, K., AuthorHoyle, B.1, Author              Jarvis, M., AuthorKim, A. G., AuthorKuehn, K., AuthorKuropatkin, N., AuthorLima, M., AuthorMaia, M. A. G., AuthorMenanteau, F., AuthorMiquel, R., AuthorOgando, R. L. C., AuthorPalmese, A., AuthorPaz-Chinchon, F., AuthorPlazas, A. A., AuthorRomer, A. K., AuthorSanchez, E., AuthorScarpine, V., AuthorSchubnell, M., AuthorSerrano, S., AuthorSevilla-Noarbe, I., AuthorSmith, M., AuthorSuchyta, E., AuthorSwanson, M. E. C., AuthorTarle, G., AuthorTarsitano, F., AuthorTo, C., AuthorTucker, D. L., AuthorVarga, T. N.1, Author              Wilkinson, R. D., Author more..
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1Optical and Interpretative Astronomy, MPI for Extraterrestrial Physics, Max Planck Society, ou_159895              

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 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.

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Language(s): eng - English
 Dates: 2021-03-02
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1093/mnras/stab594
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Title: Monthly Notices of the Royal Astronomical Society
  Abbreviation : Mon. Not. Roy. Astron. Soc.
Source Genre: Journal
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Pages: - Volume / Issue: 506 (2) Sequence Number: - Start / End Page: 1927 - 1943 Identifier: ISSN: 0035-8711
ISSN: 1365-8711