A catalog of visual-like morphologies in the 5 candels fields using deep-learning
Author
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Huertas Company, M.
Author
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Gravet, R.
Author
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Cabrera Vives, Guillermo
Author
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Pérez González, P. G.
Author
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Kartaltepe, J. S.
Author
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Barro, G.
Author
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Bernardi, M.
Author
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Mei, S.
Author
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Shankar, F.
Author
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Dimauro, P.
Author
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Bell, E. F.
Author
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Kocevski, D.
Author
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Koo, D. C.
Author
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Faber, S. M.
Author
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Mcintosh, D. H.
Admission date
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2016-01-12T14:32:13Z
Available date
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2016-01-12T14:32:13Z
Publication date
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2015
Cita de ítem
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Astrophysical Journal Supplement Series Volumen: 221 Número: 1 Número de artículo: 8 Nov. 2015
en_US
Identifier
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DOI: 10.1088/0067-0049/221/1/8
Identifier
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https://repositorio.uchile.cl/handle/2250/136386
General note
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Artículo de publicación ISI
en_US
Abstract
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We present a catalog of visual-like H-band morphologies of similar to 50.000 galaxies (H-f160w < 24.5) in the 5 CANDELS fields (GOODS-N, GOODS-S, UDS, EGS, and COSMOS). Morphologies are estimated using Convolutional Neural Networks (ConvNets). The median redshift of the sample is < z > similar to 1.25. The algorithm is trained on GOODS-S, for which visual classifications are publicly available, and then applied to the other 4 fields. Following the CANDELS main morphology classification scheme, our model retrieves for each galaxy the probabilities of having a spheroid or a disk, presenting an irregularity, being compact or a point source, and being unclassifiable. ConvNets are able to predict the fractions of votes given to a galaxy image with zero bias and similar to 10% scatter. The fraction of mis-classifications is less than 1%. Our classification scheme represents a major improvement with respect to Concentration-Asymmetry-Smoothness-based methods, which hit a 20%-30% contamination limit at high z.
en_US
Patrocinador
dc.description.sponsorship
CONICYT-Chile
DPI20140090
Institut Universitaire de France (IUF)
NSF
AST-08-08133
NASA
HST-GO-12060.10A