A catalog of visual-like morphologies in the 5 CANDELS fields using deep-learning
A catalog of visual-like morphologies in the 5 CANDELS fields using deep-learning
We present a catalog of visual-like H-band morphologies of ∼50.000 galaxies (Hf160w < 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 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 ∼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. The catalog is released with the present paper via the Rainbow database (http://rainbowx.fis.ucm.es/Rainbow-navigator-public/).
Huertas-Company, Marc
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Gravet, R
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Cabrera-Vives, G.
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Pérez-González, Pablo G.
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Kartaltepe, J.S.
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Barro, G.
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Bernardi, M.
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Mei, S.
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Shankar, F
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Dimauro, P.
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Bell, E. F.
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Kocevski, D.
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Koo, D. C.
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Faber, S. M.
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Mcintosh, D. H.
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26 October 2015
Huertas-Company, Marc
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Gravet, R
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Cabrera-Vives, G.
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Pérez-González, Pablo G.
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Kartaltepe, J.S.
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Barro, G.
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Bernardi, M.
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Mei, S.
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Shankar, F
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Dimauro, P.
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Bell, E. F.
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Kocevski, D.
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Koo, D. C.
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Faber, S. M.
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Mcintosh, D. H.
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Huertas-Company, Marc, Gravet, R, Cabrera-Vives, G., Pérez-González, Pablo G., Kartaltepe, J.S., Barro, G., Bernardi, M., Mei, S., Shankar, F, Dimauro, P., Bell, E. F., Kocevski, D., Koo, D. C., Faber, S. M. and Mcintosh, D. H.
(2015)
A catalog of visual-like morphologies in the 5 CANDELS fields using deep-learning.
The Astrophysical Journal Supplement Series, 221 (1), [8].
(doi:10.1088/0067-0049/221/1/8).
Abstract
We present a catalog of visual-like H-band morphologies of ∼50.000 galaxies (Hf160w < 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 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 ∼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. The catalog is released with the present paper via the Rainbow database (http://rainbowx.fis.ucm.es/Rainbow-navigator-public/).
Text
A CATALOG OF VISUAL-LIKE MORPHOLOGIES IN THE 5 CANDELS FIELDS USING DEEP-LEARNING
- Accepted Manuscript
More information
Accepted/In Press date: 4 September 2015
e-pub ahead of print date: 26 October 2015
Published date: 26 October 2015
Additional Information:
Arxiv copy 1509.05429 Author Shankar confirmed AM copy
Organisations:
Astronomy Group
Identifiers
Local EPrints ID: 411931
URI: http://eprints.soton.ac.uk/id/eprint/411931
ISSN: 0067-0049
PURE UUID: 349e9caf-2760-4424-82df-edd3c5835633
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Date deposited: 30 Jun 2017 16:30
Last modified: 15 Mar 2024 14:56
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Contributors
Author:
Marc Huertas-Company
Author:
R Gravet
Author:
G. Cabrera-Vives
Author:
Pablo G. Pérez-González
Author:
J.S. Kartaltepe
Author:
G. Barro
Author:
M. Bernardi
Author:
S. Mei
Author:
P. Dimauro
Author:
E. F. Bell
Author:
D. Kocevski
Author:
D. C. Koo
Author:
S. M. Faber
Author:
D. H. Mcintosh
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