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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
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/).
0067-0049
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
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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).

Record type: Article

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
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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: 25 Nov 2021 18:13

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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: F Shankar
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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