A CATALOG OF VISUAL-LIKE MORPHOLOGIES IN THE 5 CANDELS FIELDS USING DEEP LEARNING

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 t...

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Bibliographic Details
Published in:The Astrophysical journal. Supplement series Vol. 221; no. 1; pp. 8 - 23
Main Authors: Huertas-Company, M., Gravet, R., Cabrera-Vives, G., Pérez-González, P. 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., Mcintosh, D. H.
Format: Journal Article
Language:English
Published: United States The American Astronomical Society 01.11.2015
American Astronomical Society
Subjects:
ABUNDANCE
ALGORITHMS
Astrophysics
ASTROPHYSICS, COSMOLOGY AND ASTRONOMY
ASYMMETRY
Classifications
GALAXIES
galaxies: high-redshift
galaxies: structure
Mathematical models
MORPHOLOGY
Neural networks
POINT SOURCES
PROBABILITY
RED SHIFT
Sciences of the Universe
SMOOTH MANIFOLDS
SPHEROIDS
surveys
Surveys
Astrophysics - Astrophysics of Galaxies
Galaxies: high-redshift
Galaxies: structure
Catalogs
Astrophysics - Cosmology and Nongalactic Astrophysics
ISSN:0067-0049, 1538-4365, 1538-4365
Online Access:Get full text
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Summary: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/).
Bibliography:High Redshift
ApJS98439
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0067-0049
1538-4365
1538-4365
DOI:10.1088/0067-0049/221/1/8