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Título:
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A catalog of visual-like morphologies in the 5 candels fields using deep learning
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Autores:
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Huertas Company, M. ;
Gravet, R. ;
Cabrera Vives, G. ;
Pérez González, Pablo Guillermo ;
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.
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Tipo de documento:
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texto impreso
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Editorial:
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University Chicago Press, 2015-11
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Dimensiones:
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application/pdf
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Nota general:
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info:eu-repo/semantics/openAccess
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Idiomas:
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Palabras clave:
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Estado = Publicado
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Materia = Ciencias: Física: Astrofísica
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Materia = Ciencias: Física: Astronomía
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Tipo = Artículo
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Resumen:
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We present a catalog of visual-like H-band morphologies of ~50.000 galaxies (H_f160w 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 ~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.
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En línea:
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https://eprints.ucm.es/35064/1/perezgonzalez157libre.pdf
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