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 with Convolutional Neural Networks (ConvNets). The median redshift of the sample is <z>∼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 the probabilities for each galaxy of having a spheroid, a disk, presenting an irregularity, being compact or point source and being unclassifiable. ConvNets are able to predict the fractions of votes given a galaxy image with zero bias and ∼10% scatter. The fraction of miss-classifications is less than 1%. Our classification scheme represents a major improvement with respect to CAS (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 $\href{this http URL}{Rainbow\,database}$
Author
M. Huertas-Company, R. Gravet, G. Cabrera-Vives, P.G. Pérez-González, J.S. Kartaltepe, G. Barro, M. Bernardi, S. Mei, F. Shankar, P. Dimauro, E.F. Bell, D. Kocevski, D.C. Koo, S.M. Faber, D.H. Mcintosh
Journal
ApjS
Paper Publication Date
November 2015
Paper Type
Astrostatistics