%0 Journal Article %T A catalog of visual-like morphologies in the 5 CANDELS fields using deep-learning %A M. Huertas-Company %A R. Gravet %A G. Cabrera-Vives %A P. G. P¨Śrez-Gonz¨˘lez %A J. S. Kartaltepe %A G. Barro %A M. Bernardi %A S. Mei %A F. Shankar %A P. Dimauro %A E. F. Bell %A D. Kocevski %A D. C. Koo %A S. M. Faber %A D. H. Mcintosh %J Physics %D 2015 %I arXiv %R 10.1088/0067-0049/221/1/8 %X We present a catalog of visual like H-band morphologies of $\sim50.000$ galaxies ($H_{f160w}<24.5$) 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 $\sim1.25$. 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 $\sim10\%$ 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 $\href{http://rainbowx.fis.ucm.es/Rainbow_navigator_public}{Rainbow\,database}$ %U http://arxiv.org/abs/1509.05429v1