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 $\sim50.000$
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 $<z>\sim1.25$. 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
$\href{http://rainbowx.fis.ucm.es/Rainbow_navigator_public}{Rainbow\,database}$
