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 $\sim 50.000$ 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 $<z>\sim 1.25$. 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 $\sim 10\%$ 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 $\text{href}$

Accepted for publication in ApjS. Figure 10 summarizes the excellent agreement between our classification and a pure visual one. Table 3 shows the content of the catalogs. The catalogs are available from the Rainbow database (http://rainbowx.fis.ucm.es/Rainbow_navigator_public) based on the selections from the CANDELS team and cross-matched with 3D-HST v4.1 catalogs

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