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arXiv · 1509.05429

A catalog of visual-like morphologies in the 5 CANDELS fields using deep-learning

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
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Abstract

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 $\href{http://rainbowx.fis.ucm.es/Rainbow_navigator_public}{Rainbow\,database}$

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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. 2015-09-17. A catalog of visual-like morphologies in the 5 CANDELS fields using deep-learning. https://doi.org/10.1088/0067-0049%2F221%2F1%2F8

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