Physical Sciences and Mathematics Commons^™

A Catalog Of Visual-Like Morphologies In The 5 Candels Fields Using Deep Learning, M. Huertas-Company, R. Gravet, G. Cabrera-Vives, Pablo G. Pérez-González, J. Kartaltepe, Guillermo Barro, M. Bernardi, S. Mei, F. Shankar, P. Dimauro, E. F. Bell, Dale D. Kocevski, David C. Koo, Sandra M. Faber, Daniel H. Mcintosh

Physics and Astronomy Faculty Publications

We present a catalog of visual-like H-band morphologies of ~50.000 galaxies (H_f160w < 24.5) in the 5 CANDELS fields (GOODS-N, GOODS-S, UDS, EGS, and COSMOS). Morphologies are estimated using 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 for each galaxy the probabilities of having a spheroid or a disk, presenting an irregularity, being compact or a point source, and being unclassifiable. ConvNets are able to predict the fractions of votes given to a galaxy image with zero bias and ~10% scatter. The fraction of mis-classifications is …