A CATALOG OF VISUAL-LIKE MORPHOLOGIES IN THE 5 CANDELS FIELDS USING DEEP LEARNING

作者：Huertas Company M^*; Gravet R; Cabrera Vives G; Perez Gonzalez P G; Kartaltepe J S; Barro G; Bernardi M; Mei S; Shankar F; Dimauro P; Bell E F; Kocevski D; Koo D C; Faber S M; Mcintosh D H

来源：Astrophysical Journal, The - Supplement Series, 2015, 221(1): 8.

DOI：10.1088/0067-0049/221/1/8

• catalogs
• galaxies: high-redshift
• galaxies: structure
• surveys

We present a catalog of visual-like H-band morphologies of similar to 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 > similar to 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 similar to 10% scatter. The fraction of mis-classifications is less than 1%. Our classification scheme represents a major improvement with respect to Concentration-Asymmetry-Smoothness-based methods, which hit a 20%-30% contamination limit at high z.

• 出版日期2015-11