2017/11/30 by H. Domínguez Sánchez, M. Huertas-Company, Mariangela Bernardi +3 · 6 citations
Computer Science · Environmental Science · Physics and Astronomy · #Advanced Vision and Imaging #Algorithm #Artificial intelligence #Astrophysics #Computer science #Convolutional neural network #Data Visualization and Analytics #Deep learning #Galaxy #Offset (computer science) #Pattern recognition (psychology) #Physics #Remote Sensing in Agriculture #Sky #Type (biology) #astro-ph.GA
paper · pdf · doi:10.1093/mnras/sty338
18 pages, 21 figures; Accepted for publication in MNRAS
openalex created_date 2017/12/04 · arxiv created 2018/02/07 · openalex publication_date 2018/02/07 · arxiv updated 2018/02/28 · openalex updated_date 2026/08/05
Abstract We present a morphological catalogue for ∼670 000 galaxies in the Sloan Digital Sky Survey in two flavours: T-type, related to the Hubble sequence, and Galaxy Zoo 2 (GZ2 hereafter) classification scheme. By combining accurate existing visual classification catalogues with machine learning, we provide the largest and most accurate morphological catalogue up to date. The classifications are obtained with Deep Learning algorithms using Convolutional Neural Networks (CNNs). We use two visual classification catalogues, GZ2 and Nair & Abraham (2010), for training CNNs with colour images in order to obtain T-types and a series of GZ2 type questions (disc/features, edge-on galaxies, bar signature, bulge prominence, roundness, and mergers). We also provide an additional probability enabling a separation between pure elliptical (E) from S0, where the T-type model is not so efficient. For the T-type, our results show smaller offset and scatter than previous models trained with support vector machines. For the GZ2 type questions, our models have large accuracy (>97 per cent), precision and recall values (>90 per cent), when applied to a test sample with the same characteristics as the one used for training. The catalogue is publicly released with the paper.