Pushing automated morphological classifications to their limits with the Dark Energy Survey

J. Vega-Ferrero,H. Domínguez Sánchez,M. Bernardi,M. Huertas-Company,R. Morgan, B. Margalef,M. Aguena,S. Allam,J. Annis,S. Avila, D. Bacon,E. Bertin,D. Brooks,A. Carnero Rosell,M. Carrasco Kind,J. Carretero,A. Choi, C. Conselice,M. Costanzi,L. N. da Costa,M. E. S. Pereira,J. De Vicente,S. Desai,I. Ferrero,P. Fosalba, J. Frieman,J. García-Bellido,D. Gruen,R. A. Gruendl,J. Gschwend, G. Gutierrez,W. G. Hartley,S. R. Hinton,D. L. Hollowood,K. Honscheid,B. Hoyle,M. Jarvis,A. G. Kim, K. Kuehn, N. Kuropatkin,M. Lima,M. A. G. Maia,F. Menanteau,R. Miquel,R. L. C. Ogando,A. Palmese,F. Paz-Chinchón,A. A. Plazas,A. K. Romer,E. Sanchez,V. Scarpine,M. Schubnell,S. Serrano,I. Sevilla-Noarbe,M. Smith,E. Suchyta, M. E. C. Swanson,G. Tarle,F. Tarsitano,C. To,D. L. Tucker,T. N. Varga,R. D. Wilkinson

Monthly Notices of the Royal Astronomical Society（2021）

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摘要

We present morphological classifications of ∼27 million galaxies from the Dark Energy Survey (DES) Data Release 1 (DR1) using a supervised deep learning algorithm. The classification scheme separates: (a) early-type galaxies (ETGs) from late-type galaxies (LTGs); and (b) face-on galaxies from edge-on. Our convolutional neural networks (CNNs) are trained on a small subset of DES objects with previo...

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关键词

methods: observational,catalogues,galaxies: structure

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