1. Galaxy morphological classification catalogue of the Dark Energy Survey Year 3 data with convolutional neural networks
- Author
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G. Tarle, M. Carrasco Kind, F. Andrade-Oliveira, Kyler Kuehn, D. L. Burke, Josh Frieman, I. Sevilla-Noarbe, M. Soares-Santos, H. T. Diehl, N. Kuropatkin, Ting-Yun Cheng, Daniel Thomas, Alfonso Aragón-Salamanca, A. Choi, S. Allam, Robert A. Gruendl, S. Everett, Alex Drlica-Wagner, K. D. Eckert, L. N. da Costa, K. Honscheid, J. Carretero, F. Paz-Chinchón, E. J. Sanchez, David J. Brooks, Samuel Hinton, Juan Garcia-Bellido, Maria E. S. Pereira, David J. James, D. L. Hollowood, Asa F. L. Bluck, S. Serrano, Pablo Fosalba, G. Gutierrez, M. Smith, J. Annis, A. A. Plazas Malagón, M. A. G. Maia, A. Roodman, Tommaso Giannantonio, Adriano Pieres, Daniel Gruen, M. E. C. Swanson, C To, V. Scarpine, Elisabeth Krause, E. Suchyta, August E. Evrard, D. W. Gerdes, Michel Aguena, Felipe Menanteau, I. Ferrero, M. Costanzi, Ramon Miquel, J. De Vicente, J. Gschwend, Christopher J. Conselice, M. March, Ofer Lahav, Robert Morgan, National Science Foundation (US), Ministerio de Economía y Competitividad (España), European Commission, Australian Research Council, Durham University, University Park, University of Manchester, Universidade de São Paulo (USP), Laboratório Interinstitucional de e-Astronomia – LIneA, Fermi National Accelerator Laboratory, Universidade Estadual Paulista (UNESP), University of Cambridge, University College London, Stanford University, SLAC National Accelerator Laboratory, National Center for Supercomputing Applications, University of Illinois at Urbana–Champaign, Barcelona Institute of Science and Technology, Ohio State University, University of Trieste, INAF – Osservatorio Astronomico di Trieste, Institute for Fundamental Physics of the Universe, Observatório Nacional, University of Michigan, Medioambientales y Tecnológicas (CIEMAT), University of Chicago, University of Pennsylvania, Santa Cruz Institute for Particle Physics, University of Oslo, Institut d’Estudis Espacials de Catalunya (IEEC), CSIC), Universidad Autonoma de Madrid, University of Queensland, Harvard & Smithsonian, University of Arizona, Macquarie University, Lowell Observatory, Institució Catalana de Recerca i Estudis Avançats, University of Wisconsin–Madison, Peyton Hall, University of Southampton, Oak Ridge National Laboratory, University of Portsmouth, Cheng, Ting-Yun, Conselice, Christopher J., Aragón-Salamanca, Alfonso, Aguena, M., Allam, S., Andrade-Oliveira, F., Annis, J., Bluck, A. F. L., Brooks, D., Burke, D. L., Carrasco Kind, M., Carretero, J., Choi, A., Costanzi, M., da Costa, L. N., Pereira, M. E. S., De Vicente, J., Diehl, H. T., Drlica-Wagner, A., Eckert, K., Everett, S., Evrard, A. E., Ferrero, I., Fosalba, P., Frieman, J., García-Bellido, J., Gerdes, D. W., Giannantonio, T., Gruen, D., Gruendl, R. A., Gschwend, J., Gutierrez, G., Hinton, S. R., Hollowood, D. L., Honscheid, K., James, D. J., Krause, E., Kuehn, K., Kuropatkin, N., Lahav, O., Maia, M. A. G., March, M., Menanteau, F., Miquel, R., Morgan, R., Paz-Chinchón, F., Pieres, A., Plazas Malagón, A. A., Roodman, A., Sanchez, E., Scarpine, V., Serrano, S., Sevilla-Noarbe, I., Smith, M., Soares-Santos, M., Suchyta, E., Swanson, M. E. C., Tarle, G., Thomas, D., and To, C.
- Subjects
structure [Galaxies] ,FOS: Physical sciences ,Astrophysics::Cosmology and Extragalactic Astrophysics ,Astrophysics ,Disc galaxy ,01 natural sciences ,Convolutional neural network ,Methods: observational ,Methods: data analysis ,Galaxies: structure ,0103 physical sciences ,observational [Methods] ,10. No inequality ,data analysis [Methods] ,010303 astronomy & astrophysics ,Astrophysics::Galaxy Astrophysics ,methods: observational ,methods: data analysis ,catalogues ,galaxies: structure ,Astrophysics - Astrophysics of Galaxies ,Physics ,010308 nuclear & particles physics ,Astronomy and Astrophysics ,Catalogues ,Galaxy ,Redshift ,Data set ,Space and Planetary Science ,Astrophysics of Galaxies (astro-ph.GA) ,data analysi [methods] ,Magnitude (astronomy) ,Dark energy ,catalogue ,Galaxy morphological classification - Abstract
Cheng, Ting-Yun, et al., We present in this paper one of the largest galaxy morphological classification catalogues to date, including over 20 million galaxies, using the Dark Energy Survey (DES) Year 3 data based on convolutional neural networks (CNNs). Monochromatic i-band DES images with linear, logarithmic, and gradient scales, matched with debiased visual classifications from the Galaxy Zoo 1 (GZ1) catalogue, are used to train our CNN models. With a training set including bright galaxies (16 ≤ i < 18) at low redshift (z < 0.25), we furthermore investigate the limit of the accuracy of our predictions applied to galaxies at fainter magnitude and at higher redshifts. Our final catalogue covers magnitudes 16 ≤ i < 21, and redshifts z < 1.0, and provides predicted probabilities to two galaxy types – ellipticals and spirals (disc galaxies). Our CNN classifications reveal an accuracy of over 99 per cent for bright galaxies when comparing with the GZ1 classifications (i < 18). For fainter galaxies, the visual classification carried out by three of the co-authors shows that the CNN classifier correctly categorizes discy galaxies with rounder and blurred features, which humans often incorrectly visually classify as ellipticals. As a part of the validation, we carry out one of the largest examinations of non-parametric methods, including ∼100 ,000 galaxies with the same coverage of magnitude and redshift as the training set from our catalogue. We find that the Gini coefficient is the best single parameter discriminator between ellipticals and spirals for this data set., The DES data management system is supported by the National Science Foundation under grant numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MINECO under grants AYA2015- 71825, ESP2015-66861, FPA2015-68048, SEV-2016-0588, SEV2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA programme of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO), through project number CE110001020, and the Brazilian Instituto Nacional de Ciencia e Tecnologia (INCT) e-Universe (CNPq grant 465376/2014-2).
- Published
- 2021