Galaxy morphological classification catalogue of the Dark Energy Survey Year 3 data with convolutional neural networks

Journal Article · 24 July 2021 · Monthly Notices of the Royal Astronomical Society

DOI: https://doi.org/10.1093/mnras/stab2142 · OSTI ID:1819963

Cheng, Ting-Yun  ^[1]; Conselice, Christopher J. ^[2]; Aragón-Salamanca, Alfonso ^[3]; Aguena, M.  ^[4]; Allam, S. ^[5]; Andrade-Oliveira, F. ^[6]; Annis, J. ^[5]; Bluck, A. F. L. ^[7]; Brooks, D. ^[8]; Burke, D. L. ^[9]; Carrasco Kind, M. ^[10]; Carretero, J.  ^[11]; Choi, A. ^[12]; Costanzi, M. ^[13]; da Costa, L. N. ^[14]; Pereira, M. E. S. ^[15]; De Vicente, J. ^[16]; Diehl, H. T. ^[5]; Drlica-Wagner, A. ^[17]; Eckert, K. ^[18] ; Everett, S. ^[19]; Evrard, A. E.  ^[20]; Ferrero, I.  ^[21]; Fosalba, P.  ^[22]; Frieman, J. ^[23]; García-Bellido, J. ^[24]; Gerdes, D. W. ^[20]; Giannantonio, T. ^[25]; Gruen, D.  ^[26]; Gruendl, R. A. ^[10]; Gschwend, J. ^[14]; Gutierrez, G. ^[5]; Hinton, S. R.  ^[27]; Hollowood, D. L.  ^[19]; Honscheid, K. ^[28]; James, D. J. ^[29]; Krause, E. ^[30]; Kuehn, K. ^[31]; Kuropatkin, N. ^[5]; Lahav, O. ^[8]; Maia, M. A. G. ^[14]; March, M.  ^[18]; Menanteau, F. ^[10]; Miquel, R. ^[32]; Morgan, R. ^[33]; Paz-Chinchón, F. ^[34]; Pieres, A. ^[14]; Plazas Malagón, A. A. ^[35]; Roodman, A. ^[9]; Sanchez, E. ^[16]; Scarpine, V. ^[5]; Serrano, S. ^[22]; Sevilla-Noarbe, I. ^[16]; Smith, M.  ^[36]; Soares-Santos, M. ^[15]; Suchyta, E.  ^[37]; Swanson, M. E. C. ^[38]; Tarle, G. ^[15]; Thomas, D.  ^[39]; To, C. ^[26]

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1. Centre of Extragalactic Astronomy, Durham University, Stockton Road, Durham DH1 3LE, UK, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK
2. School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK, Jodrell Bank Centre for Astrophysics, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
3. School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK
4. Departamento de Física Matemática, Instituto de Física, Universidade de São Paulo, CP 66318, São Paulo, SP 05314-970, Brazil, Laboratório Interinstitucional de e-Astronomia – LIneA, Rua Gal. José Cristino 77, Rio de Janeiro, RJ 20921-400, Brazil
5. Fermi National Accelerator Laboratory, PO Box 500, Batavia, IL 60510, USA
6. Laboratório Interinstitucional de e-Astronomia – LIneA, Rua Gal. José Cristino 77, Rio de Janeiro, RJ 20921-400, Brazil, Instituto de Física Teórica, Universidade Estadual Paulista, São Paulo, 01140-070, Brazil
7. Cavendish Laboratory Astrophysics Group, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK, Kavli Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK
8. Department of Physics & Astronomy, University College London, Gower Street, London WC1E 6BT, UK
9. Kavli Institute for Particle Astrophysics & Cosmology, Stanford University, PO Box 2450, Stanford, CA 94305, USA, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
10. Center for Astrophysical Surveys, National Center for Supercomputing Applications, 1205 West Clark Street, Urbana, IL 61801, USA, Department of Astronomy, University of Illinois at Urbana–Champaign, 1002 W. Green Street, Urbana, IL 61801, USA
11. Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Campus UAB, E-08193 Bellaterra (Barcelona), Spain
12. Center for Cosmology and Astro-Particle Physics, The Ohio State University, Columbus, OH 43210, USA
13. Astronomy Unit, Department of Physics, University of Trieste, Via Tiepolo 11, I-34131 Trieste, Italy, INAF – Osservatorio Astronomico di Trieste, Via G. B. Tiepolo 11, I-34143 Trieste, Italy, Institute for Fundamental Physics of the Universe, Via Beirut 2, I-34014 Trieste, Italy
14. Laboratório Interinstitucional de e-Astronomia – LIneA, Rua Gal. José Cristino 77, Rio de Janeiro, RJ 20921-400, Brazil, Observatório Nacional, Rua Gal. José Cristino 77, Rio de Janeiro, RJ 20921-400, Brazil
15. Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA
16. Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Madrid, 28040, Spain
17. Fermi National Accelerator Laboratory, PO Box 500, Batavia, IL 60510, USA, Department of Astronomy and Astrophysics, University of Chicago, Chicago, IL 60637, USA, Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637, USA
18. Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA
19. Santa Cruz Institute for Particle Physics, Santa Cruz, CA 95064, USA
20. Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA, Department of Astronomy, University of Michigan, Ann Arbor, MI 48109, USA
21. Institute of Theoretical Astrophysics, University of Oslo, PO Box 1029 Blindern, NO-0315 Oslo, Norway
22. Institut d’Estudis Espacials de Catalunya (IEEC), E-08034 Barcelona, Spain, Institute of Space Sciences (ICE, CSIC), Campus UAB, Carrer de Can Magrans, s/n, E-08193 Barcelona, Spain
23. Fermi National Accelerator Laboratory, PO Box 500, Batavia, IL 60510, USA, Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637, USA
24. Instituto de Fisica Teorica UAM/CSIC, Universidad Autonoma de Madrid, E-28049 Madrid, Spain
25. Kavli Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK, Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK
26. Kavli Institute for Particle Astrophysics & Cosmology, Stanford University, PO Box 2450, Stanford, CA 94305, USA, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA, Department of Physics, Stanford University, 382 Via Pueblo Mall, Stanford, CA 94305, USA
27. School of Mathematics and Physics, University of Queensland, Brisbane, QLD 4072, Australia
28. Center for Cosmology and Astro-Particle Physics, The Ohio State University, Columbus, OH 43210, USA, Department of Physics, The Ohio State University, Columbus, OH 43210, USA
29. Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA
30. Department of Astronomy/Steward Observatory, University of Arizona, 933 North Cherry Avenue, Tucson, AZ 85721-0065, USA
31. Australian Astronomical Optics, Macquarie University, North Ryde, NSW 2113, Australia, Lowell Observatory, 1400 Mars Hill Road, Flagstaff, AZ 86001, USA
32. Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Campus UAB, E-08193 Bellaterra (Barcelona), Spain, Institució Catalana de Recerca i Estudis Avançats, E-08010 Barcelona, Spain
33. Physics Department, University of Wisconsin–Madison, 2320 Chamberlin Hall, 1150 University Avenue Madison, WI 53706-1390, USA
34. Center for Astrophysical Surveys, National Center for Supercomputing Applications, 1205 West Clark Street, Urbana, IL 61801, USA, Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK
35. Department of Astrophysical Sciences, Princeton University, Peyton Hall, Princeton, NJ 08544, USA
36. School of Physics and Astronomy, University of Southampton, Southampton SO17 1BJ, UK
37. Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
38. Center for Astrophysical Surveys, National Center for Supercomputing Applications, 1205 West Clark Street, Urbana, IL 61801, USA
39. Institute of Cosmology and Gravitation, University of Portsmouth, Portsmouth PO1 3FX, UK

ABSTRACT 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.

• Research Organization: Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Univ. of Michigan, Ann Arbor, MI (United States)
• Sponsoring Organization: USDOE; USDOE Office of Science (SC), High Energy Physics (HEP)
• Contributing Organization: DES Collaboration
• Grant/Contract Number: AC02-07CH11359; AC02-76SF00515; AC05-00OR22725; SC0019193
• OSTI ID: 1819963
• Report Number(s): FERMILAB-PUB--21-648-PPD; arXiv:2107.10210
• Journal Information: Monthly Notices of the Royal Astronomical Society, Journal Name: Monthly Notices of the Royal Astronomical Society Journal Issue: 3 Vol. 507; ISSN 0035-8711
• Publisher: Oxford University PressCopyright Statement
• Country of Publication: United Kingdom
• Language: English