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Title: Finding high-redshift strong lenses in DES using convolutional neural networks

Journal Article · · Monthly Notices of the Royal Astronomical Society
DOI:https://doi.org/10.1093/mnras/stz272· OSTI ID:1550886
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  1. Centre for Astrophysics & Supercomputing, Swinburne University of Technology, PO Box 218, Hawthorn, VIC 3122, Australia; ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), Swinburne University of Technology, Hawthorn, VIC 3122, Australia
  2. Institute of Cosmology & Gravitation, University of Portsmouth, Portsmouth PO1 3FX, UK
  3. School of Software and Electrical Engineering, Swinburne University of Technology, PO Box 218, Hawthorn, VIC 3122, Australia
  4. Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory, Casilla 603, 1700000 La Serena, Chile
  5. Department of Physics & Astronomy, University College London, Gower Street, London WC1E 6BT, UK; Department of Physics and Electronics, Rhodes University, PO Box 94, Grahamstown, 6140, South Africa
  6. Fermi National Accelerator Laboratory, PO Box 500, Batavia, IL 60510, USA
  7. LSST, 933 North Cherry Avenue, Tucson, AZ 85721, USA
  8. CNRS, UMR 7095, Institut d’Astrophysique de Paris, F-75014, Paris, France; Sorbonne Universités, UPMC Univ Paris 06, UMR 7095, Institut d’Astrophysique de Paris, F-75014, Paris, France
  9. Department of Physics & Astronomy, University College London, Gower Street, London WC1E 6BT, UK
  10. Kavli Institute for Particle Astrophysics & Cosmology, PO Box 2450, Stanford University, Stanford, CA 94305, USA; SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
  11. 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
  12. Department of Astronomy, University of Illinois at Urbana-Champaign, 1002 W. Green Street, Urbana, IL 61801, USA; National Center for Supercomputing Applications, 1205 West Clark Str, Urbana, IL 61801, USA
  13. Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Campus UAB, E-08193 Bellaterra (Barcelona), Spain
  14. Kavli Institute for Particle Astrophysics & Cosmology, PO Box 2450, Stanford University, Stanford, CA 94305, USA
  15. Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), 28040 Madrid, Spain
  16. Department of Physics, IIT Hyderabad, Kandi, Telangana 502285, India
  17. Department of Astronomy/Steward Observatory, 933 North Cherry Avenue, Tucson, AZ 85721-0065, USA; Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA 91109, USA
  18. Fermi National Accelerator Laboratory, PO Box 500, Batavia, IL 60510, USA; Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637, USA
  19. Instituto de Fisica Teorica UAM/CSIC, Universidad Autonoma de Madrid, E-28049 Madrid, Spain
  20. Institut d’Estudis Espacials de Catalunya (IEEC), E-08193 Barcelona, Spain; Institute of Space Sciences (ICE, CSIC), Campus UAB, Carrer de Can Magrans, s/n, E-08193 Barcelona, Spain
  21. Department of Astronomy, University of Michigan, Ann Arbor, MI 48109, USA; Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA
  22. Department of Astronomy, University of California, Berkeley, 501 Campbell Hall, Berkeley, CA 94720, USA; Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
  23. Department of Physics & Astronomy, University College London, Gower Street, London WC1E 6BT, UK; Department of Physics, ETH Zurich, Wolfgang-Pauli-Strasse 16, CH-8093 Zurich, Switzerland
  24. Santa Cruz Institute for Particle Physics, Santa Cruz, CA 95064, USA
  25. 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
  26. Max Planck Institute for Extraterrestrial Physics, Giessenbachstrasse, E-85748 Garching, Germany; Universitäts-Sternwarte, Fakultät für Physik, Ludwig-Maximilians Universität München, Scheinerstr. 1, D-81679 München, Germany
  27. Harvard-Smithsonian Center for Astrophysics, Cambridge, MA 02138, USA
  28. Australian Astronomical Observatory, North Ryde, NSW 2113, Australia
  29. Laboratório Interinstitucional de e-Astronomia – LIneA, Rua Gal. José Cristino 77, Rio de Janeiro, RJ – 20921-400, Brazil; Departamento de Física Matemática, Instituto de Física, Universidade de São Paulo, CP 66318, São Paulo, SP, 05314-970, Brazil
  30. Center for Cosmology and Astro-Particle Physics, The Ohio State University, Columbus, OH 43210, USA; Department of Astronomy, The Ohio State University, Columbus, OH 43210, USA
  31. 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
  32. Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA 91109, USA
  33. Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA
  34. School of Physics and Astronomy, University of Southampton, Southampton SO17 1BJ, UK
  35. Brandeis University, Physics Department, 415 South Street, Waltham, MA 02453, USA
  36. Laboratório Interinstitucional de e-Astronomia – LIneA, Rua Gal. José Cristino 77, Rio de Janeiro, RJ – 20921-400, Brazil; Instituto de Física Gleb Wataghin, Universidade Estadual de Campinas, Campinas, SP, 13083-859, Brazil
  37. Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
  38. National Center for Supercomputing Applications, 1205 West Clark Str, Urbana, IL 61801, USA
  39. Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA
  40. Institute for Astronomy, University of Edinburgh, Edinburgh EH9 3HJ, UK

We search Dark Energy Survey (DES) Year 3 imaging data for galaxy-galaxy strong gravitational lenses using convolutional neural networks. We generate 250 000 simulated lenses at redshifts > 0.8 from which we create a data set for training the neural networks with realistic seeing, sky and shot noise. Using the simulations as a guide, we build a catalogue of 1.1 million DES sources with 1.8 < g - i < 5, 0.6 < g - r < 3, r_mag > 19, g_mag > 20, and i_mag > 18.2. We train two ensembles of neural networks on training sets consisting of simulated lenses, simulated non-lenses, and real sources. We use the neural networks to score images of each of the sources in our catalogue with a value from 0 to 1, and select those with scores greater than a chosen threshold for visual inspection, resulting in a candidate set of 7301 galaxies. During visual inspection, we rate 84 as 'probably' or 'definitely' lenses. Four of these are previously known lenses or lens candidates. We inspect a further 9428 candidates with a different score threshold, and identify four new candidates. We present 84 new strong lens candidates, selected after a few hours of visual inspection by astronomers. This catalogue contains a comparable number of high-redshift lenses to that predicted by simulations. Based on simulations, we estimate our sample to contain most discoverable lenses in this imaging and at this redshift range.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
Contributing Organization:
DES Collaboration
DOE Contract Number:
AC02-05CH11231
OSTI ID:
1550886
Journal Information:
Monthly Notices of the Royal Astronomical Society, Vol. 484, Issue 4; ISSN 0035-8711
Publisher:
Royal Astronomical Society
Country of Publication:
United States
Language:
English

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Cited By (11)

Deep learning predictions of galaxy merger stage and the importance of observational realism text January 2019
Machine Learning Classifiers for Intermediate Redshift Emission Line Galaxies text January 2019
Detecting strongly lensed supernovae at z ∼ 5–7 with LSST journal November 2019
Strong gravitational lensing of explosive transients text January 2019
An Extended Catalog of Galaxy–Galaxy Strong Gravitational Lenses Discovered in DES Using Convolutional Neural Networks journal July 2019
Deep learning predictions of galaxy merger stage and the importance of observational realism journal October 2019
Strong gravitational lensing of explosive transients journal November 2019
Machine-learning Classifiers for Intermediate Redshift Emission-line Galaxies journal September 2019
Methods for strong gravitational lens detection and analysis using machine learning and high performance computing text January 2020
Surveying the reach and maturity of machine learning and artificial intelligence in astronomy journal September 2019
KiDS-SQuaD: II. Machine learning selection of bright extragalactic objects to search for new gravitationally lensed quasars journal November 2019

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