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

Abstract

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 hoursmore » 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.« less

Authors:
ORCiD logo [1]; ORCiD logo [2];  [1];  [3];  [3];  [4];  [5];  [6]; ORCiD logo [2];  [7];  [8];  [9];  [6];  [10]; ORCiD logo [11];  [12];  [13];  [11];  [14];  [15] more »;  [16];  [6];  [9];  [17];  [6];  [18];  [19];  [20];  [21];  [22]; ORCiD logo [10];  [12];  [11];  [6];  [23];  [24];  [25]; ORCiD logo [26];  [27];  [28];  [6];  [9];  [18];  [29];  [6];  [11];  [30];  [21];  [31];  [6]; ORCiD logo [32];  [15];  [6];  [33];  [20];  [15];  [34]; ORCiD logo [35];  [36]; ORCiD logo [37];  [38];  [33];  [39];  [4]; ORCiD logo [6]; ORCiD logo [40] « less
  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
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
Contributing Org.:
DES Collaboration
OSTI Identifier:
1550886
DOE Contract Number:  
AC02-05CH11231
Resource Type:
Journal Article
Journal Name:
Monthly Notices of the Royal Astronomical Society
Additional Journal Information:
Journal Volume: 484; Journal Issue: 4; Journal ID: ISSN 0035-8711
Publisher:
Royal Astronomical Society
Country of Publication:
United States
Language:
English

Citation Formats

Jacobs, C., Collett, T., Glazebrook, K., McCarthy, C., Qin, A. K., Abbott, T. M. C., Abdalla, F. B., Annis, J., Avila, S., Bechtol, K., Bertin, E., Brooks, D., Buckley-Geer, E., Burke, D. L., Carnero Rosell, A., Carrasco Kind, M., Carretero, J., da Costa, L. N., Davis, C., De Vicente, J., Desai, S., Diehl, H. T., Doel, P., Eifler, T. F., Flaugher, B., Frieman, J., García-Bellido, J., Gaztanaga, E., Gerdes, D. W., Goldstein, D. A., Gruen, D., Gruendl, R. A., Gschwend, J., Gutierrez, G., Hartley, W. G., Hollowood, D. L., Honscheid, K., Hoyle, B., James, D. J., Kuehn, K., Kuropatkin, N., Lahav, O., Li, T. S., Lima, M., Lin, H., Maia, M. A. G., Martini, P., Miller, C. J., Miquel, R., Nord, B., Plazas, A. A., Sanchez, E., Scarpine, V., Schubnell, M., Serrano, S., Sevilla-Noarbe, I., Smith, M., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Vikram, V., Walker, A. R., Zhang, Y., and Zuntz, J. Finding high-redshift strong lenses in DES using convolutional neural networks. United States: N. p., 2019. Web. doi:10.1093/mnras/stz272.
Jacobs, C., Collett, T., Glazebrook, K., McCarthy, C., Qin, A. K., Abbott, T. M. C., Abdalla, F. B., Annis, J., Avila, S., Bechtol, K., Bertin, E., Brooks, D., Buckley-Geer, E., Burke, D. L., Carnero Rosell, A., Carrasco Kind, M., Carretero, J., da Costa, L. N., Davis, C., De Vicente, J., Desai, S., Diehl, H. T., Doel, P., Eifler, T. F., Flaugher, B., Frieman, J., García-Bellido, J., Gaztanaga, E., Gerdes, D. W., Goldstein, D. A., Gruen, D., Gruendl, R. A., Gschwend, J., Gutierrez, G., Hartley, W. G., Hollowood, D. L., Honscheid, K., Hoyle, B., James, D. J., Kuehn, K., Kuropatkin, N., Lahav, O., Li, T. S., Lima, M., Lin, H., Maia, M. A. G., Martini, P., Miller, C. J., Miquel, R., Nord, B., Plazas, A. A., Sanchez, E., Scarpine, V., Schubnell, M., Serrano, S., Sevilla-Noarbe, I., Smith, M., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Vikram, V., Walker, A. R., Zhang, Y., & Zuntz, J. Finding high-redshift strong lenses in DES using convolutional neural networks. United States. doi:10.1093/mnras/stz272.
Jacobs, C., Collett, T., Glazebrook, K., McCarthy, C., Qin, A. K., Abbott, T. M. C., Abdalla, F. B., Annis, J., Avila, S., Bechtol, K., Bertin, E., Brooks, D., Buckley-Geer, E., Burke, D. L., Carnero Rosell, A., Carrasco Kind, M., Carretero, J., da Costa, L. N., Davis, C., De Vicente, J., Desai, S., Diehl, H. T., Doel, P., Eifler, T. F., Flaugher, B., Frieman, J., García-Bellido, J., Gaztanaga, E., Gerdes, D. W., Goldstein, D. A., Gruen, D., Gruendl, R. A., Gschwend, J., Gutierrez, G., Hartley, W. G., Hollowood, D. L., Honscheid, K., Hoyle, B., James, D. J., Kuehn, K., Kuropatkin, N., Lahav, O., Li, T. S., Lima, M., Lin, H., Maia, M. A. G., Martini, P., Miller, C. J., Miquel, R., Nord, B., Plazas, A. A., Sanchez, E., Scarpine, V., Schubnell, M., Serrano, S., Sevilla-Noarbe, I., Smith, M., Soares-Santos, M., Sobreira, F., Suchyta, E., Swanson, M. E. C., Tarle, G., Vikram, V., Walker, A. R., Zhang, Y., and Zuntz, J. Fri . "Finding high-redshift strong lenses in DES using convolutional neural networks". United States. doi:10.1093/mnras/stz272.
@article{osti_1550886,
title = {Finding high-redshift strong lenses in DES using convolutional neural networks},
author = {Jacobs, C. and Collett, T. and Glazebrook, K. and McCarthy, C. and Qin, A. K. and Abbott, T. M. C. and Abdalla, F. B. and Annis, J. and Avila, S. and Bechtol, K. and Bertin, E. and Brooks, D. and Buckley-Geer, E. and Burke, D. L. and Carnero Rosell, A. and Carrasco Kind, M. and Carretero, J. and da Costa, L. N. and Davis, C. and De Vicente, J. and Desai, S. and Diehl, H. T. and Doel, P. and Eifler, T. F. and Flaugher, B. and Frieman, J. and García-Bellido, J. and Gaztanaga, E. and Gerdes, D. W. and Goldstein, D. A. and Gruen, D. and Gruendl, R. A. and Gschwend, J. and Gutierrez, G. and Hartley, W. G. and Hollowood, D. L. and Honscheid, K. and Hoyle, B. and James, D. J. and Kuehn, K. and Kuropatkin, N. and Lahav, O. and Li, T. S. and Lima, M. and Lin, H. and Maia, M. A. G. and Martini, P. and Miller, C. J. and Miquel, R. and Nord, B. and Plazas, A. A. and Sanchez, E. and Scarpine, V. and Schubnell, M. and Serrano, S. and Sevilla-Noarbe, I. and Smith, M. and Soares-Santos, M. and Sobreira, F. and Suchyta, E. and Swanson, M. E. C. and Tarle, G. and Vikram, V. and Walker, A. R. and Zhang, Y. and Zuntz, J.},
abstractNote = {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.},
doi = {10.1093/mnras/stz272},
journal = {Monthly Notices of the Royal Astronomical Society},
issn = {0035-8711},
number = 4,
volume = 484,
place = {United States},
year = {2019},
month = {1}
}

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journal, January 2008

  • Weijmans, Anne-Marie; Krajnović, Davor; Van De Ven, Glenn
  • Monthly Notices of the Royal Astronomical Society, Vol. 383, Issue 4
  • DOI: 10.1111/j.1365-2966.2007.12680.x

A magnified young galaxy from about 500 million years after the Big Bang
journal, September 2012


Nebulae as Gravitational Lenses
journal, February 1937