Strong lensing in UNIONS: Toward a pipeline from discovery to modeling^⋆

¹ Institute of Physics, Laboratory of Astrophysics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Observatoire de Sauverny, 1290 Versoix, Switzerland
e-mail: elodie.savary@epfl.ch
² Institut d’Astrophysique de Paris, UMR7095 CNRS & Sorbonne Université, 98bis Bd Arago, 75014 Paris, France
³ Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK
⁴ Max-Planck-Institut für Astrophysik, Karl-Schwarzschild Str. 1, 85748 Garching, Germany
⁵ Technische Universität München, Physik-Department, James-Franck-Straße 1, 85748 Garching, Germany
⁶ Institute of Astronomy and Astrophysics, Academia Sinica, 11F of ASMAB, No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan
⁷ AIM, CEA, CNRS, Université Paris-Saclay, Université de Paris, 91191 Gif-sur-Yvette, France
⁸ Observatoire de Paris, PSL Research University, 61 Avenue de l’Observatoire, 75014 Paris, France
⁹ Waterloo Centre for Astrophysics, University of Waterloo, 200, University Ave W, Waterloo, ON N2L 3G1, Canada
¹⁰ Department of Physics and Astronomy, University of Waterloo, Waterloo, ON N2L 3G1, Canada
¹¹ Perimeter Institute for Theoretical Physics, 31 Caroline St N, Waterloo, ON N2L 2Y5, Canada
¹² Canadian Astronomy Data Centre, Herzberg Astronomy and Astrophysics, 5071 West Saanich Rd, Victoria, BC V9E 2E7, Canada
¹³ Department of Physics and Astronomy, University of British Columbia, 6225 Agricultural Road, Vancouver V6T 1Z1, Canada
¹⁴ Queen’s University, Dept. of Physics, Engineering Physics and Astronomy, Kingston, Canada

Received: 21 October 2021
Accepted: 3 June 2022

Abstract

We present a search for galaxy-scale strong gravitational lenses in the initial 2500 square degrees of the Canada-France Imaging Survey (CFIS). We designed a convolutional neural network (CNN) committee that we applied to a selection of 2 344 002 exquisite-seeing r-band images of color-selected luminous red galaxies. Our classification uses a realistic training set where the lensing galaxies and the lensed sources are both taken from real data, namely the CFIS r-band images themselves and the Hubble Space Telescope (HST). A total of 9460 candidates obtain a score above 0.5 with the CNN committee. After a visual inspection of the candidates, we find a total of 133 lens candidates, of which 104 are completely new. The set of false positives mainly contains ring, spiral, and merger galaxies, and to a lesser extent galaxies with nearby companions. We classify 32 of the lens candidates as secure lenses and 101 as maybe lenses. For the 32 highest quality lenses, we also fit a singular isothermal ellipsoid mass profile with external shear along with an elliptical Sersic profile for the lens and source light. This automated modeling step provides distributions of properties for both sources and lenses that have Einstein radii in the range 0.5″ < θ_E < 2.5″. Finally, we introduce a new lens and/or source single-band deblending algorithm based on auto-encoder representation of our candidates. This is the first time an end-to-end lens-finding and modeling pipeline is assembled together, in view of future lens searches in a single band, as will be possible with Euclid.