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Strong lensing by edge-on galaxies in UNIONS

Published online by Cambridge University Press:  04 March 2024

J. A. Acevedo Barroso*
Affiliation:
Institute of Physics, Laboratory of Astrophysics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Observatoire de Sauverny, 1290 Versoix, Switzerland.
B. Clément
Affiliation:
Aix-Marseille Université, CNRS, CNES, LAM, Marseille, France
F. Courbin
Affiliation:
Institute of Physics, Laboratory of Astrophysics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Observatoire de Sauverny, 1290 Versoix, Switzerland.
R. Gavazzi
Affiliation:
Aix-Marseille Université, CNRS, CNES, LAM, Marseille, France Institut d’Astrophysique de Paris, UMR 7095, CNRS, and Sorbonne Université, 98 bis boulevard Arago, 75014 Paris, France
C. Lemon
Affiliation:
Institute of Physics, Laboratory of Astrophysics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Observatoire de Sauverny, 1290 Versoix, Switzerland.
K. Rojas
Affiliation:
Institute of Cosmology and Gravitation, University of Portsmouth, Burnaby Rd, Portsmouth PO1 3FX, UK
E. Savary
Affiliation:
Institute of Physics, Laboratory of Astrophysics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Observatoire de Sauverny, 1290 Versoix, Switzerland. Departement of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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Abstract

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Current searches for galaxy-scale strong lenses focus on massive Luminous Red Galaxies but tend to overlook late-type lenses, in part because of their smaller Einstein radii. We take advantage of the superb seeing of the UNIONS survey in the r-band to perform an imaging search for edge-on late-type lenses. We use Convolutional Neural Networks trained with simulated observations composed of images of real galaxies from UNIONS and real sources from HST. Using 3600 square degrees of the survey we test ∼7 million galaxies and find 56 systems with obvious signs of lensing. In addition, we empirically estimate the true prevalence of lenses in UNIONS by visually inspecting 120,000 randomly chosen images in the survey. We find that the number of edge-on lenses we discover with CNNs is compatible with these estimates.

Type
Contributed Paper
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of International Astronomical Union

References

Almeida, A. et al. 2023, ApJS, 267(2), 44.Google Scholar
Bertin, E.. In I. N. Evans, A. Accomazzi, D. J. Mink, and A. H. Rots, editors, Astronomical Data Analysis Software and Systems XX 2011, volume 442 of Astronomical Society of the Pacific Conference Series, 435.Google Scholar
Bertin, E. 2013, Astrophysics Source Code Library, record ascl:1301.001.Google Scholar
Bertin, E. and Arnouts, S. 1996, A&AS, 117, 393404.CrossRefGoogle Scholar
Bertin, E. and Arnouts, S. 2010, Astrophysics Source Code Library, record ascl:1010.064.Google Scholar
Birrer, S. and Amara, A. 2018, Physics of the Dark Universe, 22, 189201.CrossRefGoogle Scholar
Birrer, S. et al. 2021, The Journal of Open Source Software, 6(62), 3283.CrossRefGoogle Scholar
Brewer, B. J. et al. 2012, MNRAS, 422(4), 35743590.CrossRefGoogle Scholar
Cañameras, R. et al. 2021, A&A, 653, L6.Google Scholar
Cañameras, R. et al. 2020, A&A, 644, A163.Google Scholar
Chollet, F. and others 2018, Astrophysics Source Code Library, record ascl:1806.022.Google Scholar
Dutton, A. A. et al. 2011, MNRAS, 417(3), 16211642.CrossRefGoogle Scholar
Ferreras, I. et al. 2010, MNRAS, 409(1), L30L34.CrossRefGoogle Scholar
Gwyn, S. D. J. 2008, PASP, 120(864), 212.CrossRefGoogle Scholar
Lanusse, F. et al. 2018, MNRAS, 473(3), 38953906.CrossRefGoogle Scholar
Li, R. et al. 2021, ApJ, 923(1), 16.CrossRefGoogle Scholar
Navarro, J. F., Frenk, C. S., and White, S. D. M. 1996, ApJ, 462, 563.CrossRefGoogle Scholar
Rojas, K. et al. 2022, A&A, 668, A73.Google Scholar
Savary, E. et al. 2022, A&A, 666, A1.Google Scholar
Sygnet, J. F., Tu, H., Fort, B., and Gavazzi, R. 2010, A&A, 517, A25.Google Scholar
Treu, T. et al. 2011, MNRAS, 417(3), 16011620.CrossRefGoogle Scholar