Journal
ASTRONOMY & ASTROPHYSICS
Volume 667, Issue -, Pages -Publisher
EDP SCIENCES S A
DOI: 10.1051/0004-6361/202243745
Keywords
gravitational lensing: strong; catalogs; galaxies: general
Categories
Funding
- ESA Research Fellowship
- HST programmes [9405, 9414, 9427, 9483, 9836, 10096, 10134, 10152, 10200, 10207, 10325, 10326, 10334, 10395, 10420, 10491, 10496, 10503, 10504, 10505, 10521, 10523, 10569, 10626, 10635, 10816, 10825, 10861, 10875, 10880, 10881]
- NASA [NAS 5-26555]
- A HST programmes [10997, 11142, 11588, 11597, 11613, 11697, 11734, 12063, 12064, 12104, 12166, 12195, 12209, 12238, 12253, 12286, 12313, 12319, 12362, 12476, 12477, 12515, 12546, 12549, 12555, 12575, 12591, 12756, 12884, 12898, 12937, 13023, 13024, 13307, 13352, 13364, 13393, 13412]
- The HST programmes [13657, 13695, 13698, 13711, 13750, 13845, 13942, 14096, 14098, 14118, 14165, 14199, 14594, 14662, 14766, 14808, 15063, 15117, 15121, 15183, 15212, 15230, 15275, 15287, 15307, 15320, 15378, 15446, 15495, 15608, 15642, 15644, 15654, 15696, 15843, 16025, 13442, 13495, 13496, 13514, 13641]
- Global Impact Award from Google
- Alfred P. Sloan Foundation
- National Science Foundation
- U.S. Department of Energy
- National Aeronautics and Space Administration
- Japanese Monbukagakusho
- Max Planck Society
- Higher Education Funding Council for England
- American Museum of Natural History
- Astrophysical Institute Potsdam
- University of Basel
- University of Cambridge
- Case Western Reserve University
- University of Chicago
- Drexel University
- Fermilab
- Institute for Advanced Study
- Japan Participation Group
- Johns Hopkins University
- Joint Institute for Nuclear Astrophysics
- Kavli Institute for Particle Astrophysics and Cosmology
- Korean Scientist Group
- Chinese Academy of Sciences (LAMOST)
- Los Alamos National Laboratory
- Max-Planck-Institute for Astronomy (MPIA)
- Max-Planck-Institute for Astrophysics (MPA)
- New Mexico State University
- Ohio State University
- University of Pittsburgh
- University of Portsmouth
- Princeton University
- United States Naval Observatory
- University of Washington
Ask authors/readers for more resources
This study utilized crowdsourcing and archival images from the Hubble Space Telescope to identify 198 new strong gravitational lens candidates. These lenses were found to be fainter than those previously detected, making them ideal for lens modeling and scientific analysis.
Context. The Hubble Space Telescope (HST) archives constitute a rich dataset of high-resolution images to mine for strong gravitational lenses. While many HST programmes specifically target strong lenses, they can also be present by coincidence in other HST observations. Aims. Our aim is to identify non-targeted strong gravitational lenses, without any prior selection on the lens properties, in almost two decades of images from the ESA HST archive (eHST). Methods. We used crowdsourcing on the Hubble Asteroid Hunter (HAH) citizen science project to identify strong lenses, along with asteroid trails, in publicly available large field-of-view HST images. We visually inspected 2354 objects tagged by citizen scientists as strong lenses to clean the sample and identify the genuine lenses. Results. We report the detection of 252 strong gravitational lens candidates, which were not the primary targets of the HST observations. A total of 198 of them are new, not previously reported by other studies, consisting of 45 A grades, 74 B grades and 79 C grades. The majority are galaxy-galaxy configurations. The newly detected lenses are, on average, 1.3 magnitudes fainter than previous HST searches. This sample of strong lenses with high-resolution HST imaging is ideal to follow up with spectroscopy for lens modelling and scientific analyses. Conclusions. This paper presents the unbiased search of lenses that enabled us to find a wide variety of lens configurations, including exotic lenses. We demonstrate the power of crowdsourcing in visually identifying strong lenses and the benefits of exploring large archival datasets. This study shows the potential of using crowdsourcing in combination with artificial intelligence for the detection and validation of strong lenses in future large-scale surveys such as ESA's Euclid mission or in James Webb Space Telescope (JWST) archival images.
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