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| Full text | |
| Author(s): Show less - |
Delchambre, L.
[1]
;
Krone-Martins, A.
[2]
;
Wertz, O.
[3]
;
Ducourant, C.
[4]
;
Galluccio, L.
[5]
;
Klueter, J.
[6]
;
Mignard, F.
[5]
;
Teixeira, R.
[7]
;
Djorgovski, S. G.
[8]
;
Stern, D.
[9]
;
Graham, M. J.
[8]
;
Surdej, J.
[1]
;
Bastian, U.
[6]
;
Wambsganss, J.
[6, 10]
;
Le Campion, J. -F.
[4]
;
Slezak, E.
[5]
Total Authors: 16
|
| Affiliation: | [1] Univ Liege, Inst Astrophys & Geophys, 19c, Allee 6 Aout, B-4000 Liege - Belgium
[2] Univ Lisbon, CENTRA, Fac Ciencias, P-1749016 Lisbon - Portugal
[3] Univ Bonn, Argelander Inst Astron, Hugel 71, D-53121 Bonn - Germany
[4] Univ Bordeaux, Lab Astrophys Bordeaux, CNRS, B18N, Allee Geoffroy St Hilaire, F-33615 Pessac - France
[5] Univ Cote Azur, Observ Cote Azur, CNRS, Lab Lagrange, Blvd Observ, CS 34229, F-06304 Nice - France
[6] Heidelberg Univ, Zentrum Astron, Astron Rechen Inst, Monchhofstr 12-14, D-69120 Heidelberg - Germany
[7] Univ Sao Paulo, Inst Astron Geofis & Ciencias Atmosfer, Rua Matao 1226, Cidade Univ, BR-05508900 Sao Paulo, SP - Brazil
[8] CALTECH, 1200 E Calif Blvd, Pasadena, CA 91125 - USA
[9] CALTECH, Jet Prop Lab, 4800 Oak Grove Dr, Pasadena, CA 91109 - USA
[10] ISSI, Hallerstr 6, Bern 3012 - Switzerland
Total Affiliations: 10
|
| Document type: | Journal article |
| Source: | Astronomy & Astrophysics; v. 622, FEB 15 2019. |
| Web of Science Citations: | 5 |
| Abstract | |
Aims. In this work, we aim to provide a reliable list of gravitational lens candidates based on a search performed over the entire Gaia Data Release 2 (Gaia DR2). We also aim to show that the astrometric and photometric information coming from the Gaia satellite yield sufficient insights for supervised learning methods to automatically identify strong gravitational lens candidates with an efficiency that is comparable to methods based on image processing. Methods. We simulated 106 623 188 lens systems composed of more than two images, based on a regular grid of parameters characterizing a non-singular isothermal ellipsoid lens model in the presence of an external shear. These simulations are used as an input for training and testing our supervised learning models consisting of extremely randomized trees (ERT5). These trees are finally used to assign to each of the 2 129 659 clusters of celestial objects extracted from the Gaia DR2 a discriminant value that reflects the ability of our simulations to match the observed relative positions and fluxes from each cluster. Once complemented with additional constraints, these discriminant values allow us to identify strong gravitational lens candidates out of the list of clusters. Results. We report the discovery of 15 new quadruply-imaged lens candidates with angular separations of less than 6 `' and assess the performance of our approach by recovering 12 of the 13 known quadruply-imaged systems with all their components detected in Gaia DR2 with a misclassification rate of fortuitous clusters of stars as lens systems that is below 1% Similarly, the identification capability of our method regarding quadruply-imaged systems where three images are detected in Gaia DR2 is assessed by recovering 10 of the 13 known quadruply-imaged systems having one of their constituting images discarded. The associated misclassification rate varies between 5.83% and 20%, depending on the image we decided to remove. (AU) | |
| FAPESP's process: | 09/54006-4 - A computer cluster for the Astronomy Department of the University of São Paulo Institute of Astronomy, Geophysics and Atmospheric Sciences and for the Cruzeiro do Sul University Astrophysics Center |
| Grantee: | Elisabete Maria de Gouveia Dal Pino |
| Support Opportunities: | Multi-user Equipment Program |