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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Gaia GraL: Gaia DR2 Gravitational Lens Systems III. A systematic blind search for new lensed systems

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Autor(es):
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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]
Número total de Autores: 16
Afiliação do(s) autor(es):
[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
Número total de Afiliações: 10
Tipo de documento: Artigo Científico
Fonte: Astronomy & Astrophysics; v. 622, FEB 15 2019.
Citações Web of Science: 5
Resumo

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)

Processo FAPESP: 09/54006-4 - Um cluster de computadores para o Departamento de Astronomia do IAG-USP e para o Núcleo de Astrofísica da UNICSUL
Beneficiário:Elisabete Maria de Gouveia Dal Pino
Linha de fomento: Auxílio à Pesquisa - Programa Equipamentos Multiusuários