Gaia GraL: Gaia DR2 Gravitational Lens Systems. III. A systematic blind search for new lensed systems
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 (ERTs). 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.
© 2019 ESO. Article published by EDP Sciences. Received 9 July 2018; Accepted 1 January 2019; Published online 15 February 2019. The catalogue of clusters of Gaia DR2 sources from Gaia GraL is only available at the CDS via anonymous ftp to cdsarc.u-strasbg.fr (22.214.171.124) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/622/A165. LD and JS acknowledge support from the ESA PRODEX Programme "Gaia-DPAC QSOs" and from the Belgian Federal Science Policy Office. AKM acknowledges the support from the Portuguese Fundação para a Ciência e a Tecnologia (FCT) through grants SFRH/BPD/74697/2010 and PTDC/FIS-AST/31546/2017, from the Portuguese Strategic Programme UID/FIS/00099/2013 for CENTRA, from the ESA contract AO/1-7836/14/NL/HB, and from the Caltech Division of Physics, Mathematics and Astronomy for hosting a research leave during 2017–2018, when this paper was prepared. OW is supported by the Humboldt Research Fellowship for Postdoctoral Researchers. SGD and MJG acknowledge partial support from the NSF grants AST-1413600 and AST-1518308, and the NASA grant 16-ADAP16-0232. The work of DS was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. We acknowledge partial support from "Actions sur projet INSU-PNGRAM", and from the Brazil-France exchange programmes Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) – Comité Français d'Évaluation de la Coopération Universitaire et Scientifique avec le Brésil (COFECUB). This work has made use of the computing facilities of the Laboratory of Astroinformatics (IAG/USP, NAT/Unicsul), whose purchase was made possible by the Brazilian agency FAPESP (grant 2009/54006-4) and the INCT-A, and we thank the entire LAi team, especially Carlos Paladini, Ulisses Manzo Castello, Luis Ricardo Manrique, and Alex Carciofi for their support. This work has made use of results from the ESA space mission Gaia, the data from which were processed by the Gaia Data Processing and Analysis Consortium (DPAC). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. The Gaia mission website is http://www.cosmos.esa.int/gaia. Some of the authors are members of the Gaia Data Processing and Analysis Consortium (DPAC).
Accepted Version - 1807.02845.pdf
Published - aa33802-18.pdf