Gaia GraL: Gaia gravitational lens systems. IX. Using XGBoost to explore the Gaia Focused Product Release GravLens catalogue
Creators
- Petit, Q.1
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Ducourant, C.1
- Slezak, E.2
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Krone-Martins, A.3, 4
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Bœhm, C.5
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Connor, T.6, 7
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Delchambre, L.8
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Djorgovski, S. G.9
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Galluccio, L.2
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Graham, M. J.9
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Jalan, P.10
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Klioner, S. A.11
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Klüter, J.12
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Mignard, F.2
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Negi, V.13
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Scarano Jr, S.14
- Sebastian den Brok, J.6
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Sluse, D.8
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Stern, D.7
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Surdej, J.8
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Teixeira, R.15
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Vale-Cunha, P. H.15
- Walton, D. J.16
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Wambsganss, J.17
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1.
Laboratory of Astrophysics of Bordeaux
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2.
Lagrange Laboratory
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3.
University of California, Irvine
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4.
University of Lisbon
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5.
University of Sydney
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6.
Harvard-Smithsonian Center for Astrophysics
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7.
Jet Propulsion Lab
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8.
University of Liège
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9.
California Institute of Technology
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10.
Center for Theoretical Physics
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11.
TU Dresden
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12.
Louisiana State University
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13.
Inter-University Centre for Astronomy and Astrophysics
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14.
Universidade Federal de Sergipe
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15.
Universidade de São Paulo
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16.
University of Hertfordshire
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17.
Heidelberg University
Abstract
Aims. Quasar strong gravitational lenses are important tools for putting constraints on the dark matter distribution, dark energy contribution, and the Hubble-Lemaître parameter. We aim to present a new supervised machine learning-based method to identify these lenses in large astrometric surveys. The Gaia Focused Product Release (FPR) GravLens catalogue is designed for the identification of multiply imaged quasars, as it provides astrometry and photometry of all sources in the field of 4.7 million quasars.
Methods. Our new approach for automatically identifying four-image lens configurations in large catalogues is based on the eXtreme Gradient Boosting classification algorithm. To train this supervised algorithm, we performed realistic simulations of lenses with four images that account for the statistical distribution of the morphology of the deflecting halos as measured in the EAGLE simulation. We identified the parameters discriminant for the classification and performed two different trainings, namely, with and without distance information.
Results. The performances of this method on the simulated data are quite good, with a true positive rate and a true negative rate of about 99.99% and 99.84%, respectively. Our validation of the method on a small set of known quasar lenses demonstrates its efficiency, with 75% of known lenses being correctly identified. We applied our algorithm (both trainings) to more than 0.9 million quadruplets selected from the Gaia FPR GravLens catalogue. We derived a list of 1127 candidates with at least one score larger than 0.75, where each candidate has two scores–one from the model trained with distance information and one from the model trained without distance information–and including 201 very good candidates with both high scores.
Copyright and License
© The Authors 2025.
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Acknowledgement
We acknowledge the french national program PN-GRAM and Action Spécifique Gaia as well as Observatoire Aquitain des Sciences de l’Univers (OASU) for financial support along the years. Our work was eased by the use of the data handling and visualisation software TOPCAT (Taylor 2005). This research has made use of “Aladin sky atlas” developed at CDS, Strasbourg Observatory, France (Boch & Fernique 2014; Bonnarel et al. 2000). This research has made use of the VizieR catalogue access tool, CDS, Strasbourg, France. This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement.
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Additional details
Related works
- Is new version of
- Discussion Paper: arXiv:2504.03303 (arXiv)
Funding
- Centre National d'Études Spatiales
- PN-GRAM -
- Observatoire Aquitain des Sciences de l'Univers
Dates
- Accepted
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2025-02-10
- Available
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2025-04-02Published online