Gaia GraL: Gaia gravitational lens systems

IX. Using XGBoost to explore the Gaia Focused Product Release GravLens catalogue

¹ Laboratoire d’Astrophysique de Bordeaux, Univ. Bordeaux, CNRS, B18N, Allée Geoffroy Saint-Hilaire, F-33615 Pessac, France
² Université Côte d’Azur, Observatoire de la Côte d’Azur, CNRS, Laboratoire Lagrange, Bd de l’Observatoire, CS 34229, F-06304 Nice Cedex 4, France
³ Donald Bren School of Information and Computer Sciences, University of California, Irvine, CA 92697, USA
⁴ CENTRA/SIM, Faculdade de Ciéncias, Universidade de Lisboa, Ed. C8, Campo Grande, 1749-016 Lisboa, Portugal
⁵ Sydney Institute for Astronomy, School of Physics, The University of Sydney, Physics Road, Camperdown, NSW 2006, Australia
⁶ Center for Astrophysics Harvard & Smithsonian, 60 Garden St., 02138 Cambridge, MA, USA
⁷ Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA
⁸ Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Liège, B-4000 Liège, Belgium
⁹ Division of Physics, Mathematics, and Astronomy, Caltech, Pasadena, CA 91125, USA
¹⁰ Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São Paulo, Rua do Matão, 1226, Cidade Universitária, 05508-900 São Paulo, SP, Brazil
¹¹ Center for Theoretical Physics, Polish Academy of Sciences, Warsaw, Poland
¹² Lohrmann-Observatorium, Technische Universitaet Dresden, D-01062 Dresden, Germany
¹³ Department of Physics and Astronomy, Louisiana State University, Baton Rouge, LA 70803, USA
¹⁴ Inter University Centre for Astronomy and Astrophysics, Post Bag 04, Ganeshkhind, Pune 411007, India
¹⁵ Departamento de Física CCET, Universidade Federal de Sergipe, Rod. Marechal Rondon s/n, 49.100-000, Jardim Rosa Elze, São Cristóvão, SE, Brazil
¹⁶ Centre for Astrophysics Research, University of Hertfordshire, College Lane, Hatfield AL10 9AB, UK
¹⁷ Astronomisches Rechen-Institut (ARI), Zentrum fur Astronomie der Universitaet Heidelberg (ZAH), Manchhofstr. 12-14, 69120 Heidelberg, Germany

^⋆ Corresponding author; quentin.petit.1@u-bordeaux.fr

Received: 29 July 2024
Accepted: 10 February 2025

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.