P. H. Vale-Cunha | Searcharxiv

arXiv2026

Gaia GraL X.: The GraL catalogue of gravitationally lensed quasars Matched with \textit{Gaia} data, redshifts, and time delays

Determining the Hubble constant tension requires alternative strategies, and multiply imaged quasars, with their intermediate redshifts, can potentially be used in this regard. We provide a currently complete catalogue of spectroscopically confirmed lensed quasars with ESA/{\it Gaia} astrometry and photometry, as well as redshifts and time delays when available. In addition to the improved astrometry, the catalogue increases the number of lensed quasars by a factor of 1.5 (now 364, of which 277 are doubles and 87 are quads or triples) and significantly increases the number of lensing galaxies detected (now 218), which represents a major step forward. Redshifts are provided for 347 quasars and 188 deflectors. A completely new table of time delays, required for estimates of $H_0$, is presented, with 195 time delays from 73 systems. {\it Gaia} absolute astrometry is sub-milliarcsecond and covers the entire sky. Future {\it Gaia} data releases will provide long-term photometry, which should provide many more time delays. The catalogues as presented here enable machine-learning techniques to be trained and tested and subsequently applied to the {\it Gaia} data releases. Finally, we derive simple but homogeneous models of the 18 quadruply imaged quasars for which images of all four components are presented in {\it Gaia} DR3.}

astro-ph.GA↗

arXiv2025

Gaia GraL: Gaia gravitational lens systems IX. Using XGBoost to explore the Gaia Focused Product Release GravLens catalogue

Aims. Quasar strong gravitational lenses are important tools for putting constraints on the dark matter distribution, dark energy contribution, and the Hubble-Lemaitre 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.

astro-ph.GA↗