Discovery of two bright high-redshift gravitationally lensed quasars revealed by Gaia. Issue 1 (14th October 2021)

• Record Type: Journal Article
• Authors: Desira, Christopher; Shu, Yiping; Auger, Matthew W; McMahon, Richard G; Lemon, Cameron A; Anguita, Timo; Neira, Favio
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 509:Issue 1(2022)
• Page Start: 738
• Page End: 747
• Publication Date: 2021-10-14
• Subjects: gravitational lensing: strong -- methods: observational -- quasars: general; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/stab2960
• Languages: English
• ISSNs: 0035-8711

Abstract

ABSTRACT: We present the discovery and preliminary characterisation of two high-redshift gravitationally lensed quasar systems in Gaia Data Release 2 (DR2). Candidates with multiple close-separation Gaia detections and quasar-like colours in WISE, Pan-STARRS, and DES are selected for follow-up spectroscopy with the New Technology Telescope. We confirm DES J215028.71−465251.3 as a $z$ = 4.130 ± 0.006 asymmetric, doubly imaged lensed quasar system and model the lensing mass distribution as a singular isothermal sphere. The system has an Einstein radius of 1.202 ± 0.005 arcsec and a predicted time delay of ∼122.0 d between the quasar images, assuming a lensing galaxy redshift of $z$ = 0.5, making this a priority system for future optical monitoring. We confirm PS J042913.17+142840.9 as a $z$ = 3.866 ± 0.003 four-image quasar system in a cusp configuration, lensed by two foreground galaxies. The system is well modelled using a singular isothermal ellipsoid for the primary lens and a singular isothermal sphere for the secondary lens with Einstein radii 0.704 ± 0.006 and 0.241 ± 0.030 arcsec, respectively. A maximum predicted time delay of 9.6 d is calculated, assuming lensing galaxy redshifts of $z$ = 1.0. Furthermore, PS J042913.17+142840.9 exhibits a large flux ratio anomaly, up to a factor of 2.66 ± 0.37 in i band, that varies across optical and near-infrared wavelengths. We discuss LSST and its implications for future high-redshift lens searches and outline an extension to the search using supervised machine learning techniques.