VDES J2325-5229 a z=2.7 gravitationally lensed quasar discovered using morphology independent supervised machine learning
Fernanda Ostrovski, Richard G. McMahon, Andrew J. Connolly, Cameron A., Lemon, Matthew W. Auger, Manda Banerji, Johnathan M. Hung, Sergey E. Koposov,, Christopher E. Lidman, Sophie L. Reed, Sahar Allam, Aur\'elien Benoit-L\'evy,, Emmanuel Bertin, David Brooks

TL;DR
This paper reports the discovery of a high-redshift gravitationally lensed quasar at z=2.74, identified through a novel morphology-independent supervised machine learning approach using multi-wavelength photometry, and characterized through spectroscopic follow-up.
Contribution
The authors develop and apply a new morphology-independent multi-wavelength machine learning method to identify gravitationally lensed quasars, demonstrated by the discovery of a z=2.74 lensed quasar system.
Findings
Discovered a z=2.74 gravitationally lensed quasar with a 2.9" image separation.
Estimated the lensing galaxy's Einstein radius as ~1.47".
Derived a time delay of approximately 52 days for the system.
Abstract
We present the discovery and preliminary characterization of a gravitationally lensed quasar with a source redshift and image separation of lensed by a foreground elliptical galaxy. Since the images of gravitationally lensed quasars are the superposition of multiple point sources and a foreground lensing galaxy, we have developed a morphology independent multi-wavelength approach to the photometric selection of lensed quasar candidates based on Gaussian Mixture Models (GMM) supervised machine learning. Using this technique and multicolour photometric observations from the Dark Energy Survey (DES), near IR photometry from the VISTA Hemisphere Survey (VHS) and WISE mid IR photometry, we have identified a candidate system with two catalogue components with and comprised of an elliptical galaxy and two blue point…
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