VDES J2325-5229 a z=2.7 gravitationally lensed quasar discovered using morphology independent supervised machine learning

Abstract

We present the discovery and preliminary characterization of a gravitationally lensed quasar with a source redshift $z_s=2.74$ and image separation of $2.9"$ lensed by a foreground $z_l=0.40$ 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 $gi$ multicolour photometric observations from the Dark Energy Survey (DES), near IR $JK$ photometry from the VISTA Hemisphere Survey (VHS) and WISE mid IR photometry, we have identified a candidate system with two catalogue components with $i_{AB}=18.61$ and $i_{AB}=20.44$ comprised of an elliptical galaxy and two blue point sources. Spectroscopic follow-up with NTT and the use of an archival AAT spectrum show that the point sources can be identified as a lensed quasar with an emission line redshift of $z=2.739\pm 0.003$ and a foreground early type galaxy with $z=0.400\pm 0.002$. We model the system as a single isothermal ellipsoid and find the Einstein radius ${\theta }_E\sim 1.47"$, enclosed mass $M_{enc}\sim 4\times 10^{11}$M${}_{\odot }$ and a time delay of $\sim$52 days. The relatively wide separation, month scale time delay duration and high redshift make this an ideal system for constraining the expansion rate beyond a redshift of 1.

11 pages, 7 figures, 7 tables, MNRAS accepted

References in corpus (12)

• H0LiCOW V. New COSMOGRAIL time delays of HE0435-1223: $H_0$ to 3.8% precision from strong lensing in a flat $\Lambda$CDM model
• PESSTO : survey description and products from the first data release by the Public ESO Spectroscopic Survey of Transient Objects
• Introduction to astroML: Machine Learning for Astrophysics
• The Sloan Digital Sky Survey Quasar Lens Search. I. Candidate Selection Algorithm
• The Spatial Structure of An Accretion Disk
• The Sloan Digital Sky Survey Quasar Lens Search. II. Statistical Lens Sample from the Third Data Release
• The Sloan Digital Sky Survey Quasar Lens Search. III. Constraints on Dark Energy from the Third Data Release Quasar Lens Catalog
• Multi wavelength study of the gravitational lens system RXS J1131-1231: II Lens model and source reconstruction
• The Sloan Digital Sky Survey Quasar Lens Search. IV. Statistical Lens Sample from the Fifth Data Release
• A Rapidly Spinning Black Hole Powers the Einstein Cross
• Resolving the Clumpy Structure of the Outflow Winds in the Gravitationally Lensed Quasar SDSS J1029+2623
• ULAS J234311.93-005034.0: A gravitational lens system selected from UKIDSS and SDSS

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