Abstract
Gravitationally lensed quasars are useful for studying astrophysics and cosmology, and enlarging the sample size of lensed quasars is important for multiple studies. In this work, we develop a lens search algorithm for four-image (quad) lensed quasars based on their time variability. In the development of the lens search algorithm, we constructed a pipeline simulating multi-epoch images of lensed quasars in cadenced surveys, accounting for quasar variabilities, quasar hosts, lens galaxies, and the point spread function variation. Applying the simulation pipeline to the Hyper Suprime-Cam (HSC) transient survey, an ongoing cadenced survey, we generated HSC-like difference images of the mock lensed quasars from the lens catalog of Oguri & Marshall (2010, MNRAS, 405, 2579). With the difference images of the mock lensed quasars and the variable objects from the HSC transient survey, we developed a lens search algorithm that picks out variable objects as lensed quasar candidates based on their spatial extent in the difference images. We tested the performance of our lens search algorithm on a sample combining the mock lensed quasars and variable objects from the HSC transient survey. Using difference images from multiple epochs, our lens search algorithm achieves a high true-positive rate (TPR) of 90.1% and a low false-positive rate (FPR) of 2.3% for the bright quads (the third brightest image brightness m3rd < 22.0 mag) with wide separation (the largest separation among the multiple image pairs θLP > 1.5″). With a preselection of the number of blobs in the difference image, we obtain a TPR of 97.6% and a FPR of 2.6% for the bright quads with wide separation. Even when difference images are only available in one single epoch, our lens search algorithm can still detect the bright quads with wide separation at high TPR of 97.6% and low FPR of 2.4% in the optimal seeing scenario, and at TPR of ∼94% and FPR of ∼5% in typical scenarios. Therefore, our lens search algorithm is promising and is applicable to ongoing and upcoming cadenced surveys, particularly the HSC transient survey and the Rubin Observatory Legacy Survey of Space and Time, for finding new lensed quasar systems.
Highlights
Lensed quasars are powerful tools to study astrophysics and cosmology
With the Hyper Suprime-Cam (HSC)-like difference images of lensed quasars, we develop a lens search algorithm that picks out variable objects with large spatial extent in the difference images and classifies these objects as lensed quasar candidates
We present a comprehensive simulation pipeline of time-varied lensed images and develop a new algorithm for searching lensed quasars through their time variability
Summary
Lensed quasars are powerful tools to study astrophysics and cosmology. The Sloan Digital Sky Survey (SDSS; York et al 2000) Quasar Lens Search (SQLS; Oguri et al 2006; Inada et al 2008, 2010, 2012; More et al 2016a), which has found the largest sample of lensed quasars (∼60), started with spectroscopic confirmed SDSS quasars To these, they applied a morphological selection for potential lens candidates with narrow separation and a color. Selection for potential lens candidates that are deblended in the SDSS; these authors conducted follow-up observations to confirm these candidates after a visual inspection of the objects selected by the morphological or color selection Both CLASS and SQLS start with spectroscopic searches, and further look into the morphology or images for lensed quasar candidates.
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