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SDSS-IV MaNGA: A Catalogue of Spectroscopically Detected Strong Galaxy-Galaxy Lens Candidates

Authors :
Michael S Talbot
Joel R Brownstein
Justus Neumann
Daniel Thomas
Claudia Maraston
Niv Drory
Publication Year :
2022

Abstract

We spectroscopically detected candidate emission-lines of 8 likely, 17 probable, and 69 possible strong galaxy-galaxy gravitational lens candidates found within the spectra of ~10,000 galaxy targets contained within the completed Mapping of Nearby Galaxies at Apache Point Observatory (MaNGA) survey. This search is based upon the methodology of the Spectroscopic Identification of Lensing Objects (SILO) project, which extends the spectroscopic detection methods of the BOSS Emission-Line Lensing Survey (BELLS) and the Sloan Lens ACS Survey (SLACS). We scanned the co-added residuals that we constructed from stacks of foreground subtracted row-stacked-spectra (RSS) so a sigma-clipping method can be used to reject cosmic-rays and other forms of transients that impact only a small fraction of the combined exposures. We also constructed narrow-band images from the signal-to-noise of the co-added residuals to observe signs of lensed source images. We also use several methods to compute the probable strong lensing regime for each candidate lens to determine which candidate background galaxies may reside sufficiently near the galaxy centre for strong lensing to occur. We present the spectroscopic redshifts within a value-added catalogue (VAC) for data release 17 (DR17) of SDSS-IV. We also present the lens candidates, spectroscopic data, and narrow-band images within a VAC for DR17. High resolution follow-up imaging of these lens candidates are expected to yield a sample of confirmed grade-A lenses with sufficient angular size to probe possible discrepancies between the mass derived from a best-fitting lens model, and the dynamical mass derived from the observed stellar velocities.<br />Accepted for publication in MNRAS, June 20, 2022. In press. 30 pages, 8 figures, 3 tables, 2 appendices with 15 tables

Details

Language :
English
Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....ef7c1c37d886b06595e3159ffb820a1e