Enhanced lensing rate by clustering of massive galaxies: newly discovered systems in the SLACS fields
Elisabeth R. Newton, Philip J. Marshall, Tommaso Treu (University of, California Santa Barbara)

TL;DR
This study uses galaxy clustering to efficiently identify gravitational lenses in HST data, discovering four new systems and revealing a higher lensing rate in targeted fields compared to random surveys.
Contribution
It introduces a novel, multi-method approach for lens detection in HST images and reports the discovery of new lens systems, expanding understanding of lensing in different galaxy populations.
Findings
Higher lensing rate in targeted fields than random fields
Discovery of four new gravitational lens systems
Lenses are predominantly higher-redshift, lower-mass early-type galaxies
Abstract
[Abridged] We exploit the clustering of massive galaxies to perform a high efficiency imaging search for gravitational lenses. Our dataset comprises 44 fields imaged by the Hubble Space Telescope (HST) Advanced Camera for Surveys (ACS), each of which is centered on a lens discovered by the Strong Lens ACS Survey (SLACS). We compare four different search methods: 1) automated detection with the HST Archive Galaxy-scale Gravitational Lens Survey (HAGGLeS) robot, 2) examining cutout images of bright galaxies (BGs) after subtraction of a smooth galaxy light distribution, 3) examining the unsubtracted BG cutouts, and 4) performing a full-frame visual inspection of the ACS images. We compute purity and completeness and consider investigator time for the four algorithms, using the main SLACS lenses as a testbed. The first and second algorithms perform the best. We present the four new lens…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
