Galaxy and Mass Assembly: a comparison between galaxy-galaxy lens searches in KiDS/GAMA

Shawn Knabel, Rebecca L. Steele, Benne W. Holwerda, Joanna S. Bridge, Alice Jacques, Andrew M. Hopkins, Stephen P. Bamford, Michael J. I. Brown, Sarah Brough, Lee Kelvin, Maciej Bilicki, John Kielkopf

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
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Strong gravitational lenses are a rare and instructive type of astronomical object. Identification has long relied on serendipity, but different strategies—such as mixed spectroscopy of multiple galaxies along the line of sight, machine-learning algorithms, and citizen science—have been employed to identify these objects as new imaging surveys become available. We report on the comparison between spectroscopic, machine-learning, and citizen-science identification of galaxy–galaxy lens candidates from independently constructed lens catalogs in the common survey area of the equatorial fields of the Galaxy and Mass Assembly survey. In these, we have the opportunity to compare high completeness spectroscopic identifications against high-fidelity imaging from the Kilo Degree Survey used for both machine-learning and citizen-science lens searches. We find that the three methods—spectroscopy, machine learning, and citizen science—identify 47, 47, and 13 candidates, respectively, in the 180 square degrees surveyed. These identifications barely overlap, with only two identified by both citizen science and machine learning. We have traced this discrepancy to inherent differences in the selection functions of each of the three methods, either within their parent samples (i.e., citizen science focuses on low redshift) or inherent to the method (i.e., machine learning is limited by its training sample and prefers well-separated features, while spectroscopy requires sufficient flux from lensed features to lie within the fiber). These differences manifest as separate samples in estimated Einstein radius, lens stellar mass, and lens redshift. The combined sample implies a lens candidate sky density of ∼0.59 deg−2 and can inform the construction of a training set spanning a wider mass–redshift space. A combined approach and refinement of automated searches would result in a more complete sample of galaxy–galaxy lens candidates for future surveys.

Original languageEnglish
Article number223
Pages (from-to)1-26
Number of pages26
JournalAstronomical Journal
Issue number5
Publication statusPublished - 1 Nov 2020

Bibliographical note

Copyright 2020 The American Astronomical Society. First published in The Astronomical Journal, 160(5), 223, 2020. The original publication is available at, published by IOP Publishing. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


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