New High-quality Strong Lens Candidates with Deep Learning in the Kilo-Degree Survey

Journal
Date Issued
2020
Author(s)
Li, R.
•
NAPOLITANO, Nicola Rosario
•
•
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Koopmans, L. V. E.
•
Huang, Z.
•
Roy, N.
•
Vernardos, G.
•
Chatterjee, S.
•
Giblin, B.
•
•
•
Covone, G.
•
Kuijken, K.
DOI
Abstract
We report new high-quality galaxy-scale strong lens candidates found in the Kilo-Degree Survey data release 4 using machine learning. We have developed a new convolutional neural network (CNN) classifier to search for gravitational arcs, following the prescription by Petrillo et al. and using only r-band images. We have applied the CNN to two "predictive samples": a luminous red galaxy (LRG) and a "bright galaxy" (BG) sample (r < 21). We have found 286 new high-probability candidates, 133 from the LRG sample and 153 from the BG sample. We have ranked these candidates based on a value that combines the CNN likelihood of being a lens and the human score resulting from visual inspection (P-value), and here we present the highest 82 ranked candidates with P-values ≥0.5. All of these high-quality candidates have obvious arc or pointlike features around the central red defector. Moreover, we define the best 26 objects, all with P-values ≥0.7, as a "golden sample" of candidates. This sample is expected to contain very few false positives; thus, it is suitable for follow-up observations. The new lens candidates come partially from the more extended footprint adopted here with respect to the previous analyses and partially from a larger predictive sample (also including the BG sample). These results show that machine-learning tools are very promising for finding strong lenses in large surveys and more candidates can be found by enlarging the predictive samples beyond the standard assumption of LRGs. In the future, we plan to apply our CNN to the data from next-generation surveys such as the Large Synoptic Survey Telescope, Euclid, and the Chinese Space Station Optical Survey....
Volume
899
Issue
1
Start page
30
Uri
Url
Issn Identifier
0004-637X
Ads BibCode
2020ApJ...899...30L
Rights
open.access
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