High-quality Strong Lens Candidates in the Final Kilo-Degree Survey Footprint

Journal
Date Issued
2021
Author(s)
Li, R.
•
•
•
•
Kuijken, K.
•
Koopmans, L. V. E.
•
Schneider, P.
•
•
Xie, L.
•
Long, L.
•
Shu, W.
•
Vernardos, G.
•
Huang, Z.
•
COVONE, GIOVANNI
•
Dvornik, A.
•
Heymans, C.
•
Hildebrandt, H.
•
•
Wright, A. H.
DOI
Abstract
We present 97 new high-quality strong lensing candidates found in the final ∼350 deg2 that complete the full ∼1350 deg2 area of the Kilo-Degree Survey (KiDS). Together with our previous findings, the final list of high-quality candidates from KiDS sums up to 268 systems. The new sample is assembled using a new convolutional neural network (CNN) classifier applied to r-band (best-seeing) and g, r, and i color-composited images separately. This optimizes the complementarity of the morphology and color information on the identification of strong lensing candidates. We apply the new classifiers to a sample of luminous red galaxies (LRGs) and a sample of bright galaxies (BGs) and select candidates that received a high probability to be a lens from the CNN (P CNN). In particular, setting P CNN > 0.8 for the LRGs, the one-band CNN predicts 1213 candidates, while the three-band classifier yields 1299 candidates, with only ∼30% overlap. For the BGs, in order to minimize the false positives, we adopt a more conservative threshold, P CNN > 0.9, for both CNN classifiers. This results in 3740 newly selected objects. The candidates from the two samples are visually inspected by seven coauthors to finally select 97 "high-quality"lens candidates which received mean scores larger than 6 (on a scale from 0 to 10). We finally discuss the effect of the seeing on the accuracy of CNN classification and possible avenues to increase the efficiency of multiband classifiers, in preparation of next-generation surveys from ground and space.
Volume
923
Issue
1
Start page
16
Uri
Url
Issn Identifier
0004-637X
Rights
open.access
File(s)
Loading...
Thumbnail Image
Name

Li_2021_ApJ_923_16.pdf

Description
PDF editoriale
Size

3.48 MB

Format

Adobe PDF

Checksum (MD5)

2d5e027b6e9b1a9a9fb00dbbbf197e73