High-quality strong lens candidates in the final Kilo Degree survey footprint

R. Li, N. R. Napolitano*, C. Spiniello, C. Tortora, K. Kuijken, L. V. E. Koopmans, P. Schneider, F. Getman, L. Xie, L. Long, W. Shu, G. Vernardos, Z. Huang, G. Covone, A. Dvornik, C. Heymans, H. Hildebrandt, M. Radovich, A. H. Wright

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

We present 97 new high-quality strong lensing candidates found in the final ∼350deg2, that completed the full ∼1350deg2 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, 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 (PCNN). In particular, setting PCNN>0.8 for the LRGs, the 1-band CNN predicts 1213 candidates, while the 3-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, PCNN>0.9, for both CNN classifiers. This results in 3740 newly selected objects. The candidates from the two samples are visually inspected by 7 co-authors 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 multi-band classifiers, in preparation of next-generation surveys from ground and space.
Original languageEnglish
JournalAstrophysical Journal
Publication statusAccepted/In press - 6 Oct 2021

Keywords

  • astro-ph.GA

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