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

  • Authors: R. Li, N. R. Napolitano, C. Tortora, C. Spiniello, L. V. E. Koopmans, Z. Huang, N. Roy, G. Vernardos, S. Chatterjee, B. Giblin, F. Getman, M. Radovich, G. Covone, and K. Kuijken

2020 The Astrophysical Journal 899 30.

  • Provider: AAS Journals

Caption: Figure 7.

Distribution of the 286 lens candidates in the photometric redshift–luminosity space of the foreground deflectors. The dots marked by red plus signs are the first 82 candidates shown in Figure 8. The error bars on the rauto magnitudes are smaller than the symbol sizes.
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