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Reducing False Positives in Strong-Lens Searches with Generalized-Mean Consensus of Machine-Learning Ensembles in the Kilo-Degree Survey
Context. In wide-field surveys, the main challenge is not just classifier sensitivity, but the overwhelming number of false positives. Searching for strong lenses among millions to bilions of galaxies produces many contaminants, making the bottleneck for follow-up inspection and building statistical
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