Automation of finding strong gravitational lenses in the Kilo Degree Survey with U - DenseLens (DenseLens + Segmentation).

In the context of upcoming large-scale surveys like Euclid, the necessity for the automation of strong lens detection is essential. While existing machine learning pipelines heavily rely on the classification probability (P), this study intends to address the importance of integrating additional metrics, such as Information Content (IC) and the number of pixels above the segmentation threshold ([Formula: see text]), to alleviate the false positive rate in unbalanced data-sets. In this work, we introduce a segmentation algorithm (U-Net) as a supplementary step in the established strong gravitational lens identification pipeline (Denselens), which primarily utilizes [Formula: see text] and [Formula: see text] parameters for the detection and ranking.