KiDS grav. lenses obscured by foreground light

To address the limitations of previous lens search efforts, particularly for samples with smaller RE or faint lensed images, we developed a composite CNN framework that utilizes both U-Net and ResNet architectures for feature extraction and classification. We propose a hybrid search method that combines U-Net and ResNet architectures to enhance the detection of the foreground galaxy-obscured lenses.

Our approach consists of two main stages: first, the U-Net model separates the foreground galaxy light from potential lensing signals, creating residual images that highlight the lensing features. Next, the ResNet module performs binary classification on these residual images to determine the presence of lensing signals. We evaluated the hybrid search method with real observational data to demonstrate its effectiveness, achieving a recall of 71.5% and a 4.5% false positive rate at a confidence threshold of 0.6.