Key points are not available for this paper at this time.
We have performed a systematic search for galaxy-scale strong lenses using Hyper Suprime-Cam imaging data, focusing on lenses in overdense environments. To identify these lens candidates, we exploit our neural network from HOLISMOKES VI, which is trained on realistic gri mock-images as positive examples, and real images as negative examples. Compared to our previous work, we lower the i-Kron radius limit to >0.5". This results in an increase by around 73 million sources to more than 135 million images. During our visual multi-stage grading of the network candidates, we now inspect simultaneously larger stamps (80"x80") to identify large, extended arcs cropped in the 10"x10" cutouts, and classify additionally their overall environment. Here we also reinspect our previous lens candidates and classify their environment. Using these 546 visually identified lens candidates, we further define various criteria by exploiting extensive and complementary photometric redshift catalogs, to select the candidates in overdensities. In total, we identified 24 grade-A and 138 grade-B candidates with either spatially-resolved multiple images or extended, distorted arcs in the new sample. Furthermore, with our different techniques, we identify in total 237/546 lens candidates in a cluster-like or overdense environment, containing only 49 group- or cluster-scale re-discoveries. These results demonstrate the feasibility of downloading and applying network classifiers to hundreds of million cutouts, necessary in the upcoming era of big data from deep, wide-field imaging surveys like Euclid and the Rubin Observatory Legacy Survey of Space and Time, while leading to a sample size that can be inspected by humans. These networks, with false-positive rates of ~0.01%, are very powerful tools to identify such rare galaxy-scale strong lensing systems, while also aiding in the discovery of new strong lensing clusters.
Schuldt et al. (Fri,) studied this question.