Characteristics of 162 strong lenses

Here, we also re-inspect our previous lens candidates with i-Kron radii >=0.8" and classify their environment. Using the 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, combining our different techniques to determine overdensities, we identify in total 231/546 lens candidates by at least one of our three identification methods of overdensities, containing only 49 group- or cluster-scale re-discoveries, while 43 systems are identified by all three procedures. We further perform a statistical analysis by using the neural network from HOLISMOKES IX to model these systems as singular isothermal ellipsoids with external shear and to estimate their parameter values, making this the largest uniformly modeled sample.

We find a tendency towards larger Einstein radii for galaxy-scale systems in overdense environments, while the other parameter values as well as uncertainty distributions are consistent between those in overdense and non-overdense environments. These results demonstrate the feasibility of downloading and applying neural 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 visually inspected by humans.

These deep learning pipelines, 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.