HOLISMOKES XIII: Strong-lens candidates at all mass scales and their environments from the Hyper-Suprime Cam and deep learning

TL;DR

This paper presents a systematic search for strong gravitational lenses across all mass scales using Hyper Suprime-Cam data and deep learning, resulting in a large, uniformly modeled sample that reveals environmental influences on lens properties.

Contribution

The study introduces a comprehensive deep learning pipeline combined with visual inspection to identify and analyze strong lens candidates across various environments, expanding the known sample significantly.

Findings

Larger Einstein radii are found in overdense environments.

The deep learning method achieves a false-positive rate of about 0.01%.

The sample includes 546 lens candidates, with detailed environmental and size analysis.

Abstract

We performed a systematic search for strong gravitational lenses using Hyper Suprime-Cam (HSC) data, focusing on galaxy-scale lenses combined with an environment analysis resulting in the identification of lensing clusters. To identify these lens candidates, we exploited our neural network (NN) from HOLISMOKES VI. During our visual grading, we also simultaneously inspected larger stamps (80'' x 80'') to identify large, extended arcs and also classify their overall environment. Here, we also re-inspected our previous lens candidates with i-Kron radii larger than 0.8''. Using the 546 visually identified lens candidates, we further defined various criteria to select the candidates in overdensities. In total, we identified 24 grade A and 138 grade B candidates that exhibit either spatially-resolved multiple images or extended, distorted arcs in the new sample. Furthermore, combining our different techniques to determine overdensities, we identified a total of 231/546 lens candidates by at least one of our three identification methods for overdensities. This new sample contains only 49 group- or cluster-scale re-discoveries, while 43 systems had been identified by all three procedures. Furthermore, we performed a statistical analysis by using the NN from HOLISMOKES IX to model these systems, making this the largest uniformly modeled sample to date. We find a tendency towards larger Einstein radii for galaxy-scale systems in overdense environments. These results demonstrate the feasibility of applying NNs to hundreds of million cutouts, while resulting in 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.