  Strong lensing in galaxy clusters probes properties of dense cores of dark
matter halos in mass, studies the distant universe at flux levels and spatial
resolutions otherwise unavailable, and constrains cosmological models
independently. The next-generation large scale sky imaging surveys are expected
to discover thousands of cluster-scale strong lenses, which would lead to
unprecedented opportunities for applying cluster-scale strong lenses to solve
astrophysical and cosmological problems. However, the large dataset challenges
astronomers to identify and extract strong lensing signals, particularly
strongly lensed arcs, because of their complexity and variety. Hence, we
propose a framework to detect cluster-scale strongly lensed arcs, which
contains a transformer-based detection algorithm and an image simulation
algorithm. We embed prior information of strongly lensed arcs at cluster-scale
into the training data through simulation and then train the detection
algorithm with simulated images. We use the trained transformer to detect
strongly lensed arcs from simulated and real data. Results show that our
approach could achieve 99.63 % accuracy rate, 90.32 % recall rate, 85.37 %
precision rate and 0.23 % false positive rate in detection of strongly lensed
arcs from simulated images and could detect almost all strongly lensed arcs in
real observation images. Besides, with an interpretation method, we have shown
that our method could identify important information embedded in simulated
data. Next step, to test the reliability and usability of our approach, we will
apply it to available observations (e.g., DESI Legacy Imaging Surveys) and
simulated data of upcoming large-scale sky surveys, such as the Euclid and the
CSST.
