  Upcoming large astronomical surveys are expected to capture an unprecedented
number of strong gravitational lensing systems. Deep learning is emerging as a
promising practical tool for the detection and quantification of these
galaxy-scale image distortions. The absence of large quantities of
representative data from current astronomical surveys motivates the development
of a robust forward-modeling approach using synthetic lensing images. Using a
mock sample of strong lenses created upon a state-of-the-art extragalactic
catalogs, we train a modular deep learning pipeline for uncertainty-quantified
detection and modeling with intermediate image processing components for
denoising and deblending the lensing systems. We demonstrate a high degree of
interpretability and controlled systematics due to domain-specific task modules
trained with different stages of synthetic image generation. For lens detection
and modeling, we obtain semantically meaningful latent spaces that separate
classes of strong lens images and yield uncertainty estimates that explain the
origin of misclassified images and provide probabilistic predictions for the
lens parameters. Validation of the inference pipeline has been carried out
using images from the Subaru telescope's Hyper Suprime-Cam camera, and LSST
DESC simulated DC2 sky survey catalogues.
