Differentiable Strong Lensing: Uniting Gravity and Neural Nets through Differentiable Probabilistic Programming

Understand

Since upcoming telescopes will observe thousands of strong lensing systems, creating fully-automated analysis pipelines for these images becomes increasingly important.

• In this work, we make a step towards that direction by developing the first end-to-end differentiable strong lensing pipeline.
• Our approach leverages and combines three important computer science developments: (a) convolutional neural networks, (b) efficient gradient-based sampling techniques, and (c) deep probabilistic programming languages.
• The latter automatize parameter inference and enable the combination of generative deep neural networks and physics components in a single model.