# Differentiable strong lensing: uniting gravity and neural nets through differentiable probabilistic programming

@article{Chianese2019DifferentiableSL, title={Differentiable strong lensing: uniting gravity and neural nets through differentiable probabilistic programming}, author={Marco Chianese and Adam Coogan and Paul Hofma and Sydney Otten and Christoph Weniger}, journal={Monthly Notices of the Royal Astronomical Society}, year={2019} }

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: (i) convolutional neural networks (CNNs), (ii) efficient gradient-based sampling techniques, and (iii) deep…

## 22 Citations

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- Computer SciencePublications of the Astronomical Society of Australia
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Analysis of galaxy–galaxy strong lensing systems is strongly dependent on any prior assumptions made about the appearance of the source. Here we present a method of imposing a data-driven prior /…

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The GPry algorithm for fast Bayesian inference of general (non-Gaussian) posteriors with a moderate number of parameters is presented, based on generating a Gaussian Process surrogate model of the log-posterior aided by a Support Vector Machine classiﬁer that excludes extreme or non-ﬂnite values.

### Strong Lensing Source Reconstruction Using Continuous Neural Fields

- PhysicsArXiv
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