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. In the current work, we demonstrate that it is possible to combine a convolutional neural network trained on galaxy images as a source model with a fully-differentiable and exact implementation of gravitational lensing physics in a single probabilistic model. This does away with hyperparameter tuning for the source model, enables the simultaneous optimization of nearly one hundred source and lens parameters with gradient-based methods, and allows the use of efficient gradient-based posterior sampling techniques. These features make this automated inference pipeline potentially suitable for processing a large amount of data. By analyzing mock lensing systems with different signal-to-noise ratios, we show that lensing parameters are reconstructed with percent-level accuracy. More generally, we consider this work as one of the first steps in establishing differentiable probabilistic programming techniques in the particle astrophysics community, which have the potential to significantly accelerate and improve many complex data analysis tasks.

中文翻译：

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可微强透镜：通过可微概率编程将重力和神经网络结合起来

由于即将到来的望远镜将观察到数千个强大的透镜系统，因此为这些图像创建全自动分析管道变得越来越重要。在这项工作中，我们通过开发第一个端到端可微分强透镜管道朝着这个方向迈出了一步。我们的方法利用并结合了三个重要的计算机科学发展：（a）卷积神经网络，（b）基于梯度的高效采样技术，以及（c）深度概率编程语言。后者使参数推断自动化，并使生成式深度神经网络和物理组件在单个模型中组合成为可能。在目前的工作中，我们证明，可以将在星系图像上训练的卷积神经网络作为源模型与引力透镜物理学的完全可微分和精确实现结合在单个概率模型中。这消除了对源模型的超参数调整，可以使用基于梯度的方法同时优化近一百个源和透镜参数，并允许使用基于梯度的高效后验采样技术。这些功能使这种自动推理管道可能适合处理大量数据。通过分析具有不同信噪比的模拟透镜系统，我们表明透镜参数以百分比级精度重建。更普遍，