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The analysis of optical images of galaxy-galaxy strong gravitational lensingsystems can provide important information about the distribution of dark matterat small scales. However, the modeling and statistical analysis of these imagesis extraordinarily complex, bringing together source image and main lensreconstruction, hyper-parameter optimization, and the marginalization oversmall-scale structure realizations. We present here a new analysis pipelinethat tackles these diverse challenges by bringing together many recent machinelearning developments in one coherent approach, including variationalinference, Gaussian processes, differentiable probabilistic programming, andneural likelihood-to-evidence ratio estimation. Our pipeline enables: (a) fastreconstruction of the source image and lens mass distribution, (b) variationalestimation of uncertainties, (c) efficient optimization of sourceregularization and other hyperparameters, and (d) marginalization overstochastic model components like the distribution of substructure. We presenthere preliminary results that demonstrate the validity of our approach.

Targeted Likelihood-Free Inference of Dark Matter Substructure in Strongly-Lensed Galaxies