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Hybrid Physical-Deep Learning Model for Astronomical Inverse Problems
[article]
2019
arXiv
pre-print
We present a Bayesian machine learning architecture that combines a physically motivated parametrization and an analytic error model for the likelihood with a deep generative model providing a powerful data-driven prior for complex signals. This combination yields an interpretable and differentiable generative model, allows the incorporation of prior knowledge, and can be utilized for observations with different data quality without having to retrain the deep network. We demonstrate our
arXiv:1912.03980v1
fatcat:rtqnopmnangflntdawsrqseuuy