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 approach with an example of astronomical source separation in current imaging data, yielding a physical and interpretable model of astronomical scenes.
- Pub Date:
- December 2019
- Astrophysics - Instrumentation and Methods for Astrophysics;
- Computer Science - Machine Learning
- 8 pages, accepted submission to the NeurIPS 2019 Machine Learning and the Physical Sciences Workshop