alpha-Deep Probabilistic Inference (alpha-DPI): efficient uncertainty quantification from exoplanet astrometry to black hole feature extraction
This work addresses the need for reliable uncertainty quantification in astronomy, such as for exoplanet detection and black hole analysis, offering a practical solution that balances speed and accuracy, though it appears incremental as it builds on existing variational and sampling techniques.
The paper tackles the challenge of efficient and accurate posterior estimation in high-dimensional astronomical inference problems by proposing alpha-DPI, a deep learning framework that combines alpha-divergence variational inference with importance re-weighting, achieving fast and scalable uncertainty quantification with improved accuracy over traditional methods.
Inference is crucial in modern astronomical research, where hidden astrophysical features and patterns are often estimated from indirect and noisy measurements. Inferring the posterior of hidden features, conditioned on the observed measurements, is essential for understanding the uncertainty of results and downstream scientific interpretations. Traditional approaches for posterior estimation include sampling-based methods and variational inference. However, sampling-based methods are typically slow for high-dimensional inverse problems, while variational inference often lacks estimation accuracy. In this paper, we propose alpha-DPI, a deep learning framework that first learns an approximate posterior using alpha-divergence variational inference paired with a generative neural network, and then produces more accurate posterior samples through importance re-weighting of the network samples. It inherits strengths from both sampling and variational inference methods: it is fast, accurate, and scalable to high-dimensional problems. We apply our approach to two high-impact astronomical inference problems using real data: exoplanet astrometry and black hole feature extraction.