nbi: the Astronomer's Package for Neural Posterior Estimation
This addresses the problem of customizing NPE methods for astronomers, offering a more accessible tool for inference tasks, though it is incremental as it builds on existing NPE frameworks.
The authors tackled the slow adoption of Neural Posterior Estimation (NPE) in astronomy by developing nbi, a package that provides built-in featurizer networks for sequential data like light curves and spectra and introduces SNPE-IS for asymptotically exact inference, enabling off-the-shelf application to astronomical problems.
Despite the promise of Neural Posterior Estimation (NPE) methods in astronomy, the adaptation of NPE into the routine inference workflow has been slow. We identify three critical issues: the need for custom featurizer networks tailored to the observed data, the inference inexactness, and the under-specification of physical forward models. To address the first two issues, we introduce a new framework and open-source software nbi (Neural Bayesian Inference), which supports both amortized and sequential NPE. First, nbi provides built-in "featurizer" networks with demonstrated efficacy on sequential data, such as light curve and spectra, thus obviating the need for this customization on the user end. Second, we introduce a modified algorithm SNPE-IS, which facilities asymptotically exact inference by using the surrogate posterior under NPE only as a proposal distribution for importance sampling. These features allow nbi to be applied off-the-shelf to astronomical inference problems involving light curves and spectra. We discuss how nbi may serve as an effective alternative to existing methods such as Nested Sampling. Our package is at https://github.com/kmzzhang/nbi.