Astronomical imaging: The theory of everything

TL;DR

This paper proposes developing automated, generative models of astronomical images that can synthesize any data, enabling comprehensive catalogs and improved automated discovery of celestial phenomena.

Contribution

It introduces a framework for creating homogeneous calibration meta-data and generative models that encompass all astronomical sources, advancing automated analysis and discovery.

Findings

A generative model can synthesize any astronomical image.

The best-fit model yields the most complete astronomical catalog.

Residual analysis can identify novel phenomena or model failures.

Abstract

We are developing automated systems to provide homogeneous calibration meta-data for heterogeneous imaging data, using the pixel content of the image alone where necessary. Standardized and complete calibration meta-data permit generative modeling: A good model of the sky through wavelength and time--that is, a model of the positions, motions, spectra, and variability of all stellar sources, plus an intensity map of all cosmological sources--could synthesize or generate any astronomical image ever taken at any time with any equipment in any configuration. We argue that the best-fit or highest likelihood model of the data is also the best possible astronomical catalog constructed from those data. A generative model or catalog of this form is the best possible platform for automated discovery, because it is capable of identifying informative failures of the model in new data at the pixel level, or as statistical anomalies in the joint distribution of residuals from many images. It is also, in some sense, an astronomer's "theory of everything".