Robust Registration of Astronomy Catalogs with Applications to the Hubble Space Telescope

TL;DR

This paper introduces a robust method for registering astronomy catalogs, specifically improving the astrometric calibration of Hubble Space Telescope images by handling false associations and bad data effectively.

Contribution

It proposes a Bayesian, robust registration approach using infinitesimal 3D rotations and an improved objective function for better calibration accuracy.

Findings

Enhanced calibration accuracy demonstrated on simulated data.

Effective handling of false candidate associations.

Potential improvements for HST and similar telescopes.

Abstract

Astrometric calibration of images with a small field of view is often inferior to the internal accuracy of the source detections due to the small number of accessible guide stars. One important experiment with such challenges is the Hubble Space Telescope (HST). A possible solution is to cross-calibrate overlapping fields instead of just relying on standard stars. Following the approach of \citet{2012ApJ...761..188B}, we use infinitesimal 3D rotations for fine-tuning the calibration but devise a better objective that is robust to a large number of false candidates in the initial set of associations. Using Bayesian statistics, we accommodate bad data by explicitly modeling the quality, which yields a formalism essentially identical to an $M$-estimation in robust statistics. Our results on simulated and real catalogs show great potentials for improving the HST calibration, and those with similar challenges.