Catalog Matching with Astrometric Correction and its Application to the Hubble Legacy Archive
Tamas Budavari (JHU), Stephen H. Lubow (STScI)

TL;DR
This paper introduces a new astrometric correction method and a Bayesian matching approach to improve object identification across overlapping astronomical images, demonstrated on the Hubble Legacy Archive.
Contribution
It presents a novel, efficient solution for relative astrometry and a Bayesian method for matching objects across multiple images, enhancing catalog accuracy.
Findings
Achieved sub-pixel positional accuracy of a few milli-arcseconds.
Developed a fast, analytic solution for astrometric corrections.
Created a high-quality, publicly available catalog of matched objects.
Abstract
Object cross-identification in multiple observations is often complicated by the uncertainties in their astrometric calibration. Due to the lack of standard reference objects, an image with a small field of view can have significantly larger errors in its absolute positioning than the relative precision of the detected sources within. We present a new general solution for the relative astrometry that quickly refines the World Coordinate System of overlapping fields. The efficiency is obtained through the use of infinitesimal 3-D rotations on the celestial sphere, which do not involve trigonometric functions. They also enable an analytic solution to an important step in making the astrometric corrections. In cases with many overlapping images, the correct identification of detections that match together across different images is difficult to determine. We describe a new greedy Bayesian…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
