Astrometry.net: Blind astrometric calibration of arbitrary astronomical images

TL;DR

Astrometry.net is a robust system that automatically determines the pointing, scale, and orientation of astronomical images without prior information, achieving near-perfect success rates by matching star patterns to catalog data.

Contribution

It introduces a fully automated, reliable method for blind astrometric calibration of arbitrary images using geometric hashing and Bayesian hypothesis testing.

Findings

99.9% success rate with USNO-B catalog data

Augmentation with 2MASS catalog achieves 100% completeness

No false positives in calibration results

Abstract

We have built a reliable and robust system that takes as input an astronomical image, and returns as output the pointing, scale, and orientation of that image (the astrometric calibration or WCS information). The system requires no first guess, and works with the information in the image pixels alone; that is, the problem is a generalization of the "lost in space" problem in which nothing--not even the image scale--is known. After robust source detection is performed in the input image, asterisms (sets of four or five stars) are geometrically hashed and compared to pre-indexed hashes to generate hypotheses about the astrometric calibration. A hypothesis is only accepted as true if it passes a Bayesian decision theory test against a background hypothesis. With indices built from the USNO-B Catalog and designed for uniformity of coverage and redundancy, the success rate is 99.9% for contemporary near-ultraviolet and visual imaging survey data, with no false positives. The failure rate is consistent with the incompleteness of the USNO-B Catalog; augmentation with indices built from the 2MASS Catalog brings the completeness to 100% with no false positives. We are using this system to generate consistent and standards-compliant meta-data for digital and digitized imaging from plate repositories, automated observatories, individual scientific investigators, and hobbyists. This is the first step in a program of making it possible to trust calibration meta-data for astronomical data of arbitrary provenance.