Astrometry.net: Blind astrometric calibration of arbitrary astronomical images

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.

• The paper introduces a fully automated method that uses geometric hashing and Bayesian verification to calibrate astronomical images.
• It demonstrates a success rate exceeding 99.9% in image recognition across surveys like SDSS, GALEX, and HST.
• The system recovers and calibrates archival and diverse images, enhancing data integration within the Virtual Observatory.

Overview of "Astrometry.net: Blind astrometric calibration of arbitrary astronomical images"

The paper presents Astrometry.net, a robust and fully automated system for blind astrometric calibration of astronomical images. This system efficiently determines the pointing, scale, and orientation (commonly referred to as World Coordinate System or WCS information) of any input astronomical image with remarkable accuracy by relying solely on the information contained within the image pixels. This capability addresses the broader "lost in space" problem, where none of the image characteristics, including its scale, are known a priori.

Methodology

The core methodology involves detecting sources in the input image, constructing asterisms (typically sets of four or five stars called "quads"), and applying geometric hashing to generate hypotheses about the image's astrometric calibration. The system constructs these hypotheses by comparing the hashed configurations against pre-indexed configurations derived from a reference star catalog. Each hypothesis undergoes rigorous verification through a Bayesian decision theory process to ensure its accuracy. The indices used are built from comprehensive catalogs such as the USNO-B and 2MASS, optimizing for uniformity and redundancy across the sky.

Results

The system demonstrates a high success rate exceeding 99.9% for near-ultraviolet and visual imaging survey data without producing false positives. Specific numerical results highlight that, for SDSS r-band images, the system successfully recognizes over 99.97% of the images, even in cases where the bandpass differs considerably from the reference index. The inclusion of indices from multiple catalogs, like USNO-B and 2MASS, further enhances completeness and reliability, achieving a recognition rate of 100% when combining both. CPU time per image is minimized, with many images being recognized within a second. The robustness of this system is further evidenced by its ability to perform well across different astronomical datasets, including those from GALEX and the Hubble Space Telescope.

Implications

The introduction of Astrometry.net has substantial implications for astronomical research, particularly in data sharing and archive utilization. By enabling precise astrometric calibration from image data alone, it reclaims a significant portion of "lost" observational capabilities such as archival photographic plates or amateur astronomers' images, broadening the temporal and spatial research domains. This capability is critical in the context of the Virtual Observatory, where trust in calibration meta-data is paramount to conducting scientific analyses across heterogeneous datasets. Moreover, the system's adaptability to various image scales and bandpasses implies its utility in supporting future initiatives involving large, distributed datasets and surveys like LSST.

Future Prospects

Future directions for this research could involve enhancing the system's adaptability across even wider scales and bandpasses, possibly through the inclusion of additional reference catalogs that broaden spectral coverage. It could also continue refining computational efficiencies to handle even larger datasets as the volume of publicly available astronomical imagery grows. Additionally, further integration with the Virtual Observatory's standards could streamline the process of data exchange and utilization among astronomers globally.

In summary, Astrometry.net stands as a significant advancement in the field due to its reliability, robustness, and automation in performing astrometric calibrations. It represents a vital tool for maximizing the scientific utility of astronomical images of varied scales and provenances, thereby contributing significantly to ongoing and future astronomical research efforts.