ASTROMETRY.NET: BLIND ASTROMETRIC CALIBRATION OF ARBITRARY ASTRONOMICAL IMAGES
University of New Brunswick · University of Toronto · +6 more institutions
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…
Citation impact
- FWCI
- 24.43
- Percentile
- 100%
- References
- 25
Authors
5- DLDustin LangCorresponding
University of New Brunswick, University of Toronto, Princeton University, Canada Research Chairs
- DWDavid W. Hogg
Max Planck Institute for Astronomy, New York University
- KMKeir Mierle
University of New Brunswick, Google (United States), University of Toronto, Canada Research Chairs
- MRMichael R. Blanton
New York University
- STSam T. Roweis
University of New Brunswick, Google (United States), Mercer University, University of Toronto, Canada Research Chairs, New York University
Topics & keywords
- Computer science
- Astrometry
- False positive paradox
- Artificial intelligence
- Scale (ratio)
- Stars
- Computer vision
- Physics