We present Astrometry.net, a system for automatically recognizing and astrometrically calibrating astronomical images, using the information in the image pixels alone. The system is based on the geometric hashing approach in computer vision: We use the geometric relationships between low-level features (stars and galaxies), which are relatively indistinctive, to create geometric features that are distinctive enough that we can recognize images that cover less than one-millionth of the area of the sky. The geometric features are used to generate rapidly hypotheses about the location---the pointing, scale, and rotation---of an image on the sky. Each hypothesis is then evaluated in a Bayesian decision theory framework in order to ensure that most correct hypotheses are accepted while false hypotheses are almost never accepted. The feature-matching process is accelerated by using a new fast and space-efficient kd-tree implementation. The Astrometry.net system is available via a web interface, and the software is released under an open-source license. It is being used by hundreds of individual astronomers and several large-scale projects, so we have at least partially achieved our goal of helping ``to organize, annotate and make searchable all the world's astronomical information.''
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/19281 |
| Date | 03 March 2010 |
| Creators | Lang, Dustin |
| Contributors | Roweis, Sam, Hogg, David W. |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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