Department of Computer Science, University of Toronto, Canada
Wednesday 30 April 2008
Seminar Room B10 (Basement)
Alexandra House, 17 Queen Square, London, WC1N 3AR
Making The Sky Searchable: Large Scale Astronomical Pattern Recognition
Imagine you have an uncalibrated picture of the night sky and you want to know where the telescope was pointing when the picture was taken. Since several digital catalogues are available, containing (among other data) positions and magnitudes of billions of stars, you should, in principle, be able to find source locations by analyzing the pixels of your image and then exhaustively search the catalogues and find where that pattern of sources occurs. The only catch is that the sky is pretty big and that both images and catalogues are pretty noisy. Nonetheless, by using efficient geometric hashing techniques, our group has built a universal astrometric calibration robot which, roughly speaking, takes as input a picture of the night sky and returns as output the location on the sky at which the picture was taken. This is the first step in a more ambitious effort to learn a probabilistic model which accounts for *every* image of the night sky ever taken (including all professional, amateur and historical pixles) by modeling not only astrometry but also bandpass, time, and instrument properties.
Joint work with between University of Toronto and NYU.
Project Website: http://astrometry.net