Asteroid Data Hunter

The results of the Asteroid Data Hunter challenge were intended to provide new algorithms to promote science. Scientists find asteroids by taking images of the same place in the sky and find the star-like objects that move. With many telescopes scanning the sky during the time around the new moon, the large data volumes prevent individual inspection of every image. Traditionally, the identification of asteroids and other moving bodies in the Solar System has been achieved by acquiring images over several epochs and detecting changes between frames. This general approach has been used since before the discovery of Pluto and continues to this day. With the vast amount of data available now flowing from modern instruments, there is no good way for professional astronomers to verify every detection. In particular, looking in the future as large surveys grow ever larger, the ability to autonomously and rapidly check the images and determine which objects are suitable for follow up will be crucial. Current analysis implies that at best the CSS data pipeline is 80 ? 90% accurate and there are (based on CSS discovery numbers) several thousand additional objects that could be recovered per year. Starting from a fresh position allows specific optimizations of data analysis, which would be useful as a general moving object pipeline system for other observatories as well.
NASA desires a more computationally efficient, general purpose algorithm to detect moving objects using Catalina Sky Survey (CSS) data. Specifically, to increase the detection sensitivity, minimize the number of false positives, ignore imperfections in the data, and ensure that the algorithm can run effectively on all computers. The results of the Asteroid Data Hunter challenge were intended to provide new algorithms to promote science. Scientists find asteroids by taking images of the same place in the sky and find the star-like objects that move. With many telescopes scanning the sky during the time around the new moon, the large data volumes prevent individual inspection of every image. Traditionally, the identification of asteroids and other moving bodies in the Solar System has been achieved by acquiring images over several epochs and detecting changes between frames. This general approach has been used since before the discovery of Pluto and continues to this day. With the vast amount of data available now flowing from modern instruments, there is no good way for professional astronomers to verify every detection. In particular, looking in the future as large surveys grow ever larger, the ability to autonomously and rapidly check the images and determine which objects are suitable for follow up will be crucial. Current analysis implies that at best the CSS data pipeline is 80 ? 90% accurate and there are (based on CSS discovery numbers) several thousand additional objects that could be recovered per year. Starting from a fresh position allows specific optimizations of data analysis, which would be useful as a general moving object pipeline system for other observatories as well.

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