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Artificial Intelligence Techniques for Automating the CAMS Processing Pipeline to Direct the Search for Long-Period Comets

semanticscholar

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Abstract
We describe an effort to automate the CAMS (Cameras for All-sky Meteor Surveillance) data reduction pipeline using artificial intelligence techniques to discriminate meteors from other types of detections and to determine correct solutions during triangulation. The effort will make it possible to have the results from a night of low-light video observations available to the observers the following day. As part of the data reduction pipeline, meteors are classified as real and assigned to showers. Results are presented in such a way that each shower can be identified, and new showers from the occasional encounter with the dust trails of long-period comets can be recognized. The detection of such rare showers will allow to direct the search for long-period comets in dedicated deep surveys.
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