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Exploring deep learning as an event classification method for the Cherenkov Telescope Array
2017
Proceedings of 35th International Cosmic Ray Conference — PoS(ICRC2017)
unpublished
Telescopes based on the imaging atmospheric Cherenkov technique (IACTs) detect images of the atmospheric showers generated by gamma rays and cosmic rays as they are absorbed by the atmosphere. The much more frequent cosmic-ray events form the main background when looking for gamma-ray sources, and therefore IACT sensitivity is significantly driven by the capability to distinguish between these two types of events. Supervised learning algorithms, like random forests and boosted decision trees,
doi:10.22323/1.301.0809
fatcat:fohbie7lpzbzbk7ptbbfycxrqy