Abstract
The MAGIC and FACT telescopes on the Canary Island of La Palma are both imaging Cherenkov telescopes. Their purpose is to detect highly energetic gamma particles sent out by various astrophysical sources. Due to characteristics of the detection process not only gamma particles are recorded, but also other particles summarized as hadrons. For further analysis the gamma ray signal has to be separated from the hadronic background. So far, so-called Hillas parameters (Hillas, Proceedings of the 19th International Cosmic Ray Conference ICRC, San Diego, 1985) are used as features in a classification algorithm for the separation. These parameters are only a first heuristic approach to describe signal events, so that it is desirable to find better features for the classification. We construct new features by using distance measures between the observed Cherenkov light distribution in the telescope camera and an idealized model distribution for the signal events, which we deduce from simulations and which takes, for example, the alignment and shape of an event into account. The new features added to the Hillas parameters lead to substantial gains in terms of classification.
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Acknowledgements
Part of the work on this paper has been supported by Deutsche Forschungsgemeinschaft (DFG) within the Collaborative Research Center SFB 876 “Providing Information by Resource-Constrained Analysis,” project C3. We gratefully thank the FACT collaboration for supplying us with the test data sets and the ITMC at TU Dortmund University for providing computer resources on LiDO. We are grateful to the reviewers for their helpful comments.
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Voigt, T., Fried, R. (2015). Distance Based Feature Construction in a Setting of Astronomy. In: Lausen, B., Krolak-Schwerdt, S., Böhmer, M. (eds) Data Science, Learning by Latent Structures, and Knowledge Discovery. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44983-7_42
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DOI: https://doi.org/10.1007/978-3-662-44983-7_42
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