Abstract
Astrophysical experiments produce Big Data which need efficient and effective data analytics. In this paper we present a general data analysis process which has been successfully applied to data from IceCube, a cubic kilometer neutrino detector located at the geographic South Pole.
The goal of the analysis is to separate neutrinos from atmospheric muons within the data to determine the muon neutrino energy spectrum. The presented process covers straight cuts, variable selection, classification, and unfolding. A major challenge in the separation is the unbalanced dataset. The expected signal to background ratio in the initial data (trigger level) is roughly 1:\(10^6\). The overall process was embedded in a multi-fold cross-validation to control its performance. A subsequent regularized unfolding yields the sought after neutrino energy spectrum.
This paper is based on work with the IceCube collaboration [3] and work in project C3 of the Collaborative Research Center SFB 876 which is funded by the DFG.
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Börner, M., Rhode, W., Ruhe, T., for the IceCube Collaboration., Morik, K. (2015). Discovering Neutrinos Through Data Analytics. In: Bifet, A., et al. Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2015. Lecture Notes in Computer Science(), vol 9286. Springer, Cham. https://doi.org/10.1007/978-3-319-23461-8_15
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DOI: https://doi.org/10.1007/978-3-319-23461-8_15
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