Application of data mining techniques in atmospheric neutrino analyses with IceCube
Department of Physics, TU Dortmund, Germany
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Published online: 11 April 2016
The selection of event candidates by machine learning algorithms has become an important analysis tool. Data mining, however, goes beyond the simple training and application of a learning algorithm. It also incorporates finding a good representation of data in fewer dimensions without losing relevant information, as well as a thorough validation of the results throughout the entire analysis. A data mining-based event selection chain has been developed for the measurement of the atmospheric νμ spectrum with IceCube in the 59-string configuration. It yielded a high statistics and high purity sample (99.59 ± 0.37%) of νμ, while allowing only 1.0 × 10−4% of the incoming background muons to pass. In this paper the setup of the analysis chain is presented and the results are discussed in the context of atmospheric νμ analyses.
© Owned by the authors, published by EDP Sciences, 2016
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