Speaker
Tim Ruhe
(TU Dortmund)
Description
The selection of event candidates by using machine 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 reprensation of the data in less 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 muon neutrino spectrum with IceCube in the 59-string configuration. It yielded a high statistics and high purity sample of neutrino candidates, while rejecting 99.9999% of the incoming background muons. Since then the analysis chain could be applied in analyses of the atmospheric muon neutrino spectrum using IceCube in the 79- and 86-string configuration with only minor changes. The setup of the analysis chain will be presented and the results will be discussed in the scope of atmospheric neutrino analyses.
Primary author
Tim Ruhe
(TU Dortmund)
