EPJ Web of Conferences
Volume 116, 2016Very Large Volume Neutrino Telescope (VLVnT-2015)
|Number of page(s)||4|
|Section||Computing Models, Data Repositories, Virtual Observatory, Data Formats, Software Systems, User Training|
|Published online||11 April 2016|
Application of data mining techniques in atmospheric neutrino analyses with IceCube
Department of Physics, TU Dortmund, Germany
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
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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