| Issue |
EPJ Web of Conferences
Volume 116, 2016
Very Large Volume Neutrino Telescope (VLVnT-2015)
|
|
|---|---|---|
| Article Number | 07006 | |
| Number of page(s) | 4 | |
| Section | Computing Models, Data Repositories, Virtual Observatory, Data Formats, Software Systems, User Training | |
| DOI | https://doi.org/10.1051/epjconf/201611607006 | |
| Published online | 11 April 2016 | |
https://doi.org/10.1051/epjconf/201611607006
Application of data mining techniques in atmospheric neutrino analyses with IceCube
Department of Physics, TU Dortmund, Germany
a e-mail: tim.ruhe@tu-dortmund.de
b http://icecube.wisc.edu
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.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.