Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2015: Machine Learning and Knowledge Discovery in Databases pp 208-212

Discovering Neutrinos Through Data Analytics

  • Mathis Börner
  • Wolfgang Rhode
  • Tim Ruhe
  • for the IceCube Collaboration
  • Katharina Morik
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9286)

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.

Keywords

Neutrinos IceCube Machine learning Random forest Feature selection Cross-validation Signal and background separation 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mathis Börner
    • 1
  • Wolfgang Rhode
    • 1
  • Tim Ruhe
    • 1
  • for the IceCube Collaboration
  • Katharina Morik
    • 1
  1. 1.TU Dortmund University, Experimental Physics, Computer ScienceDortmundGermany

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