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A Case Study on the Use of Statistical Classification Methods in Particle Physics

  • Claus Weihs
  • Olaf Mersmann
  • Bernd Bischl
  • Arno Fritsch
  • Heike Trautmann
  • Till Moritz Karbach
  • Bernhard Spaan
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Current research in experimental particle physics is dominated by high profile and large scale experiments. One of the major tasks in these experiments is the selection of interesting or relevant events. In this paper we propose to use statistical classification algorithms for this task. To illustrate our method we apply it to an Monte-Carlo (MC) dataset from the BaBar experiment. One of the major obstacles in constructing a classifier for this task is the imbalanced nature of the dataset. Only about 0.5% of the data are interesting events. The rest are background or noise events. We show how ROC curves can be used to find a suitable cutoff value to select a reasonable subset of a stream for further analysis. Finally, we estimate the CP asymmetry of the \({B}^{\pm }\rightarrow D{K}^{\pm }\) decay using the samples extracted by the classifiers.

Notes

Acknowledgements

This work was partly supported by the Collaborative Research Center SFB 823 and the Research Training Group “Statistical Modelling” of the German Research Foundation. Due to the computationally intensive nature of this case study, all calculations were performed on the LiDO HPC cluster at the TU Dortmund. We would like to thank the LiDO team for their support.

References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Claus Weihs
    • 1
  • Olaf Mersmann
    • 1
  • Bernd Bischl
    • 1
  • Arno Fritsch
    • 1
  • Heike Trautmann
    • 1
  • Till Moritz Karbach
    • 2
  • Bernhard Spaan
    • 2
  1. 1.Statistics DepartmentTU DortmundDortmundGermany
  2. 2.Physics DepartmentTU DortmundDortmundGermany

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