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Unsupervised statistical learning applied to experimental high-energy physics and related areas

Author

Listed:
  • Eduardo F. Simas Filho

    (Electrical Engineering Program, Federal University of Bahia, Rua Aristides Novis, 02, Salvador, Bahia 40210-630, Brazil)

  • José M. Seixas

    (Signal Processing Laboratory, COPPE/Poli, Federal University of Rio de Janeiro, Brazil)

Abstract

Unsupervised statistical learning (USL) techniques, such as self-organizing maps (SOMs), principal component analysis (PCA) and independent component analysis explore different statistical properties to efficiently process information from multiple variables. USL algorithms have been successfully applied in experimental high-energy physics (HEP) and related areas for different purposes, such as feature extraction, signal detection, noise reduction, signal-background separation and removal of cross-interference from multiple signal sources in multisensor measurement systems. This paper presents both a review of the theoretical aspects of these signal processing methods and examples of some successful applications in HEP and related areas experiments.

Suggested Citation

  • Eduardo F. Simas Filho & José M. Seixas, 2016. "Unsupervised statistical learning applied to experimental high-energy physics and related areas," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 27(05), pages 1-16, May.
    • Handle: RePEc:wsi:ijmpcx:v:27:y:2016:i:05:n:s0129183116300025
    DOI: 10.1142/S0129183116300025
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