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

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.