Topics in statistical data analysis for high-energy physics

Details Topics in statistical data analysis for high-energy physics Topics in statistical data analysis for high-energy physics

2010-12-16

arxiv.org/abs/1012.3589v1

CERN Yellow Report CERN-2010-002, pp.197-218

Physics

Data Analysis, Statistics and Probability

22 pages, Lectures given at the 2009 European School of High-Energy Physics, Bautzen, Germany, 14-27 Jun 2009

Scientific paper

These lectures concern two topics that are becoming increasingly important in the analysis of High Energy Physics (HEP) data: Bayesian statistics and multivariate methods. In the Bayesian approach we extend the interpretation of probability to cover not only the frequency of repeatable outcomes but also to include a degree of belief. In this way we are able to associate probability with a hypothesis and thus to answer directly questions that cannot be addressed easily with traditional frequentist methods. In multivariate analysis we try to exploit as much information as possible from the characteristics that we measure for each event to distinguish between event types. In particular we will look at a method that has gained popularity in HEP in recent years: the boosted decision tree (BDT).

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