Application of the rule-growing algorithm RIPPER to particle physics analysis

TL;DR

This paper explores the use of the RIPPER rule-growing algorithm for particle selection in high-energy physics, emphasizing its efficiency and effectiveness compared to other multivariate analysis methods.

Contribution

It demonstrates the application of RIPPER with bagging and cost-sensitivity in particle physics, showing its advantages over other techniques.

Findings

RIPPER effectively reduces background while selecting signals.

Bagging and cost-sensitivity improve analysis quality.

Compared favorably to other multivariate methods.

Abstract

A large hadron machine like the LHC with its high track multiplicities always asks for powerful tools that drastically reduce the large background while selecting signal events efficiently. Actually such tools are widely needed and used in all parts of particle physics. Regarding the huge amount of data that will be produced at the LHC, the process of training as well as the process of applying these tools to data, must be time efficient. Such tools can be multivariate analysis -- also called data mining -- tools. In this contribution we present the results for the application of the multivariate analysis, rule growing algorithm RIPPER on a problem of particle selection. It turns out that the meta-methods bagging and cost-sensitivity are essential for the quality of the outcome. The results are compared to other multivariate analysis techniques.