Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider

TL;DR

This paper reviews recent advances in multivariate analysis and statistical learning techniques tailored for high-energy physics, highlighting their application and performance improvements in searches at the Large Hadron Collider.

Contribution

It introduces new multivariate analysis tools developed within the AMVA4NewPhysics project and evaluates their effectiveness in LHC data analysis.

Findings

Improved sensitivity in ATLAS and CMS data analyses.

Development of new tools enhancing measurement precision.

Promising methods for future physics searches.

Abstract

Between the years 2015 and 2019, members of the Horizon 2020-funded Innovative Training Network named "AMVA4NewPhysics" studied the customization and application of advanced multivariate analysis methods and statistical learning tools to high-energy physics problems, as well as developed entirely new ones. Many of those methods were successfully used to improve the sensitivity of data analyses performed by the ATLAS and CMS experiments at the CERN Large Hadron Collider; several others, still in the testing phase, promise to further improve the precision of measurements of fundamental physics parameters and the reach of searches for new phenomena. In this paper, the most relevant new tools, among those studied and developed, are presented along with the evaluation of their performances.