Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider
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.
Keywords
Cite
@article{arxiv.2105.07530,
title = {Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider},
author = {Anna Stakia and Tommaso Dorigo and Giovanni Banelli and Daniela Bortoletto and Alessandro Casa and Pablo de Castro and Christophe Delaere and Julien Donini and Livio Finos and Michele Gallinaro and Andrea Giammanco and Alexander Held and Fabricio Jiménez Morales and Grzegorz Kotkowski and Seng Pei Liew and Fabio Maltoni and Giovanna Menardi and Ioanna Papavergou and Alessia Saggio and Bruno Scarpa and Giles C. Strong and Cecilia Tosciri and João Varela and Pietro Vischia and Andreas Weiler},
journal= {arXiv preprint arXiv:2105.07530},
year = {2021}
}
Comments
101 pages, 21 figures, submitted to Elsevier. [v2]: Updated to published version (in 'Reviews in Physics')