Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider
This work addresses the problem of enhancing data analysis sensitivity and precision for physicists at the LHC, representing an incremental advancement in domain-specific tools.
The paper tackled the customization and development of advanced multivariate analysis methods for high-energy physics at the Large Hadron Collider, resulting in improved sensitivity for data analyses and promising further precision gains in measurements and searches for new phenomena.
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.