Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider
Résumé
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.
Mots clés
Particle physics
CERN LHC
CMS
ATLAS
AMVA4NewPhysics
Machine learning
Neural networks
Supervised classification
Anomaly detection
Gaussian processes
Statistical inference
p p: scattering
p p: colliding beams
CERN LHC Coll
new physics: search for
neural network
CERN Lab
sensitivity
performance
statistical analysis
data analysis method
programming