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

Anna Stakia , Tommaso Dorigo , Giovanni Banelli , Daniela Bortoletto , Alessandro Casa , Pablo de Castro , Christophe Delaere , Julien Donini (1) , Livio Finos , Michele Gallinaro , Andrea Giammanco , Alexander Held , Fabricio Jiménez Morales (1, 2) , Grzegorz Kotkowski , Seng Pei Liew , Fabio Maltoni , Giovanna Menardi , Ioanna Papavergou , Alessia Saggio , Bruno Scarpa , Giles C. Strong , Cecilia Tosciri , João Varela , Pietro Vischia , Andreas Weiler

1 LPC - Laboratoire de Physique de Clermont
2 LLR - Laboratoire Leprince-Ringuet

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

Domaines: Physique des Hautes Energies - Expérience [hep-ex]; Informatique [cs]; Physique des Hautes Energies - Phénoménologie [hep-ph]; Analyse de données, Statistiques et Probabilités [physics.data-an]

Anna Stakia, Tommaso Dorigo, Giovanni Banelli, Daniela Bortoletto, Alessandro Casa, et al.. Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider. Rev.Phys., 2021, 7, pp.100063. ⟨10.1016/j.revip.2021.100063⟩. ⟨hal-03243399⟩