Anna Stakia

Jet flavour classification is of paramount importance for a broad range of applications in modern-day high-energy-physics experiments, particularly at the LHC. In this paper we propose a novel architecture for this task that exploits modern deep learning techniques. This new model, called DeepJet, overcomes the limitations in input size that affected previous approaches. As a result, the heavy flavour classification performance improves, and the model is extended to also perform quark-gluon tagging.

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.

Abstract In the course of the past four years, AMVA4NewPhysics, a Horizon2020-funded Marie Skłodowska-Curie (MSCA) Innovative Training Network, focused on the study of Multivariate Analysis Methods and Statistical Learning tools for the High Energy Physics research. Through the individual and collaborative work of its members, AMVA4NewPhysics succeeded in developing and optimising several such tools for use by the ATLAS and CMS experiments at the Large Hadron Collider, at CERN, promising to improve their measurement and search sensitivity. In this paper, some of these new tools are presented, along with their related results.