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 …

Classification of jets as originating from light-flavor or heavy-flavor quarks is an important
task for inferring the nature of particles produced in high-energy collisions. The large and …

A bstract While “quark” and “gluon” jets are often treated as separate, well-defined objects in
both theoretical and experimental contexts, no precise, practical, and hadron-level definition …

A bstract There has been substantial progress in applying machine learning techniques to
classification problems in collider and jet physics. But as these techniques grow in …

A bstract Artificial intelligence offers the potential to automate challenging data-processing
tasks in collider physics. To establish its prospects, we explore to what extent deep learning …

We report on the progress in flavor identification tools developed for a future e+ e− linear
collider such as the International Linear Collider (ILC) and Compact Linear Collider (CLIC) …

A bstract Machine learning techniques are increasingly being applied toward data analyses
at the Large Hadron Collider, especially with applications for discrimination of jets with …

A bstract Modern machine learning techniques, such as convolutional, recurrent and
recursive neural networks, have shown promise for jet substructure at the Large Hadron …

A bstract We introduce a novel approach to jet tagging and classification through the use of
techniques inspired by computer vision. Drawing parallels to the problem of facial …

A bstract Understanding jets initiated by quarks and gluons is of fundamental importance in
collider physics. Efficient and robust techniques for quark versus gluon jet discrimination …