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 …

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 …

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 describe a strategy for constructing a neural network jet substructure tagger which
powerfully discriminates boosted decay signals while remaining largely uncorrelated with …

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 A key question for machine learning approaches in particle physics is how to best
represent and learn from collider events. As an event is intrinsically a variable-length …

At the extreme energies of the Large Hadron Collider, massive particles can be produced at
such high velocities that their hadronic decays are collimated and the resulting jets overlap …

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 The identification of boosted heavy particles such as top quarks or vector bosons is
one of the key problems arising in experimental studies at the Large Hadron Collider. In this …

A bstract Machine learning based on convolutional neural networks can be used to study jet
images from the LHC. Top tagging in fat jets offers a well-defined framework to establish our …