Jet substructure has emerged to play a central role at the Large Hadron Collider (LHC),
where it has provided numerous innovative new ways to search for new physics and to …

Jet substructure has emerged to play a central role at the Large Hadron Collider, where it
has provided numerous innovative ways to search for new physics and to probe the …

How to represent a jet is at the core of machine learning on jet physics. Inspired by the
notion of point clouds, we propose a new approach that considers a jet as an unordered set …

Recent results at the Large Hadron Collider (LHC) have pointed to enhanced physics
capabilities through the improvement of the real-time event processing techniques. Machine …

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 …

The study of the internal structure of hadronic jets has become in recent years a very active
area of research in particle physics. Jet substructure techniques are increasingly used in …

A bstract Deep learning methods have been increasingly adopted to study jets in particle
physics. Since symmetry-preserving behavior has been shown to be an important factor for …

The performance of identification algorithms (“taggers”) for hadronically decaying top quarks
and W bosons in pp collisions at ss= 13 TeV recorded by the ATLAS experiment at the Large …

Based on the established task of identifying boosted, hadronically decaying top quarks, we
compare a wide range of modern machine learning approaches. Unlike most established …

A bstract Building on the notion of a particle physics detector as a camera and the collimated
streams of high energy particles, or jets, it measures as an image, we investigate the …