Despite its great success, machine learning can have its limits when dealing with insufficient
training data. A potential solution is the additional integration of prior knowledge into the …

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 …

Particle physics is a branch of science aiming at discovering the fundamental laws of matter
and forces. Graph neural networks are trainable functions which operate on graphs—sets of …

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 …

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 …

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 …

We introduce a potentially powerful new method of searching for new physics at the LHC,
using autoencoders and unsupervised deep learning. The key idea of the autoencoder is …

A bstract Autoencoders are widely used in machine learning applications, in particular for
anomaly detection. Hence, they have been introduced in high energy physics as a …