Advances in machine learning methods provide tools that have broad applicability in
scientific research. These techniques are being applied across the diversity of nuclear …

Deep learning tools can incorporate all of the available information into a search for new
particles, thus making the best use of the available data. This paper reviews how to optimally …

In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct
a comprehensive particle-level view of the event by combining information from the …

Using detailed simulations of calorimeter showers as training data, we investigate the use of
deep learning algorithms for the simulation and reconstruction of single isolated particles …

We explore the use of graph networks to deal with irregular-geometry detectors in the
context of particle reconstruction. Thanks to their representation-learning capabilities, graph …

The ABCD method is one of the most widely used data-driven background estimation
techniques in high energy physics. Cuts on two statistically independent classifiers separate …

We introduce a method to reconstruct the kinematics of neutral-current deep inelastic
scattering (DIS) using a deep neural network (DNN). Unlike traditional methods, it exploits …

Jet clustering is traditionally an unsupervised learning task because there is no unique way
to associate hadronic final states with the quark and gluon degrees of freedom that …

We present an end-to-end reconstruction algorithm to build particle candidates from detector
hits in next-generation granular calorimeters similar to that foreseen for the high-luminosity …

High-energy physics detectors, images, and point clouds share many similarities in terms of
object detection. However, while detecting an unknown number of objects in an image is …