From raw detector activations to reconstructed particles, data at the Large Hadron Collider
(LHC) are sparse, irregular, heterogeneous and highly relational in nature. Graph neural …

In this community review report, we discuss applications and techniques for fast machine
learning (ML) in science—the concept of integrating powerful ML methods into the real-time …

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 …

The full optimization of the design and operation of instruments whose functioning relies on
the interaction of radiation with matter is a super-human task, due to the large dimensionality …

Abstract The Exa. TrkX project has applied geometric learning concepts such as metric
learning and graph neural networks to HEP particle tracking. Exa. TrkX's tracking pipeline …

Machine learning methods have a long history of applications in high-energy physics (HEP).
Recently, there is a growing interest in exploiting these methods to reconstruct particle …

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 …

We provide details on the implementation of a machine-learning based particle flow
algorithm for CMS. The standard particle flow algorithm reconstructs stable particles based …

This paper reports on the second “Throughput” phase of the Tracking Machine Learning
(TrackML) challenge on the Codalab platform. As in the first “Accuracy” phase, the …