… We extend the prototypical message passing Graph Neural Network architecture as described
in [… Once hits are assembled into input graphs Gin in embedded space (section 2), the hit …

… Improved track seeding, based on global pattern recognition, can dramatically improve …
embedded space. The embedded graphs are then fed into doublet, triplet, and end-to-end track …

… This paper introduces ETX4VELO, a Graph Neural Network (GNN)-based track-finding …
Label each remaining link (which corresponds to articulations engaged in forks) as a unique track…

… connected component label to each node, indicating the track to which each node belongs.
Each node is initially assigned a unique label, typically its own node index. Next the label of …

… graph neural networks to HEP particle tracking. Exa.TrkX’s … to the same track to be close in
the embedded space, according … -k NN graph construction, and performing track labelling with …

… are designed to recover tracks from the edge-weighted graph. … with set-level (graph-level)
truth labels, which are the target … Track seeding performance via hit triplet identification was …

… learning and graph neural networks to HEP particle tracking. … the same track to be close in
the embedded space, according … -k NN graph construction, and performing track labelling with …

… techniques such as graph neural networks are being … quantum graph neural networks to
reconstruct the particle track … Each of these doublets have a label that defines whether they …

… hadrons, therefore we label generated charged hadrons with \(|… calorimeter clusters not
matched to tracks. In addition, we … corresponding track, therefore, for electron and muon tracks we …

… detector), a path is seeded by several physically possible points, … In that case, techniques
such as graph neural networks can … simple clustering in an embedded space, there are several …