Track Seeding and Labelling with Embedded-space Graph Neural Networks
To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is investigating a variety of machine learning approaches to particle track reconstruction. The most promising of these solutions, graph neural networks (GNN), process the event as a graph that connects track measurements (detec...
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Zusammenfassung: | To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is
investigating a variety of machine learning approaches to particle track
reconstruction. The most promising of these solutions, graph neural networks
(GNN), process the event as a graph that connects track measurements (detector
hits corresponding to nodes) with candidate line segments between the hits
(corresponding to edges). Detector information can be associated with nodes and
edges, enabling a GNN to propagate the embedded parameters around the graph and
predict node-, edge- and graph-level observables. Previously, message-passing
GNNs have shown success in predicting doublet likelihood, and we here report
updates on the state-of-the-art architectures for this task. In addition, the
Exa.TrkX project has investigated innovations in both graph construction, and
embedded representations, in an effort to achieve fully learned end-to-end
track finding. Hence, we present a suite of extensions to the original model,
with encouraging results for hitgraph classification. In addition, we explore
increased performance by constructing graphs from learned representations which
contain non-linear metric structure, allowing for efficient clustering and
neighborhood queries of data points. We demonstrate how this framework fits in
with both traditional clustering pipelines, and GNN approaches. The embedded
graphs feed into high-accuracy doublet and triplet classifiers, or can be used
as an end-to-end track classifier by clustering in an embedded space. A set of
post-processing methods improve performance with knowledge of the detector
physics. Finally, we present numerical results on the TrackML particle tracking
challenge dataset, where our framework shows favorable results in both seeding
and track finding. |
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DOI: | 10.48550/arxiv.2007.00149 |