Graph Neural Networks.pdf

GNNs_for_Track_Finding.pdf
Preview of Graph Neural Networks
🔗 Source: indico.cern.ch
📊 Size: 2.81 MB
👤 Author: Daniel Murnane
⬇️ Downloads: 115

Summary

The Exa.TrkX project aims to optimize ML approaches for the Exascale tracking problem, enabling production-level tracking on next-generation detector systems. The project involves using graph neural networks for sub-second processing of HL-LHC event data to find seeds or tracks. The track finding pipeline includes embedding raw hit data, filtering doublets and triplets, training and classifying them in GNNs, and applying cuts for seeds and DBSCAN for track labels. Previous ML approaches include tracks as images using CNNs and tracks as sequences of points using LSTMs.

Description

The Exa.TrkX project aims to optimize ML approaches for the Exascale tracking problem, enabling production-level tracking on next-generation detector systems.

Technical Information

  • File Format: PDF
  • File Size: 2.81 MB
  • Pages: 39
  • Language: EN
  • Author: Daniel Murnane
  • Total Downloads: 115
  • Last Updated: 1 week ago

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This PDF document about Graph Neural Networks provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Graph Neural Networks.

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