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Fast triggering in high-energy physics experiments using hardware neural networks.

B Denby1, P Garda, B Granado

  • 1Lab. des Instrum. et Syst. d'Ile de France, Univ. Pierre et Marie Curie, Paris, France.

IEEE Transactions on Neural Networks
|February 5, 2008
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Summary
This summary is machine-generated.

High-energy physics needs fast hardware neural networks for triggering. This study examines the H1 level 2 trigger and proposes new field-programmable gate array architectures for future high-speed neural network triggers.

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Area of Science:

  • High-energy physics
  • Particle physics instrumentation
  • Machine learning hardware

Background:

  • High-energy physics experiments demand sophisticated triggering systems.
  • Hardware-based neural networks are crucial for high-speed pattern recognition (MHz-GHz).
  • The H1 level 2 trigger at the HERA accelerator serves as a case study.

Purpose of the Study:

  • To analyze the challenges of neural triggering in high-energy physics.
  • To highlight the significance of hardware preprocessing.
  • To introduce novel field-programmable gate array (FPGA) architectures for future neural network triggers.

Main Methods:

  • Detailed examination of the H1 level 2 trigger system.
  • Analysis of hardware preprocessing requirements.
  • Exploration of FPGA-based neural network trigger designs.

Main Results:

  • The H1 level 2 trigger demonstrates the feasibility of neural networks for triggering.
  • Hardware preprocessing is essential for efficient data handling.
  • New FPGA architectures offer potential for enhanced trigger performance.

Conclusions:

  • Hardware neural networks are vital for high-energy physics triggering.
  • FPGA-based designs show promise for next-generation trigger systems.
  • Further research into FPGA architectures can advance trigger capabilities.