Scientific Reports (May 2024)

A low-latency graph computer to identify metastable particles at the Large Hadron Collider for real-time analysis of potential dark matter signatures

  • Ashutosh Vijay Kotwal,
  • Hunter Kemeny,
  • Zijie Yang,
  • Jiqing Fan

DOI
https://doi.org/10.1038/s41598-024-60319-9
Journal volume & issue
Vol. 14, no. 1
pp. 1 – 15

Abstract

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Abstract Image recognition is a pervasive task in many information-processing environments. We present a solution to a difficult pattern recognition problem that lies at the heart of experimental particle physics. Future experiments with very high-intensity beams will produce a spray of thousands of particles in each beam-target or beam-beam collision. Recognizing the trajectories of these particles as they traverse layers of electronic sensors is a massive image recognition task that has never been accomplished in real time. We present a real-time processing solution that is implemented in a commercial field-programmable gate array using high-level synthesis. It is an unsupervised learning algorithm that uses techniques of graph computing. A prime application is the low-latency analysis of dark-matter signatures involving metastable charged particles that manifest as disappearing tracks.

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