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

• Published in: Scientific reports Vol. 14; no. 1; pp. 10181 - 15
• Main Authors: Kotwal, Ashutosh Vijay, Kemeny, Hunter, Yang, Zijie, Fan, Jiqing
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 03.05.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/987; 639/705/1042; 639/705/258; 639/766/419/1131; 639/766/930/2735; Algorithms; Charged particles; Computational science; Dark matter; Digital signal processors; Disappearing track trigger; Electrical and electronic engineering; Experimental particle physics; Experiments; Fast pattern recognition; Field programmable gate arrays; Humanities and Social Sciences; Imaging techniques; Information processing; Information technology; Large Hadron Collider; Latency; Low latency; multidisciplinary; OTHER INSTRUMENTATION; Pattern recognition; Physics; Science; Science & Technology; Science (multidisciplinary); Sensors; Silicon; Software; Unsupervised learning; Low latency; Disappearing track trigger; Large Hadron Collider; Unsupervised learning; Fast pattern recognition
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-60319-9

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.