Ultrafast jet classification at the HL-LHC

• Published in: Machine learning: science and technology Vol. 5; no. 3
• Main Authors: Odagiu, Patrick, Que, Zhiqiang, Duarte, Javier, Haller, Johannes, Kasieczka, Gregor, Lobanov, Artur, Loncar, Vladimir, Luk, Wayne, Ngadiuba, Jennifer, Pierini, Maurizio, Rincke, Philipp, Seksaria, Arpita, Summers, Sioni, Sznajder, Andre, Tapper, Alexander, Årrestad, Thea K.
• Format: Journal Article
• Language: English
• Published: United States IOP Publishing 18.07.2024
• Subjects: INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; PHYSICS OF ELEMENTARY PARTICLES AND FIELDS
• ISSN: 2632-2153; 2632-2153

Three machine learning models are used to perform jet origin classification. These models are optimized for deployment on a field-programmable gate array device. In this context, we demonstrate how latency and resource consumption scale with the input size and choice of algorithm. Moreover, the models proposed here are designed to work on the type of data and under the foreseen conditions at the CERN large hadron collider during its high-luminosity phase. Through quantization-aware training and efficient synthetization for a specific field programmable gate array, we show that $\mathcal{O}(100)$ ns inference of complex architectures such as Deep Sets and Interaction Networks is feasible at a relatively low computational resource cost.