Ultrafast jet classification at the HL-LHC

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.

doi:10.1088/2632-2153/ad5f10

Title: Ultrafast jet classification at the HL-LHC

Journal Article · 18 July 2024 · Machine Learning: Science and Technology

DOI: https://doi.org/10.1088/2632-2153/ad5f10 · OSTI ID:2404436

; Que, Zhiqiang; Duarte, Javier; Haller, Johannes; Kasieczka, Gregor; Lobanov, Artur; Loncar, Vladimir; Luk, Wayne;

;

; Seksaria, Arpita; Summers, Sioni;

; Tapper, Alexander;

Abstract 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 $O (100)$ ns inference of complex architectures such as Deep Sets and Interaction Networks is feasible at a relatively low computational resource cost.

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Sponsoring Organization:: USDOE Office of Science (SC), High Energy Physics (HEP)

Grant/Contract Number:: SC0021187; AC02-07CH11359

OSTI ID:: 2404436

Journal Information:: Machine Learning: Science and Technology, Journal Name: Machine Learning: Science and Technology Journal Issue: 3 Vol. 5; ISSN 2632-2153

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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