Ultrafast jet classification at the HL-LHC
File(s)
Published version
Author(s)
Type
Journal Article
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.
Date Issued
2024-09
Date Acceptance
2024-07-03
Citation
Machine Learning: Science and Technology, 2024, 5 (3)
ISSN
2632-2153
Publisher
IOP Publishing
Journal / Book Title
Machine Learning: Science and Technology
Volume
5
Issue
3
Copyright Statement
© 2024 The Author(s). Published by IOP Publishing Ltd Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
License URL
Identifier
http://dx.doi.org/10.1088/2632-2153/ad5f10
Publication Status
Published
Article Number
035017
Date Publish Online
2024-07-18