Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics

Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics

<jats:p>Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering. An important domain for the application of these networks is the FGPA-based first layer of real-time dat...

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Bibliographic Details
Main Authors: Iiyama, Yutaro, Cerminara, Gianluca, Gupta, Abhijay, Kieseler, Jan, Loncar, Vladimir, Pierini, Maurizio, Qasim, Shah Rukh, Rieger, Marcel, Summers, Sioni, Van Onsem, Gerrit, Wozniak, Kinga Anna, Ngadiuba, Jennifer, Di Guglielmo, Giuseppe, Duarte, Javier, Harris, Philip, Rankin, Dylan, Jindariani, Sergo, Liu, Mia, Pedro, Kevin, Tran, Nhan, Kreinar, Edward, Wu, Zhenbin
Other Authors: Massachusetts Institute of Technology. Laboratory for Nuclear Science
Format: Article
Language:English
Published: Frontiers Media SA 2022
Online Access:https://hdl.handle.net/1721.1/142102

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