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

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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 data filtering at...

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Detalles Bibliográficos
Autores principales: 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
Lenguaje:eng
Publicado: 2020
Materias:
physics.ins-det
Detectors and Experimental Techniques
cs.LG
Computing and Computers
hep-ex
Particle Physics - Experiment
Acceso en línea:https://dx.doi.org/10.3389/fdata.2020.598927
http://cds.cern.ch/record/2728798

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