Speaker
Jean-Roch Vlimant
(California Institute of Technology (US))
Description
We study the use of interaction networks to perform tasks related to jet reconstruction. In particular, we consider jet tagging for generic boosted-jet topologies, tagging of large-momentum H$\to$bb decays, and anomalous-jet detection. The achieved performance is compared to state-of-the-art deep learning approaches, based on Convolutional or Recurrent architectures. Unlike these approaches, Interaction Networks allow to reach state-of-the art performance without making assumptions on the underlying data (e.g., detector geometry or resolution, particle ordering criterion, etc.). Given their flexibility, Interaction Networks provide an interesting possibility for deployment-friendly deep learning algorithms for the LHC experiments.
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Primary authors
Maurizio Pierini
(CERN)
Jennifer Ngadiuba
(CERN)
Kinga Anna Wozniak
(University of Vienna (AT))
Olmo Cerri
(California Institute of Technology (US))
Thong Nguyen
(California Institute of Technology (US))
Javier Mauricio Duarte
(Fermi National Accelerator Lab. (US))
Maria Spiropulu
(California Institute of Technology (US))
Eric Moreno
(California Institute of Technology)
Jean-Roch Vlimant
(California Institute of Technology (US))
