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  1. We provide details on the implementation of a machine-learning based particle flow algorithm for CMS. The standard particle flow algorithm reconstructs stable particles based on calorimeter clusters and tracks to provide a global event reconstruction that exploits the combined information of multiple detector subsystems, leading to strong improvements for quantities such as jets and missing ...
    • Physics

      Massimo Tessarotto (Department of Mathematics, Informatics and Geosciences, University of Trieste, Trieste, Italy, Research Center for Theoretical Physics and Astrophysics, Institute of Physics, Silesian University in Opava, Opava, Czech Republic), Claudio Asci (Department of Mathematics, Informatics and Geosciences, University of Trieste, Trieste, Italy), Alessandro Soranzo (Department of ...

  2. Machine Learning for Particle Flow Reconstruction at CMS Joosep Pata1,∗, Javier Duarte2, Farouk Mokhtar2, Eric Wulff3, Jieun Yoo4, Jean-Roch Vlimant5, Maurizio Pierini3, Maria Girone3 (on behalf of the CMS Collaboration) 1NICPB, R¨avala pst 10, 10143 Tallinn, Estonia 2University of California San Diego, La Jolla, CA 92093, USA
  3. The machine-learned PF model reconstructs particle candidates based on the full list of tracks and calorimeter clusters in the event. For validation, we determine the physics performance directly in the CMS software framework when the proposed algorithm is interfaced with the offline reconstruction of jets and missing transverse energy.
  4. researchgate.net

    Feb 1, 2023We provide details on the implementation of a machine-learning based particle flow algorithm for CMS. The standard particle flow algorithm reconstructs stable particles based on calorimeter ...
  5. indico.cern.ch

    Progress towards an improved particle-flow algorithm at CMS with machine learning Joosep Pata1, Farouk Mokhtar2, Javier Duarte2, Eric Wulff3, Maurizio Pierini3, Jean-Roch Vlimant4 1National Institute of Chemical Physics and Biophysics, 2UC San Diego, 3CERN, 4California Institute of Technology • Compare the usefulness of additional event-level loss terms
  6. semanticscholar.org

    A possible evolution of particle flow towards heterogeneous computing platforms such as GPUs using a graph neural network is studied and the machine-learned PF model reconstructs particle candidates based on the full list of tracks and calorimeter clusters in the event. We provide details on the implementation of a machine-learning based particle flow algorithm for CMS.
  7. indico.cern.ch

    Explaining machine-learned particle-flow reconstruction 5th Inter-experiment Machine Learning Workshop. ... • Apply LRP to an MLPF model trained on "gen-level" targets in CMS. Thanks! Feel free to email: fmokhtar@ucsd.edu 17. Backup 18.
  8. iopscience.iop.org

    Particleow reconstruction for the CMS Phase-II Level-1 Trigger ... assisted in many cases by machine learning to benefit from the complete particle-level event record. We present the logic of these algorithms, ... Total resource and latency requirements for the Layer-1 Particle Flow prototype firmware. VU9P DSPs FFs BRAMs LUTs Latency
  9. Mar 30, 2023The particle-flow (PF) algorithm, which infers particles based on tracks and calorimeter clusters, is of central importance to event reconstruction in the CMS experiment at the CERN LHC, and has been a focus of development in light of planned Phase-2 running conditions with an increased pileup and detector granularity. In recent years, the machine learned particle-flow (MLPF) algorithm, a ...

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