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Comp F3 White Paper planning

US/Pacific
Description
    • 1
      Introduction/WP schedule
    • 2
      WP update
      Speaker: Brian Nord (Fermilab)
    • 3
      GNN/Instance Segmentation WPs
      Speaker: Savannah Thais (Princeton University)
    • 4
      WP on Detector Simulation
      Speaker: Mikuni Vinicius (LBNL)
    • 5
      Symmetry Group Equivariant Architectures
      Speaker: Mariel Pettee (Yale University)
    • 6
      Data Science/ML education in HEP
      Speaker: Mark Neubauer (University of Illinois at Urbana-Champaign)
    • 7
      Self-Driving System for Physics
      Speaker: Cecilia Tosciri
    • 8
      Applications of Machine Learning to Lattice Quantum Field Theory
      Speakers: Daniel Hackett (University of Colorado, Boulder), Daniel Hackett
    • 9
      Model Independent Searches for New Physics using Machine Learning

      Our main idea is to assess the performance of different semi-supervised or unsupervised methods using different benchmark data sets. Besides commonly explored multi-jet signatures, we also want to consider multi-lepton processes. In order to compare the performance of different methods, we also consider several types of data input, e.g., kinematic variables and image data sets. This will provide a comparison of different methods on the same footing.

      Speaker: Charanjit Kaur
    • 10
      Community Needs, Tools and Resources
      Speaker: Dylan Rankin (Massachusetts Institute of Technology)