IML Machine Learning Working Group

Europe/Zurich
40/S2-C01 - Salle Curie (CERN)

40/S2-C01 - Salle Curie

CERN

115
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Description

Topic: Optimal transport and invertible algorithms

Videoconference
IML Machine Learning Working Group
Zoom Meeting ID
96543252431
Host
Simon Akar
Alternative hosts
Riccardo Torre, Fabio Catalano
Useful links
Join via phone
Zoom URL
    • 1
      News
      Speakers: Anja Butter, Fabio Catalano (University and INFN Torino (IT)), Lorenzo Moneta (CERN), Michael Kagan (SLAC National Accelerator Laboratory (US)), Dr Pietro Vischia (Universite Catholique de Louvain (UCL) (BE)), Simon Akar (University of Cincinnati (US)), Stefano Carrazza (CERN)
    • 2
      Normalizing Flows for Differentiable Expectation Values
      Speaker: Thorsten Glüsenkamp (Universität Erlangen-Nürnberg)
    • 15:30
      Question time
    • 3
      Normalising Flows for Particle Cloud Generation
      Speaker: Benno Kach (Deutsches Elektronen-Synchrotron (DE))
    • 16:00
      Question time
    • 4
      Normalising Flows for Calorimeter Simulation
      Speaker: Imahn Shekhzadeh (Haute école de gestion de Genève)
    • 16:30
      Question time
    • 5
      Two Invertible Networks for the Matrix Element Method

      The matrix element method is widely considered the ultimate LHC inference tool for small event numbers, but computationally expensive. We show how a combination of two conditional generative neural networks encodes the QCD radiation and detector effects without any simplifying assumptions and allows us to efficiently compute the likelihood for individual hard-scattering events. We illustrate our approach for the CP-violating phase of the top Yukawa coupling in associated Higgs and single-top production. The limiting factor for the precision of our approach is jet combinatorics.

      Speaker: Theo Heimel (Heidelberg University)
    • 17:00
      Question time