Name: IML Machine Learning Working Group
Start: 2022-10-11T15:00:00+02:00
End: 2022-10-11T18:00:00+02:00
Location: CERN

- 15:00 → 15:05
  
  News 5m
  
  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)
  
  IML_meeting_news_111022.pdf
- 15:05 → 15:30
  
  Normalizing Flows for Differentiable Expectation Values 25m
  
  Speaker: Thorsten Glüsenkamp (Universität Erlangen-Nürnberg)
  
  nfs_cern.pdf
- 15:30 → 15:35
  
  Question time 5m
- 15:35 → 16:00
  
  Normalising Flows for Particle Cloud Generation 25m
  
  Speaker: Benno Kach (Deutsches Elektronen-Synchrotron (DE))
  
  IML_Meeting16_9.pdf
- 16:00 → 16:05
  
  Question time 5m
- 16:05 → 16:30
  
  Normalising Flows for Calorimeter Simulation 25m
  
  Speaker: Imahn Shekhzadeh (Haute école de gestion de Genève)
  
  iml_workshop_imahn_shekhzadeh_11p10p22_pres.pdf
- 16:30 → 16:35
  
  Question time 5m
- 16:35 → 17:00
  
  Two Invertible Networks for the Matrix Element Method 25m
  
  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)
  
  INN_for_MEM_Theo_Heimel.pdf
- 17:00 → 17:05
  
  Question time 5m

IML Machine Learning Working Group

40/S2-C01 - Salle Curie

CERN