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

- 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)
  
  IML_meeting_news_111022.pdf
- 2
  
  Normalizing Flows for Differentiable Expectation Values
  
  Speaker: Thorsten Glüsenkamp (Universität Erlangen-Nürnberg)
  
  nfs_cern.pdf
- 15:30
  
  Question time
- 3
  
  Normalising Flows for Particle Cloud Generation
  
  Speaker: Benno Kach (Deutsches Elektronen-Synchrotron (DE))
  
  IML_Meeting16_9.pdf
- 16:00
  
  Question time
- 4
  
  Normalising Flows for Calorimeter Simulation
  
  Speaker: Imahn Shekhzadeh (Haute école de gestion de Genève)
  
  iml_workshop_imahn_shekhzadeh_11p10p22_pres.pdf
- 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)
  
  INN_for_MEM_Theo_Heimel.pdf
- 17:00
  
  Question time

IML Machine Learning Working Group

40/S2-C01 - Salle Curie

CERN