Name: IML Machine Learning Working Group: unfolding
Start: 2023-07-27T15:00:00+02:00
End: 2023-07-27T18:00:00+02:00
Location: Virtual

- 15:00 → 15:05
  
  News 5m
  
  Speakers: Anja Butter (Centre National de la Recherche Scientifique (FR)), Fabio Catalano (CERN), Julian Garcia Pardinas (CERN), Lorenzo Moneta (CERN), Michael Kagan (SLAC National Accelerator Laboratory (US)), Dr Pietro Vischia (Universidad de Oviedo and Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA)), Simon Akar (University of Cincinnati (US)), Stefano Carrazza (CERN)
- 15:05 → 15:35
  
  Unfolding and machine learning: introduction 30m
  
  Speaker: Bogdan Malaescu (LPNHE-Paris CNRS/IN2P3 (FR))
  
  IML_UnfoldingIntro_Malaescu.pdf
- 15:35 → 15:45
  
  Question time 10m
- 15:45 → 16:15
  
  Biases and pitfalls in unfolding 30m
  
  Speaker: Igor Volobouev (Texas Tech University (US))
  
  unfolding_pitfalls.pdf
- 16:15 → 16:25
  
  Discussion 10m
- 16:25 → 16:40
  
  ν2-Flows: Fast and improved neutrino reconstruction in multi-neutrino final states with conditional normalizing flows 15m
  
  https://arxiv.org/abs/2307.02405
  Available time may be extended depending on the number of additional contributions.
  
  Speaker: Mr Matthew Leigh (University of Geneva)
  
  nuFlows talk 27-07-22.pdf
- 16:40 → 16:45
  
  Discussion 5m
- 16:45 → 17:00
  
  ML Unfolding based on conditional Invertible Neural Networks using Iterative Training 15m
  
  https://arxiv.org/abs/2212.08674
  
  Speaker: Mathias Josef Backes (Heidelberg University (DE))
  
  IML_unfolding_mBackes.pdf
- 17:00 → 17:05
  
  Discussion 5m