News

• Julian Garcia Pardinas (CERN)
• Anja Butter (Centre National de la Recherche Scientifique (FR))
• Fabio Catalano (CERN)
• Lorenzo Moneta (CERN)
• Michael Kagan (SLAC National Accelerator Laboratory (US))
• Pietro Vischia (Universidad de Oviedo and Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA))
• Simon Akar (University of Cincinnati (US))
• Stefano Carrazza (CERN)

Unfolding and machine learning: introduction

• Bogdan Malaescu (LPNHE-Paris CNRS/IN2P3 (FR))

Biases and pitfalls in unfolding

• Igor Volobouev (Texas Tech University (US))

ν2-Flows: Fast and improved neutrino reconstruction in multi-neutrino final states with conditional normalizing flows

• Matthew Leigh (University of Geneva)

https://arxiv.org/abs/2307.02405 Available time may be extended depending on the number of additional contributions.

ML Unfolding based on conditional Invertible Neural Networks using Iterative Training

• Mathias Josef Backes (Heidelberg University (DE))

https://arxiv.org/abs/2212.08674