Orateur
Javier Mariño Villadamigo
(Institut für Theoretische Physik - University of Heidelberg)
Description
Unfolding is a transformative method that is key to analyze LHC data. More recently, modern machine learning tools enable its implementation in an unbinned and high-dimensional manner. The basic techniques to perform unfolding include event reweighting, direct mapping between distributions and conditional phase space sampling, each of them providing a way to unfold LHC data accounting for all correlations in many dimensions. We describe a set of known and new unfolding methods and tools and discuss their respective advantages. Their combination allows for a systematic comparison and performance control for a given unfolding problem.
Auteur principal
Javier Mariño Villadamigo
(Institut für Theoretische Physik - University of Heidelberg)
