Speaker
Javier Mariño Villadamigo
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.
Primary authors
Anja Butter
(Centre National de la Recherche Scientifique (FR))
Ben Nachman
(Lawrence Berkeley National Lab. (US))
Javier Mariño Villadamigo
Nathan Huetsch
(Heidelberg University, ITP Heidelberg)
Sascha Diefenbacher
(Lawrence Berkeley National Lab. (US))
Theo Heimel
(Heidelberg University)
Tilman Plehn
Vinicius Massami Mikuni
(Lawrence Berkeley National Lab. (US))
