Speaker
Theo Heimel
(UCLouvain)
Description
Machine learning methods enable unbinned and full-dimensional unfolding. However, existing approaches, both classifier-based and generative, suffer from prior dependence. We propose a new method for ML-based unfolding that is completely prior independent and infers the unfolded distribution in a fully frequentist manner. Using several benchmark datasets, we demonstrate that the method can infer unfolded distributions to percent-level precision.
Authors
Anja Butter
(Centre National de la Recherche Scientifique (FR))
Michael Kagan
(SLAC National Accelerator Laboratory (US))
Nathan Huetsch
(Heidelberg University, ITP Heidelberg)
Theo Heimel
(UCLouvain)
Tilman Plehn
