Orateur
Nathan Huetsch
(Heidelberg University)
Description
Machine learning enables unbinned, highly-differential cross section measurements. A recent idea uses generative models to morph the measured distribution into the unfolded distribution. We show how to extend two morphing techniques, Schrödinger Bridges and Direct Diffusion, in order to ensure that the models learn the correct conditional probabilities. This brings distribution mapping to a similar level of accuracy as the state-of-the-art conditional generative unfolding methods. Numerical results are presented with a standard benchmark dataset of single jet substructure as well as for a new dataset describing a 22-dimensional phase space of Z + 2-jets.
Authors
Anja Butter
(LPNHE)
Benjamin Nachman
(LBNL)
Nathan Huetsch
(Heidelberg University)
Sascha Diefenbacher
(Lawrence Berkeley National Laboratory)
Sofia Palacios Schweitzer
(ITP, Heidelberg University)
Tilman Plehn
(Heidelberg University)
Vinicius Mikuni
(Lawrence Berkeley National Laboratory)
