  Machine learning-based unfolding has enabled unbinned and high-dimensional
differential cross section measurements. Two main approaches have emerged in
this research area: one based on discriminative models and one based on
generative models. The main advantage of discriminative models is that they
learn a small correction to a starting simulation while generative models scale
better to regions of phase space with little data. We propose to use
Schroedinger Bridges and diffusion models to create SBUnfold, an unfolding
approach that combines the strengths of both discriminative and generative
models. The key feature of SBUnfold is that its generative model maps one set
of events into another without having to go through a known probability density
as is the case for normalizing flows and standard diffusion models. We show
that SBUnfold achieves excellent performance compared to state of the art
methods on a synthetic Z+jets dataset.
