Improving Generative Model-based Unfolding with Schr\"{o}dinger Bridges

Understand

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.