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

• URL: http://arxiv.org/abs/2308.12351v2
• Date: Fri, 22 Sep 2023 17:28:21 GMT
• Title: Improving Generative Model-based Unfolding with Schr\"{o}dinger Bridges
• Authors: Sascha Diefenbacher, Guan-Horng Liu, Vinicius Mikuni, Benjamin Nachman, and Weili Nie
• Abstract summary: Machine learning-based unfolding has enabled unbinned and high-dimensional differential cross section measurements. 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. We show that SBUnfold achieves excellent performance compared to state of the art methods on a synthetic Z+jets dataset.
• Score: 14.989614554242229
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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