Improving generative model-based unfolding with Schrödinger bridges

Journal Article · 11 April 2024 · Physical Review. D.

DOI: https://doi.org/10.1103/PhysRevD.109.076011 · OSTI ID:2336630

Diefenbacher, Sascha; Liu, Guan-Horng; Mikuni, Vinicius ; Nachman, Benjamin ; Nie, Weili

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 Schrödinger bridges and diffusion models to create , an unfolding approach that combines the strengths of both discriminative and generative models. The key feature of 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 achieves excellent performance compared to state of the art methods on a synthetic $Z+\text{jets}$ dataset. Published by the American Physical Society 2024

• Sponsoring Organization: USDOE
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 2336630
• Journal Information: Physical Review. D., Journal Name: Physical Review. D. Journal Issue: 7 Vol. 109; ISSN PRVDAQ; ISSN 2470-0010
• Publisher: American Physical SocietyCopyright Statement
• Country of Publication: United States
• Language: English