Machine Learning-based Unfolding for Cross Section Measurements in the Presence of Nuisance Parameters

• URL: http://arxiv.org/abs/2512.07074v1
• Date: Mon, 08 Dec 2025 01:21:34 GMT
• Title: Machine Learning-based Unfolding for Cross Section Measurements in the Presence of Nuisance Parameters
• Authors: Huanbiao Zhu, Krish Desai, Mikael Kuusela, Vinicius Mikuni, Benjamin Nachman, Larry Wasserman,
• Abstract summary: In particle physics, the distortions they introduce are often known only implicitly through simulations of the detector.<n>Modern machine learning has enabled efficient simulation-based approaches for unfolding high-dimensional data.<n>We show how to extend machine learning-based unfolding to incorporate nuisance parameters.
• Score: 0.15325041686671656
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Statistically correcting measured cross sections for detector effects is an important step across many applications. In particle physics, this inverse problem is known as \textit{unfolding}. In cases with complex instruments, the distortions they introduce are often known only implicitly through simulations of the detector. Modern machine learning has enabled efficient simulation-based approaches for unfolding high-dimensional data. Among these, one of the first methods successfully deployed on experimental data is the \textsc{OmniFold} algorithm, a classifier-based Expectation-Maximization procedure. In practice, however, the forward model is only approximately specified, and the corresponding uncertainty is encoded through nuisance parameters. Building on the well-studied \textsc{OmniFold} algorithm, we show how to extend machine learning-based unfolding to incorporate nuisance parameters. Our new algorithm, called Profile \textsc{OmniFold}, is demonstrated using a Gaussian example as well as a particle physics case study using simulated data from the CMS Experiment at the Large Hadron Collider.

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