[Submitted on 8 Jul 2025] (cross-list from hep-ph)
High-Dimensional Unfolding in Large Backgrounds
Alexandre Falcão🇳🇴 · Adam Takacs🇩🇪
We propose new methodologies in multi-dimensional unfolding in dense environments, and show that incorporating auxiliary observables can significantly improve performance. Our approach builds on the ML-based OmniFold algorithm, which we extend to account for background, detector acceptance, efficiency, and uncertainties, enabling its application in high-luminosity and heavy-ion collision settings. We derive this algorithm and demonstrate its mathematical and numerical equivalence to expectation-maximization and Iterative Bayesian Unfolding (IBU). We illustrate our method with a realistic jet substructure analysis incorporating both large background and detector simulation. Our analysis includes up to 18 observables, leading to significantly improved performance in the unfolding. We propose a method that integrates calibration and unfolding into a single, consistent framework, and demonstrate enhanced performance relative to traditional methods. These developments lay the groundwork for robust, high-dimensional, ML-based unfolding and calibration in complex collider environments across a wide range of analyses.
- Comments:
- Accepted version, 30 pages, 9 figures, datasets and codes at https://github.com/OmniFoldHI/OmniFoldHI
- Subjects:
- High Energy Physics — Phenomenology (hep-ph); High Energy Physics — Experiment (hep-ex); Nuclear Experiment (nucl-ex); Nuclear Theory (nucl-th)
- arXiv:
- 2507.06291 [pdf]