High-Dimensional Unfolding in Large Backgrounds
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911046518702080 |
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| author | Falcão, Alexandre Takacs, Adam |
| author_facet | Falcão, Alexandre Takacs, Adam |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06291 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | High-Dimensional Unfolding in Large Backgrounds Falcão, Alexandre Takacs, Adam High Energy Physics - Phenomenology High Energy Physics - Experiment Nuclear Experiment Nuclear Theory 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. |
| title | High-Dimensional Unfolding in Large Backgrounds |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment Nuclear Experiment Nuclear Theory |
| url | https://arxiv.org/abs/2507.06291 |