Machine Learning-Assisted Unfolding for Neutrino Cross-section Measurements with the OmniFold Technique
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916838815825920 |
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| author | Huang, Roger G. Cudd, Andrew Kawaue, Masaki Kikawa, Tatsuya Nachman, Benjamin Mikuni, Vinicius Wilkinson, Callum |
| author_facet | Huang, Roger G. Cudd, Andrew Kawaue, Masaki Kikawa, Tatsuya Nachman, Benjamin Mikuni, Vinicius Wilkinson, Callum |
| contents | The choice of unfolding method for a cross-section measurement is tightly coupled to the model dependence of the efficiency correction and the overall impact of cross-section modeling uncertainties in the analysis. A key issue is the dimensionality used in unfolding, as the kinematics of all outgoing particles in an event typically affect the reconstruction performance in a neutrino detector. OmniFold is an unfolding method that iteratively reweights a simulated dataset, using machine learning to utilize arbitrarily high-dimensional information, that has previously been applied to proton-proton and proton-electron datasets. This paper demonstrates OmniFold's application to a neutrino cross-section measurement for the first time using a public T2K near detector simulated dataset, comparing its performance with traditional approaches using a mock data study. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06857 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning-Assisted Unfolding for Neutrino Cross-section Measurements with the OmniFold Technique Huang, Roger G. Cudd, Andrew Kawaue, Masaki Kikawa, Tatsuya Nachman, Benjamin Mikuni, Vinicius Wilkinson, Callum Data Analysis, Statistics and Probability High Energy Physics - Experiment High Energy Physics - Phenomenology The choice of unfolding method for a cross-section measurement is tightly coupled to the model dependence of the efficiency correction and the overall impact of cross-section modeling uncertainties in the analysis. A key issue is the dimensionality used in unfolding, as the kinematics of all outgoing particles in an event typically affect the reconstruction performance in a neutrino detector. OmniFold is an unfolding method that iteratively reweights a simulated dataset, using machine learning to utilize arbitrarily high-dimensional information, that has previously been applied to proton-proton and proton-electron datasets. This paper demonstrates OmniFold's application to a neutrino cross-section measurement for the first time using a public T2K near detector simulated dataset, comparing its performance with traditional approaches using a mock data study. |
| title | Machine Learning-Assisted Unfolding for Neutrino Cross-section Measurements with the OmniFold Technique |
| topic | Data Analysis, Statistics and Probability High Energy Physics - Experiment High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2504.06857 |