A Practical Guide to Unbinned Unfolding
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918344229126144 |
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| author | Canelli, Florencia Cormier, Kyle Cudd, Andrew Gillberg, Dag Huang, Roger G. Jin, Weijie Lee, Sookhyun Mikuni, Vinicius Miller, Laura Nachman, Benjamin Pan, Jingjing Pani, Tanmay Pettee, Mariel Song, Youqi Torales, Fernando |
| author_facet | Canelli, Florencia Cormier, Kyle Cudd, Andrew Gillberg, Dag Huang, Roger G. Jin, Weijie Lee, Sookhyun Mikuni, Vinicius Miller, Laura Nachman, Benjamin Pan, Jingjing Pani, Tanmay Pettee, Mariel Song, Youqi Torales, Fernando |
| contents | Unfolding, in the context of high-energy particle physics, refers to the process of removing detector distortions in experimental data. The resulting unfolded measurements are straightforward to use for direct comparisons between experiments and a wide variety of theoretical predictions. For decades, popular unfolding strategies were designed to operate on data formatted as one or more binned histograms. In recent years, new strategies have emerged that use machine learning to unfold datasets in an unbinned manner, allowing for higher-dimensional analyses and more flexibility for current and future users of the unfolded data. This guide comprises recommendations and practical considerations from researchers across a number of major particle physics experiments who have recently put these techniques into practice on real data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_09582 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Practical Guide to Unbinned Unfolding Canelli, Florencia Cormier, Kyle Cudd, Andrew Gillberg, Dag Huang, Roger G. Jin, Weijie Lee, Sookhyun Mikuni, Vinicius Miller, Laura Nachman, Benjamin Pan, Jingjing Pani, Tanmay Pettee, Mariel Song, Youqi Torales, Fernando High Energy Physics - Phenomenology High Energy Physics - Experiment Data Analysis, Statistics and Probability Unfolding, in the context of high-energy particle physics, refers to the process of removing detector distortions in experimental data. The resulting unfolded measurements are straightforward to use for direct comparisons between experiments and a wide variety of theoretical predictions. For decades, popular unfolding strategies were designed to operate on data formatted as one or more binned histograms. In recent years, new strategies have emerged that use machine learning to unfold datasets in an unbinned manner, allowing for higher-dimensional analyses and more flexibility for current and future users of the unfolded data. This guide comprises recommendations and practical considerations from researchers across a number of major particle physics experiments who have recently put these techniques into practice on real data. |
| title | A Practical Guide to Unbinned Unfolding |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2507.09582 |