A practical guide to unbinned unfolding.
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| Title: | A practical guide to unbinned unfolding. |
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| Authors: | Canelli, Florencia1 (AUTHOR), Cormier, Kyle1 (AUTHOR), Cudd, Andrew2 (AUTHOR), Gillberg, Dag3 (AUTHOR), Huang, Roger G.4 (AUTHOR), Jin, Weijie1 (AUTHOR), Lee, Sookhyun5 (AUTHOR), Mikuni, Vinicius6 (AUTHOR), Miller, Laura7 (AUTHOR), Nachman, Benjamin4,8,9 (AUTHOR), Pan, Jingjing4,10 (AUTHOR), Pani, Tanmay11 (AUTHOR), Pettee, Mariel12 (AUTHOR) mpettee@wisc.edu, Song, Youqi10 (AUTHOR), Acosta, Fernando Torales13 (AUTHOR) |
| Source: | European Physical Journal C -- Particles & Fields. Feb2026, Vol. 86 Issue 2, p1-11. 11p. |
| Subjects: | Deconvolution (Mathematics), Machine learning, Measurement errors, Data analysis, Acquisition of data, Particle physics |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
| Copyright of European Physical Journal C -- Particles & Fields is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 14346044 |
| DOI: | 10.1140/epjc/s10052-025-15265-9 |
