Event-by-event comparison between machine-learning- and transfer-matrix-based unfolding methods
The unfolding of detector effects is a key aspect of comparing experimental data with theoretical predictions. In recent years, different Machine-Learning methods have been developed to provide novel features, e.g. high dimensionality or a probabilistic single-event unfolding based on generative neu...
Gespeichert in:
| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
3 August 2024
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| In: |
The European physical journal. C, Particles and fields
Year: 2024, Jahrgang: 84, Heft: 8, Pages: 1-23 |
| ISSN: | 1434-6052 |
| DOI: | 10.1140/epjc/s10052-024-13136-3 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1140/epjc/s10052-024-13136-3
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| Verfasserangaben: | Mathias Backes, Anja Butter, Monica Dunford, Bogdan Malaescu |
MARC
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| 520 | |a The unfolding of detector effects is a key aspect of comparing experimental data with theoretical predictions. In recent years, different Machine-Learning methods have been developed to provide novel features, e.g. high dimensionality or a probabilistic single-event unfolding based on generative neural networks. Traditionally, many analyses unfold detector effects using transfer-matrix-based algorithms, which are well established in low-dimensional unfolding. They yield an unfolded distribution of the total spectrum, together with its covariance matrix. This paper proposes a method to obtain probabilistic single-event unfolded distributions, together with their uncertainties and correlations, for the transfer-matrix-based unfolding. The algorithm is first validated on a toy model and then applied to pseudo-data for the $$pp\rightarrow Z\gamma \gamma $$process. In both examples the performance is compared to the Machine-Learning-based single-event unfolding using an iterative approach with conditional invertible neural networks (IcINN). | ||
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