Amplitude Surrogates for Multi-Jet Processes
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909962133831680 |
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| author | Beccatini, Luca Maltoni, Fabio Mattelaer, Olivier Winterhalder, Ramon |
| author_facet | Beccatini, Luca Maltoni, Fabio Mattelaer, Olivier Winterhalder, Ramon |
| contents | Accurate and efficient amplitude predictions are essential for precision studies of multi-jet processes at the LHC. We introduce a novel neural network architecture that predicts multi-jet amplitudes by leveraging the Catani-Seymour factorization scheme and related lower-jet amplitudes, requiring the network to learn only a correction factor. This hybrid approach combines theoretical factorization with a data-driven ansatz, enabling fast and scalable amplitude predictions. Our networks also estimate the accuracy of each prediction, allowing us to selectively use results that meet a predefined accuracy threshold. In the context of leading-order event generation, this approach achieves speed-up factors of up to 20 while maintaining all observables at the percent-level accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11036 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Amplitude Surrogates for Multi-Jet Processes Beccatini, Luca Maltoni, Fabio Mattelaer, Olivier Winterhalder, Ramon High Energy Physics - Phenomenology Accurate and efficient amplitude predictions are essential for precision studies of multi-jet processes at the LHC. We introduce a novel neural network architecture that predicts multi-jet amplitudes by leveraging the Catani-Seymour factorization scheme and related lower-jet amplitudes, requiring the network to learn only a correction factor. This hybrid approach combines theoretical factorization with a data-driven ansatz, enabling fast and scalable amplitude predictions. Our networks also estimate the accuracy of each prediction, allowing us to selectively use results that meet a predefined accuracy threshold. In the context of leading-order event generation, this approach achieves speed-up factors of up to 20 while maintaining all observables at the percent-level accuracy. |
| title | Amplitude Surrogates for Multi-Jet Processes |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2512.11036 |