How to Trust Learned Loop Amplitudes
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912800923713536 |
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| author | Bahl, Henning Braun, Jens Heinrich, Gudrun Plehn, Tilman Revelli, Rebecca |
| author_facet | Bahl, Henning Braun, Jens Heinrich, Gudrun Plehn, Tilman Revelli, Rebecca |
| contents | Higher-order theory predictions are crucial for the precision LHC program, but the time-consuming amplitude evaluation challenges the corresponding Monte-Carlo simulations. Machine-learned amplitude surrogates can resolve this problem, if we can guarantee their precision over the entire phase space. First, we show that our surrogates provide a calibrated learned uncertainty, even for non-Gaussian systematics; second, we describe how less accurate phase space regions can be identified; third, we demonstrate how the precision in these regions can be improved reliably. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_00950 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | How to Trust Learned Loop Amplitudes Bahl, Henning Braun, Jens Heinrich, Gudrun Plehn, Tilman Revelli, Rebecca High Energy Physics - Phenomenology Higher-order theory predictions are crucial for the precision LHC program, but the time-consuming amplitude evaluation challenges the corresponding Monte-Carlo simulations. Machine-learned amplitude surrogates can resolve this problem, if we can guarantee their precision over the entire phase space. First, we show that our surrogates provide a calibrated learned uncertainty, even for non-Gaussian systematics; second, we describe how less accurate phase space regions can be identified; third, we demonstrate how the precision in these regions can be improved reliably. |
| title | How to Trust Learned Loop Amplitudes |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2601.00950 |