Amplitude Uncertainties Everywhere All at Once
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866908946623627264 |
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| author | Bahl, Henning Elmer, Nina Plehn, Tilman Winterhalder, Ramon |
| author_facet | Bahl, Henning Elmer, Nina Plehn, Tilman Winterhalder, Ramon |
| contents | Ultra-fast, precise, and controlled amplitude surrogates are essential for future LHC event generation. First, we investigate the noise reduction and biases of network ensembles and outline a new method to learn well-calibrated systematic uncertainties for them. We also establish evidential regression as a sampling-free method for uncertainty quantification. In a second part, we tackle localized disturbances for amplitude regression and demonstrate that learned uncertainties from Bayesian networks, ensembles, and evidential regression all identify numerical noise or gaps in the training data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00155 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Amplitude Uncertainties Everywhere All at Once Bahl, Henning Elmer, Nina Plehn, Tilman Winterhalder, Ramon High Energy Physics - Phenomenology Ultra-fast, precise, and controlled amplitude surrogates are essential for future LHC event generation. First, we investigate the noise reduction and biases of network ensembles and outline a new method to learn well-calibrated systematic uncertainties for them. We also establish evidential regression as a sampling-free method for uncertainty quantification. In a second part, we tackle localized disturbances for amplitude regression and demonstrate that learned uncertainties from Bayesian networks, ensembles, and evidential regression all identify numerical noise or gaps in the training data. |
| title | Amplitude Uncertainties Everywhere All at Once |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2509.00155 |