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arXiv · 2509.00155

Amplitude Uncertainties Everywhere All at Once

Henning Bahl·Nina Elmer·Tilman Plehn·Ramon Winterhalder
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Abstract

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.

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Henning Bahl, Nina Elmer, Tilman Plehn, Ramon Winterhalder. 2025-08-29. Amplitude Uncertainties Everywhere All at Once. https://doi.org/10.21468/scipostphys.20.3.083

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