arXiv · 2601.00950
How to Trust Learned Loop Amplitudes
Abstract
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.
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Henning Bahl, Jens Braun, Gudrun Heinrich, Tilman Plehn, Rebecca Revelli. 2026-01-02. How to Trust Learned Loop Amplitudes. https://arxiv.org/abs/2601.00950
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