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.
@article{arxiv.2601.00950,
title = {How to Trust Learned Loop Amplitudes},
author = {Henning Bahl and Jens Braun and Gudrun Heinrich and Tilman Plehn and Rebecca Revelli},
journal= {arXiv preprint arXiv:2601.00950},
year = {2026}
}