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Accurate Surrogate Amplitudes with Calibrated Uncertainties

by Henning Bahl, Nina Elmer, Luigi Favaro, Manuel Haußmann, Tilman Plehn, Ramon Winterhalder

Submission summary

Authors (as registered SciPost users):

Nina Elmer · Luigi Favaro · Tilman Plehn · Ramon Winterhalder

Abstract

Neural networks for LHC physics have to be accurate, reliable, and controlled. Using surrogate loop amplitudes as a use case, we first show how activation functions can be systematically tested with KANs. For reliability and control, we learn uncertainties together with the target amplitude over phase space. Systematic uncertainties can be learned by a heteroscedastic loss, but a comprehensive learned uncertainty requires Bayesian networks or repulsive ensembles. We compute pull distributions to show to what level learned uncertainties are calibrated correctly for cutting-edge precision surrogates.

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• Provide a novel and synergetic link between different research areas.
• Open a new pathway in an existing or a new research direction, with clear potential for multi-pronged follow-up work
• Detail a groundbreaking theoretical/experimental/computational discovery
• Present a breakthrough on a previously-identified and long-standing research stumbling block