Loop Amplitudes from Precision Networks

Understand

Evaluating loop amplitudes is a time-consuming part of LHC event generation.

• For di-photon production with jets we show that simple, Bayesian networks can learn such amplitudes and model their uncertainties reliably.
• A boosted training of the Bayesian network further improves the uncertainty estimate and the network precision in critical phase space regions.
• In general, boosted network training of Bayesian networks allows us to move between fit-like and interpolation-like regimes of network training.