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.
Cited by 2
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- 1 Università degli Studi di Torino / University of Turin [UNITO]
- 2 Sorbonne Université / Sorbonne University
- 3 Heidelberger Institut für Theoretische Studien / Heidelberg Institute for Theoretical Studies [HITS]
- Deutsche Forschungsgemeinschaft / German Research FoundationDeutsche Forschungsgemeinschaft [DFG]
- Horizon 2020 (through Organization: European Commission [EC])