Affiliation:
1. University of Turin
2. Heidelberg Institute for Theoretical Studies
3. Sorbonne University
Abstract
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.
Funder
Deutsche Forschungsgemeinschaft
Horizon 2020
Subject
Statistical and Nonlinear Physics,Atomic and Molecular Physics, and Optics,Nuclear and High Energy Physics,Condensed Matter Physics
Cited by
4 articles.
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