Forecasting generative amplification

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Abstract
Generative networks are perfect tools to enhance the speed and precision of LHC simulations. Especially when generating events beyond the size of the training dataset, it is important to understand their statistical precision. We present two complementary methods to estimate the amplification factor without large holdout datasets. Averaging amplification uses Bayesian networks or ensembling to estimate amplification from the precision of integrals over given phase-space volumes. Differential amplification uses hypothesis testing to quantify amplification without any resolution loss. Applied to state-of-the-art event generators, both methods indicate that amplification is already possible in specific regions of phase space.
Funding Information
  • Baden-Württemberg Stiftung (BWST_IF2020-010)
  • Bundesministerium für Forschung, Technologie und Raumfahrt (05D2022)
  • Deutsche Forschungsgemeinschaft (EXC 2181/1 - 390900948)
  • Deutsche Forschungsgemeinschaft (396021762 — TRR 257)
  • U.S. Department of Energy (DE-AC02-05CH11231)

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