Forecasting Generative Amplification
This addresses the need for reliable statistical precision estimation in high-energy physics simulations without requiring large holdout datasets, though the findings are incremental as amplification is limited to specific regions.
The paper tackled the problem of estimating the statistical precision of generative networks when generating events beyond training dataset size in LHC simulations, presenting two complementary methods that showed amplification is possible in specific phase-space regions but not across entire distributions.
Generative networks are perfect tools to enhance the speed and precision of LHC simulations. It is important to understand their statistical precision, especially when generating events beyond the size of the training dataset. 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 possible in specific regions of phase space, but not yet across the entire distribution.