提出两种无需大量预留数据的生成模型放大效应估算方法。
Forecasting Generative Amplification
- 用贝叶斯网络或集成学习估计相空间积分精度来推算放大因子。
- 通过假设检验量化放大效应，避免分辨率损失。
- 适用于高能物理事件生成器，可定位放大发生区域。
生成式网络是提升大型强子对撞机（LHC）模拟速度与精度的理想工具。尤其在生成超出训练数据规模的事件时，理解其统计精度至关重要。本文提出两种互补方法，在无需大规模预留数据集的前提下估算放大因子：平均放大法利用贝叶斯网络或集成学习，从给定相空间体积的积分精度中估计放大因子；微分放大法通过假设检验量化放大效应，且不引入分辨率损失。应用于当前最先进的事件生成器，两种方法均表明，在相空间特定区域已存在放大可能性。
原文摘要 · Abstract (English)
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.
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