论文标题
循环幅度来自精密网络
Loop Amplitudes from Precision Networks
论文作者
论文摘要
评估循环幅度是LHC事件生成的耗时部分。对于用喷气式飞机生产的Di-Photon，我们表明，简单的贝叶斯网络可以学习此类幅度并可靠地对其不确定性进行建模。对贝叶斯网络的增强培训进一步改善了关键相空间区域中的不确定性估计和网络精度。一般而言，贝叶斯网络的增强网络培训使我们能够在类似拟合的网络培训方面移动。
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.
