论文标题
CAL填充 - 生成量热模型的力量
Calomplification -- The Power of Generative Calorimeter Models
论文作者
论文摘要
由经典模拟的高计算成本激励，机器学习的生成模型在粒子物理和其他地方可能非常有用。当替代模型可以有效地学习潜在的分布时，它们变得特别有吸引力，从而使生成的样品的表现优于有限尺寸的训练样本。对于简单的高斯模型，已经观察到了这种ganplification。对于物理模拟，我们显示出相同的效果，特别是电磁骑热仪中的光子阵雨。
Motivated by the high computational costs of classical simulations, machine-learned generative models can be extremely useful in particle physics and elsewhere. They become especially attractive when surrogate models can efficiently learn the underlying distribution, such that a generated sample outperforms a training sample of limited size. This kind of GANplification has been observed for simple Gaussian models. We show the same effect for a physics simulation, specifically photon showers in an electromagnetic calorimeter.
