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arXiv 2202.07352hep-phhep-ex
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热量补充--生成量热计模型的力量

Calomplification -- The Power of Generative Calorimeter Models

Sebastian Bieringer, Anja Butter, Sascha Diefenbacher, Engin Eren, Frank Gaede, Daniel Hundhausen, Gregor Kasieczka, Benjamin Nachman, Tilman Plehn, Mathias Trabs

摘要:

由于经典模拟的高计算成本,机器学习生成模型在粒子物理学和其他领域非常有用。当代理模型可以有效地学习底层分布时,它们变得特别有吸引力,使得生成的样本优于有限大小的训练样本。对于简单的高斯模型,已经观察到这种GANplification。我们显示了相同的效果的物理模拟,特别是在电磁量能器的光子簇射。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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