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arXiv 2102.11524hep-ex
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基于Wasserstein生成对抗网络的强子对撞机数据驱动事件Generator

A Data-driven Event Generator for Hadron Colliders using Wasserstein Generative Adversarial Network

Suyong Choi, Jae Hoon Lim

摘要:

高可靠性的蒙特卡罗事件发生器和探测器模拟程序是高能物理精确测量的重要手段。需要大量的计算资源来产生足够数量的模拟事件。此外,模拟参数必须进行微调,以重现在高能粒子相互作用的情况下,这是不平凡的,在一些物理学的兴趣相空间。在本文中,我们提出了一种新的方法,基于Wasserstein生成对抗网络(WGAN),可以学习真实数据的概率分布。我们的方法是能够在一个非常短的计算时间相比,传统的MC发电机的事件生成。经过训练的WGAN能够以高保真度再现真实数据的形状。

英文摘要:

Highly reliable Monte-Carlo event generators and detector simulation programs are important for the precision measurement in the high energy physics. Huge amounts of computing resources are required to produce a sufficient number of simulated events. Moreover, simulation parameters have to be fine-tuned to reproduce situations in the high energy particle interactions which is not trivial in some phase spaces in physics interests. In this paper, we suggest a new method based on the Wasserstein Generative Adversarial Network (WGAN) that can learn the probability distribution of the real data. Our method is capable of event generation at a very short computing time compared to the traditional MC generators. The trained WGAN is able to reproduce the shape of the real data with high fidelity.

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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