论文标题
通过功能增强和转化的生成对抗网络（FAT-GAN）模拟电子蛋白散射事件
Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)
论文作者
论文摘要
我们应用生成的对抗网络（GAN）技术来构建事件发生器，该事件发生器模拟了电子蛋白散射中的粒子产生，该粒子散射中没有关于潜在粒子动力学的理论假设。有效训练GAN事件模拟器的困难在于学习颗粒物理特性的分布的复杂模式。我们开发了一个gan，该gan从粒子矩形中选择一组转换的特征，该特征可以由发电机轻松生成，并使用它们来产生一组增强特征，以提高歧视器的灵敏度。新的功能增强和转化的GAN（FAT-GAN）能够忠实地重现包容性电子散射中最终态电子动量的分布，而无需从基于域的理论假设中得出的输入。开发的技术可以在促进现有和未来加速器设施的科学（例如电子离子对撞机）的科学方面发挥重要作用。
We apply generative adversarial network (GAN) technology to build an event generator that simulates particle production in electron-proton scattering that is free of theoretical assumptions about underlying particle dynamics. The difficulty of efficiently training a GAN event simulator lies in learning the complicated patterns of the distributions of the particles physical properties. We develop a GAN that selects a set of transformed features from particle momenta that can be generated easily by the generator, and uses these to produce a set of augmented features that improve the sensitivity of the discriminator. The new Feature-Augmented and Transformed GAN (FAT-GAN) is able to faithfully reproduce the distribution of final state electron momenta in inclusive electron scattering, without the need for input derived from domain-based theoretical assumptions. The developed technology can play a significant role in boosting the science of existing and future accelerator facilities, such as the Electron-Ion Collider.
