用于射流模拟的变分自编码器
Variational Autoencoders for Jet Simulation
摘要：
我们介绍了一种新的变分自动编码器（VAE）架构，可以产生逼真的和多样化的高能物理事件。我们提出的模型，利用VAE文献中的几种技术，以模拟高保真射流图像。除了通过各种评估展示模型产生高保真射流图像的能力之外，我们还展示了它控制从潜在空间产生的事件的能力。这可能对其他任务有用，例如喷射标记，在那里我们可以测试喷射标记器如何将VAE生成的事件的背景信号分类。我们通过观察我们的模型产生的不同类型的射流图像的信号效率与背景抑制来测试这个想法。我们在几个方面比较了我们的VAE与生成对抗网络（GAN），最明显的是速度。我们提出的架构最终是一个快速，稳定，易于训练的深度生成模型，展示了VAE在模拟高能物理事件中的潜力。
英文摘要：
We introduce a novel variational autoencoder (VAE) architecture that can generate realistic and diverse high energy physics events. The model we propose utilizes several techniques from VAE literature in order to simulate high fidelity jet images. In addition to demonstrating the model's ability to produce high fidelity jet images through various assessments, we also demonstrate its ability to control the events it generates from the latent space. This can be potentially useful for other tasks such as jet tagging, where we can test how well jet taggers can classify signal from background for events generated by the VAE. We test this idea by seeing the signal efficiency vs background rejection for different types of jet images produced by our model. We compare our VAE with generative adversarial networks (GAN) in several ways, most notably in speed. The architecture we propose is ultimately a fast, stable, and easy-to-train deep generative model that demonstrates the potential of VAEs in simulating high energy physics events.
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。
