论文标题
喷气模拟的变异自动编码器
Variational Autoencoders for Jet Simulation
论文作者
论文摘要
我们介绍了一种新型的变异自动编码器（VAE）结构，该体系结构可以产生现实和多样化的高能量物理事件。我们提出的模型利用了VAE文献中的几种技术来模拟高保真喷射图像。除了展示该模型通过各种评估产生高保真喷气图像的能力外，我们还展示了其控制其从潜在空间产生的事件的能力。这对于其他任务（例如Jet Taging）可能很有用，我们可以在其中测试Jet标记器可以从VAE生成的事件中对信号进行分类的很好。我们通过查看信号效率与背景排斥的不同类型的喷气图像来测试这个想法。我们以几种方式将VAE与生成对抗网络（GAN）进行比较，最著名的是速度。我们提出的结构最终是一个快速，稳定且易于培训的深层生成模型，它证明了VAE在模拟高能物理事件中的潜力。
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.
