解码光子：BIB-AE生成网络的潜在空间中的物理学
Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network
摘要：
由于未来对撞机实验的数据收集能力不断提高，计算资源有限，因此使用生成神经网络快速模拟对撞机事件的兴趣正在增长。在我们之前的研究中，用于在高粒度量热计中产生光子簇射的有界信息瓶颈自动编码器（BIB-AE）架构显示了各种全局差分簇射分布的高精度建模。在这项工作中，我们研究了BIB-AE如何在其潜在空间中编码这些物理信息。我们对这种编码的理解使我们能够提出进一步优化生成性能的方法，例如，通过改变潜在空间采样或建议对超参数进行特定更改。特别是，我们改进了沿粒子入射轴的簇射形状的建模。
英文摘要：
Given the increasing data collection capabilities and limited computing resources of future collider experiments, interest in using generative neural networks for the fast simulation of collider events is growing. In our previous study, the Bounded Information Bottleneck Autoencoder (BIB-AE) architecture for generating photon showers in a high-granularity calorimeter showed a high accuracy modeling of various global differential shower distributions. In this work, we investigate how the BIB-AE encodes this physics information in its latent space. Our understanding of this encoding allows us to propose methods to optimize the generation performance further, for example, by altering latent space sampling or by suggesting specific changes to hyperparameters. In particular, we improve the modeling of the shower shape along the particle incident axis.
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