论文标题
高能物理模拟的生成对抗网络模型的高参数优化
Hyperparameter Optimization of Generative Adversarial Network Models for High-Energy Physics Simulations
论文作者
论文摘要
生成对抗网络（GAN）是一种功能强大且灵活的工具，可以通过学习来生成高保真性综合数据。它在模拟高能量物理（HEP）中的事件中看到了许多应用，包括模拟检测器响应和物理事件。但是，众所周知，训练甘斯（Gans）很难，并且更优化其超参数。通常，它需要进行许多试用培训尝试，以强迫稳定的培训并达到合理的忠诚度。必须进行重要的调整工作，以实现物理分析所需的准确性。这项工作使用物理不可或缺的和高性能计算机友好的超参数优化工具Hyppo来优化和检查GAN对两个独立HEP数据集的GAN超参数的敏感性。这项工作为有效调整大型强子撞机数据的有效调整gan提供了第一个见解。我们表明，鉴于适当的高参数调整，我们可以找到提供所需数量的高质量近似值的gan。我们还提供了如何使用Hyppo中的分析工具进行GAN体系结构调整的指南。
The Generative Adversarial Network (GAN) is a powerful and flexible tool that can generate high-fidelity synthesized data by learning. It has seen many applications in simulating events in High Energy Physics (HEP), including simulating detector responses and physics events. However, training GANs is notoriously hard and optimizing their hyperparameters even more so. It normally requires many trial-and-error training attempts to force a stable training and reach a reasonable fidelity. Significant tuning work has to be done to achieve the accuracy required by physics analyses. This work uses the physics-agnostic and high-performance-computer-friendly hyperparameter optimization tool HYPPO to optimize and examine the sensitivities of the hyperparameters of a GAN for two independent HEP datasets. This work provides the first insights into efficiently tuning GANs for Large Hadron Collider data. We show that given proper hyperparameter tuning, we can find GANs that provide high-quality approximations of the desired quantities. We also provide guidelines for how to go about GAN architecture tuning using the analysis tools in HYPPO.
