论文标题
带有辅助回归器的GAN，用于快速模拟电磁训练表响应
GAN with an Auxiliary Regressor for the Fast Simulation of the Electromagnetic Calorimeter Response
论文作者
论文摘要
高能量物理实验本质上依赖于模拟数据进行物理分析。但是，运行详细的仿真模型需要大量的计算资源。因此，需要加快检测器仿真的新方法。量热响应的产生通常是HEP实验模拟链中最昂贵的组成部分。结果表明，深度学习技术，尤其是生成的对抗网络，可用于重现量热计的响应。但是，这些应用程序具有挑战性，因为生成的响应不仅需要在图像一致性方面进行评估：还应考虑不同的基于物理的质量指标。在我们的工作中，我们开发了一个基于多任务的基于GAN的框架，其目标是加快LHC LHCB检测器的电磁热量计（ECAL）的响应生成。我们将辅助回归器作为第二任务，以评估GAN歧视者使用的给定输入的代理度量。我们表明，这种方法可以提高GAN的稳定性，该模型产生具有更好物理分布的样品。
High energy physics experiments essentially rely on simulated data for physics analyses. However, running detailed simulation models requires a tremendous amount of computation resources. New approaches to speed up detector simulation are therefore needed. The generation of calorimeter responses is often the most expensive component of the simulation chain for HEP experiments. It was shown that deep learning techniques, especially Generative Adversarial Networks, may be used to reproduce the calorimeter response. However, those applications are challenging, as the generated responses need evaluation not only in terms of image consistency: different physics-based quality metrics should be also taken into consideration. In our work, we develop a multitask GAN-based framework with the goal to speed up the response generation of the Electromagnetic Calorimeter (ECAL) of the LHCb detector at LHC. We introduce the Auxiliary Regressor as a second task to evaluate a proxy metric of the given input that is used by the Discriminator of the GAN. We show that this approach improves the stability of GAN and the model produces samples with better physics distributions.
