We introduce CaloFlow, a fast detector simulation framework based on normalizing flows. For the first time, we demonstrate that normalizing flows can reproduce many-channel calorimeter showers with extremely high fidelity, providing a fresh alternative to computationally expensive GEANT4 simulations, as well as other state-of-the-art fast simulation frameworks based on GANs and VAEs. Besides the usual histograms of physical features and images of calorimeter showers, we introduce a new metric for judging the quality of generative modeling: the performance of a classifier trained to differentiate real from generated images. We show that GAN-generated images can be identified by the classifier with 100% accuracy, while images generated from CaloFlow are able to fool the classifier much of the time. More broadly, normalizing flows offer several advantages compared to other state-of-the-art approaches (GANs and VAEs), including: tractable likelihoods; stable and convergent training; and principled model selection. Normalizing flows also provide a bijective mapping between data and the latent space, which could have other applications beyond simulation, for example, to detector unfolding.
翻译:我们引入了基于正常流流的快速检测模拟框架CaloFlow。 我们第一次展示了正常流能复制非常忠实的多通道热量阵列,为计算昂贵的GENANT4模拟提供了新的替代方案,以及基于GANs和VAEs的其他最先进的快速模拟框架。除了通常的卡罗里米阵列物理特征和图像直观图外,我们还引入了用于判断变异模型质量的新标准:受过训练、能够区分真实和生成图像的分类器的性能。我们显示,分类者可以100%精确地识别GAN生成的图像,而CaloFlow生成的图像在很多时间里能够愚弄分类者。更广泛地说,与其它最先进的方法(GANs和VAEs)相比,正常流动提供了几种优势,其中包括:可移动的可能性;稳定与趋同的训练;以及有原则的模型选择。 标准化流还能提供数据与潜伏空间之间的双向映射图,这些应用可以超越模拟。