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 nearly 100% accuracy, while images generated from CaloFlow are better able to fool the classifier. 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：我们提出CaloFlow，一种基于归一化流的快速探测器模拟框架。首次证明归一化流能够以极高保真度再现多通道量热计簇射，为计算密集型的GEANT4模拟及现有基于GAN和VAE的最优快速模拟框架提供了全新替代方案。除物理特征直方图和量热计簇射图像等常规评估手段外，我们引入判定生成模型质量的新指标：训练用于区分真实图像与生成图像的分类器性能。实验表明，分类器能以近100%准确率识别GAN生成图像，而CaloFlow生成的图像更能有效欺骗分类器。从更宏观角度看，归一化流相比其他前沿方法（GAN和VAE）具备多重优势：可处理的似然函数、稳定收敛的训练过程以及规范化的模型选择。此外，归一化流在数据与潜空间之间建立的双射映射，可拓展至模拟之外的其它应用场景，例如探测器中子解卷积。