Recently, we introduced CaloFlow, a high-fidelity generative model for GEANT4 calorimeter shower emulation based on normalizing flows. Here, we present CaloFlow v2, an improvement on our original framework that speeds up shower generation by a further factor of 500 relative to the original. The improvement is based on a technique called Probability Density Distillation, originally developed for speech synthesis in the ML literature, and which we develop further by introducing a set of powerful new loss terms. We demonstrate that CaloFlow v2 preserves the same high fidelity of the original using qualitative (average images, histograms of high level features) and quantitative (classifier metric between GEANT4 and generated samples) measures. The result is a generative model for calorimeter showers that matches the state-of-the-art in speed (a factor of $10^4$ faster than GEANT4) and greatly surpasses the previous state-of-the-art in fidelity.

翻译：最近，我们提出了CaloFlow，一种基于归一化流的高保真生成模型，用于GEANT4量热器簇射模拟。在此，我们介绍CaloFlow v2——对原始框架的改进，该模型将簇射生成速度较原始版本进一步提升了500倍。这一改进基于机器学习文献中最初为语音合成开发的概率密度蒸馏技术，并通过引入一组强大的新损失函数对其进行了拓展。我们通过定性（平均图像、高层特征直方图）和定量（GEANT4与生成样本之间的分类器指标）评估证明，CaloFlow v2保持了原始模型相同的高保真度。最终得到的量热器簇射生成模型在速度上达到当前最优水平（比GEANT4快10⁴倍），并在保真度上大幅超越此前的最佳方案。