CaloFlow II：基于归一化流的更快且仍保持精度的量能器簇射生成
CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter Showers with Normalizing Flows
AI总结：
本研究对基于归一化流的量能器簇射生成模型CaloFlow进行改进，引入拓展后的概率密度蒸馏技术，推出的CaloFlow v2速度比原版快500倍、比GEANT4快$10^4$倍，同时保持高保真度，在速度达最优的同时保真度超越此前最优水平。
摘要：
近期，我们提出了CaloFlow，这是一种基于归一化流、用于GEANT4量能器簇射模拟的高保真生成模型。本文中，我们推出CaloFlow v2，它是对原有框架的改进，相比原始版本的簇射生成速度再提升500倍。该改进基于名为概率密度蒸馏（Probability Density Distillation）的技术，该技术最初在机器学习领域用于语音合成，我们通过引入一组强效的新型损失项对其做了进一步拓展。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）评估手段，证明CaloFlow v2保留了原始模型的高保真度。最终得到的量能器簇射生成模型，速度达到当前最优水平（比GEANT4快$10^4$倍），且保真度大幅超越此前的最优水平。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
