标题： CALPAGAN：使用生成对抗网络的粒子量热法 显示英文标题

标题： CALPAGAN: Calorimetry for Particles using GANs

摘要： 在本研究中，展示了一种新方法，用于将快速模拟的量热计图像转换为类似于全面完整模拟的图像，该方法利用了条件生成对抗网络（GANs）。 pix2pix的概念被定制用于CALPAGAN，其中来自快速模拟的图像作为生成与详细模拟图像紧密相似输出的基础（条件）。 研究结果表明生成的图像与完整模拟的图像之间存在很强的相关性，尤其是在关键可观测量如喷注横向动量分布、喷注质量、喷注次级结构和喷注周长方面。 此外，本文探讨了该方法的有效性及其固有局限性。 这项研究标志着在高能粒子物理领域探索更高效模拟方法方面的重要一步。 显示英文摘要

摘要： In this study, a novel approach is demonstrated for converting calorimeter images from fast simulations to those akin to comprehensive full simulations, utilizing conditional Generative Adversarial Networks (GANs). The concept of pix2pix is tailored for CALPAGAN, where images from fast simulations serve as the basis(condition) for generating outputs that closely resemble those from detailed simulations. The findings indicate a strong correlation between the generated images and those from full simulations, especially in terms of key observables like jet transverse momentum distribution, jet mass, jet subjettiness, and jet girth. Additionally, the paper explores the efficacy of this method and its intrinsic limitations. This research marks a significant step towards exploring more efficient simulation methodologies in High Energy Particle Physics.