We study single-image super-resolution algorithms for photons at collider experiments based on generative adversarial networks. We treat the energy depositions of simulated electromagnetic showers of photons and neutral-pion decays in a toy electromagnetic calorimeter as 2D images and we train super-resolution networks to generate images with an artificially increased resolution by a factor of four in each dimension. The generated images are able to reproduce features of the electromagnetic showers that are not obvious from the images at nominal resolution. Using the artificially-enhanced images for the reconstruction of shower-shape variables and of the position of the shower center results in significant improvements. We additionally investigate the utilization of the generated images as a pre-processing step for deep-learning photon-identification algorithms and observe improvements in the case of training samples of small size.

翻译：我们研究了基于生成对抗网络的对撞机实验中光子的单图像超分辨率算法。将模拟的光子电磁簇射和中性π介子衰变在玩具电磁量能器中的能量沉积视为二维图像，训练超分辨率网络以生成每个维度分辨率人为提升四倍的图像。生成的图像能够复现标称分辨率图像中不明显的电磁簇射特征。利用人工增强图像重建簇射形状变量及簇射中心位置，显著提升了性能。此外，我们探索了将生成图像作为深度学习光子鉴别算法预处理步骤的应用，并在小规模训练样本场景下观察到性能提升。