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We study single-image super-resolution algorithms for photons at colliderexperiments based on generative adversarial networks. We treat the energydepositions of simulated electromagnetic showers of photons and neutral-piondecays in a toy electromagnetic calorimeter as 2D images and we trainsuper-resolution networks to generate images with an artificially increasedresolution by a factor of four in each dimension. The generated images are ableto reproduce features of the electromagnetic showers that are not obvious fromthe images at nominal resolution. Using the artificially-enhanced images forthe reconstruction of shower-shape variables and of the position of the showercenter results in significant improvements. We additionally investigate theutilization of the generated images as a pre-processing step for deep-learningphoton-identification algorithms and observe improvements in the case oftraining samples of small size.

SR-GAN for SR-gamma: super resolution of photon calorimeter images at collider experiments