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Cosmological simulations are a fundamental tool for studying the evolution of the Universe and the formation of structures across different scales. However, their computational cost increases steeply with resolution, thus limiting the scales and level of detail that can be achieved. Consequently, a trade-off between simulated volume and spatial resolution is typically required.
This limitation can be alleviated by leveraging modern deep learning-based super-resolution approaches, that can learn mappings between corresponding samples from low- and high-resolution simulations. Once such a transformation has been learned, it can be applied to computationally less expensive coarse simulations to approximate the results that would be obtained with a higher resolution.
In cosmology, super-resolution techniques based on generative adversarial networks (GANs) have shown promising results for dark-matter-only simulations. Building on this line of research, this work employs a WGAN-GP architecture tailored for the super-resolution task on DEMNUni (Dark Energy and Massive Neutrino Universe) cosmological simulations. Evaluation shows that the reconstructions achieve reasonable agreement with both the matter power spectrum and the halo mass function of the original realizations.