Fast and Accurate HI Intensity Maps with Latent Overlap Diffusion

This research introduces a novel machine learning pipeline for generating large, fast, and accurate HI intensity maps. The pipeline combines an attention-based ResUNet (HALO) for predicting dark matter halo maps with a conditional variational diffusion model (LODI) for producing 21 cm brightness temperature maps. A key innovation is the use of 'latent overlap,' which allows for the seamless combination of smaller sub-volumes into arbitrarily large simulations.

The results demonstrate that the pipeline can predict the 21 cm power spectrum on an unseen dark matter map to within 10% for wavenumbers k <= 10 h Mpc^-1, deep inside the non-linear regime. This level of accuracy is achieved with a computational effort of only a few minutes, representing a significant improvement over traditional hydrodynamic simulations.

The ability to generate accurate HI intensity maps with reduced computational cost has significant implications for cosmological studies. These maps can be used to probe the distribution of dark matter and cold gas throughout cosmic times, providing insights into the formation and evolution of galaxies and the large-scale structure of the universe. However, it is important to acknowledge the limitations of the approach, including the reliance on training data and the assumptions made in the machine learning models. Further research is needed to validate the pipeline's predictions with observational data and explore its applicability to different cosmological scenarios.

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