Towards super-resolution simulations of the fuzzy dark matter cosmological model

AI super-resolution, combining deep learning and N-body simulations, has been shown to successfully reproduce the large-scale structure and halo abundances in the Lambda cold dark matter cosmological model. Here, we extend its use to models with a different dark matter content, in this case fuzzy dark matter (FDM), in the approximation that the difference is encoded in the initial power spectrum. We focus on redshift z = 2, with simulations that model smaller scales and lower masses, the latter by two orders of magnitude, than has been done in previous AI super-resolution work. We find that the super-resolution technique can reproduce the power spectrum and halo mass function to within a few per cent of full high-resolution calculations. We also find that halo artefacts, caused by spurious numerical fragmentation of filaments, are equally present in the super-resolution outputs. Although we have not trained the super-resolution algorithm using full quantum pressure FDM simulations, the fact that it performs well at the relevant length and mass scales means that it has promise as a technique that could avoid the very high computational cost of the latter, in some contexts. We conclude that AI super-resolution can become a useful tool to extend the range of dark matter models covered in mock catalogues.