AI-assisted super-resolution cosmological simulations V: Cosmology-aware super-resolution

Abstract

We present a cosmology-aware generative model for super-resolution (SR) of cosmological $N$-body simulations in particle phase space. The model jointly generates displacements and velocities, conditioning a generative adversarial network (GAN) based generator and discriminator on evolved low-resolution (LR) fields and five $Λ$CDM parameters $(Ω_m,Ω_b,h,n_s,σ_8)$. We train a single model on 100 matched low-high resolution pairs from the Quijote Latin-hypercube suite at $z = 0$, mapping $512^3$ to $1024^3$ particles in $1\,h^{-1}\,\mathrm{Gpc}$ boxes. On ten cosmologies excluded from SR training, the model reproduces the broad matter distribution and the cosmological variation of clustering and halo abundances. Over $0.1<k<3\,h\,\mathrm{Mpc}^{-1}$, the mean of the per-cosmology maximum absolute power-spectrum errors is $3.6\%$, while FoF halo abundances agree with HR to within $15\%$ in the analyzed mass bins below $10^{14.8}\,h^{-1}M_\odot$. Satellite abundances and occupations retain larger, mass-dependent discrepancies. Corrections measured at one near-fiducial cosmology reduce a shared component of these systematic errors when applied to the other nine test cosmologies. A power spectrum based inference test also shows that calibration reduces the mean shifts in all parameters. These results extend our SR model across cosmologies and provide 6D phase space realizations for subsequent studies of non-linear structure and field-level cosmological inference.