AI-assisted super-resolution cosmological simulations III: Time evolution

Understand

In this work, we extend our recently developed super-resolution (SR) model for cosmological simulations to produce fully time consistent evolving representations of the particle phase-space distribution.

• We employ a style-based constrained generative adversarial network (Style-GAN) where the changing cosmic time is an input style parameter to the network.
• The matter power spectrum and halo mass function agree well with results from high-resolution N-body simulations over the full trained redshift range ($10 \le z \le 0$).
• Furthermore, we assess the temporal consistency of our SR model by constructing halo merger trees.