DOI: 10.1093/mnras/stad3940 ISSN: 0035-8711

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

Xiaowen Zhang, Patrick Lachance, Yueying Ni, Yin Li, Rupert A C Croft, Tiziana Di Matteo, Simeon Bird, Yu Feng
  • Space and Planetary Science
  • Astronomy and Astrophysics


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 (StyleGAN) 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 ≤ z ≤ 0). Furthermore, we assess the temporal consistency of our SR model by constructing halo merger trees. We examine progenitors, descendants and mass growth along the tree branches. All statistical indicators demonstrate the ability of our SR model to generate satisfactory high-resolution simulations based on low-resolution inputs.

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