AI-assisted super-resolution cosmological simulations III: Time evolution
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2023
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910822301696000 |
|---|---|
| author | Zhang, Xiaowen Lachance, Patrick Ni, Yueying Li, Yin Croft, Rupert A. C. Di Matteo, Tiziana Bird, Simeon Feng, Yu |
| author_facet | Zhang, Xiaowen Lachance, Patrick Ni, Yueying Li, Yin Croft, Rupert A. C. Di Matteo, Tiziana Bird, Simeon Feng, Yu |
| contents | 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. 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_12222 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | AI-assisted super-resolution cosmological simulations III: Time evolution Zhang, Xiaowen Lachance, Patrick Ni, Yueying Li, Yin Croft, Rupert A. C. Di Matteo, Tiziana Bird, Simeon Feng, Yu Cosmology and Nongalactic 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 (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. 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. |
| title | AI-assisted super-resolution cosmological simulations III: Time evolution |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2305.12222 |