AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution
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arXiv
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| Format: | Preprint |
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2024
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| author | Xiao, Xu Ding, Jiacheng Luo, XiaoLin Lan, Sun Ke Xiao, Liang Liu, Shuai Wang, Xin Zhang, Le Li, Xiao-Dong |
| author_facet | Xiao, Xu Ding, Jiacheng Luo, XiaoLin Lan, Sun Ke Xiao, Liang Liu, Shuai Wang, Xin Zhang, Le Li, Xiao-Dong |
| contents | We propose a UNet-based deep learning model to reconstruct the real-space dark matter (DM) velocity field from the redshift-space distribution of sparse DM halos. Using various statistical measures, we show that the reconstructed velocity components--including velocity magnitude, momentum, and divergence--closely match the ground truth, achieving better than 10% relative error and a correlation coefficient of 0.88. In the power spectrum comparison over $k \in [0.05, 0.3] h/{\rm Mpc}$, the UNet reconstruction outperforms linear theory and agrees with the true field within $2σ$. The model also effectively corrects redshift-space distortions (RSD), yielding unbiased power spectrum multipoles of DM fields within $2σ$. Notably, the UNet remains robust even with incomplete halo mass information. These results highlight the model's broad applicability to cosmological analyses, including RSD, cosmic web studies, the kinetic Sunyaev-Zel'dovich effect, and BAO reconstruction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_11280 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution Xiao, Xu Ding, Jiacheng Luo, XiaoLin Lan, Sun Ke Xiao, Liang Liu, Shuai Wang, Xin Zhang, Le Li, Xiao-Dong Cosmology and Nongalactic Astrophysics We propose a UNet-based deep learning model to reconstruct the real-space dark matter (DM) velocity field from the redshift-space distribution of sparse DM halos. Using various statistical measures, we show that the reconstructed velocity components--including velocity magnitude, momentum, and divergence--closely match the ground truth, achieving better than 10% relative error and a correlation coefficient of 0.88. In the power spectrum comparison over $k \in [0.05, 0.3] h/{\rm Mpc}$, the UNet reconstruction outperforms linear theory and agrees with the true field within $2σ$. The model also effectively corrects redshift-space distortions (RSD), yielding unbiased power spectrum multipoles of DM fields within $2σ$. Notably, the UNet remains robust even with incomplete halo mass information. These results highlight the model's broad applicability to cosmological analyses, including RSD, cosmic web studies, the kinetic Sunyaev-Zel'dovich effect, and BAO reconstruction. |
| title | AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2411.11280 |