AI-powered Reconstruction of Dark Matter Velocity Fields from Redshift-space Halo Distribution

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 ∈ [0.05, 0.3] h Mpc−1, 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, 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 baryon acoustic oscillation reconstruction.