Revealing Hidden Cosmic Flows through the Zone of Avoidance with Deep Learning
We present a refined deep-learning-based method to reconstruct the three-dimensional dark matter density, gravitational potential, and peculiar velocity fields in the Zone of Avoidance (ZOA), a region near the galactic plane with limited observational data. Using a convolutional neural network (V-Net) trained on A-SIM simulation data, our approach reconstructs density or potential fields from galaxy positions and radial peculiar velocities. The full 3D peculiar velocity field is then derived from the reconstructed potential. We validate the method with mocks that mimic the spatial distribution of the Cosmicflows-4 (CF4) catalog and apply it to actual data. Given CF4's significant observational uncertainties and since our model does not yet account for them, we use peculiar velocities corrected via an existing Hamiltonian Monte Carlo reconstruction, rather than raw catalog distances. Our results demonstrate that the reconstructed density field recovers known galaxy clusters detected in an H \textsc{i} survey of the ZOA, despite this dataset not being used in the reconstruction. This agreement underscores the potential of our method to reveal structures in data-sparse regions. Most notably, streamline convergence and watershed analysis identify a mass concentration consistent with the Great Attractor, at $(l, b) = (308.4^\circ \pm 2.4^\circ, 29.0^\circ \pm 1.9^\circ)$ and $cz = 4960.1 \pm 404.4,{\rm km/s}$, for 64\% of realizations. Our method is particularly valuable as it does not rely on data point density, enabling accurate reconstruction in data-sparse regions and offering strong potential for future surveys with more extensive galaxy datasets.