Reconstructing the cosmological density and velocity fields from redshifted galaxy distributions using V-net

Abstract

The distribution of matter that is measured through galaxy redshift and peculiar velocity surveys can be harnessed to learn about the physics of dark matter, dark energy, and the nature of gravity. To improve our understanding of the matter of the Universe, we can reconstruct the full density and velocity fields from the galaxies that act as tracer particles. In this paper, we use the simulated halos as proxies for the galaxies. We use a convolutional neural network, a V-net, trained on numerical simulations of structure formation to reconstruct the density and velocity fields. We find that, with detailed tuning of the loss function, the V-net could produce better fits to the density field in the high-density and low-density regions, and improved predictions for the probability distribution of the amplitudes of the velocities. However, the weights will reduce the precision of the estimated $\beta$ parameter. We also find that the redshift-space distortions of the halo catalogue do not significantly contaminate the reconstructed real-space density and velocity field. We estimate the velocity field $\beta$ parameter by comparing the peculiar velocities of halo catalogues to the reconstructed velocity fields, and find the estimated $\beta$ values agree with the fiducial value at the 68\% confidence level.

25 pages, 18 figures, 2 tables. Published JCAP

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Cited by in corpus (10)

• Target Selection for the DESI Peculiar Velocity Survey
• Cosmology inference at the field level from biased tracers in redshift-space
• Neural network reconstruction of density and velocity fields from the 2MASS Redshift Survey
• Generalized framework for likelihood-based field-level inference of growth rate from velocity and density fields
• DarkAI: Reconstructing the density, velocity and tidal field of dark matter from a DESI-like bright galaxy sample
• Peculiar Velocity Reconstruction From Simulations and Observations Using Deep Learning Algorithms
• kSZ Pairwise Velocity Reconstruction with Machine Learning
• Lagrangian space remapping and the angular momentum reconstruction from cosmic structures
• Fast Sampling of Cosmological Initial Conditions with Gaussian Neural Posterior Estimation
• On the Connection between Field-Level Inference and $n$-point Correlation Functions