AI-assisted reconstruction of cosmic velocity field from redshift-space spatial distribution of halos

Wu, Ziyong; Liang, Xiao; Xu, Xin; Wang, Jie; Kang, Xi; Wang, Yan; Wang, Xin; Zhang, Le; Li, Xiaodong

doi:10.48550/arxiv.2301.04586

Cited by 2 publications

(1 citation statement)

References 75 publications

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“…Then, the trained V-net with the minimum validation loss is applied to the 538 reconstructed test density fields to obtain the reconstructed test velocity fields. Following the arguments in [25] and [77], we define the loss function as:…”

Section: Reconstructing the Velocity Field From The Reconstructed Den...mentioning

confidence: 99%

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

Qin¹,

Parkinson²,

Hong³

et al. 2023

J. Cosmol. Astropart. Phys.

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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 β 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 β parameter by comparing the peculiar velocities of halo catalogues to the reconstructed velocity fields, and find the estimated β values agree with the fiducial value at the 68% confidence level.

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Section: Reconstructing the Velocity Field From The Reconstructed Den...mentioning

confidence: 99%

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

Qin¹,

Parkinson²,

Hong³

et al. 2023

J. Cosmol. Astropart. Phys.

View full text Add to dashboard Cite

show abstract

Estimation of line-of-sight velocities of individual galaxies using neural networks – I. Modelling redshift–space distortions at large scales

Chen,

Wang,

Mao

et al. 2024

Monthly Notices of the Royal Astronomical Society

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We present a scheme based on artificial neural networks (ANNs) to estimate the line-of-sight velocities of individual galaxies from an observed redshift–space galaxy distribution. We find an estimate of the peculiar velocity at a galaxy based on galaxy counts and barycentres in shells around it. By training the network with environmental characteristics, such as the total mass and mass centre within each shell surrounding every galaxy in redshift space, our ANN model can accurately predict the line-of-sight velocity of each individual galaxy. When this velocity is used to eliminate the RSD effect, the two-point correlation function (TPCF) in real space can be recovered with an accuracy better than 1 per cent at s > 8 $\, h^{-1}\, \mathrm{Mpc}$, and 4 per cent on all scales compared to ground truth. The real-space power spectrum can be recovered within 3 per cent on k< 0.5 $\, \mathrm{Mpc}^{-1}\, h$, and less than 5 per cent for all k modes. The quadrupole moment of the TPCF or power spectrum is almost zero down to s = 10 $\, h^{-1}\, \mathrm{Mpc}$ or all k modes, indicating an effective correction of the spatial anisotropy caused by the RSD effect. We demonstrate that on large scales, without additional training with new data, our network is adaptable to different galaxy formation models, different cosmological models, and mock galaxy samples at high-redshifts and high biases, achieving less than 10 per cent error for scales greater than 15 $\, h^{-1}\, \mathrm{Mpc}$. As it is sensitive to large-scale densities, it does not manage to remove Fingers of God in large clusters, but works remarkably well at recovering real-space galaxy positions elsewhere. Our scheme provides a novel way to predict the peculiar velocity of individual galaxies, to eliminate the RSD effect directly in future large galaxy surveys, and to reconstruct the three-dimensional cosmic velocity field accurately.

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AI-assisted reconstruction of cosmic velocity field from redshift-space spatial distribution of halos

Cited by 2 publications

References 75 publications

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

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

Estimation of line-of-sight velocities of individual galaxies using neural networks – I. Modelling redshift–space distortions at large scales

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