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Gravitational coupling between large- and small-scale density perturbations leads to anisotropic distortions to local small-scale matter fluctuations. Such local anisotropic distortions can be used to reconstruct large-scale matter distribution, known as tidal reconstruction. In this paper, we apply the tidal reconstruction methods to simulated galaxies in redshift space. We find that redshift-space distortions (RSDs) lead to anisotropic reconstruction results. While the reconstructed radial modes are more noisy mainly due to the small-scale velocity dispersion, the transverse modes are still reconstructed with high fidelity, and well correlated with the original large-scale density modes. The bias of the reconstructed field at large scales shows a simple angular dependence, which can be described by a form similar to that of the linear RSD. The noise power spectrum is nearly isotropic and scale independent on large scales. This makes the reconstructed tide fields an ideal tracer for cosmic variance cancellation and multi-tracer analysis and has profound implications for future 21 cm intensity mapping surveys.

Nonlinear gravitational evolution induces strong nonlinearities in the observed cosmological density fields, leading to positive off-diagonal correlations in the power spectrum covariance. This has caused the information saturation in the power spectrum, e.g., the neutrino mass constraints from the nonlinear power spectra are lower than their linear counterparts by a factor of ∼2 at z = 0. In this paper, we explore how nonlinear reconstruction methods improve the cosmological information from nonlinear cosmic fields. By applying nonlinear reconstruction to cold dark matter fields from the Quijote simulations, we find that nonlinear reconstruction can improve the constraints on cosmological parameters significantly, nearly reaching the linear theory limit. For neutrino mass, the result is only 12% lower than the linear power spectrum, i.e., the theoretical best result. This makes nonlinear reconstruction an efficient and useful method to extract neutrino information from current and upcoming galaxy surveys.

The angular momentum of galaxies (galaxy spin ) contains rich information about the initial condition of the universe, yet it is challenging to efficiently measure the spin direction for the tremendous amount of galaxies that are being mapped by ongoing and forthcoming cosmological surveys. We present a machine-learning-based classifier for the Z-wise versus S-wise spirals, which can help to break the degeneracy in the galaxy spin direction measurement. The proposed chirality equivariant residual network (CE-ResNet) is manifestly equivariant under a reflection of the input image, which guarantees that there is no inherent asymmetry between the Z-wise and S-wise probability estimators. We train the model with Sloan Digital Sky Survey images, with the training labels given by the Galaxy Zoo 1 project. A combination of data augmentation techniques is used during the training, making the model more robust to be applied to other surveys. We find an ∼30% increase in both types of spirals when Dark Energy Spectroscopic Instrument (DESI) images are used for classification, due to the better imaging quality of DESI. We verify that the ∼7 σ difference between the numbers of Z-wise and S-wise spirals is due to human bias, since the discrepancy drops to

We propose a new velocity reconstruction method based on the displacement estimation of recently developed methods. The velocity is first reconstructed by transfer functions in Lagrangian space and then mapped into Eulerian space. High-resolution simulations are used to test the performance. We find that the new reconstruction method outperforms the standard velocity reconstruction in the sense of better cross-correlation coefficient, less velocity misalignment, and smaller amplitude difference. We conclude that this new method has the potential to improve large-scale structure sciences involving velocity reconstruction, such as kinetic Sunyaev–Zel'dovich measurement and supernova cosmology. [ABSTRACT FROM AUTHOR]

We apply the nonlinear reconstruction method to simulated halo fields. For halo number density at z = 0, corresponding to the SDSS main sample density, we find that the scale where the noise saturates the linear signal is improved to , which is a factor of 2.29 improvement in scale, or 12 in number of linear modes. The improvement is less for higher redshift or lower halo density. We expect this to substantially improve the BAO accuracy of dense, low-redshift surveys, including the SDSS main sample, 6dFGS and 21 cm intensity mapping initiatives. [ABSTRACT FROM AUTHOR]