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We investigate the impact of spatial survey non-uniformity on the galaxy redshift distributions for forthcoming data releases of the Rubin Observatory Legacy Survey of Space and Time (LSST). Specifically, we construct a mock photometry dataset degraded by the Rubin OpSim observing conditions, and estimate photometric redshifts of the sample using a template-fitting photo-$z$ estimator, BPZ, and a machine learning method, FlexZBoost. We select the Gold sample, defined as $i<25.3$ for 10 year LSST data, with an adjusted magnitude cut for each year and divide it into five tomographic redshift bins for the weak lensing lens and source samples. We quantify the change in the number of objects, mean redshift, and width of each tomographic bin as a function of the coadd $i$-band depth for 1-year (Y1), 3-year (Y3), and 5-year (Y5) data. In particular, Y3 and Y5 have large non-uniformity due to the rolling cadence of LSST, hence provide a worst-case scenario of the impact from non-uniformity. We find that these quantities typically increase with depth, and the variation can be $10-40\%$ at extreme depth values. Based on these results and using Y3 as an example, we propagate the variable depth effect to the weak lensing $3\times2$pt data vector in harmonic space. We find that galaxy clustering is most susceptible to variable depth, causing significant deviations at large scales if not corrected for, due to the depth-dependent number density variations. For galaxy-shear and shear-shear power spectra, we find little impact given the expected LSST Y3 noise., Comment: 26 pages, 20 figures. Submitted to MNRAS

We have studied the galaxy-group cross-correlations in redshift space for the Galaxy And Mass Assembly (GAMA) Survey. We use a set of mock GAMA galaxy and group catalogues to develop and test a novel 'halo streaming' model for redshift-space distortions. This treats 2-halo correlations via the streaming model, plus an empirical 1-halo term derived from the mocks, allowing accurate modelling into the nonlinear regime. In order to probe the robustness of the growth rate inferred from redshift-space distortions, we divide galaxies by colour, and divide groups according to their total stellar mass, calibrated to total mass via gravitational lensing. We fit our model to correlation data, to obtain estimates of the perturbation growth rate, $f\sigma_8$, validating parameter errors via the dispersion between different mock realizations. In both mocks and real data, we demonstrate that the results are closely consistent between different subsets of the group and galaxy populations, considering the use of correlation data down to some minimum projected radius, $r_{\rm min}$. For the mock data, we can use the halo streaming model to below $r_{\rm min} = 5h^{-1}$ Mpc, finding that all subsets yield growth rates within about 3% of each other, and consistent with the true value. For the actual GAMA data, the results are limited by cosmic variance: $f\sigma_8=0.29\pm 0.10$ at an effective redshift of 0.20; but there is every reason to expect that this method will yield precise constraints from larger datasets of the same type, such as the DESI bright galaxy survey., Comment: 17 pages, 12 figures. Matched to the accepted version

The imprints of large-scale structures on the Cosmic Microwave Background can be studied via the CMB lensing and Integrated Sachs-Wolfe (ISW) signals. In particular, the stacked ISW signal around supervoids has been claimed in several works to be anomalously high. In this study, we find cluster and void superstructures using four tomographic redshift bins with $0

We use data from the DESI Legacy Survey imaging to probe the galaxy density field in tomographic slices covering the redshift range $0