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Abstract:

Measurements of weak gravitational lensing using the cosmic microwave background and the shapes of galaxies have refined our understanding of the late-time history of the Universe. While optical surveys have been the primary source for cosmic shear measurements, radio continuum surveys offer a promising avenue. Relevant radio sources, principally star-forming galaxies, have populations with higher mean redshifts and are less affected by dust extinction compared to optical sources. We focus on the future mid frequency SKA radio telescope and explore the cross-correlation between radio cosmic shear and CMB lensing convergence (γR × κ CMB). We investigate its potential in constraining the redshift distribution of radio galaxy samples and improving cosmological parameter constraints, including the neutrino sector. Using simulations of the first phase of the SKA and the Simons Observatory as a CMB experiment, we show how this γR × κ CMB cross-correlation can provide ∼ 1 - 10% calibration of the overall radio source redshift distribution, which in turn can significantly tighten otherwise degenerate measurements of radio galaxy bias. For the case of the next-generation full SKA, we find that the full combination of auto- and cross-spectra between γR and κ CMB becomes more powerful than the equivalent case using γO from a Euclid-like survey, with constraints 30% tighter on ΛCDM parameters and narrower bounds on sum of neutrino masses at the level of ∼ 24%. These constraints are also driven by higher redshifts and larger scales than other galaxy-CMB cross-correlations, potentially shedding light on different physical models. Our findings demonstrate the potential of radio weak lensing in improving constraints, and establish the groundwork for future joint analyses of CMB experiments and radio continuum surveys

Abstract:

We apply a suite of different estimators to the Quijote-png halo catalogs to find the best approach to constrain Primordial non-Gaussianity (PNG) at nonlinear cosmological scales, up to kmax = 0.5 h Mpc^−1. The set of summary statistics considered in our analysis includes the power spectrum, bispectrum, halo mass function, marked power spectrum, and marked modal bispectrum. Marked statistics are used here for the first time in the context of the PNG study. We perform a Fisher analysis to estimate their cosmological information content, showing substantial improvements when marked observables are added to the analysis. Starting from these summaries, we train deep neural networks to perform likelihood-free inference of cosmological and PNG parameters. We assess the performance of different subsets of summary statistics; in the case of fNL^equil, we find that a combination of the power spectrum and a suitable marked power spectrum outperforms the combination of power spectrum and bispectrum, the baseline statistics usually employed in PNG analysis. A minimal pipeline to analyze the statistics we identified can be implemented either with our ML algorithm or via more traditional estimators, if these are deemed more reliable.

Abstract:

Searches for primordial non-Gaussianity in cosmological perturbations are a key means of revealing novel primordial physics. However, robustly extracting signatures of primordial non-Gaussianity from non-linear scales of the late-time Universe is an open problem. In this paper, we apply k-Nearest Neighbour cumulative distribution functions, kNN-CDFs, to the QUIJOTE-PNG simulations to explore the sensitivity of kNN-CDFs to primordial non-Gaussianity. An interesting result is that for halo samples with Mh〈10¹⁴ M⃠ h^−1, the kNN-CDFs respond to equilateral PNG in a manner distinct from the other parameters. This persists in the galaxy catalogues in redshift space and can be differentiated from the impact of galaxy modelling, at least within the halo occupation distribution (HOD) framework considered here. kNN-CDFs are related to counts-in-cells and, through mapping a subset of the kNN-CDF measurements into the count-in-cells picture, we show that our results can be modelled analytically. A caveat of the analysis is that we only consider the HOD framework, including assembly bias. It will be interesting to validate these results with other techniques for modelling the galaxy–halo connection, e.g. (hybrid) effective field theory or semi-analytical methods.

Abstract:

Optimal extraction of cosmological information from observations of the cosmic microwave background (CMB) critically relies on our ability to accurately undo the distortions caused by weak gravitational lensing. In this work, we demonstrate the use of denoising diffusion models in performing Bayesian lensing reconstruction. We show that score-based generative models can produce accurate, uncorrelated samples from the CMB lensing convergence map posterior, given noisy CMB observations. To validate our approach, we compare the samples of our model to those obtained using established Hamiltonian Monte Carlo methods, which assume a Gaussian lensing potential. We then go beyond this assumption of Gaussianity, and train and validate our model on non-Gaussian lensing data, obtained by ray-tracing N-body simulations. We demonstrate that in this case, samples from our model have accurate non-Gaussian statistics beyond the power spectrum. The method provides an avenue towards more efficient and accurate lensing reconstruction, which does not rely on an approximate analytical description of the posterior probability. The reconstructed lensing maps can be used as an unbiased tracer of the matter distribution, and to improve delensing of the CMB, resulting in more precise cosmological parameter inference.