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Abstract:

With the advent of high-resolution, low-noise CMB measurements, the ability to extract cosmological information from thermal Sunyaev-Zel'dovich effect and kinetic Sunyaev-Zel'dovich effect will be limited not by statistical uncertainties but rather by systematic and theoretical uncertainties. The theoretical uncertainty is driven by the lack of knowledge about the electron pressure and density. Thus, we explore the electron pressure and density distributions in the IllustrisTNG hydrodynamical simulations, and we demonstrate that the cluster properties exhibit a strong dependence on the halo concentration - providing some of the first evidence of cluster assembly bias in the electron pressure and density. Further, our work shows evidence for a broken power-law mass dependence, with lower pressure in lower mass haloes than previous work and a strong evolution with mass of the radial correlations in the electron density and pressure. Both of these effects highlight the differing impact of active galactic nuclei and supernova feedback on the gas in galaxy groups compared to massive clusters. We verified that we see qualitatively similar features in the SIMBA hydrodynamical simulations, suggesting these effects could be generic features. Finally, we provide a parametric formula for the electron pressure and density profile as a function of dark matter halo mass, halo concentration, and redshift. Despite our extensions, the best-fitting formulae are not able to fully capture the features seen in certain mass, radius, and redshift regimes, thereby highlighting the challenges of precision modelling gas profiles.

Abstract:

Future Large Scale Structure surveys are expected to improve over current bounds on primordial non-Gaussianity (PNG), with a significant impact on our understanding of early Universe physics. The level of such improvements will however strongly depend on the extent to which late time non-linearities erase the PNG signal on small scales. In this work, we show how much primordial information remains in the bispectrum of the non-linear dark matter density field by implementing a new, simulation-based, methodology for joint estimation of PNG amplitudes ($f_{\rm NL}$) and standard $\Lambda$CDM parameters. The estimator is based on optimally compressed statistics, which, for a given input density field, combine power spectrum and modal bispectrum measurements, and numerically evaluate their covariance and their response to changes in cosmological parameters. We train and validate the estimator using a large suite of N-body simulations (QUIJOTE-PNG), including different types of PNG (local, equilateral, orthogonal). We explicitly test the estimator's unbiasedness, optimality and stability with respect to changes in the total number of input realizations. While the dark matter power spectrum itself contains negligible PNG information, as expected, including it as an ancillary statistic increases the PNG information content extracted from the bispectrum by a factor of order $2$. As a result, we prove the capability of our approach to optimally extract PNG information on non-linear scales beyond the perturbative regime, up to $k_{\rm max} = 0.5~h\,{\rm Mpc}^{-1}$, obtaining marginalized $1$-$\sigma$ bounds of $\Delta f_{\rm NL}^{\rm local} \sim 16$, $\Delta f_{\rm NL}^{\rm equil} \sim 77$ and $\Delta f_{\rm NL}^{\rm ortho} \sim 40$ on a cubic volume of $1~(\mathrm{Gpc}/h)^3$ at $z=1$. At the same time, we discuss the significant information on cosmological parameters contained on these scales.Comment: 22 pages, 12 figure