Self-consistent secondary cosmic microwave background anisotropies and extragalactic foregrounds in the FLAMINGO simulations

TL;DR

This work delivers self-consistent, full-sky mock CMB maps of secondary anisotropies and extragalactic foregrounds derived from the FLAMINGO hydrodynamical simulations, including CMB lensing, tSZ, kSZ, CIB, radio sources, and patchy screening. The maps are built from lightcone outputs and analyzed via power spectra, validated against observations and compared to dark-matter–only predictions to demonstrate consistent multi-component predictions. By varying feedback models and cosmologies, the study shows that CIB and kSZ signals are particularly sensitive to baryonic physics, while cross-correlations between CIB and other tracers remain informative even when SEDs are adjusted to data. The resulting suite provides a valuable, publicly available resource for testing component-separation pipelines, forecast studies for upcoming surveys, and deeper insights into the interplay between baryons, galaxy formation, and large-scale structure.

Abstract

Secondary anisotropies in the cosmic microwave background (CMB) contain information that can be used to test both cosmological models and models of galaxy formation. Starting from lightcone-based HEALPix maps and catalogues, we present a new set of mock CMB maps constructed in a self-consistent manner from the FLAMINGO suite of cosmological hydrodynamical simulations, including CMB lensing, thermal and kinetic Sunyaev-Zeldovich effects, cosmic infrared background, radio point source and anisotropic screening maps. We show that these simulations reproduce a wide range of observational constraints. We also compare our simulations with previous predictions based on dark matter-only simulations which generally model the secondary anisotropies independently from one another, concluding that our hydrodynamical simulation mocks perform at least as well as previous mocks in matching the observations whilst retaining self-consistency in the predictions of the different components. Using the model variations in FLAMINGO, we further explore how the signals depend on cosmology and feedback modelling, and we predict cross-correlations between some of the signals that differ significantly from those in previous mocks. The mock CMB maps should provide a valuable resource for exploring correlations between different secondary anisotropies and other large-scale structure tracers, and can be applied to forecasts for upcoming surveys.

Figures (24)

• Figure 1: Constraints on the $\beta_{d}$, $T_{0}$, and $\alpha$ parameters of the SED of the CIB model, obtained by fitting to the 353/545/857 GHz auto-power spectrum measurements from L19_CIB.
• Figure 2: The 150 MHz radio luminosity function (RLF) reconstructed from the black hole particle lightcone in the fiducial $(1~\mathrm{Gpc})^{3}$ run. This is an example showing the case for a black hole selection of $\lambda_{\rm Edd} < 10^{-2}$ over the redshift range $0.5 < z < 1.0$. The observed RLF from the LOFAR survey within the same redshift interval (black points), as well as its best-fitting parametric model (red thick dashed line), are overplotted for comparison LOFAR_RLF. Abundance matching is used to map from the bolometric luminosity function of black holes in the simulation to the observed RLF (see text).
• Figure 3: Comparison of the differential source number counts between observations and simulations. The blue, orange, and green solid curves are the best-fitting source count models for the three black hole selections considered in this study (corresponding to different Eddington rate cuts), with the best-fitting $\alpha_{\rm radio}$ values obtained by fitting to the measured SPT data at three frequencies Everett_SPT (black diamonds) in the flux range $S_{\nu} = 10^{-2}$–$10^{-1}$ Jy. Other observations from the ACT Vargas_ACT and PlanckP18_radio are shown as violet triangles and light green crosses respectively. Results from the AGORA simulation are shown as a red thick dashed line for comparison. Overall, the simulations reproduce the number counts well.
• Figure 4: Comparison of the lensed and unlensed auto-power spectra of different LSS tracers from the fiducial $(2.8~\rm Gpc)^3$ run. All curves are obtained by averaging over 8 independent lightcones. A 1-$2\%$ suppression of power at small scales is expected from foreground lensing effects.
• Figure 5: Full-sky mock intensity maps of the thermal Sunyaev–Zel’dovich (tSZ) effect and its relativistic correction. These maps are generated from the lightcone outputs of the fiducial $(2.8 ~\rm Gpc)^{3}$ run integrated up to $z = 4.5$. Left: tSZ intensity map at 857 GHz, computed using Equation \ref{['eqn::delta_tSZ']} (i.e., non-relativistic tSZ). Middle: Difference map between the relativistically corrected and non-relativistic tSZ intensity maps at the same frequency, with the relativistic corrections applied using SZpack. Right: the corresponding $y$-weighted temperature map. Note the large differences in the color bar ranges across the three maps.