Generating all-sky radio continuum clustering simulations with GHOST

TL;DR

GHOST delivers an all-sky, clustered radio mock anchored to the FLAMINGO halo lightcone, enabling end-to-end testing of multi-tracer estimators for local-type PNG through a monotonic L–$M_h$ abundance-matching framework. By calibrating a radio-first SFG RLF at 1.4 GHz and TRECS-inspired AGN populations within redshift shells, the catalog reproduces observed counts, redshift distributions, and angular clustering while preserving a clear bias hierarchy across tracer classes. The work shows that selection effects reshape projection kernels and thus observed clustering without reversing intrinsic bias ordering, making the mock a practical sandbox for $f_{\rm NL}$ forecasts and related cosmological probes. The dataset supports not only PNG analyses but also ISW, magnification, and dipole studies, with planned upgrades to spectral modeling, satellites, and observation-realistic pipelines for future releases.

Abstract

Techniques using multiple tracers of the large scale structure of the universe show great promise for examining the fundamentals of our Universe's cosmology. Such techniques rely on the different relationship between the overdensity of tracers and the broader matter overdensity, enabling cosmic-variance-free tests of primordial non-Gaussianity in the initial curvature perturbations. There is a great opportunity for current and future all-sky extra-galactic radio surveys to make use of this technique to test for non-Gaussianity at a precision greater than existing all-sky constraints from the cosmic microwave background. To realize this goal there is a need for accurate simulations. Previous radio galaxy simulations have either been realistic but covering only a small area (and so unhelpful for cosmological forecasts), or all-sky dark matter only cosmological simulations but having no connection to a real radio galaxy population. In this study, we use the FLAMINGO suite of cosmological surveys, as well as the matching of dark matter halos to radio galaxy population, to create an accurate sky simulation in order to examine the feasibility of multi-tracer techniques. We present an analysis of the clustering (with a bias model for the simulation), as well as redshift distributions, source counts and radio luminosity functions, and discuss future work on non-Gaussianity detection.

Figures (20)

• Figure 1: Population workflow used in ghost. The diagram summarizes how radio sources are generated for the two branches, with relevant subsections of the paper indicated by the bracketed numbers: SFG (left) and AGNs (right). Green slanted boxes are external inputs (halo mass, redshift, base/other frequencies, external counts); cyan chevrons are operations/relations (e.g. SFR sampling, synchrotron/free–free relations, evolving RLF $\Phi(L)$, spectral–index mapping, abundance matching); red rounded boxes are intermediate products or outputs (component luminosities $L_{\rm ff}$/$L_{\rm sync}$, SFG/AGN luminosities, simulated counts, observer-frame fluxes). On the SFG side, sampled SFRs are converted to radio luminosity via synchrotron and free–free relations, then propagated to other frequencies. On the AGN side, type parameters (HERG/LERG, SSAGN, etc.) and the evolving RLF (via $L_\star$ and $\Phi(L)$) set the AGN luminosity, with spectral indices mapping to other bands. Both branches feed an abundance-matching step (using halo mass and redshift) to assign hosts and form the final matched-halo catalogue; the counts/interval-sum nodes provide predicted number counts used for verification. Arrows indicate data flow from inputs to catalogue outputs.
• Figure 2: All populations — radio luminosity vs. halo mass with per–population marginals (shared density scale). Each panel shows 2D binned abundances in a fixed redshift window; colored semi–transparent maps overlay populations (SFG solid, SSAGN dashed, FSRQ dash–dot, BL Lac dotted). All density layers share the same logarithmic colorbar, so intensity is directly comparable across populations and panels. Solid lines trace the median (“ridgeline”) of $\log_{10}L_\nu$ at fixed $\log_{10}M_h$. The small top (right) axes show, for each population, the normalized marginal PDFs $p(\log_{10}M_h)$ and $p(\log_{10}L_\nu)$—with $\int p\,\mathrm{d}\log M_h=1$ and $\int p\,\mathrm{d}\log L=1$—using the same color/linestyle as the ridgelines. Reading the figure:SFG peak at lower host masses and luminosities; SSAGN extend to higher $M_h$ with a steeper $L_\nu$–$M_h$ trend; FSRQ and BL Lac preferentially inhabit the most massive halos and dominate the bright radio tail. With increasing redshift, ridgelines shift to higher $M_h$ and the PDFs narrow, reflecting both selection effects and the evolving halo–occupation mix.
• Figure 3: raw GHOST sample: Angular correlation function $w(\theta)$ for AGN (blue triangles) and SFG (orange circles). Solid lines show inverse-variance weighted power-law fits $w(\theta)=A(\theta/\theta_0)^{1-\gamma}$ over $\theta\in[0.02^\circ,\,1.0^\circ]$; shaded bands are the propagated $1\sigma$ model uncertainties from the $(A,\gamma)$ covariance. Best-fit slopes are $\gamma_{\rm AGN}=1.886\pm0.028$ and $\gamma_{\rm \ac{SFG}}=1.647\pm0.076$ . The lower panel shows data$-$fit residuals. The amplitude ordering AGN$>$SFG reflects the intrinsic bias hierarchy, while the slightly shallower SFG slope is consistent with a broader, higher-$z$ kernel $\phi(z)$ that mixes more $k=\ell/\chi(z)$ in projection (Equations \ref{['eq:wtheta_def']}--\ref{['eq:Cl_limber']}).
• Figure 4: VLA-COSMOS selection (1.4 GHz): $w(\theta)$ for AGN and SFG after the $L_{1.4}\gtrless L_{\rm cross}(z)$ selection function detailed in Section \ref{['sec:maglioccettiselection']}. Solid curves are our power-law fits over $\theta\in[0.02^\circ,\,1.0^\circ]$ (shaded $1\sigma$ bands); dotted lines and translucent bands show the literature reference for AGN and SFG, respectively. We obtain $\gamma_{\rm AGN}=1.858\pm0.041$ and $\gamma_{\rm \ac{SFG}}=1.667\pm0.010$. The SFG amplitude lies above the reference at large scales and the slope is slightly flatter. From the effective-depth summary (Table \ref{['tab:proj_numbers']}) the SFG kernel compresses to $z_{\rm eff}\simeq0.094$ and $\chi_{\rm eff}\simeq0.41$ Gpc, so a fixed $\theta$ probes small $r_\perp$ (e.g. $r_\perp(0.05^\circ)\approx0.35$ Mpc), flattening the angular scaling (Equations \ref{['eq:zeff_chieff']}--\ref{['eq:rperp']}).
• Figure 5: VLA-COSMOS selection (3 GHz): Same as Fig. \ref{['fig:acf_mag']} but for the 3 GHz cut (Section \ref{['sec:haleselection']}. Our fits give $\gamma_{\rm AGN}=1.903\pm0.037$ and $\gamma_{\rm \ac{SFG}}=1.662\pm0.010$. AGN closely follow the band from Hale2018, while SFG sit high and slightly flatter. The projection summary (Table \ref{['tab:proj_numbers']}) shows $z_{\rm eff,\,SFG}\simeq0.161$ and $\chi_{\rm eff,\,SFG}\simeq0.67$ Gpc (so $r_\perp(0.05^\circ)\approx0.59$ Mpc), again explaining the slope change as a selection-driven projection effect rather than frequency-dependent systematics. Residuals (lower panel) are shown relative to the fitted power laws.