Primordial Physics in the Nonlinear Universe: signatures of inflationary resonances, excitations, and scale dependence

Abstract

Primordial non-Gaussianities (PNGs) are imprints in the initial density field sourced by the dynamics of inflation. These dynamics can induce scale dependence, oscillations, and other features in the primordial bispectrum. We analyze a suite of over thirty PNG templates, including those used in the _Planck_ analyses of the Cosmic Microwave Background (CMB), and resolve their signatures in the deeply nonlinear regime of the late-time density field. Using simulations, we forecast results from a lensing analysis of the Year-10 data from the Rubin Observatory Legacy Survey of Space and Time (LSST). We find that lensing achieves sensitivity comparable to the CMB for many models, and even surpasses it for templates whose features peak on smaller scales, $k \gtrsim 0.2 h/{\rm Mpc}$. Many templates generate non-monotonic behaviors in mass and length scales, providing a distinct phenomenology in the resulting late-time structure. We simulate, for the first time, resonant signatures consistently in both the primordial power spectrum and bispectrum. The constraints on their amplitudes $(A_{\rm pk}, f_{\rm NL})$ are essentially independent, as each affects structure formation in distinct ways. Overall, we find that lensing data can provide competitive and complementary constraints on these models, and can deliver leading constraints when the primordial features are predominantly on smaller scales. The data products are publicly released as part of the Ulagam simulation suite. Our initial conditions generator is publicly available at https://github.com/DhayaaAnbajagane/Aarambam.

Figures (15)

• Figure 1: The different templates considered in this work, shown in three specific limits, alongside the approximated versions using our basis functions (black lines). The shape function is defined as $S(\ldots) = (k_1k_2k_3)^2\times B(\ldots)$. In all cases, the templates are adequately reproduced by our approximations; see Section \ref{['sec:sims:Models']} for details on the templates. Here, $k_{\rm F} = 0.006\, \, h{\rm /Mpc}$ is the fundamental frequency of the simulation volume.
• Figure 2: Constraints on the amplitude $f_{\rm NL}$ for different models/templates and parameter spaces. Gray lines show our constraints from using the public CMB-BEST pipeline of Sohn:2024:Colliders; see Appendix \ref{['appx:Planck2018']} for more details. The lensing constraints do not marginalize over additional parameters, where such marginalization is expected to degrade constraints by 20--30%; see Section \ref{['sec:results:lensing']} for details. The forecasted lensing constraints are generally competitive with the data constraints of Planck 2018, and can even surpass the latter for templates that have excess power on smaller scales. This highlights the synergy from using different probes with varying sensitivities.
• Figure 3: The correlation in the joint Fisher posterior for pairs of models. Values close to 1 and 0 indicate strong degeneracy and orthogonality, respectively, between the late-time predictions of two models. The upper triangle uses the parameter covariance matrix for LSST Y10, and the lower one the same for a cosmic variance (CV) limited survey. Redder colors indicate more uncorrelated signals. The correlations in an LSST Y10 measurement are similar to those from a CV-limited survey. The diagonal (which has a trivial correlation of 1) is shaded black for clearer visuals. The black outlines denote different classes of templates.
• Figure 4: The derivative of the power spectrum with input $f_{\rm NL}$ value, presented for different templates and at different redshifts. For values of $f_{\rm NL} = 100$, there is a 1% to 2% change in the power spectrum across all scales. Oscillatory features are more prevalent at high redshift, and are washed away at lower redshifts as nonlinear structure evolves over time. However, the amplitude of the derivatives still increases towards lower redshifts with the prolonged impacts of nonlinear structure formation. The error bars show the uncertainty on the mean derivative, obtained from bootstrapping over ten independent realizations. For visibility, we only show uncertainties at $z = 0$. Though, in most cases the error bars are hidden behind the lines.
• Figure 5: Similar to Figure \ref{['fig:results:mPk']} but for the matter density bispectrum. For visibility reasons, we only show uncertainties for the equilateral and squeezed limits at $z = 0$. The measurements are smoothed with a narrow Gaussian kernel for visualization.