The shape function of the observed growth index

TL;DR

This work reframes the growth index $\gamma$ as a function of observables $f\sigma_8(z)$, $f(z)$, and $H(z)$, avoiding fixed-parametric forms. By deriving an analytic $\gamma(a)$ from these observables and implementing a variational shape-function interpolation grounded in the virtual-work principle, it produces a continuous $\gamma(z)$ with error propagation across redshift. The approach enables model-independent tests of $\Lambda$CDM against modified gravity and dynamical dark energy, and forecasts indicate that Stage-IV surveys could detect discrepancies among the three key observables, especially in $f\sigma_8$, while still challenging dynamical dark energy scenarios. Overall, the method provides a robust framework for reconstructing the growth index and testing cosmological models with upcoming high-precision data.

Abstract

The growth index $γ$ is a powerful trigger for detecting deviations from $Λ$CDM. However, its value is often determined by considering an asymptotic constant value that works for all redshift, or else following a chosen parameterisation. Here we formulate the growth index as function of three quantities that could be directly related to observables in redshift bins, $fσ_8(z_i)$, $f(z_i)$ and $H(z_i)$. We determine its value and its derivative at observed nodal center of redshift bins and use the shape function method, after showing insightful connection with its underlying governing virtual-work conservation principle, to construct a redshift dependence of the $γ$ without assuming a specific parameterization. We then use the resulting shape function to test if we can disentangle between different scenarios where there are discrepancies between its three constituent measured components. We also tested whether it can be used to rule out models of modified gravity, or extended parametric models of the growth index that capture more general behaviors with an additional parameter as function of the scale factor or dark energy. Adopting forecasted measurements from next generation surveys on the three quantities used to construct $γ$, we find that reported discrepancies between them could be detected with our method, but at the bins where the errors and lost of precision from our addition of degrees of freedom is small with respect to the deviation of $γ$. The same could be concluded for first order extensions to $γ$ or common modified gravity models, and to a lesser degree for dynamical dark energy models after supposing the latest DESI values. We conclude that this method is a strong tool to investigate cosmology in a model-independent way especially with forthcoming data delivered by further stage-IV surveys with more stringent uncertainties.(Abridged)

Figures (5)

• Figure 1: A comparison from our constructed $\gamma$ obtained at different bins in comparison to analytic values. The grey band marks the 1% limit.
• Figure 2: Different interpolation methods including simple linear interpolation (red lines), continuity preserving spline interpolation (green lines), B-spline smoothing wise interpolation (pink lines), cubic spline interpolation (orange lines) and this work shape function interpolation (blue lines)
• Figure 3: Top panel: Forecast on $\gamma(a)$ supposing different observed values for $H(a)$, $f(a)$ and $f\sigma_8(a)$ following different asymptotic constant $\gamma$ values suitable for describing the shown modified gravity models (see Sect. \ref{['sect:TestandForcast']} for details). Note that nDGP$_{\Lambda}$ signifies that we tuned it to have a $\Lambda$CDM background expansion. Middle and bottom panel: same as above but for different parametrisations of $\gamma$ with a scale factor or $\Omega_{\rm DE}$ variation through an additional $\gamma_1$ parameter.
• Figure 4: Forecast on $\gamma(a)$ supposing different observed values for $H(a)$, $f(a)$ and $f\sigma_8(a)$ following different modified gravity model. Top panel: $w$ constant or CPL dynamical dark energy models. Middle panel: $f(R)$ model. Bottom panel: nDGP model.
• Figure 5: Top panel: Forecast on $\gamma(a)$ supposing three different observed values for $H(a)$ while $f(a)$ and $f\sigma_8(a)$ follow the fiducial model. Middle panel: Same as above but here $f(a)$ is being discrepant. Bottom panel: Same as above but supposing different $f\sigma_8(a)$ observations from different probes.