Efficient Compression of Redshift-Space Distortion Data for Late-Time Modified Gravity Models

TL;DR

This paper introduces a method to compress redshift-space distortion data into a few parameters to efficiently test modified gravity models, demonstrating its effectiveness with mock and current data, and forecasting improved constraints with future surveys.

Contribution

The authors develop a model-independent data compression technique for redshift-space distortions, enabling efficient testing of late-time modified gravity models with current and future cosmological data.

Findings

The compression method accurately reproduces model constraints.

Current data shows hints of suppressed matter growth at 2.7σ confidence level.

Forecasts indicate significant improvements in parameter constraints with DESI data.

Abstract

Current cosmological observations allow for deviations from the standard growth of large-scale structures in the universe. These deviations could indicate modifications to General Relativity on cosmological scales or suggest the dynamical nature of dark energy. It is important to characterize these departures in a model-independent manner to understand their significance objectively and explore their fundamental causes more generically across a wider spectrum of theories and models. In this paper, we compress the information from redshift-space distortion data into 2-3 parameters $\mu_i$, which control the ratio between the effective gravitational coupling in Poisson's equation and Newton's constant in several redshift bins in the late universe. We test the efficiency of this compression using mock final-year data from the Dark Energy Spectroscopic Instrument (DESI) and considering three different models within the class of effective field theories of dark energy. The constraints on the parameters of these models, obtained from both the direct fit to the data and the projection of the compressed parameters onto the parameters of the models, are fully consistent, demonstrating the method's good performance. Then, we apply it to current data and find hints of a suppressed matter growth in the universe at $\sim 2.7\sigma$ C.L., in full accordance with previous works in the literature. Finally, we perform a forecast with DESI data and show that the uncertainties on the parameters $\mu_1$ at $z<1$ and $\mu_2$ at $1<z<3$ are expected to decrease by approximately $40\%$ and $20\%$, respectively, compared to those obtained with current data. Additionally, we project these forecasted constraints onto the parameters of the aforesaid models.