http://arxiv.org/abs/2209.12799

We use a particular machine learning approach, called the genetic algorithms (GA), in order to place constraints on deviations from general relativity (GR) via a possible evolution of Newton’s constant $\mu\equiv G_\mathrm{eff}/G_\mathrm{N}$ and of the dark energy anisotropic stress $\eta$, both defined to be equal to one in GR. Specifically, we use a plethora of background and linear-order perturbations data, such as type Ia supernovae, baryon acoustic oscillations, cosmic chronometers, redshift space distortions and $E_g$ data. We find that although the GA is affected by the lower quality of the currently available data, especially from the $E_g$ data, the reconstruction of Newton’s constant is consistent with both a constant value and with unity within the errors. On the other hand, the anisotropic stress deviates strongly from unity due to the sparsity and the systematics of the $E_g$ data. Finally, we also create synthetic data based on an LSST-like survey and forecast the limits of any possible detection of deviations from GR. In particular we use two fiducial models, namely the cosmological constant model $\Lambda$CDM and a model with an evolving Newton’s constant, and we find that the GA reconstructions of $\mu(z)$ and $\eta(z)$ are in most cases constrained to within a few percent of the fiducial models, thus demonstrating the utility of the GA reconstruction approach.

Read this paper on arXiv…

G. Alestas, L. Kazantzidis and S. Nesseris
Tue, 27 Sep 22
30/89

Comments: 15 pages, 7 figures, 3 tables, comments welcome