Papers › Machine learning constraints on deviations from general relativity from the large...
Machine learning constraints on deviations from general relativity from the large scale structure of the Universe
George Alestas, Lavrentios Kazantzidis, Savvas Nesseris
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
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 μ≡G_(eff)/G_N and of the dark energy anisotropic stress η, 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 a constant value 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 a next-generation survey and forecast the limits of any possible detection of deviations from GR. In particular, we use two fiducial models: one based on the cosmological constant ΛCDM model and another on a model with an evolving Newton's constant, dubbed μCDM. We find that the GA reconstructions of μ(z) and η(z) can be constrained to within a few percent of the fiducial models and in the case of the μCDM mocks, they can also provide a strong detection of several σs, thus demonstrating the utility of the GA reconstruction approach.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections