Exploring Hu-Sawicki-like modified gravity with Genetic Algorithms
arXiv:2607.12745
The paper uses genetic algorithms to generate and test Hu‑Sawicki‑like f(R) modified‑gravity models against current background cosmological observations, finding only modest deviations from the standard ΛCDM scenario.
Abstract
We investigate whether viable Hu-Sawicki-like models can produce deviations from that can be tested against current background cosmological data. We adopt a machine-learning approach based on Genetic Algorithms (GA) to reconstruct analytical perturbations around the Hu-Sawicki class of models. We develop a pipeline that interfaces the \texttt{GATO} GA code with the \texttt{CANDI} cosmology code. Each function generated by the GA is first tested against theoretical viability conditions, including stability, the recovery of a standard matter-dominated epoch, the General Relativity limit, and chameleon screening mechanism. Viable candidates are then passed to \texttt{CANDI} to reconstruct the corresponding background cosmology and are tested against DESI DR2 BAO measurements and the Pantheon+ Type Ia supernova catalogue. %\newline The deviations we find are largest at late times, where the lower curvature makes modified-gravity effects more relevant, and are rapidly suppressed at higher redshift, in agreement with the imposed matching to the matter-dominated era. To further quantify deviations from the standard cosmological model, we compute the diagnostic. It shows only a very small departure from the constant behaviour. The effective dark energy equation of state associated with the reconstructed function also evolves only weakly, showing a mild transition from an effective quintessence-like nature to an effective phantom-like regime. Overall, our results indicate that, within perturbations around the Hu-Sawicki class of models, current background data allow only limited deviations from .
Prepared for submission to PRD. 15 pages and 5 figures