📝 SummarXiv

Abstract

The standard $\Lambda$CDM paradigm of the physical Universe suffers from well-known conceptual problems and is challenged by observational data. Alternative models exist in the literature, both phenomenological and physically motivated, many of them suffering from similar or new problems. We propose a method to mechanically generate alternative models in a procedure informed by data and tuned to mitigate specific problems. We implement a computational framework, dubbed CosmoGen, that integrates evolutionary algorithms for symbolic regression, with computation of structure formation and background cosmological quantities that are used to guide the evolutionary process. As a proof-of-concept we apply the procedure to the specific case of dark energy fluid models, and ask the framework to generate models capable of alleviating the cosmological tensions $S_8$ and $H_0$. The system generates models with high fitness values, and through a Bayesian analysis of an illustrative model, we show that the model indeed alleviates the tensions.