Speaker
Description
The standard Lambda cold dark matter (LCDM) 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, but many of them suffer from similar or new problems.
We propose a method to mechanically generate alternative models in a data-informed procedure tuned to mitigate specific problems.
We implemented a computational framework, dubbed CosmoGen, based on evolutionary algorithms for symbolic regression. The evolutionary process is guided by the computation of structure formation and background cosmological quantities. This is done by integrating supervised learning symbolic regression methods with the cosmological Boltzmann code CLASS and the Bayesian inference tool MontePython. In this framework, each candidate model proposed by the symbolic regression algorithm is implemented on the fly as a modified version of CLASS, treating it as a new dark energy or dark matter component. MontePython then performs a preliminary parameter estimation using a likelihood tuned to the objectives to be achieved, and it returns a fitness score. The score is fed back into the evolutionary loop of the symbolic regression algorithm. This is a hybrid approach between traditional sampling methods and evolutionary strategies. The evolutionary methods are tasked with discovering functional forms, whereas the traditional sampling methods are responsible for evaluating each functional form and extracting the best-fit values for its parameters.
As a proof-of-concept, we applied the procedure to the specific case of dark energy fluid models and asked the framework to generate models capable of alleviating the cosmological tensions S8 and H0.
The system generated models with high fitness values, and through a Bayesian analysis of an illustrative model, we show that the model indeed alleviates the tensions, even though the Bayes factor indicates a weaker preference for LCDM.
This talk is based on the article (arxiv: 2509.15453), accepted for publication in A&A.