%0 Generic %1 bernardo2022energy %A Bernardo, Reginald Christian %A Grandón, Daniela %A Said, Jackson Levi %A Cárdenas, Víctor H. %D 2022 %K cosmology machine_learning phd %T Dark energy by natural evolution: Constraining dark energy using Approximate Bayesian Computation %U http://arxiv.org/abs/2211.05482 %X We look at dark energy from a biology inspired viewpoint by means of the Approximate Bayesian Computation (ABC) and late time cosmological observations. We find that dynamical dark energy comes out on top, or in the ABC language naturally selected, over the standard $Łambda$CDM cosmological scenario. We confirm this conclusion is robust to whether baryon acoustic oscillations and Hubble constant priors are considered. Our results show that the algorithm prefers low values of the Hubble constant, consistent or at least a few standard deviation away from the cosmic microwave background estimate, regardless of the priors taken initially in each model. This supports the result of the traditional MCMC analysis and could be viewed as strengthening evidence for dynamical dark energy being a more favorable model of late time cosmology.

@misc{bernardo2022energy, abstract = {We look at dark energy from a biology inspired viewpoint by means of the Approximate Bayesian Computation (ABC) and late time cosmological observations. We find that dynamical dark energy comes out on top, or in the ABC language naturally selected, over the standard $\Lambda$CDM cosmological scenario. We confirm this conclusion is robust to whether baryon acoustic oscillations and Hubble constant priors are considered. Our results show that the algorithm prefers low values of the Hubble constant, consistent or at least a few standard deviation away from the cosmic microwave background estimate, regardless of the priors taken initially in each model. This supports the result of the traditional MCMC analysis and could be viewed as strengthening evidence for dynamical dark energy being a more favorable model of late time cosmology.}, added-at = {2023-01-01T13:51:25.000+0100}, author = {Bernardo, Reginald Christian and Grandón, Daniela and Said, Jackson Levi and Cárdenas, Víctor H.}, biburl = {https://www.bibsonomy.org/bibtex/285ad7650137dc1aec46cffa3bc7f9b49/intfxdx}, description = {Dark energy by natural evolution: Constraining dark energy using Approximate Bayesian Computation}, interhash = {3c9fcac20fec95a6554887f08efac0c5}, intrahash = {85ad7650137dc1aec46cffa3bc7f9b49}, keywords = {cosmology machine_learning phd}, note = {cite arxiv:2211.05482Comment: 22 pages, 14 figures, 4 tables, comments welcome}, timestamp = {2023-01-01T13:51:25.000+0100}, title = {Dark energy by natural evolution: Constraining dark energy using Approximate Bayesian Computation}, url = {http://arxiv.org/abs/2211.05482}, year = 2022 }