Constraints on prospective deviations from the cold dark matter model using a Gaussian process
Recently, using Bayesian Machine Learning, a deviation from the cold dark matter model on cosmological scales has been put forward. Such a model might replace the proposed non-gravitational interaction between dark energy and dark matter, and help solve the 0 tension problem. The idea behind the lea...
| Autores: | , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/362733 |
| Acceso en línea: | http://hdl.handle.net/10261/362733 |
| Access Level: | acceso abierto |
| Palabra clave: | H0 tension Bayesian machine learning Gaussian processes |
| Sumario: | Recently, using Bayesian Machine Learning, a deviation from the cold dark matter model on cosmological scales has been put forward. Such a model might replace the proposed non-gravitational interaction between dark energy and dark matter, and help solve the 0 tension problem. The idea behind the learning procedure relies on a generated expansion rate, while the real expansion rate is just used to validate the learned results. In the present work, however, the emphasis is put on a Gaussian Process (GP), with the available () data confirming the possible existence of the already learned deviation. Three cosmological scenarios are considered: a simple one, with an equation-of-state parameter for dark matter =0≠0, and two other models, with corresponding parameters =0+1 and =0+1/(1+). The constraints obtained on the free parameters 0 and 1 hint towards a dynamical nature of the deviation. The dark energy dynamics is also reconstructed, revealing interesting aspects connected with the 0 tension problem. It is concluded, however, that improved tools and more data are needed, to reach a better understanding of the reported deviation. |
|---|