Test to early dark energy models using the Hubble expansion rate

In this work, we present two independent metrics to compute today’s value of the Hubble parameter Ho.Firstly, we implement the median statistics, a robust method unaffected by outliers and variations in thedata distribution. Under only a few assumptions and a large dataset of Ho, built from different observationalmethods in more than 90 years, this non-parametric scheme provides an estimate of 68.0 ± 4.5 km/s/Mpc forHo. We submit the catalog to a second test: the least squares function χ2. We compare the predictions fromthe ΛCDM model (and the Planck collaboration 2018 cosmology) with the early dark energy parametrizationpresented in García et al. 2021. The best fit values with this method are 68.5 ± 0.1 and 66.1 ± 0.1 km/s/Mpc,for the former and latter models, respectively. We highlight that these robust statistical methods such as1 the median statistics, are a powerful tool to solve the current Hubble tension (as well as other possibleinconsistencies among astronomical datasets). Notably, this method does not rely on any cosmologicalmodels; therefore, it provides a clean (unbiased) prediction of the Universe’s expansion rate today. Finally,we find that our results are consistent with the forecast for the Hubble parameter from the early Universeestimates, rather than the local measurements, with two statistical schemes based on completely differentassumptions (parametric vs. non-parametric metrics) and a catalog of 574 values for Ho recovered from theliterature

• Fecha de publicación: 2022
• Institución: Universidad Pedagógica y Tecnológica de Colombia
• Repositorio: RiUPTC: Repositorio Institucional UPTC
• Idioma: spa
• OAI Identifier: oai:repositorio.uptc.edu.co:001/15374
• Acceso en línea: https://revistas.uptc.edu.co/index.php/ciencia_en_desarrollo/article/view/15216; https://repositorio.uptc.edu.co/handle/001/15374
• Palabra clave: Energía Oscura, Cosmología, Tensión de Hubble, Inferencia Bayesiana.; Dark Energy, Cosmology, Hubble tension, Bayesian Inference. 2
• License: http://purl.org/coar/access_right/c_abf2