The growth factor parametrization versus numerical solutions in flat and non-flat dark energy models

Abstract

In the present investigation we use observational data of $$ f \sigma _ {8} $$ f σ 8 to determine observational constraints in the plane $$(\Omega _{m0},\sigma _{8})$$ ( Ω m 0 , σ 8 ) using two different methods: the growth factor parametrization and the numerical solutions method for density contrast, $$\delta _{m}$$ δ m . We verified the correspondence between both methods for three models of accelerated expansion: the $$\Lambda CDM$$ Λ C D M model, the $$ w_{0}w_{a} CDM$$ w 0 w a C D M model and the running cosmological constant RCC model. In all case we consider also the curvature as free parameter. The study of this correspondence is important because the growth factor parametrization method is frequently used to discriminate between competitive models. Our results we allow us to determine that there is a good correspondence between the observational constrains using both methods. We also test the power of the $$ f\sigma _ {8} $$ f σ 8 data to constraints the curvature parameter within the $$ \Lambda CDM $$ Λ C D M model. For this we use a non-parametric reconstruction using Gaussian processes. Our results show that the $$ f\sigma _ {8}$$ f σ 8 data with the current precision level does not allow to distinguish between a flat and non-flat universe.

Using quasar X-ray and UV flux measurements to constrain cosmological model parameters

“…3 For observational constraints on spatial curvature see Farooq et al (2015), Chen et al (2016), Rana et al (2017), Ooba et al (2018a,c), Yu et al (2018), Park & Ratra (2019c,a), Wei (2018), DES Collaboration (2019), Li et al (2020), Handley (2019), Efstathiou & Gratton (2020), Di Valentino et al (2021b), Velasquez-Toribio & Fabris (2020, Vagnozzi et al (2020Vagnozzi et al ( , 2021, KiDS Collaboration (2021), Arjona & Nesseris (2021), Dhawan et al (2021), and references therein. 4 For observational constraints on the CDM model see Avsajanishvili et al (2015), Ryan et al (2018), Solà Peracaula et al (2018Peracaula et al ( , 2019, Zhai et al (2017), Ooba et al (2018bOoba et al ( , 2019, Park & Ratra (2018, 2019b, 2020, Sangwan et al (2018), Singh et al (2019), Ureña-López & Roy (2020), Sinha & Banerjee (2021), and references therein.…”

Analytical growth functions for cosmic structures in a $Λ$CDM Universe

“…Our methodology can also accommodate for non-trivial modifications at the background level. This in particular allows for the inclusion of a spatial background curvature term, as well as certain (parametrisations of) dark energy models (Linder & Jenkins 2003;Huterer et al 2015;Velasquez-Toribio & Fabris 2020). At the fluctuation level, the corresponding linear growth function D will look vastly different for non-standard cosmologies (see e.g.…”

Latest data constraint of some parameterized dark energy models

“…The results show that these DE models can be clearly distinguished. Actually, direct parameterization of growth factor or growth index can also be used to understand the properties of DE [94,[103][104][105][106][107][108], and observational constraints may further distinguish different models. In the future, this issue is worth further study by considering new forms of parameterization and using more new observational data.…”