Cosmological constraints on γ -gravity models

Abstract

In this paper we place observational constraints on the well-known γ-gravity f (R) model using the latest cosmological data, namely we use the latest growth rate, Cosmic Microwave Background, Baryon Acoustic Oscillations, Supernovae type Ia and Hubble parameter data. Performing a joint likelihood analysis we find that the γ-gravity model is in very good agreement with observations. Utilizing the AIC statistical test we statistically compare the current f (R) model with ΛCDM cosmology and find that they are statistically equivalent. Therefore, γ-gravity can be seen as a useful scenario toward testing deviations from General Relativity. Finally, we note that we find somewhat higher values for the f (R) best-fit values compared to those mentioned in the past in the literature and we argue that this could potential alleviate the halo-mass function problem. PACS numbers: 95.36.+x, 04.50.Kd, 98.80.Es

Constraints on $$\sigma _8$$ and degeneracies from linear Nash-Greene perturbations in subhorizon scale

“…The methodology used to handle the data relies on the Markov Chain Monte Carlo (MCMC) technique based on the Metropolis-Hasting algorithm with a modified version of the original available publicly Mathematica TM code from refs. [37,38]. We apply our χ 2 -statistics to the joint likelihood of the 1107 data points.…”

“…We perform the Markov Chain Monte Carlo (MCMC) sample technique with a modified version of the available publicly code [37,38] written in Mathematica TM software using the joint likelihood of kinematical probes as of the Cosmic Microwave Background (CMB) Planck 2018 [1] datasets of TT,TE,EE+lowE on 68% interval of the related cosmolog-0123456789(). : V,-vol ical parameters, the largest dataset Pantheon SnIa [39] with redshift ranging from 0.01 < z < 2.3, the Hubble parameter a function of redshift H (z) [40] and Baryonic Acoustic Oscillations (BAO) from points of the joint surveys 6dFGS [41], BOSS DR12 [42], SDSS DR7 MGS [43], eBOSS DR14 [44], BOSS DR12 Lyα forest [45] and BOSS DR11 Lyα forest [46].…”

Sub-horizon modes and growth index in a linear scalar cosmological perturbations

“…The methodology used in this paper relies on the Markov Chain Monte Carlo (MCMC) sample technique adapted from a publicly available code of a modified Metropolis-Hastings algorithm [31,32] used as a parameter estimator. We perform our analysis using the joint likelihood of kinematical probes on the CMB Planck 2018 data [2], the Pantheon SNIa [33] with redshift ranging from 0.01 < z < 2.3, the Hubble parameter H (z) as a function of redshift [34][35][36][37][38][39] and the "extended Gold 2018" growth-rate data compilation of SDSS [40][41][42], 6dFGS [43], IRAS [44,45], 2MASS [44,46], 2dFGRS [47], GAMA [48], BOSS [49], WiggleZ [50], Vipers [51], FastSound [52], BOSS Q [53] and additional points from the 2018 SDSS-IV [54][55][56].…”

“…In this direction, we used a Markov Chain Monte Carlo analysis (MCMC) from a modified Metropolis-Hastings algorithm [31,32] to determine the cosmic parameters. We studied matter density profiles δ m (z) for the models indicating a higher growth density for the β-model in the scale k ∼ 0.1.…”

“…The Markov Chain Monte Carlo (MCMC) is used as a cosmological parameter estimator from a publicly available code [30][31][32]. The joint analysis is made from the latest data on 0123456789().…”