Model-independent constraints on $Ω_m$ and $H(z)$ from the link between geometry and growth

Ruiz-Zapatero, Jaime; García-García, Carlos; Alonso, David; Ferreira, Pedro G.; Grumitt, Richard D. P.

doi:10.48550/arxiv.2201.07025

“…This suggests that if late-time solutions are invoked to address the new physics required for resolving the Hubble tension, then the modifications must go beyond the background level [43,44]. Our conclusion is consistent with recent studies [114][115][116], together with the recent analytic criterion [117,118], rendering a no-go guide for the late-time solutions on the Hubble tension.…”

Section: Conclusion and Discussionsupporting

confidence: 89%

No-go guide for late-time solutions to the Hubble tension: Matter perturbations

Cai¹,

Guo²,

Wang³

et al. 2022

Preprint

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The Hubble tension seems to be a crisis with ∼ 5σ discrepancy between the most recent local distance ladder measurement from Type Ia supernovae calibrated by Cepheids and the global fitting constraint from the cosmic microwave background data. To narrow down the possible late-time solutions to the Hubble tension, we have used in a recent study [Phys. Rev. D 105 (2022) L021301] an improved inverse distance ladder method calibrated by the absolute measurements of the Hubble expansion rate at high redshifts from the cosmic chronometer data, and found no appealing evidence for new physics at the late time beyond the ΛCDM model characterized by a parametrization based on the cosmic age. In this paper, we further investigate the perspective of this improved inverse distance ladder method by including the late-time matter perturbation growth data. Independent of the dataset choices, model parametrizations, and diagnostic quantities (S8 and S12), the new physics at the late time beyond the ΛCDM model is strongly disfavoured so that the previous late-time no-go guide for the Hubble tension is further strengthened.

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“…The mass density parameter Ω M is rather weakly constrained if we don't assume a cosmological model. For example, recent attempts at model-independent estimates are provided by the works [49,50] that consider direct observations of the expansion and gravitational dynamics on the expanding background to place constraints on Ω M . The latter paper reports a range Ω M = 0.224±0.066.…”

Section: Observational Data For Scale Factor Evolutionmentioning

confidence: 99%

Suggestions of decreasing dark energy from supernova and BAO data

Raamsdonk,

Waddell

2024

J. Cosmol. Astropart. Phys.

5

0

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The potential energy from a time-dependent scalar field provides a possible explanation for the observed cosmic acceleration. In this paper, we investigate how data from supernova and bary acoustic oscillation surveys constrain the possible evolution of a single scalar field over the period of time (roughly half the age of the universe) for which these data are available. Taking a linear approximation to the scalar potential V(ϕ) = V 0 + V 1 ϕ around the present value, a likelihood analysis appears to significantly prefer models with a decreasing potential energy at present, with approximately 99.99 % of the exp(-χ 2/2) distribution having V 1 > 0 in a convention where ϕ̇ ≤ 0 at present. The models favoured by the distribution typically have an order one decrease 〈|Range[V(ϕ(t))]/V(t 0)|〉 ≈ 0.36 in the scalar potential energy over the time frame corresponding to z < 2. According to the likelihood analysis, the ΛCDM model with no variation in dark energy appears to be significantly disfavoured in the context of the linear potential model, but this should be interpreted cautiously since model selection criteria that make use of Δχ 2 while ignoring parameter space volumes still favour ΛCDM. Working with a second order approximation to the potential, the supernova data can be fit well for a wide range of possible potentials, including models where the universe has already stopped accelerating.

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“…The GP consists of generic supervised learning method designed to solve regression and probabilistic classification problems, where we can interpolate the observations and compute empirical confidence intervals and a prediction in some region of interest [54]. In the cosmological context, GP techniques has been used to reconstruct cosmological parameters, like the dark energy equation of state, w(z), the expansion rate of the universe, the cosmic growth rate, and other cosmological functions (see, e.g., [5,[55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70][71][72][73][74] for a short list of references).…”

Section: Gaussian Processes Regressionmentioning

confidence: 99%

Inferring $$S_8(z)$$ and $$\gamma (z)$$ with cosmic growth rate measurements using machine learning

Avila

¹

,

Bernui

²

,

Bonilla

³

et al. 2022

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Measurements of the cosmological parameter $$S_8$$ S 8 provided by cosmic microwave background and large scale structure data reveal some tension between them, suggesting that the clustering features of matter in these early and late cosmological tracers could be different. In this work, we use a supervised learning method designed to solve Bayesian approach to regression, known as Gaussian Processes regression, to quantify the cosmic evolution of $$S_8$$ S 8 up to $$z \sim 1.5$$ z ∼ 1.5 . For this, we propose a novel approach to find firstly the evolution of the function $$\sigma _8(z)$$ σ 8 ( z ) , then we find the function $$S_8(z)$$ S 8 ( z ) . As a sub-product we obtain a minimal cosmological model-dependent $$\sigma _8(z=0)$$ σ 8 ( z = 0 ) and $$S_8(z=0)$$ S 8 ( z = 0 ) estimates. We select independent data measurements of the growth rate f(z) and of $$[f\sigma _8](z)$$ [ f σ 8 ] ( z ) according to criteria of non-correlated data, then we perform the Gaussian reconstruction of these data sets to obtain the cosmic evolution of $$\sigma _8(z)$$ σ 8 ( z ) , $$S_8(z)$$ S 8 ( z ) , and the growth index $$\gamma (z)$$ γ ( z ) . Our statistical analyses show that $$S_8(z)$$ S 8 ( z ) is compatible with Planck $$\Lambda $$ Λ CDM cosmology; when evaluated at the present time we find $$\sigma _8(z=0) = 0.766 \pm 0.116$$ σ 8 ( z = 0 ) = 0.766 ± 0.116 and $$S_8(z=0) = 0.732 \pm 0.115$$ S 8 ( z = 0 ) = 0.732 ± 0.115 . Applying our methodology to the growth index, we find $$\gamma (z=0) = 0.465 \pm 0.140$$ γ ( z = 0 ) = 0.465 ± 0.140 . Moreover, we compare our results with others recently obtained in the literature. In none of these functions, i.e. $$\sigma _8(z)$$ σ 8 ( z ) , $$S_8(z)$$ S 8 ( z ) , and $$\gamma (z)$$ γ ( z ) , do we find significant deviations from the standard cosmology predictions.

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Model-independent constraints on $Ω_m$ and $H(z)$ from the link between geometry and growth

Cited by 4 publications

References 58 publications

No-go guide for late-time solutions to the Hubble tension: Matter perturbations

No-go guide for late-time solutions to the Hubble tension: Matter perturbations

Suggestions of decreasing dark energy from supernova and BAO data

Inferring $$S_8(z)$$ and $$\gamma (z)$$ with cosmic growth rate measurements using machine learning

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