Elucidating cosmological model dependence with $$H_0$$

Colgáin, Eoin Ó; Sheikh-Jabbari, M. M.

doi:10.1140/epjc/s10052-021-09708-2

Cited by 53 publications

(28 citation statements)

References 109 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, in the inset, we show how the different priors on H 0 give slightly different reconstructed H 0 values. Understandably, the GP also comes with quirks, the most notable of these are overfitting [77], underestimating uncertainties [79], and kernel selection [57,80]. Later on, we shall witness this overfitting in our assessment of the GP and the parameteric methods in the next section.…”

Section: Nonparametric Reconstruction Methodsmentioning

confidence: 98%

Parametric and nonparametric methods hint dark energy evolution

Bernardo,

Grandón,

Said

et al. 2021

Preprint

View full text Add to dashboard Cite

We openly study dark energy through the viewpoints of parametric and nonparametric analyses of late-time cosmological data. We consider three Hubble parameter priors reflecting the Hubble tension and make use of two phenomenological functions, namely, a normalized dark energy density and a compactified dark energy equation of state. We predict the shape of both functions and present new constraints on the dark energy equation of state. The results hint at dark energy evolution regardless of the choice of the method and of the priors. Given that dark energy evolution is arrived at by drastically different approaches, it emphasizes the result that the cosmological data prefers a nontrivial evolution.

show abstract

Section: Nonparametric Reconstruction Methodsmentioning

confidence: 98%

Parametric and nonparametric methods hint dark energy evolution

Bernardo,

Grandón,

Said

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Other recent work also includes a possible resolution to the Hubble tension problem within ΛCDM in Refs. [18,52] where the model independent nature of GP is brought into question due to its dependence on the kernel choice in the regression procedure of the GP reconstruction algorithm. Furthermore, in quintessence and k-essence models from Horndeski theories of gravity [27] that represent an extension to quintessence can reproduce reconstructions of the late expansion of the Universe within 2σ.…”

Section: Gaussian Processes Reconstruction For Cosmologymentioning

confidence: 99%

Performance of Non-Parametric Reconstruction Techniques in the Late-Time Universe

Escamilla-Rivera,

Said,

Mifsud

2021

Preprint

View full text Add to dashboard Cite

In the context of a Hubble tension problem that is growing in its statistical significance, we reconsider the effectiveness of non-parametric reconstruction techniques which are independent of prescriptive cosmological models. By taking cosmic chronometers, Type Ia Supernovae and baryonic acoustic oscillation data, we compare and contrast two important reconstruction approaches, namely Gaussian processes (GP) and the Locally weighted Scatterplot Smoothing together with Simulation and extrapolation method (LOESS-Simex or LS). We firstly show how both GP and LOESS-Simex can be used to successively reconstruct various data sets to a high level of precision. We then directly compare both approaches in a quantitative manner by considering several factors, such as how well the reconstructions approximate the data sets themselves to how their respective uncertainties evolve. In light of the puzzling Hubble tension, it is important to consider how the uncertain regions evolve over redshift and the methods compare for estimating cosmological parameters at current times. For cosmic chronometers and baryonic acoustic oscillation compiled data sets, we find that GP generically produce smaller variances for the reconstructed data with a minimum value of σ GP−min = 1.1, while the situation for LS is totally different with a minimum of σ LS−min = 50.8. Moreover, some of these characteristics can be alliviate at low z, where LS presents less underestimation in comparison to GP.

show abstract

“…More concretely there are auxiliary issues with selecting the covariance function, but this can be circumvented with genetic algorithms [28] to a certain extent. However, GP suffers from a bigger issue with over-fitting for low redshift data [29], which impacts inferred values of H 0 in the Hubble diagram, and f σ 8 0 for growth data. These challenges of GP seem to be generically problematic features that are hard to avoid within this approach.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Reconstruction of Late-Time Cosmology and Null Tests

Dialektopoulos,

Said,

Mifsud

et al. 2021

Preprint

View full text Add to dashboard Cite

The prospect of nonparametric reconstructions of cosmological parameters from observational data sets has been a popular topic in the literature for a number of years. This has mainly taken the form of a technique based on Gaussian processes but this approach is exposed to several foundational issues ranging from overfitting to kernel consistency problems. In this work, we explore the possibility of using artificial neural networks (ANN) to reconstruct late-time expansion and large scale structure cosmological parameters. We first show how mock data can be used to design an optimal ANN for both parameters, which we then use with real data to infer their respective redshift profiles. In addition, we also consider null tests on the reconstructed data against a concordance cosmology.

show abstract

Elucidating cosmological model dependence with $$H_0$$

Cited by 53 publications

References 109 publications

Parametric and nonparametric methods hint dark energy evolution

Parametric and nonparametric methods hint dark energy evolution

Performance of Non-Parametric Reconstruction Techniques in the Late-Time Universe

Neural Network Reconstruction of Late-Time Cosmology and Null Tests

Contact Info

Product

Resources

About