Revisiting a non-parametric reconstruction of the deceleration parameter from combined background and the growth rate data

Mukherjee, Purba; Banerjee, Narayan

doi:10.1016/j.dark.2022.100998

Cited by 25 publications

(5 citation statements)

References 109 publications

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“…Thus, the optimal values of the hyperparameters are derived from the maximisation of the probability of the GP to generate our considered set of data, that is implemented [88,[91][92][93] via the minimisation of the GP marginal likelihood, which is similar to the hierarchical Bayesian approach. GP have now been exhaustively used for the reconstruction of cosmological functions, particularly related to the late-time cosmic accelerated expansion observables [75,76,88,89,[94][95][96][97][98][99][100][101][102][103][104][105]. We should remark that although GP are independent from any cosmological model, GP rely on the choice of the kernel function which governs the correlations between distinct points in the GP reconstructed function, and hence its profile.…”

Section: Gaussian Processesmentioning

confidence: 99%

Reconstructing teleparallel gravity with cosmic structure growth and expansion rate data

Said,

Mifsud,

Sultana

et al. 2021

Preprint

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We consider the application of a machine learning technique to growth and expansion rate data in the context of teleparallel gravity (TG). We do this by using a combined approach of Hubble data together with redshift-space-distortion f σ 8 data which together are used to reconstruct the TG Lagrangian via Gaussian processes (GP), where the Hubble data mainly comes from cosmic chronometer and supernova type Ia data from the Pantheon release. In this work, we consider two main GP covariance functions, namely the squaredexponential and Cauchy kernels in order to show consistency (to within 1σ uncertainties). The core results of this work are the numerical constructions of the TG Lagrangian from GP reconstructed Hubble and growth data. We take different possible combinations of the datasets and kernels to show any potential differences in this regard. We show that nontrivial cosmology beyond ΛCDM falls within the uncertainties of the reconstruction from growth data.

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Section: Gaussian Processesmentioning

confidence: 99%

Reconstructing teleparallel gravity with cosmic structure growth and expansion rate data

Said,

Mifsud,

Sultana

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Another approach is to parameterize the modifications of the DM perturbations equations in a model-independent way so different models can be described with the same formalism and discriminate modifications of gravity from other effects [30]. In [246,247] the interaction is not postulated but reconstructed from the data in a non-parametric way.…”

Section: New Phenomenological Modelsmentioning

confidence: 99%

Further understanding the interaction between dark energy and dark matter: current status and future directions

Wang,

Abdalla,

Atrio-Barandela

et al. 2024

Rep. Prog. Phys.

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The interaction between dark matter and dark energy can be in- corporated into field theory models of dark energy that have proved successful in alleviating the coincidence problem. We review recent advances in this field, including new models and constraints from dif- ferent astronomical data sets. We show that interactions are allowed by observations and can reduce the current tensions among different measurements of cosmological parameters. We extend our discussion to include constraints from non-linear effects and results from cosmo- logical simulations. Finally, we discuss forthcoming multi-messenger data from current and future observational facilities that will help to improve our understanding of the interactions within the dark sector.

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“…This has mainly taken the form of using Gaussian processes (GP) [48] which is based on training a covariance function to reconstruct the Hubble diagram together with uncertainties at each point such as in refs. [49][50][51][52][53][54][55][56][57][58]. In refs.…”

Section: Jcap12(2022)029mentioning

confidence: 99%

Neural network reconstruction of H'(z) and its application in teleparallel gravity

Mukherjee

Said

Mifsud

2022

J. Cosmol. Astropart. Phys.

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In this work, we explore the possibility of using artificial neural networks to impose constraints on teleparallel gravity and its f(T) extensions. We use the available Hubble parameter observations from cosmic chronometers and baryon acoustic oscillations from different galaxy surveys. We discuss the procedure for training a network model to reconstruct the Hubble diagram. Further, we describe the procedure to obtain H'(z), the first order derivative of H(z), using artificial neural networks which is a novel approach to this method of reconstruction. These analyses are complemented with further studies on the impact of two priors which we put on H 0 to assess their impact on the analysis, which are the local measurements by the SH0ES team (H 0 R20 = 73.2 ± 1.3 km Mpc-1 s-1) and the updated TRGB calibration from the Carnegie Supernova Project (H 0 TRGB = 69.8 ± 1.9 km Mpc-1 s-1), respectively. Additionally, we investigate the validity of the concordance model, through some cosmological null tests with these reconstructed data sets. Finally, we reconstruct the allowed f(T) functions for different combinations of the observational Hubble data sets. Results show that the ΛCDM model lies comfortably included at the 1σ confidence level for all the examined cases.

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Revisiting a non-parametric reconstruction of the deceleration parameter from combined background and the growth rate data

Cited by 25 publications

References 109 publications

Reconstructing teleparallel gravity with cosmic structure growth and expansion rate data

Reconstructing teleparallel gravity with cosmic structure growth and expansion rate data

Further understanding the interaction between dark energy and dark matter: current status and future directions

Neural network reconstruction of H'(z) and its application in teleparallel gravity

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