Pith. sign in

REVIEW 1 cited by

Reconstructing teleparallel gravity with cosmic structure growth and expansion rate data

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2103.05021 v2 pith:RRRFXOOY submitted 2021-03-08 astro-ph.CO gr-qc

classification astro-ph.COgr-qc
keywords datagrowthhubblecosmicgravitykernelslagrangianreconstructions
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
abstract

In this work, we use a combined approach of Hubble parameter data together with redshift-space-distortion $(f\sigma_8)$ data, which together are used to reconstruct the teleparallel gravity (TG) Lagrangian via Gaussian processes (GP). The adopted Hubble data mainly comes from cosmic chronometers, while for the Type Ia supernovae data we use the latest jointly calibrated Pantheon compilation. Moreover, we consider two main GP covariance functions, namely the squared-exponential and Cauchy kernels in order to show consistency (to within 1$\sigma$ uncertainties). The core results of this work are the numerical reconstructions of the TG Lagrangian from GP reconstructed Hubble and growth data. We take different possible combinations of the datasets and kernels to illustrate any potential differences in this regard. We show that nontrivial cosmology beyond $\Lambda$CDM falls within the uncertainties of the reconstructions from growth data, which therefore indicates no significant departure from the concordance cosmological model.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Cosmo-Learn: code for learning cosmology using different methods and mock data

    astro-ph.CO 2025-08 conditional novelty 5.0 of 10

    An open-source toolkit that simulates late-universe cosmological observations and benchmarks MCMC, genetic algorithms, Gaussian processes, Bayesian ridge regression, and neural networks in one pipeline.

Pith tools