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Null test of the cosmic curvature using $H(z)$ and supernovae data

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arxiv 1509.06283 v2 pith:RKR72ZUL submitted 2015-09-21 astro-ph.CO gr-qc

classification astro-ph.COgr-qc
keywords nulltestcosmiccurvatureomegadatamodel-independentobservations
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

We introduce a model-independent approach to the null test of the cosmic curvature which is geometrically related to the Hubble parameter $H(z)$ and luminosity distance $d_L(z)$. Combining the independent observations of $H(z)$ and $d_L(z)$, we use the model-independent smoothing technique, Gaussian processes, to reconstruct them and determine the cosmic curvature $\Omega_K^{(0)}$ in the null test relation. The null test is totally geometrical and without assuming any cosmological model. We show that the cosmic curvature $\Omega_K^{(0)}=0$ is consistent with current observational data sets, falling within the $1\sigma$ limit. To demonstrate the effect on the precision of the null test, we produce a series of simulated data of the models with different $\Omega_K^{(0)}$. Future observations in better quality can provide a greater improvement to constrain or refute the flat universe with $\Omega_K^{(0)}=0$.

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Cited by 2 Pith papers

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.

  2. Non-parametric reconstructions of cosmic curvature: current constraints and forecasts

    astro-ph.CO 2024-11 conditional novelty 4.0 of 10

    Using Gaussian-process reconstructions of cosmic distances and expansion rates, the authors find no statistically significant departure from flatness or from the cosmological principle in current data, and they foreca...

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