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Defying the laws of Gravity I: model-independent reconstruction of the Universe expansion from growth data

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arxiv 1906.05991 v3 pith:ZC2CRDYS submitted 2019-06-14 astro-ph.CO gr-qc

Benjamin L'Huillier , Arman Shafieloo , David Polarski , Alexei A. Starobinsky This is my paper · ORCID
classification astro-ph.COgr-qc
keywords datagrowthuniverseexpansiongravityhistoryinhomogeneitymodel-independent
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abstract

Using redshift space distortion data, we perform model-independent reconstructions of the growth history of matter inhomogeneity in the expanding Universe using two methods: crossing statistics and Gaussian processes. We then reconstruct the corresponding history of the Universe background expansion and fit it to type Ia supernovae data, putting constraints on $(\Omega_\mathrm{m,0},\sigma_{8,0})$. The results obtained are consistent with the concordance flat-$\Lambda$CDM model and General Relativity as the gravity theory given the current quality of the inhomogeneity growth data.

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Forward citations

Cited by 3 Pith papers

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

  1. Hubble tension: the shape wall

    astro-ph.CO 2026-07 accept novelty 6.0 of 10

    Late-time modifications to the expansion history can raise H0 by at most about 2% (conservative) to 3.7% (permissive) if the CMB acoustic scale is fixed.

  2. Probing the Cosmic Distance Duality Relation via Non-Parametric Reconstruction for High Redshifts

    astro-ph.CO 2025-09 conditional novelty 4.0 of 10

    A Gaussian process reconstruction of the cosmic distance duality parameter eta(z) from BAO, galaxy clusters, supernovae, and quasars finds consistency with eta=1 at the 2-sigma level out to z about 2.33.

  3. Is $\omega_0 \omega_a$CDM a good model for the clumpy Universe?

    astro-ph.CO 2025-07 reject novelty 4.0 of 10

    Against a Gaussian-process reconstruction of 15 sigma8(z) measurements, the DESI w0waCDM model fits slightly better than LambdaCDM, but the difference is tiny and the comparison metric is biased.

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