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Reconstructing Gravity on Cosmological Scales

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arxiv 1902.01366 v1 pith:VZVOHPQR submitted 2019-02-04 astro-ph.CO gr-qchep-th

classification astro-ph.COgr-qchep-th
keywords cosmologicalmodelsdatacosmicdarkenergygravitymodel
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We present the data-driven reconstruction of gravitational theories and Dark Energy models on cosmological scales. We showcase the power of present cosmological probes at constraining these models and quantify the knowledge of their properties that can be acquired through state of the art data. This reconstruction exploits the power of the Effective Field Theory approach to Dark Energy and Modified Gravity phenomenology, which compresses the freedom in defining such models into a finite set of functions that can be reconstructed across cosmic times using cosmological data. We consider several model classes described within this framework and thoroughly discuss their phenomenology and data implications. We find that some models can alleviate the present discrepancy in the determination of the Hubble constant as inferred from the cosmic microwave background and as directly measured. This results in a statistically significant preference for the reconstructed theories over the standard cosmological model.

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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. Constraints on Dark Energy Models Using Late Universe Probes

    astro-ph.CO 2025-06 conditional novelty 4.0 of 10

    Using late-universe probes only, the authors find all dark energy models remain within 1-2 sigma of Lambda CDM, which wins the Bayesian model comparison, and confirm that DESI's LRG1 and LRG2 points drive the dynamica...

  2. Alleviating the Hubble Tension via Cosmological Time Dilation in the meVSL Model

    physics.gen-ph 2025-09 reject novelty 3.0 of 10

    The meVSL model's parameter b reduces the baryon drag sound horizon, raising inferred H0, and changes the cosmological time-dilation exponent to n=1-b/4; the paper forecasts SN sample sizes to detect this.

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