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Constraints on coasting cosmological models from gravitational-wave standard sirens

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arxiv 2310.16556 v2 pith:6I5GHY7J submitted 2023-10-25 astro-ph.CO

classification astro-ph.CO
keywords coastingmodelsconstraintscosmologicaldetectionsgravitational-wavemathrmcosmologies
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

We present the first test of coasting cosmological models with gravitational-wave standard sirens observed in the first three observing runs of the LIGO-Virgo-KAGRA detector network. We apply the statistical galaxy catalog method adapted to coasting cosmologies and infer constraints on the $H_0$ Hubble constant for the three fixed values of the curvature parameter $k=\left\{ -1,0,+1 \right\}$ in $H_0^2 c^{-2}$ units. The maximum posteriors and $68.3\%$ highest density intervals we obtained from a combined analysis of $46$ dark siren detections and a single bright siren detection are $H_0=\left\{68.1^{+8.5}_{-5.6},67.5^{+8.3}_{-5.2},67.1^{+6.6}_{-5.8} \right\}~\mathrm{km\ s^{-1}\ Mpc^{-1}}$, respectively. All our constraints on $H_0$ are consistent within one sigma with the $H_0$ measured with the differential age method, which provides a constraint on $H_0$ in coasting cosmologies independently from $k$. Our results constrain all cosmological models with $a(t)\propto t$ linear expansion in the luminosity distance and redshift range of the $47$ LIGO-Virgo detections, i.e. $d_\mathrm{L}\lesssim 5~\mathrm{Gpc}$ and $z\lesssim 0.8$, which practically include all (both strictly linear and quasi-linear) models in the coasting model family. As we have found, the coasting models and the $\Lambda$CDM model fit equally well to the applied set of gravitational-wave detections.

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  1. Testing Quasi-Linear Coasting Cosmologies with Late-Time Large-Scale Structure Growth

    astro-ph.CO 2025-06 conditional novelty 5.0 of 10

    A closed-form growth factor for linearly expanding (coasting) cosmologies is derived and fit to fσ8 data, showing all tested models are consistent but Lambda-CDM is statistically preferred.

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