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Can we learn from matter creation to solve the $H_{0}$ tension problem?
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abstract
The $H_{0}$ tension problem is studied in the light of a matter creation mechanism (an effective approach to replacing dark energy), the way to define the matter creation rate being of pure phenomenological nature. Bayesian (probabilistic) Machine Learning is used to learn the constraints on the free parameters of the models, with the learning being based on the generated expansion rate, $H(z)$. Taking advantage of the method, the constraints for three redshift ranges are learned. Namely, for the two redshift ranges: $z\in [0,2]$~(cosmic chronometers) and $z\in [0,2.5]$~(cosmic chronometers + BAO), covering already available $H(z)$ data, to validate the learned results; and for a third redshift interval, $z\in[0,5]$, for forecasting purposes. It is learned that the $3\alpha H_{0}$ term in the creation rate provides options that have the potential to solve the $H_{0}$ tension problem.
Forward citations
Cited by 3 Pith papers
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Is Dark Energy an Effective Manifestation of Non-equilibrium Thermodynamics? -- Insights from DESI
Two phenomenological dark matter creation rates can reproduce the accelerated expansion of the universe and fit current background data as well as or slightly better than LambdaCDM for some DESI-based data combinations.
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Dark energy era with a resolution of Hubble tension in generalized entropic cosmology
A generalized-entropy dark energy model with one fitted extra parameter returns H0 near 73 km/s/Mpc on some datasets, but the reported model-comparison statistics do not favor it over LambdaCDM.
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Investigating $f(R)$-Inflation: background evolution and constraints
A Jordan-frame f(R) model with particle creation during inflation is fitted to DESI and Pantheon+ data, yielding H0=71.04 and a claimed reduction of the Hubble tension to about 1.6-2.8 sigma.
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