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dc.contributor.author | Anagnostopoulos, Fotios K.![]() |
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dc.contributor.author | Bonanno, Alfio![]() |
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dc.contributor.author | Mitra, Ayan![]() |
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dc.contributor.author | Zarikas, Vasilios![]() |
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dc.date.accessioned | 2023-03-29T09:40:38Z | |
dc.date.available | 2023-03-29T09:40:38Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Anagnostopoulos, F., Bonanno, A., Mitra, A., & Zarikas, V. (2022). Swiss-cheese cosmologies with variable G and Λ from the renormalization group. Physical Review, 105(8). https://doi.org/10.1103/physrevd.105.083532 | en_US |
dc.identifier.uri | http://nur.nu.edu.kz/handle/123456789/7004 | |
dc.description.abstract | A convincing explanation for the nature of the dark energy and dark matter is still missing. In recent works a RG-improved swiss-cheese cosmology with an evolving cosmological constant dependent on the Schücking radius has been proven to be a promising model to explain the observed cosmic acceleration. In this work we extend this model to consider the combined scaling of the Newton constant G and the cosmological constant Λ according to the IR-fixed point hypothesis. We shall show that our model easily generates the observed recent passage from deceleration to acceleration without need of extra energy scales, exotic fields or fine-tuning. In order to check the generality of the concept, two different scaling relations have been analyzed and we proved that both are in very good agreement with Λ CDM cosmology while one of them has no fine-tuning problem. We also show that our model satisfies the observational local constraints on ˙ G / G . | en_US |
dc.language.iso | en | en_US |
dc.publisher | PHYSICAL REVIEW D | en_US |
dc.rights | Attribution-NonCommercial-ShareAlike 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/us/ | * |
dc.subject | Type of access: Open Access | en_US |
dc.subject | swiss-cheese cosmology | en_US |
dc.title | SWISS-CHEESE COSMOLOGIES WITH VARIABLE G AND Λ FROM THE RENORMALIZATION GROUP | en_US |
dc.type | Article | en_US |
workflow.import.source | science |
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