JOURNAL ARTICLE

Theoretical and observational implications of Planck's constant as a running fine structure constant.

  • Published In: International Journal of Modern Physics D: Gravitation, Astrophysics & Cosmology, 2024, v. 33, n. 9/10. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Ali, Ahmed Farag; Mureika, Jonas; Vagenas, Elias C.; Elmashad, Ibrahim 3 of 3

Abstract

This paper explores how a reinterpretation of the generalized uncertainty principle as an effective variation of Planck's constant provides a physical explanation for a number of fundamental quantities and couplings. In this context, a running fine structure constant is naturally emergent and the cosmological constant problem is solved, yielding a novel connection between gravitation and quantum field theories. The model could potentially clarify the recent experimental observations by the DESI Collaboration that could imply a fading of dark energy over time. When applied to quantum systems and their characteristic length scales, a simple geometric relationship between energy and entropy is disclosed. Lastly, a mass–radius relation for both quantum and classical systems reveals a phase transition-like behavior similar to thermodynamical systems, which we speculate to be a consequence of topological defects in the universe. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Modern Physics D: Gravitation, Astrophysics & Cosmology. 2024/07, Vol. 33, Issue 9/10, p1
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2024
  • ISSN:0218-2718
  • DOI:10.1142/S0218271824500366
  • Accession Number:178882769
  • Copyright Statement:Copyright of International Journal of Modern Physics D: Gravitation, Astrophysics & Cosmology is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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