PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 5, 20260 citationsOpen Access

Radion-Mediated Regularization of Gravitational Singularities: A 5D Origin for the CCEGA Framework

View Full Paper
LMLópez Sánchez Marc

Key Points

  • The aim is to investigate the stability of compact objects in a five-dimensional extension of the CCEGA framework.
  • Extended CCEGA framework to five dimensions
  • Analyzed the stability of compact objects
  • Utilized perturbation analysis based on the sixth-order WKB method
  • Examined the impact of radion dynamics on gravitational wave echoes.
  • Regular black hole metric is achieved with a de Sitter core
  • Connection established between regularization scale and vacuum expectation value of radion
  • Frequency sidebands in echo signals are generated by radion modulation
  • Results suggest unique observational signatures for third-generation interferometers.

Abstract

We extend the CCEGA framework to a five-dimensional scenario to investigate the stability of compact objects in the strong-field regime. We demonstrate that the exponential curvature-matter coupling induces a regular black hole metric with a de Sitter core, effectively avoiding the central singularity. We explicitly connect the regularization scale Rc to the vacuum expectation value of a stabilized radion in the bulk. Through a perturbation analysis based on the sixth-order WKB method, we characterize the resulting gravitational wave echoes. The dynamics of the radion introduce a characteristic modulation, generating frequency sidebands in the echo signal. Our results suggest that this fine structure provides a unique observational signature, detectable by third-generation interferometers such as LISA or the Einstein Telescope.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

López Sánchez Marc (2025) studied this question.

synapsesocial.com/papers/69a91dc3d6127c7a504c0e98https://doi.org/10.5281/zenodo.18843895
Ask AI
Helpful
Bookmark
Share
View Full Paper