PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
April 30, 20260 citationsOpen Access

Curvature-Driven Modifications to Photon Propagation: A Kretschmann-Based Phenomenological Framework for Strong-Field Gravity

View Full Paper
DSDey Shivsankar

Key Points

  • The aim is to develop a model that captures photon propagation in curved spacetime using curvature-driven dynamics.
  • Introduced a bounded Lagrangian through EFT-inspired resummation.
  • Compared sigmoidal, exponential, and Padé regularization schemes for their impact on photon trajectories.
  • Constrained parameter space using solar-system, strong-lensing, and multi-messenger data.
  • Predicted corrections to lensing effects and time delay for photons in strong-field gravity.
  • Lagrangian modifications produced finite photon trajectories in strong-field regimes.
  • Agreements noted between regularization schemes at low curvature, with deviations observed at high curvature.

Abstract

This paper presents a phenomenological model for photon propagation in curved spacetime that replaces the usual null-geodesic picture with a curvature-regularized dynamics built from the Kretschmann scalar. A bounded Lagrangian is introduced through an EFT-inspired resummation, producing modified photon trajectories that remain finite in strong-field regimes while reducing to standard general relativity in the weak-field limit. The model compares sigmoidal, exponential, and Padé regularization schemes, showing that they agree at low curvature but differ in the high-curvature domain. The framework predicts corrections to lensing, time delay, and dispersion, and the allowed parameter space is constrained by solar-system, strong-lensing, and multi-messenger data. The approach is explicitly phenomenological, with the underlying field-theoretic derivation left open.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Dey Shivsankar (2026) studied this question.

synapsesocial.com/papers/69f2f1dc1e5f7920c638780bhttps://doi.org/10.5281/zenodo.19835570
Ask AI
Helpful
Bookmark
Share
View Full Paper