From Frame Covariance to the Swampland Distance Conjecture

This paper resolves the ambiguity of field space geometry in gravitational effective field theories by developing a frame-covariant framework that interprets conformal frames as distinct foliations of a higher-dimensional auxiliary geometry, thereby demonstrating that key Swampland Distance Conjectures are universal consequences of frame covariance rather than specific quantum gravity constraints.

Sotirios Karamitsos, Benjamin Muntz2026-03-02⚛️ hep-th

Covariant eigenmode overlap formalism for gravitational wave signals in electromagnetic cavities

This paper presents a coordinate-invariant formalism using eigenmode expansion to model the interaction between gravitational waves and resonant detectors, specifically deriving coupling coefficients that account for damping and back-action to facilitate numerical analysis of high-frequency experiments in arbitrary electromagnetic cavity geometries.

Jordan Gué, Tom Krokotsch, Gudrid Moortgat-Pick2026-03-02⚛️ hep-th

Universality of the Blandford-Znajek emission in stationary and axisymmetric spacetimes

This paper demonstrates that while the lowest-order Blandford-Znajek jet power is universal across generic stationary and axisymmetric black-hole spacetimes, the next-leading-order corrections depend on the specific spacetime geometry, offering a potential method to distinguish rapidly rotating black holes through combined measurements of jet luminosity and angular velocity.

Filippo Camilloni, Luciano Rezzolla2026-03-02⚛️ hep-th

Acoustic Black Hole in Hayward Spacetime: Shadow, Quasinormal Modes and Analogue Hawking Radiation

This paper investigates an acoustic black hole within Hayward spacetime derived from relativistic Gross-Pitaevskii theory, numerically analyzing its shadow, quasinormal modes, and analogue Hawking radiation to demonstrate how a tuning parameter enhances emission rates and shadow size while stabilizing oscillation frequencies through effective potential modifications.

Zhong-Yi Hui, Yu-Ye Cheng, Jia-Rui Sun2026-03-02⚛️ hep-th

Deep Horizon; a machine learning network that recovers accreting black hole parameters

This paper introduces "Deep Horizon," a machine learning framework utilizing two convolutional neural networks to accurately recover black hole physical parameters from simulated Event Horizon Telescope images, demonstrating that while current ground-based resolution limits recovery to mass and accretion rate, higher-resolution space-based observations at 690 GHz would enable the precise estimation of additional parameters including spin and viewing angle.

Jeffrey van der Gucht, Jordy Davelaar, Luc Hendriks + 5 more2019-10-29🔭 astro-ph.HE