Cosmic Censorship in Quantum Gravity
Ichiro Oda
Abstract
We study cosmic censorship in the Reissner-Nordstrom charged black hole by means of quantum gravity holding near the apparent horizons. We consider a gedanken experiment whether or not a black hole with the electric charge Q less than the mass M (Q < M) could increase its charge to go beyond the extremal limit Q = M by absorbing the external charged matters. If the black hole charge could exceed the extremal value, a naked singularity would be liberated from the protection of the outer horizon and visible to distant observers, which means weak cosmic censorship to be violated in this process. It is shown that the black hole never exceeds the extremal black hole this way in quantum gravity as in classical general gravity. An increment of the trapped external charged matters by the black hole certainly causes the inner Cauchy horizon to approach the outer horizon, but its relative approaching speed gradually slows down and stops precisely at the extremal limit. It is quite remarkable that cosmic censorship remains true even in quantum gravity. This study is the first attempt of examining weak cosmic censorship beyond the classical analysis.
Create a lesson
Related papers
All Quiet on the Vector Front: Scalar Growth and Perturbative Control in Horndeski--Weyl-squared Gravity
Antonio De Felice, Guillem Domènech, Jan Tränkle
Directional effects of the Unruh effect on steering harvesting
Shu-Min Wu, Rui-Yang Xu, Zhihong Liu
Testing Charged Black Holes Embedded in Anisotropic Matter and Surrounded by Plasma through Gravitational Lensing: Bounds from the Sloan Lens ACS Survey
Ritesh Pandey, Shubham Kala, Amare Abebe et al.
Quantum noise and torque correlations in a torsion-bar Sagnac speed meter
Yuki Inoue, Mario Onglao, Daiki Tanabe et al.
A catalog of long, eccentric and precessing binary black hole simulations from ICTS-TIFR
Vaishak Prasad, Prayush Kumar, Michael Boyle et al.
Hierarchical Parameter Estimation for Intrinsic Parameters of Extreme-mass-ratio Inspirals Using Deep Learning
Ping Shen, Wen-Biao Han, Minghui Du