Gravity on parallelizable manifold
S. Kaniel, Y. Itin
Abstract
General relativity postulates that the gravity field is defined on a Riemannian manifold. The field equations are Rμν= 0 i.e. Ricci's curvature tensor vanishes. The field equations have to be augmented by natural physical requirements like orientability, time orientability and existence of a spinorial structure. Moreover, it is impossible to define the energy of the gravity field only by the metric tensor. We suggest to impose an additional structure and consider parallelizable manifold i.e. manifolds for which a smooth field of frames exists. The derivation of the field equations is by an action principle. A Lagrangian, which is quadratic in the differentials, is defined. The minimum of the action is achieved at a 16 × 16 second order quasi linear system. It is of Laplacian type. The system admits a unique exact solution for a centrally symmetric, static and assimptotically flat field. The resulting metric is the celebrated Rosen metric, which is very close to the Schwarzchild metric. The two are intrinsicly different since the scalar curvature of the Schuarzschild metric is zero, in contrast to Rosen's which is positive. The suggested field admits black holes which are briefly discussed.
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