The symplectic 2-form and Poisson bracket of null canonical gravity
Michael P. Reisenberger
Abstract
It is well known that free (unconstrained) initial data for the gravitational field in general relativity can be identified on an initial hypersurface consisting of two intersecting null hypersurfaces. Here the phase space of vacuum general relativity associated with such an initial data hypersurface is defined; a Poisson bracket is defined, via Peierls' prescription, on sufficiently regular functions on this phase space, called ``observables''; and a bracket on initial data is defined so that it reproduces the Peierls bracket between observables when these are expressed in terms of the initial data. The brackets between all elements of a free initial data set are calculated explicitly. The bracket on initial data presented here has all the characteristics of a Poisson bracket except that it does not satisfy the Jacobi relations (even though the brackets between the observables do). The initial data set used is closely related to that of Sachs. However, one significant difference is that it includes a ``new'' pair of degrees of freedom on the intersection of the two null hypersurfaces which are present but quite hidden in Sachs' formalism. As a step in the calculation an explicit expression for the symplectic 2-form in terms of these free initial data is obtained.
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