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Twistors in spin networks and loop quantum gravity
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˜ Nl Nl s(l) j l t(l) Simone Speziale Oxford, 3 Jan. 2017
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X34 Intro and overview Loop quantum gravity: a background independent approach to quantum gravity based on the use of real Ashtekar variables and spin networks
• Main results: space is quantised, geometric operators have discrete spectra and non-commuting properties; applications to cosmology and black holes
• Main open questions: recovering smooth geometries following Einstein’s equations in the semiclassical limit; systematically compute transition amplitudes
Each `Feynman diagram’ of the theory corresponds to a truncation to a finite number of degrees of freedom: this truncation describes the evolution of a fuzzy quantum geometry.
In recent years many properties of this fuzzy quantum geometry have been clarified, and twistors have emerged as natural tools to provide a covariant description of the geometry, to answer open questions, and to lead to new research directions.
It is a rich research area, and I will only present some isolated results, more closely related to twistors, without attempting due to the limited time a comprehensive and coherent overview.
1. SU(2) spin networks represent a collection of fuzzy polyhedra glued together along faces 2. Their spacetime embedding, based on SU(2) SL(2,C), is naturally described by twistors ! 3. Using twistors to construct SU(2,2) spin networks I. Loop Quantum Gravity Spin networksQuanta and quantumof space in geometry loop quantum gravity QuantumQFT field theory Loop quantumLQG gravity
= n = F n H H H
n, pi,hi quanta of fields ,je,iv quanta of space | i! | i! • number of quanta • number of quanta and their relations Spin• momenta networks are eigenstates of geometric• volumes operators of regions such as surface areas • helicites • areas of interconnecting surfaces spins quanta of area • 7! Fuzzy spinning particles Fuzzy linked polyhedra intertwiners quanta of volumes • 7! dynamics described by dynamics described by Feynman diagrams spin foams Key result
geometric operators turn out to have discrete spectra with minimal excitations proportional to the Planck length
diagrams can be organised diagram organisation not yet established! in PT or EFT hands-on approach for the moment:
Speziale — LQG and polyhedracompute Motivations andin overviewa given truncation, then change truncation 8/38 Classical limit of a spin network
L N ,jl,in = L2[SU(2) /SU(2) ,dµHaar]= j n Inv l n Vj | i2 H l ⌦ ⌦ 2 l ⇣ ⇥ ⇤⌘
On a single link: L [SU(2),dµ ] T ⇤SU(2) the classical phase space of a spinning top 2 Haar !
On a single node: Inv V a certain phase space corresponding to ⌦i ji ! SKM h i shapes of polyhedra (a classical counterpart to Penrose’s spin-geometry theorem)
L On the full graph: T ⇤SU(2) // C~ turns out to describe the H ! n intrinsic and extrinsic geometry of a discretised space-like hypersurface II. SU(2) singlets and polyhedra (Freidel-Krasnov-Livine ’09, Bianchi-Dona-S ’10) SU(2) singlets and polyhedra
n 2 j i=1Sji iVji 3 ⇥ ! ⌦ j2 ~ j123 C =0 j12 # # j1 ...
2 := S // C~ KM Inv i Vji j1,j2,...; j12,j123,... SKM ⇥i ji S ! ⌦ | i h i dim = 2(n 3) virtual spins = n 3 SKM
It follows from three theorems: A4n4
A3n3 • Kapovich-Millson:
reduction by the closure constraint gives a symplectic manifold A2n2 with action angle variables the diagonals and dihedral angles of the polygon A1n1 O • Guillermin-Stenberg: (for compact orbits) quantisation commutes with reduction
• Minkowski: a closed sum of vectors in R3 defines a unique bounded, convex polyhedron Polyhedra reconstruction Bianchi, Doná, and Speziale Bianchi and HMH Bohr-Sommerfeld Minkowski theorem: a convex flat polyhedron at fixed areas has 2(n-3) degrees of freedom, and can be numerical reconstruction ’11 tetrahedral volume spectrum ’11 uniquely reconstructed from the normalsGeometry of polyhedra
14 Explicit reconstruction procedure: Xl edge lengths, volume, adjacency matrix N~i jiN~i =0 fiÑ { } 12 i For F 4 there are many X di↵erent combinatorial structures, or classes ° 10
F 6 Dominant: Codimension 2: Notice that a polyhedron has in general many possible adjacency classes: the adjacency matrix and valence of “ 8 each face is uniquely determined by the normals 6 Codimension 1: Codimension 3: