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Reconstructing the Universe

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arxiv hep-th/0505154 v2 pith:3RHTEQA4 submitted 2005-05-17 hep-th gr-qchep-lat

J. Ambjorn (NBI Copenhagen , U. Utrecht) , J. Jurkiewicz (U. Krakow) , R. Loll (U. Utrecht) This is my paper · ORCID
classification hep-thgr-qchep-lat
keywords dynamicaluniversecausalquantumreconstructingspacetimetimeaspects
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We provide detailed evidence for the claim that nonperturbative quantum gravity, defined through state sums of causal triangulated geometries, possesses a large-scale limit in which the dimension of spacetime is four and the dynamics of the volume of the universe behaves semiclassically. This is a first step in reconstructing the universe from a dynamical principle at the Planck scale, and at the same time provides a nontrivial consistency check of the method of causal dynamical triangulations. A closer look at the quantum geometry reveals a number of highly nonclassical aspects, including a dynamical reduction of spacetime to two dimensions on short scales and a fractal structure of slices of constant time.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Statistical properties of quadrangular surfaces

    cond-mat.stat-mech 2026-07 conditional novelty 6.0 of 10

    GKNS random quadrangulations have Hausdorff dimension ~2 and percolation exponents not in the ordinary percolation class, while dynamical and general quadrangulations have dimension ~4 like dynamical triangulations.

  2. Asymptotically Safe Gravitational Form Factors from the Proper-Time Flow Equation

    hep-th 2026-05 unverdicted novelty 6.0 of 10

    Asymptotically safe gravitational form factors are obtained by integrating the proper-time flow to k=0; finite cutoff-independent results with 1/q² UV decay require selecting the non-Gaussian fixed point as UV boundar...

  3. Machine learning in phase transition analysis of lattice quantum gravity

    hep-lat 2025-10 conditional novelty 5.0 of 10

    Off-the-shelf supervised ML models trained on geometric CDT observables reproduce known quantum-gravity phase transitions and can give sharper transition signals than standard order parameters.

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