Fri - 02/23/24

Effective four-dimensional loop quantum black hole with a cosmological constant

  • Authors: Jianhui Lin, Xiangdong Zhang
  • Subjects: General Relativity and Quantum Cosmology (gr-qc)
  • Arxiv link: https://arxiv.org/abs/2402.13638
  • Abstract In this paper, we utilize the effective corrections of the $\bar{\mu}$-scheme in loop quantum black holes to obtain a 4-dimensional spherically symmetric metric with a cosmological constant. By imposing the areal gauge on the components of Ashtekar variables in the classical theory and applying the holonomy corrections, we derive the equations of motion, which can be solved to obtain the expression for the effective metric in the Painlev'{e}-Gullstrand coordinates. A comparison with the $\Lambda=0$ case reveals minimal modifications near the outer horizon, while significant differences are observed far from the outer horizon. Moreover, the physical properties of these quantum-corrected solutions are also discussed.

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Thu - 02/22/24

Relaxation of first-class constraints and the quantization of gauge theories: from “matter without matter” to the reappearance of time in quantum gravity

  • Authors: Roberto Casadio, Leonardo Chataignier, Alexander Yu. Kamenshchik, Francisco G. Pedro, Alessandro Tronconi, Giovanni Venturi
  • Subjects: General Relativity and Quantum Cosmology (gr-qc); High Energy Physics - Theory (hep-th); Mathematical Physics (math-ph); Quantum Physics (quant-ph)
  • Arxiv link: https://arxiv.org/abs/2402.12437
  • Abstract We make a conceptual overview of a particular approach to the initial-value problem in canonical gauge theories. We stress how the first-class phase-space constraints may be relaxed if we interpret them as fixing the values of new degrees of freedom. This idea goes back to Fock and Stueckelberg, leading to restrictions of the gauge symmetry of a theory, and it corresponds, in certain cases, to promoting constants of Nature to physical fields. Recently, different versions of this formulation have gained considerable attention in the literature, with several independent iterations, particularly in classical and quantum descriptions of gravity, cosmology, and electromagnetism. In particular, in the case of canonical quantum gravity, the Fock–Stueckelberg approach is relevant to the so-called problem of time. Our overview recalls the work of Fock and Stueckelberg and its physical interpretation with the aim of conceptually unifying the different iterations of the idea that appear in the literature and of motivating further research.

There is no new related paper today

  1. [2402.12578] - Evidence for Planck Luminosity Bound in Quantum Gravity - Wolfgang Wieland

Wed - 02/21/24

There is no new related paper today

There is no new related paper today

  1. [2402.11016] - Holographic phenomenology via overlapping degrees of freedom - Oliver Friedrich, ChunJun Cao, Sean Carroll, Gong Cheng, Ashmeet Singh

Tue - 02/20/24

Towards quantum gravity with neural networks: Solving the quantum Hamilton constraint of U(1) BF theory

  • Authors: Hanno Sahlmann, Waleed Sherif
  • Subjects: General Relativity and Quantum Cosmology (gr-qc); High Energy Physics - Theory (hep-th); Computational Physics (physics.comp-ph)
  • Arxiv link: https://arxiv.org/abs/2402.10622
  • Abstract In the canonical approach of loop quantum gravity, arguably the most important outstanding problem is finding and interpreting solutions to the Hamiltonian constraint. In this work, we demonstrate that methods of machine learning are in principle applicable to this problem. We consider U(1) BF theory in 3 dimensions, quantized with loop quantum gravity methods. In particular, we formulate a master constraint corresponding to Hamilton and Gauss constraints using loop quantum gravity methods. To make the problem amenable for numerical simulation we fix a graph and introduce a cutoff on the kinematical degrees of freedom, effectively considering U$_q$(1) BF theory at a root of unity. We show that the Neural Network Quantum State (NNQS) ansatz can be used to numerically solve the constraints efficiently and accurately. We compute expectation values and fluctuations of certain observables and compare them with exact results or exact numerical methods where possible. We also study the dependence on the cutoff.

Towards quantum gravity with neural networks: Solving the quantum Hamilton constraint of U(1) BF theory

  • Authors: Hanno Sahlmann, Waleed Sherif
  • Subjects: General Relativity and Quantum Cosmology (gr-qc); High Energy Physics - Theory (hep-th); Computational Physics (physics.comp-ph)
  • Arxiv link: https://arxiv.org/abs/2402.10622
  • Abstract In the canonical approach of loop quantum gravity, arguably the most important outstanding problem is finding and interpreting solutions to the Hamiltonian constraint. In this work, we demonstrate that methods of machine learning are in principle applicable to this problem. We consider U(1) BF theory in 3 dimensions, quantized with loop quantum gravity methods. In particular, we formulate a master constraint corresponding to Hamilton and Gauss constraints using loop quantum gravity methods. To make the problem amenable for numerical simulation we fix a graph and introduce a cutoff on the kinematical degrees of freedom, effectively considering U$_q$(1) BF theory at a root of unity. We show that the Neural Network Quantum State (NNQS) ansatz can be used to numerically solve the constraints efficiently and accurately. We compute expectation values and fluctuations of certain observables and compare them with exact results or exact numerical methods where possible. We also study the dependence on the cutoff.
  1. [2402.10620] - Testing Higher Derivative Gravity Through Tunnelling - Ruth Gregory, Shi-Qian Hu

Mon - 02/19/24

Towards quantum gravity with neural networks: Solving the quantum Hamilton constraint of U(1) BF theory

  • Authors: Hanno Sahlmann, Waleed Sherif
  • Subjects: General Relativity and Quantum Cosmology (gr-qc); High Energy Physics - Theory (hep-th); Computational Physics (physics.comp-ph)
  • Arxiv link: https://arxiv.org/abs/2402.10622
  • Abstract In the canonical approach of loop quantum gravity, arguably the most important outstanding problem is finding and interpreting solutions to the Hamiltonian constraint. In this work, we demonstrate that methods of machine learning are in principle applicable to this problem. We consider U(1) BF theory in 3 dimensions, quantized with loop quantum gravity methods. In particular, we formulate a master constraint corresponding to Hamilton and Gauss constraints using loop quantum gravity methods. To make the problem amenable for numerical simulation we fix a graph and introduce a cutoff on the kinematical degrees of freedom, effectively considering U$_q$(1) BF theory at a root of unity. We show that the Neural Network Quantum State (NNQS) ansatz can be used to numerically solve the constraints efficiently and accurately. We compute expectation values and fluctuations of certain observables and compare them with exact results or exact numerical methods where possible. We also study the dependence on the cutoff.

Towards quantum gravity with neural networks: Solving the quantum Hamilton constraint of U(1) BF theory

  • Authors: Hanno Sahlmann, Waleed Sherif
  • Subjects: General Relativity and Quantum Cosmology (gr-qc); High Energy Physics - Theory (hep-th); Computational Physics (physics.comp-ph)
  • Arxiv link: https://arxiv.org/abs/2402.10622
  • Abstract In the canonical approach of loop quantum gravity, arguably the most important outstanding problem is finding and interpreting solutions to the Hamiltonian constraint. In this work, we demonstrate that methods of machine learning are in principle applicable to this problem. We consider U(1) BF theory in 3 dimensions, quantized with loop quantum gravity methods. In particular, we formulate a master constraint corresponding to Hamilton and Gauss constraints using loop quantum gravity methods. To make the problem amenable for numerical simulation we fix a graph and introduce a cutoff on the kinematical degrees of freedom, effectively considering U$_q$(1) BF theory at a root of unity. We show that the Neural Network Quantum State (NNQS) ansatz can be used to numerically solve the constraints efficiently and accurately. We compute expectation values and fluctuations of certain observables and compare them with exact results or exact numerical methods where possible. We also study the dependence on the cutoff.
  1. [2402.10620] - Testing Higher Derivative Gravity Through Tunnelling - Ruth Gregory, Shi-Qian Hu

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