Analysing (cosmological) singularity avoidance in loop quantum gravity using U(1)3 coherent states and Kummer's functions. (20th April 2023)

• Record Type: Journal Article
• Authors: Giesel, Kristina; Winnekens, David
• Is Part Of: Classical and quantum gravity. Volume 40:Number 8(2023)
• Publication Date: 2023-04-20
• Subjects: (cosmological) singularity avoidance -- loop quantum gravity -- coherent states -- Kummer functions; Quantum gravity -- Periodicals; Gravitation -- Periodicals; Relativity (Physics) -- Periodicals; Space and time -- Periodicals; Periodicals; 521.1
• DOI: 10.1088/1361-6382/acc0c7
• Languages: English
• ISSNs: 0264-9381

Abstract

Abstract: Using a new procedure based on Kummer's Confluent Hypergeometric Functions, we investigate the question of singularity avoidance in loop quantum gravity (LQG) in the context of U ( 1 ) 3 complexifier coherent states and compare obtained results with already existing ones. Our analysis focuses on the dynamical operators, denoted by q ˆ I 0 i 0 ( r ), whose products are the analogue of the inverse scale factor in LQG and also play a pivotal role for other dynamical operators such as matter Hamiltonians or the Hamiltonian constraint. For graphs of cubic topology and linear powers in q ˆ I 0 i 0 ( r ), we obtain the correct classical limit and demonstrate how higher order corrections can be computed with this method. This extends already existing techniques in the way how the involved fractional powers are handled. We also extend already existing formalisms to graphs with higher-valent vertices. For generic graphs and products of q ˆ I 0 i 0 ( r ), using estimates becomes inevitable and we investigate upper bounds for these semiclassical expectation values. Compared to existing results, our method allows to keep fractional powers involved in q ˆ I 0 i 0 ( r ) throughout the computations, which have been estimated by integer powers elsewhere. Similar to former results, we find a non-zero upper bound for the inverse scale factor at the initial singularity. Additionally, our findings provide some insights into properties and related implications of the results that arise when using estimates and can be used to look for improved estimates.