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A Sharp Deconfinement Transition for Potts Lattice Gauge Theory in Codimension Two
Paul Duncan1, Benjamin Schweinhart2
1Department of Mathematics, Indiana University, Bloomington, 47405 USA.
This study extends the sharp phase transition result for Bernoulli plaquette percolation to higher dimensions and a dependent model. This work establishes sharp phase transitions for Wilson loop expectations in gauge theories.
Area of Science:
- Statistical Mechanics
- Percolation Theory
- Lattice Gauge Theory
Background:
- The seminal 1983 work by Aizenman et al. established a sharp phase transition for 2D Bernoulli plaquette percolation.
- This transition relates to the boundedness of large rectangular loops by surfaces of plaquettes in 3D lattices.
Purpose of the Study:
- To generalize the sharp phase transition result to higher-dimensional lattices.
- To investigate phase transitions in dependent percolation models, specifically the plaquette random-cluster model.
- To establish sharp phase transitions for Wilson loop expectations in dual Potts lattice gauge theories.
Main Methods:
- Extension of the original proof techniques to (d-1)-dimensional plaquette percolation in d-dimensional lattices.
- Application of these methods to the plaquette random-cluster model, a dependent percolation model.
- Leveraging the duality between the random-cluster model and Potts lattice gauge theory.
Main Results:
- Demonstrated sharp phase transitions for (d-1)-dimensional plaquette percolation in Z^d.
- Proved sharp phase transitions for Wilson loop expectations in (d-2)-dimensional q-state Potts hyperlattice gauge theory.
- Developed the general theory of the i-plaquette random cluster model and its connection to (i-1)-dimensional Potts lattice gauge theory.
Conclusions:
- The sharp phase transition phenomenon in percolation theory is robust and extendable to higher dimensions and dependent models.
- The results provide a rigorous foundation for understanding phase transitions in related lattice gauge theories.
- The study highlights the power of random-cluster models in analyzing complex statistical physics systems.
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