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Content archived on 2024-06-16

Simulating Chiral Gauge theory with Matrix Models : Non-commutative Geometry and Fuzzy Approximations


Non-commutative geometry was proposed earlier than renormalization as a possible way to eliminate ultraviolet divergences in quantum field theories. On the other hand quantum field theories on non-commutative spaces are generally unknown beyond the one-lo op approximation. A gauge-covariant, chiral-invariant regularization of gauge theories can be achieved by quantizing the underlying space-time manifold thereby replacing it by a non-commutative matrix model or a "fuzzy manifold''. Indeed if the underlying space-time manifold can be treated as a phase space one can quantize it in the usual way with a parameter theta assuming the role of hbar. Naturally the emergent quantum space is fuzzy with non-commuting coordinates and a finite number of degrees of freedom and as a consequence it is ultraviolet finite. The continuum limit is the semi-classical theta goes to zero limit. These are essentially matrix models. The advantage of this regulator compared to ordinary lattice prescription is that discretization by quantization is remarkably successful in preserving symmetries and topological features and altogether avoiding the fermion-doubling problem. As it turns out fuzzy spaces can also be used to regularize infinite dimensional non-commutative spaces such as Moya l-Weyl spaces. The main focus of this proposal is the construction of a new non-perturbative method for chiral gauge theories based on the fuzzy tw-spheres and their Cartesian products.

More precisely we will use Monte Carlo numerical simulations to:
a) determine the phase structure of 2-dimensional non-commutative fuzzy Yang-Mills theory , and
b) to solve the non-commutative fuzzy Schwinger model.

This will provide a crucial step towards understanding chiral gauge theories on infinite dimensional non-commutative Moyal-Weyl spaces. But it will also lay the foundation for the study of ordinary QCD in two and four dimensions using fuzzy approximations.

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