Tensorial generalization of characters
H. Itoyama, A. Mironov, A. Morozov

TL;DR
This paper introduces a new basis for gauge-invariant operators in rainbow tensor models, generalizing characters from matrix models to tensors, with specific properties related to averages and representation theory.
Contribution
It develops a tensorial generalization of characters, providing a basis for gauge-invariant operators with properties analogous to Schur functions in matrix models.
Findings
Operators are labeled by Young diagrams and relate to representation dimensions.
A new basis simplifies the understanding of gauge-invariant operators in tensor models.
Operators are eigenfunctions of generalized cut-and-join operators.
Abstract
In rainbow tensor models, which generalize rectangular complex matrix model (RCM) and possess a huge gauge symmetry , we introduce a new sub-basis in the linear space of gauge invariant operators, which is a redundant basis in the space of operators with non-zero Gaussian averages. Its elements are labeled by -tuples of Young diagrams of a given size equal to the power of tensor field. Their tensor model averages are just products of dimensions: of representations of the linear group , with made of the Clebsch-Gordan coefficients of representations of the symmetric group. Moreover, not only the averages but the operators themselves exist only when these are non-vanishing. This sub-basis is…
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