2018
DOI: 10.1007/jhep02(2018)089
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Orthogonal bases of invariants in tensor models

Abstract: Representation theory provides an efficient framework to count and classify invariants in tensor models of (gauge) symmetryWe show that there are two natural ways of counting invariants, one for arbitrary G d and another valid for large rank of G d . We construct basis of invariant operators based on the counting, and compute correlators of their elements. The basis associated with finite rank of G d diagonalizes two-point function. It is analogous to the restricted Schur basis used in matrix models. We commen… Show more

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Cited by 30 publications
(29 citation statements)
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“…In what follows we will simply state and use the results we need. The reader requiring more details is encouraged to consult [59,23], as well as [83,84,85,86].…”
Section: Finite N Contributionsmentioning
confidence: 99%
“…In what follows we will simply state and use the results we need. The reader requiring more details is encouraged to consult [59,23], as well as [83,84,85,86].…”
Section: Finite N Contributionsmentioning
confidence: 99%
“…Each invariant is associated with the specific way indices of Φ and indices of Φ are contracted subjected to a double coset equivalence, see [42] for details. Counting the number of invariant operators, building a basis which diagonalizes the two-point function of the free theory and computing correlators has been a recent subject of study [29,33,30,31,32]. In those studies it was manifest the prominent role of Kronecker coefficients in organizing the spectrum of energy eigenstates.…”
Section: Tensor Partition Functionmentioning
confidence: 99%
“…The recent discovery of the relation between some sectors of Kronecker coefficients and Littlewood-Richardson numbers in the field of combinatorics and group theory. This is relevant for us since Kronecker coefficients organize the spectrum of eigenstates of free tensor models [29,30,31,32], whereas Littlewood-Richardson numbers have long been known to organize the spectrum of matrix models [34,35,36,37,38,39,40,41].…”
Section: Introductionmentioning
confidence: 99%
“…Some of the insights come from the Virasoro structure of the matrix models [27][28][29][30][31][32][33][34] and its combinatorics and finding its counterpart in tensor models in general is expected to pave the way to bring progress in this field. Recent references include [35][36][37][38][39][40][41][42][43][44][45][46][47][48].…”
Section: Introductionmentioning
confidence: 99%