2023
DOI: 10.48550/arxiv.2303.03713
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Weight module classifications for Bershadsky--Polyakov algebras

Abstract: A. The Bershadsky-Polyakov algebras are the subregular quantum hamiltonian reductions of the affine vertex operator algebras associated with š”°š”© 3 . In [5], we realised these algebras in terms of the regular reduction, Zamolodchikov's W 3 -algebra, and an isotropic lattice vertex operator algebra. We also proved that a natural construction of relaxed highestweight Bershadsky-Polyakov modules has the property that the result is generically irreducible. Here, we prove that this construction, when combined with … Show more

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Cited by 1 publication
(2 citation statements)
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“…This inverse reduction was used in [12] to determine modular transformations and conjecture Grothendiek fusion rules for Bershadsky-Polyakov minimal models in terms of such data for W 3 minimal models. The same inverse reduction also facilitates a classification of irreducible weight modules (with finite-dimensional weight spaces) for W k ( 3 , ) and, for certain k, its simple quotient [13].…”
Section: Partial and Inverse Reductionmentioning
confidence: 98%
See 1 more Smart Citation
“…This inverse reduction was used in [12] to determine modular transformations and conjecture Grothendiek fusion rules for Bershadsky-Polyakov minimal models in terms of such data for W 3 minimal models. The same inverse reduction also facilitates a classification of irreducible weight modules (with finite-dimensional weight spaces) for W k ( 3 , ) and, for certain k, its simple quotient [13].…”
Section: Partial and Inverse Reductionmentioning
confidence: 98%
“…It was later shown by Adamović that this inverse reduction can be deployed to understand some of the representation theory of V k ( 2 ) and, at certain levels, L k ( 2 ) [1]. An analogous embedding for 3 was studied in [2], and an inverse to a partial reduction for 3 was studied in [11][12][13]. These inverse reductions have proven exceptionally useful in constructing interesting modules, as well as computing data important for conformal field theoretic applications.…”
mentioning
confidence: 99%