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Annales Henri Poincaré

, Volume 16, Issue 10, pp 2399–2463 | Cite as

Jack Polynomials with Prescribed Symmetry and Some of Their Clustering Properties

  • Patrick DesrosiersEmail author
  • Jessica Gatica
Article

Abstract

We study Jack polynomials in N variables, with parameter α, and having a prescribed symmetry with respect to two disjoint subsets of variables. For instance, these polynomials can exhibit a symmetry of type AS, which means that they are antisymmetric in the first m variables and symmetric in the remaining Nm variables. One of our main goals is to extend recent works on symmetric Jack polynomials (Baratta and Forrester in Nucl Phys B 843:362–381, 2011; Berkesch et al. in Jack polynomials as fractional quantum Hall states and the Betti numbers of the (k + 1)-equals ideal, 2013; Bernevig and Haldane in Phys Rev Lett 101:1–4, 2008) and prove that the Jack polynomials with prescribed symmetry also admit clusters of size k and order r, that is, the polynomials vanish to order r when k + 1 variables coincide. We first prove some general properties for generic α, such as their uniqueness as triangular eigenfunctions of operators of Sutherland type, and the existence of their analogues in infinity many variables. We then turn our attention to the case with α = −(k + 1)/(r − 1). We show that for each triplet (k, r, N), there exist admissibility conditions on the indexing sets, called superpartitions, that guaranty both the regularity and the uniqueness of the polynomials. These conditions are also used to establish similar properties for non-symmetric Jack polynomials. As a result, we prove that the Jack polynomials with arbitrary prescribed symmetry, indexed by (k, r, N)-admissible superpartitions, admit clusters of size k = 1 and order r ≥ 2. In the last part of the article, we find necessary and sufficient conditions for the invariance under translation of the Jack polynomials with prescribed symmetry AS. This allows to find special families of superpartitions that imply the existence of clusters of size k > 1 and order r ≥ 2.

Keywords

Cluster Property Symmetric Polynomial Admissibility Condition Symmetry Type Invariant Polynomial 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Basel 2014

Authors and Affiliations

  1. 1.Centre de recherche de l’Institut universitaire, en santé mentale de Québec (CRIUSMQ)QuebecCanada
  2. 2.Département de physique, de génie physique et d’optiqueUniversité LavalQuebecCanada
  3. 3.Instituto Matemática y FísicaUniversidad de TalcaTalcaChile

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