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Record W2034541598 · doi:10.1063/1.4790417

The algebra of dual −1 Hahn polynomials and the Clebsch-Gordan problem of <i>sl</i>−1(2)

2013· article· en· W2034541598 on OpenAlexaff
Vincent X. Genest, Luc Vinet, Alexei Zhedanov

Bibliographic record

VenueJournal of Mathematical Physics · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum chaos and dynamical systems
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMathematicsPure mathematicsHahn polynomialsCombinatoricsOrthogonal polynomialsAlgebra over a fieldMathematical physicsClassical orthogonal polynomialsGegenbauer polynomials

Abstract

fetched live from OpenAlex

The algebra \documentclass[12pt]{minimal}\begin{document}$\mathcal {H}$\end{document}H of the dual −1 Hahn polynomials is derived and shown to arise in the Clebsch-Gordan problem of sl−1(2). The dual −1 Hahn polynomials are the bispectral polynomials of a discrete argument obtained from the q → −1 limit of the dual q-Hahn polynomials. The Hopf algebra sl−1(2) has four generators including an involution, it is also a q → −1 limit of the quantum algebra slq(2) and furthermore, the dynamical algebra of the parabose oscillator. The algebra \documentclass[12pt]{minimal}\begin{document}$\mathcal {H}$\end{document}H, a two-parameter generalization of \documentclass[12pt]{minimal}\begin{document}$\mathfrak {u}(2)$\end{document}u(2) with an involution as additional generator, is first derived from the recurrence relation of the −1 Hahn polynomials. It is then shown that \documentclass[12pt]{minimal}\begin{document}$\mathcal {H}$\end{document}H can be realized in terms of the generators of two added sl−1(2) algebras, so that the Clebsch-Gordan coefficients of sl−1(2) are dual −1 Hahn polynomials. An irreducible representation of \documentclass[12pt]{minimal}\begin{document}$\mathcal {H}$\end{document}H involving five-diagonal matrices and connected to the difference equation of the dual −1 Hahn polynomials is constructed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.238

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.214
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations18
Published2013
Admission routes1
Has abstractyes

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