Orthogonal Polynomials for a Class of Measures with Discrete Rotational Symmetries in the Complex Plane
Bibliographic record
Abstract
We obtain the strong asymptotics of polynomials $$p_n(\lambda )$$ p n ( λ ) , $$\lambda \in {\mathbb {C}}$$ λ ∈ C , orthogonal with respect to measures in the complex plane of the form $$\begin{aligned} \hbox {e}^{-N(|\lambda |^{2s}-t\lambda ^s-\overline{t}\overline{\lambda }^s)}\hbox {d}A(\lambda ), \end{aligned}$$ e - N ( | λ | 2 s - t λ s - t ¯ λ ¯ s ) d A ( λ ) , where s is a positive integer, t is a complex parameter, and $$\hbox {d}A$$ d A stands for the area measure in the plane. This problem has its origin in normal matrix models. We study the asymptotic behavior of $$p_n(\lambda )$$ p n ( λ ) in the limit $$n,N\rightarrow \infty $$ n , N → ∞ in such a way that $$n/N\rightarrow T$$ n / N → T constant. Such asymptotic behavior has two distinguished regimes according to the topology of the limiting support of the eigenvalues distribution of the normal matrix model. If $$0<|t|^2 T/s$$ | t | 2 > T / s , the eigenvalue distribution support consists of s connected components. Correspondingly, the support of the limiting zero distribution of the orthogonal polynomials consists of a closed contour contained in each connected component. Our asymptotic analysis is obtained by reducing the planar orthogonality conditions of the polynomials to equivalent contour integral orthogonality conditions. The strong asymptotics for the orthogonal polynomials is obtained from the corresponding Riemann–Hilbert problem by the Deift–Zhou nonlinear steepest descent method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".