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Uniform Resolvent Asymptotics for Growing Theta-Null Source Families

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Abstract

We prove a relative matrix asymptotic for growing families of derivative-null sources in a reciprocal-theta realization of the Weil form. Writing U = log(1/ε), the actual constrained inverse Gram converges relatively to its explicit outer-Hardy/Cauchy leading matrix uniformly when the source count is o(log U). A finite exponential coefficient budget gives an eventual sufficient schedule and effective absolute, but unevaluated, onset constants. The proof combines a gapped positive inverse-factor kernel, logarithmic model-space plateau leverage, exact polar recovery and complete dual-leakage estimates. Separately, a logarithmically displaced physical wall gives a literal normalized inverse upper 2²³ n I for every U ≥ 64n², replacing a factorial source-count price. Its composition with the original physical null Gram sharpens a signed null-elimination resource bound. The resulting estimates quantify the positive archimedean reference and its exact-null elimination resources.

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Edition files

Available editions

  1. Edition 2 · 2026-10-31 · public
  2. Edition 1 · 2026-10-27 · public

Research details

Subjects
Mathematics · Number Theory · Complex Analysis · Functional Analysis
Keywords
theta-null sources, uniform resolvent asymptotics, Hardy spaces, Cauchy matrices, Stieltjes factors, null elimination
Manuscript date
2026-10-27
Reader reuse
No additional permission granted

Scope

Uniform relative whole-matrix convergence for growing actual-source families under explicit exponential-budget admissions, a complete finite-U thin-wall upper bound, and exact-null elimination resource estimates on the original polar-constrained domain.

Disclosures

AI-assisted mathematical research, computation and manuscript preparation.

Reproduction

The package contains complete editable analytical proofs and portable exact/Arb finite controls for atom bounds, wall admissions and Cauchy/exponential budgets. All 10,834 controls pass identically in normal and optimized Python; the effective absolute recovery constants are specified analytically.