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Transpose-odd operator chirality in co-moving multiplicative processes: exact tail-level structure and a detectability obstruction

Nihat Çağrı Çalışkan

Source record

Source: arXiv

Published: Sep 28, 2026

arXiv: 2609.35923

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Source abstract

We ask whether transposition changes the stationary heavy tail of the co-moving recursion xt+1=QtBQt⊤xt+Qtηt\mathbf x_{t+1}=Q_tBQ_t^\top\mathbf x_t+Q_t\boldsymbolη_t in d=3d=3. The model has independent Haar rotations, Gaussian body-frame noise with covariance ΣΣ, and an explicit positive metric g\mathsf g. The tail index α⋆α_\star depends only on singular values, so α⋆(B)=α⋆(B⊤)α_\star(B)=α_\star(B^\top). The structure theorem and polar-twist mechanism are unconditional. In the g=I\mathsf g=I chart, write B=S+ω^B=S+\widehat{\boldsymbolω}: Δ6(B)=−16det⁡[ω,Sω,S2ω]Δ_6(B)=-16\det[\boldsymbolω,S\boldsymbolω,S^2\boldsymbolω]. Then B̸∼O(3)B⊤B\not\sim_{O(3)}B^\top, Δ6(B)≠0Δ_6(B)\ne0, rank⁡[ω,Sω,S2ω]=3\operatorname{rank}[\boldsymbolω,S\boldsymbolω,S^2\boldsymbolω]=3, and controllability of (S,ω)(S,\boldsymbolω) are equivalent; intrinsic operator chirality is a Kalman rank condition. Unlike the exponent, the tail level need not be transpose-invariant. The exact polar-twist identity rotates the anisotropic noise frame under mirroring, showing how level asymmetry can enter. A nonzero asymmetry is established only conditionally and numerically, not by a closed-form example: under Assumption R on the SPD chart, Δlog⁡C=⟨A,G⟩+o(∥A∥)Δ\log C=\langle A,G\rangle+o(\|A\|) as A→0A\to0, with G=2H∘[Σ,∂Σlog⁡C]G=2H\circ[Σ,\partial_Σ\log C]; finite-threshold simulations are consistent with this first-order law. Strict-Haar averaging makes every lagged second-order cross-moment transpose-blind; a fourth radial moment retains an odd channel. No finite scalar-weighted radial-moment combination uniformly cancels the even quadratic form while retaining the odd covector.

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Transpose-odd operator chirality in co-moving multiplicative processes: exact tail-level structure and a detectability obstruction — Mathematical Frontier Network