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2209.00612

Analytic Smoothing and Nekhoroshev Estimates for Hölder Steep Hamiltonians

Santiago Barbieri, Jean-Pierre Marco, Jessica Elisa Massetti

correcthigh confidence
Category
math.DS
Journal tier
Strong Field
Processed
Sep 28, 2025, 12:56 AM

Audit review

The uploaded paper proves Theorem 1.1 with exponents a=(ℓ−1)/(2n α1⋯αn−2)+1/2 and b=1/(2n α1⋯αn−1), and stability time T(ε) ≤ C''_T/(|ln ε|^{ℓ−1} ε^a), radius R(ε) ≤ C''_I ε^b, via analytic smoothing with a weighted Fourier estimate (Theorem 4.1) combined with a Pöschel normal form and a steep resonant geometry/trap; see the statement and proof roadmap in the paper’s Theorem 1.1, Lemma B.1, and Section 5 (notably (5.23)) . The candidate solution reproduces the final exponents but its derivation departs materially from the paper: it selects s≈c/|ln ε| (instead of the ε-dependent choice in (5.23)), asserts an extra polynomial-in-K remainder term ||f_s^*|| ≲ K^{-(ℓ−1)}||f_s|| which is not established by the cited normal-form lemma, and misplaces the logarithmic factor when computing T from μ. In the paper, the dominant remainder is ε s^{ℓ−1} (from smoothing), with e^{-Ks} controlled by choosing K,s as in (5.23), yielding μ∼ε^{1+a(ℓ−1)}|ln ε|^{ℓ−1} and T∼1/(|ln ε|^{ℓ−1} ε^{a(ℓ−1)+1/2}) , whereas the candidate alternately claims μ∼ε/|ln ε|^{ℓ−1} and μ∼ε^{1+a(ℓ−1)}/|ln ε|^{ℓ−1}, which would put |ln ε| in the numerator of T. Thus, while the end exponents match, the model’s proof is internally inconsistent and uses an unproven estimate.

Referee report (LaTeX)

\textbf{Recommendation:} no revision

\textbf{Journal Tier:} strong field

\textbf{Justification:}

The manuscript introduces a weighted-Fourier analytic smoothing tailored to Hamiltonian perturbation theory and leverages it to obtain the first Nekhoroshev bounds for Hölder steep systems with sharp exponents. The proof integrates classical analytic normal forms with modern steep resonance geometry in a transparent way. The result is a valuable contribution likely to spur further developments in other regularity classes and in applications.