2407.16632
Birkhoff Sum Convergence of Fréchet Observables to Stable Laws for Gibbs-Markov Systems and Applications
An Chen, Matthew Nicol, Andrew Török
correcthigh confidence
- Category
- Not specified
- Journal tier
- Specialist/Solid
- Processed
- Sep 28, 2025, 12:56 AM
- arXiv Links
- Abstract ↗PDF ↗
Audit review
The paper proves small-jumps negligibility (Theorem 8.1) for α in (1,2) by verifying a Davis–Hsing-type mixing/covariance condition using BV decay of correlations, then combines this with point-process limits for Fréchet observables to obtain stable laws (Corollary 8.3) and treats intermittent (LSV) maps via inducing, yielding the three-case theorem (Theorem 8.5) . The candidate solution reaches essentially the same conclusions: (A) small-jumps negligibility for α∈(1,2) via a martingale/Poisson-equation approach and Freedman’s inequality; (B) α-stable limits for φ(x)=d(x,x0)^{-1/α} on Gibbs–Markov maps; and (C) the LSV trichotomy with the same parameter regimes. However, the candidate’s proof strategy differs and has technical gaps: it sketches a reverse-martingale decomposition based on (I−L)h=f with an extra remainder term and asserts a variance control E[V_{n,ε}]≲nE[f_{n,ε}^2] without justification, and it does not account for Poisson clustering near periodic points (addressed in the paper via point-process limits of Freitas–Freitas–Magalhães and Davis–Hsing) . Despite these gaps in the model’s outline, the results it states match the paper’s theorems and parameter thresholds (including the cancellation regime bound α<1+1/γ^2−1/γ) .
Referee report (LaTeX)
\textbf{Recommendation:} minor revisions \textbf{Journal Tier:} specialist/solid \textbf{Justification:} A clear and rigorous treatment of stable-limit laws for heavy-tailed observables on Gibbs–Markov maps is provided, together with a careful application to LSV maps that elucidates the competition between heavy tails and slow mixing. The methods are standard but well executed, and the results consolidate and extend the literature. Minor improvements in reference attributions and a brief discussion of scale/skew in the clustered case would enhance clarity.