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Hyperbolic sine-Gordon model beyond the first threshold

2 Pith papers cite this work. Polarity classification is still indexing.

2 Pith papers citing it
abstract

We study the hyperbolic sine-Gordon model, with a parameter $\be^2 > 0$, and its associated Gibbs dynamics on the two-dimensional torus. By introducing a physical space approach to the Fourier restriction norm method and establishing nonlinear dispersive smoothing for the imaginary multiplicative Gaussian chaos, we construct invariant Gibbs dynamics for the hyperbolic sine-Gordon model beyond the first threshold $\be^2 = 2\pi$. The deterministic step of our argument hinges on establishing key bilinear estimates, featuring weighted bounds for cone multipliers. Moreover, the probabilistic component involves a careful analysis of the imaginary Gaussian multiplicative chaos and reduces to integrating singularities along space-time light cones. As a by-product of our proof, we identify $\be^2 = 6\pi$ as a critical threshold for the hyperbolic sine-Gordon model, which is quite surprising given that the associated parabolic model has a critical threshold at $\be^2 =8\pi$.

fields

math.AP 2

years

2026 2

verdicts

ACCEPT 2

representative citing papers

On probabilistic ill-posedness

math.AP · 2026-07-08 · accept · novelty 6.0

The paper defines enhanced probabilistic well-posedness by imposing stability at the origin and reinterprets recent 'beyond variance blowup' results for dispersive PDEs as probabilistic ill-posedness.

citing papers explorer

Showing 2 of 2 citing papers.

  • Fourier restriction norm method adapted to controlled paths: stochastic wave equations math.AP · 2026-07-08 · accept · none · ref 172 · internal anchor

    Pathwise local well-posedness of stochastic nonlinear wave equations with multiplicative noise is established in optimal regularity ranges by unifying Fourier restriction norm methods with rough path integration.

  • On probabilistic ill-posedness math.AP · 2026-07-08 · accept · none · ref 95 · internal anchor

    The paper defines enhanced probabilistic well-posedness by imposing stability at the origin and reinterprets recent 'beyond variance blowup' results for dispersive PDEs as probabilistic ill-posedness.