English

Disordered Gibbs measures and Gaussian conditioning

Probability 2025-12-11 v2 Mathematical Physics math.MP

Abstract

We study the law of a random field fN(σ)f_N(\boldsymbol{\sigma}) evaluated at a random sample from the Gibbs measure associated to a Gaussian field HN(σ)H_N(\boldsymbol{\sigma}). In the high-temperature regime, we show that bounds on the probability that fN(σ)Af_N(\boldsymbol{\sigma})\in A for σ\boldsymbol{\sigma} randomly sampled from the Gibbs measure can be deduced from similar bounds for deterministic σ\boldsymbol{\sigma} under the conditional Gaussian law given that HN(σ)/N=EH_N(\boldsymbol{\sigma})/N=E for EE close to the derivative F(β)F'(\beta) of the free energy (which is the typical value of HN(σ)/NH_N(\boldsymbol{\sigma})/N under the Gibbs measure). In the more challenging low-temperature regime we restrict to kk-RSB spherical spin glasses, proving a similar result, now with a more elaborate conditioning. Namely, with qiq_i denoting the locations of the non-zero atoms of the Parisi measure, in addition to specifying that HN(σ)/N=EH_N(\boldsymbol{\sigma})/N=E, here one needs to also condition on the energy and its gradient at points x1,,xk\mathbf{x}_1,\ldots,\mathbf{x}_k such that xi,xj/N=qij\langle \mathbf{x}_i,\mathbf{x}_j\rangle/N=q_{i\wedge j} and xi,σ/Nqi\langle \mathbf{x}_i,\boldsymbol{\sigma}\rangle/N\approx q_{i}. Like in the high-temperature phase, the energy and gradient values on which one conditions are also specified by the model's Parisi measure. We apply our general results to two important problems from statistical physics. That is, computing the Franz-Parisi potential at any temperature and, reducing certain asymptotics of Langevin dynamics with initial conditions distributed according to the Gibbs measure, to the more manageable problem of studying dynamics with non-random initial conditions and conditional disorder.

Keywords

Cite

@article{arxiv.2409.19453,
  title  = {Disordered Gibbs measures and Gaussian conditioning},
  author = {Amir Dembo and Eliran Subag},
  journal= {arXiv preprint arXiv:2409.19453},
  year   = {2025}
}
R2 v1 2026-06-28T19:00:41.809Z