Manifolds Pinned by a High-Dimensional Random Landscape: Hessian at the Global Energy Minimum - Sorbonne Université
Article Dans Une Revue Journal of Statistical Physics Année : 2020

Manifolds Pinned by a High-Dimensional Random Landscape: Hessian at the Global Energy Minimum

Résumé

We consider an elastic manifold of internal dimension d and length L pinned in a N dimensional random potential and confined by an additional parabolic potential of curvature μ. We are interested in the mean spectral density ρ(λ) of the Hessian matrix K at the absolute minimum of the total energy. We use the replica approach to derive the system of equations for ρ(λ) for a fixed L d in the N → ∞ limit extending d = 0 results of our previous work (Fyodorov et al. in Ann Phys 397:1-64, 2018). A particular attention is devoted to analyzing the limit of extended lattice systems by letting L → ∞. In all cases we show that for a confinement curvature μ exceeding a critical value μ c , the so-called "Larkin mass", the system is replica-symmetric and the Hessian spectrum is always gapped (from zero). The gap vanishes quadratically at μ → μ c. For μ < μ c the replica symmetry breaking (RSB) occurs and the Hessian spectrum is either gapped or extends down to zero, depending on whether RSB is 1-step or full. In the 1-RSB case the gap vanishes in all d as (μ c − μ) 4 near the transition. In the full RSB case the gap is identically zero. A set of specific landscapes realize the so-called "marginal cases" in d = 1, 2 which share both feature of the 1-step and the full RSB solution, and exhibit some scale invariance. We also obtain the average Green function associated to the Hessian and find that at the edge of the spectrum it decays exponentially in the distance within the internal space of the manifold with a length scale equal in all cases to the Larkin length introduced in the theory of pinning.
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Dates et versions

hal-02552325 , version 1 (23-04-2020)

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Yan Fyodorov, Pierre Le Doussal. Manifolds Pinned by a High-Dimensional Random Landscape: Hessian at the Global Energy Minimum. Journal of Statistical Physics, 2020, 179 (1), pp.176-215. ⟨10.1007/s10955-020-02522-2⟩. ⟨hal-02552325⟩
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