The universal maximum conjecture for log-correlated fields

From papers

Let X(n)={Xv(n),vVn}X(n)=\{X_v(n),v\in V_n\} be a log-correlated field in the sense of logarithmic covariance, with E[Xv(n)]=0E[X_v(n)]=0 and E[Xv(n)2]σ2nE[X_v(n)^2]\approx\sigma^2n. Set

c=2log2σ,an=cn32σ2clogn,bn=1.c=\sqrt{2\log 2}\,\sigma,\qquad a_n=cn-\frac{3}{2}\frac{\sigma^2}{c}\log n,\qquad b_n=1.

Universal maximum conjecture. The maximum satisfies, for some constant CC and a random variable ZZ called the derivative martingale,

limnP(maxvVnXv(n)an+bnx)=E[eCZecx].\lim_{n\to\infty}P\left(\max_{v\in V_n}X_v(n)\leq a_n+b_nx\right)=E\left[e^{-CZ e^{-cx}}\right].

This prediction expresses the expected universal extreme-value law for log-correlated fields that are sufficiently close to Gaussian fields. It is motivated by branching-random-walk heuristics and is intended to apply across a broad class of models, although the required hypotheses and the general proof remain open.

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Sources & referencesView supporting material

Primary source

Louis-Pierre Arguin, “Extrema of log-correlated random variables: Principles and Examples”, arXiv:1601.00582 (2016).

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