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Extrema of log-correlated random variables: Principles and Examples

1 Pith paper cite this work. Polarity classification is still indexing.

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abstract

These notes were written for the mini-course "Extrema of log-correlated random variables: Principles and Examples" at the Introductory School held in January 2015 at the Centre International de Rencontres Math\'ematiques in Marseille. There have been many advances in the understanding of the high values of log-correlated random fields from the physics and mathematics perspectives in recent years. These fields admit correlations that decay approximately like the logarithm of the inverse of the distance between index points. Examples include branching random walks and the two-dimensional Gaussian free field. In this paper, we review the properties of such fields and survey the progress in describing the statistics of their extremes. The branching random walk is used as a guiding example to prove the correct leading and subleading order of the maximum following the multiscale refinement of the second moment method of Kistler. The approach sheds light on a conjecture of Fyodorov, Hiary & Keating on the maximum of the Riemann zeta function on an interval of the critical line and of the characteristic polynomial of random unitary matrices.

fields

hep-th 1

years

2026 1

verdicts

UNVERDICTED 1

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Black Holes and Random Variables

hep-th · 2026-07-02 · unverdicted · novelty 6.0

Formulates an avatar of the Fyodorov-Hiary-Keating conjecture for black hole microstate counts, implying sharp bounds on CFT primary operator interval counts and suggesting that AdS spectra exhibit extreme value statistics of Gaussian log-correlated random matrices.

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  • Black Holes and Random Variables hep-th · 2026-07-02 · unverdicted · none · ref 28 · internal anchor

    Formulates an avatar of the Fyodorov-Hiary-Keating conjecture for black hole microstate counts, implying sharp bounds on CFT primary operator interval counts and suggesting that AdS spectra exhibit extreme value statistics of Gaussian log-correlated random matrices.