In a random model of the Riemann zeta function, the normalized total mass of high points a linear order below the maximum converges almost surely to Gaussian multiplicative chaos of an approximating process times a random function.
Multiplicative chaos measures for a random model of the Riemann zeta function
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abstract
We prove convergence of a stochastic approximation of powers of the Riemann $\zeta$ function to a non-Gaussian multiplicative chaos measure, and prove that this measure is a non-trivial multifractal random measure. The results cover both the subcritical and critical chaos. A basic ingredient of the proof is a 'good' Gaussian approximation of the induced random fields that is potentially of independent interest.
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High points of a random model of the Riemann-zeta function and Gaussian multiplicative chaos
In a random model of the Riemann zeta function, the normalized total mass of high points a linear order below the maximum converges almost surely to Gaussian multiplicative chaos of an approximating process times a random function.