The macroscopic shape of Gelfand-Tsetlin patterns and free probability
Samuel G. G. Johnston, Joscha Prochno

TL;DR
This paper investigates the macroscopic shape of Gelfand-Tsetlin patterns, revealing their surface tension and large deviation principles, and connects these findings to free probability theory and free entropy.
Contribution
It provides a detailed analysis of Gelfand-Tsetlin patterns' surface tension and establishes a link between their large deviations and free probability, confirming a conjecture by Shlyakhtenko and Tao.
Findings
Derived explicit surface tension formula for Gelfand-Tsetlin functions.
Proved large deviation principle for scaled Gelfand-Tsetlin patterns.
Connected the minimizers of the rate functional to free probability operations.
Abstract
A Gelfand-Tsetlin function is a real-valued function defined on a finite subset of the lattice with the property that for every edge directed north or east between two elements of . We study the statistical physics properties of random Gelfand-Tsetlin functions from the perspective of random surfaces, showing in particular that the surface tension of Gelfand-Tsetlin functions at gradient is given by \begin{align*} \sigma(u_1,u_2) = - \log (u_1 + u_2 ) - \log \sin (\pi u_1/(u_1+u_2)) -1 + \log \pi. \end{align*} A Gelfand-Tsetlin pattern is a Gelfand-Tsetlin function defined on the triangle . We show that after rescaling, a sequence of random Gelfand-Tsetlin patterns with fixed diagonal heights…
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Taxonomy
TopicsMathematical Dynamics and Fractals · Stochastic processes and statistical mechanics · Theoretical and Computational Physics
