Zeta measures and Thermodynamic Formalism for temperature zero

TL;DR

This paper investigates the behavior of zeta measures associated with H"older potentials on Bernoulli spaces as parameters vary, focusing on their convergence to maximizing measures and establishing large deviation principles.

Contribution

It introduces a new analysis of zeta measures' limits when parameters tend to infinity and one, without assuming uniqueness of the maximizing measure.

Findings

Zeta measures converge to maximizing measures as parameters vary.

Established a Large Deviation Principle for the limit behavior.

Analyzed the case when the product of parameters approaches a positive constant.

Abstract

We address the analysis of the following problem: given a real H\"older potential $f$ defined on the Bernoulli space and $\mu_f$ its equilibrium state, it is known that this shift-invariant probability can be weakly approximated by probabilities in periodic orbits associated to certain zeta functions. Given a H\"older function $f>0$ and a value $s$ such that $0<s<1$, we can associate a shift-invariant probability $\nu_{s}$ such that for each continuous function $k$ we have \[\int k d\nu_{s}=\frac{\sum_{n=1}^{\infty}\sum_{x\in Fix_{n}}e^{sf^{n}(x)-nP(f)}\frac{k^{n}(x)}{n}}{\sum_{n=1}^{\infty}\sum_{x\in Fix_{n}}e^{sf^{n}(x)-nP(f)}},\] where $P(f)$ is the pressure of $f$, $Fix_n$ is the set of solutions of $\sigma^n(x)=x$, for any $n\in \mathbb{N}$, and $f^{n}(x) = f(x) + f(\sigma(x)) + f(\sigma^2(x))+... + f(\sigma^{n-1} (x)).$ We call $\nu_{s}$ a zeta probability for $f$ and $s$. It is known that $\nu_s \to \mu_{f}$, when $s \to 1$. We consider for each value $c$ the potential $c f$ and the corresponding equilibrium state $\mu_{c f}$. What happens with $\nu_{s}$ when $c$ goes to infinity and $s$ goes to one? This question is related to the problem of how to approximate the maximizing probability for $f$ by probabilities on periodic orbits. We study this question and also present here the deviation function $I$ and Large Deviation Principle for this limit $c\to \infty, s\to 1$. We will make an assumption: $\lim_{c\to \infty, s\to 1} c(1-s)= L>0$. We do not assume here the maximizing probability for $f$ is unique.