{"title":"Weak expansion properties and a large deviation principle for coarse\n expanding conformal systems","authors":"Li, Zhiqiang, Zheng, Hanyun","doi":"10.48550/arxiv.2311.07305","DOIUrl":null,"url":null,"abstract":"In this paper, we prove that for a metric coarse expanding conformal system $f\\:(\\mathfrak{X}_1,X)\\rightarrow (\\mathfrak{X}_0,X)$ with repellor $X$, the map $f|_X\\:X\\rightarrow X$ is asymptotically $h$-expansive. Moreover, we show that $f|_X$ is not $h$-expansive if there exists at least one branch point in the repellor. As a consequence of asymptotic $h$-expansiveness, for $f|_X$ and each real-valued continuous potential on $X$, there exists at least one equilibrium state. For such maps, if some additional assumptions are satisfied, we can furthermore establish a level-2 large deviation principle for iterated preimages, followed by an equidistribution result.","PeriodicalId":496270,"journal":{"name":"arXiv (Cornell University)","volume":"115 22","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv (Cornell University)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.48550/arxiv.2311.07305","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, we prove that for a metric coarse expanding conformal system $f\:(\mathfrak{X}_1,X)\rightarrow (\mathfrak{X}_0,X)$ with repellor $X$, the map $f|_X\:X\rightarrow X$ is asymptotically $h$-expansive. Moreover, we show that $f|_X$ is not $h$-expansive if there exists at least one branch point in the repellor. As a consequence of asymptotic $h$-expansiveness, for $f|_X$ and each real-valued continuous potential on $X$, there exists at least one equilibrium state. For such maps, if some additional assumptions are satisfied, we can furthermore establish a level-2 large deviation principle for iterated preimages, followed by an equidistribution result.