{"title":"Heyde Theorem for Locally Compact Abelian Groups Containing No Subgroups Topologically Isomorphic to the 2-Dimensional Torus","authors":"Gennadiy Feldman","doi":"10.1007/s00041-024-10092-0","DOIUrl":null,"url":null,"abstract":"<p>We prove the following group analogue of the well-known Heyde theorem on a characterization of the Gaussian distribution on the real line. Let <i>X</i> be a second countable locally compact Abelian group containing no subgroups topologically isomorphic to the 2-dimensional torus. Let <i>G</i> be the subgroup of <i>X</i> generated by all elements of <i>X</i> of order 2 and let <span>\\(\\alpha \\)</span> be a topological automorphism of the group <i>X</i> such that <span>\\(\\textrm{Ker}(I+\\alpha )=\\{0\\}\\)</span>. Let <span>\\(\\xi _1\\)</span> and <span>\\(\\xi _2\\)</span> be independent random variables with values in <i>X</i> and distributions <span>\\(\\mu _1\\)</span> and <span>\\(\\mu _2\\)</span> with nonvanishing characteristic functions. If the conditional distribution of the linear form <span>\\(L_2 = \\xi _1 + \\alpha \\xi _2\\)</span> given <span>\\(L_1 = \\xi _1 + \\xi _2\\)</span> is symmetric, then <span>\\(\\mu _j\\)</span> are convolutions of Gaussian distributions on <i>X</i> and distributions supported in <i>G</i>. We also prove that this theorem is false if <i>X</i> is the 2-dimensional torus.</p>","PeriodicalId":15993,"journal":{"name":"Journal of Fourier Analysis and Applications","volume":"116 1","pages":""},"PeriodicalIF":1.2000,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Fourier Analysis and Applications","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s00041-024-10092-0","RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
引用次数: 0
Abstract
We prove the following group analogue of the well-known Heyde theorem on a characterization of the Gaussian distribution on the real line. Let X be a second countable locally compact Abelian group containing no subgroups topologically isomorphic to the 2-dimensional torus. Let G be the subgroup of X generated by all elements of X of order 2 and let \(\alpha \) be a topological automorphism of the group X such that \(\textrm{Ker}(I+\alpha )=\{0\}\). Let \(\xi _1\) and \(\xi _2\) be independent random variables with values in X and distributions \(\mu _1\) and \(\mu _2\) with nonvanishing characteristic functions. If the conditional distribution of the linear form \(L_2 = \xi _1 + \alpha \xi _2\) given \(L_1 = \xi _1 + \xi _2\) is symmetric, then \(\mu _j\) are convolutions of Gaussian distributions on X and distributions supported in G. We also prove that this theorem is false if X is the 2-dimensional torus.
期刊介绍:
The Journal of Fourier Analysis and Applications will publish results in Fourier analysis, as well as applicable mathematics having a significant Fourier analytic component. Appropriate manuscripts at the highest research level will be accepted for publication. Because of the extensive, intricate, and fundamental relationship between Fourier analysis and so many other subjects, selected and readable surveys will also be published. These surveys will include historical articles, research tutorials, and expositions of specific topics.
TheJournal of Fourier Analysis and Applications will provide a perspective and means for centralizing and disseminating new information from the vantage point of Fourier analysis. The breadth of Fourier analysis and diversity of its applicability require that each paper should contain a clear and motivated introduction, which is accessible to all of our readers.
Areas of applications include the following:
antenna theory * crystallography * fast algorithms * Gabor theory and applications * image processing * number theory * optics * partial differential equations * prediction theory * radar applications * sampling theory * spectral estimation * speech processing * stochastic processes * time-frequency analysis * time series * tomography * turbulence * uncertainty principles * wavelet theory and applications