Geodesic Beams in Eigenfunction Analysis
Geodesic Beams in Eigenfunction Analysis
复制标题
特征函数分析中的测地梁
DOI:
10.1007/978-3-031-31586-2
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发表时间:
2023
期刊:
影响因子:
--
通讯作者:
Canzani Y
中科院分区:
文献类型:
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作者:
Canzani Y
This book aims to explain the concepts behind the geodesic beam method that we have developed to study the behavior of high energy eigenfunctions. The idea for geodesic beams was inspired by the work of Koch-Tataru-Zworski [KTZ07] as well as the second author’s work with Toth [GT17]. Geodesic beams originally appeared in [Gal19] and were developed into their current form in [CG19b, CG21, CG19a, CG20b]. The book is intended to be accessible to graduate students and is aimed at readers interested in studying the behavior of eigenfunctions. Although familiarity with semiclassical analysis will be helpful when reading this text, we provide a quick, axiomatic introduction to the subject in Chap. 3. This chapter, in principle, contains all of the semiclassical analysis results necessary to understand the rest of the text. For more detailed treatments of the semiclassical machinery, we refer the reader to the books [Zwo12, DS99, DZ19]. Throughout the book, we chose to present full proofs when they illustrate important ideas and are central to our method. We instead present outlines of proofs when the technical details can obscure the ideas. In this case, we refer the reader to the corresponding article for the full details of the proof. We hope that our choices will make the text relatively easy to read, while still containing the main points of our analysis. Our presentation of various parts of this text was heavily influenced by Zworski’s book [Zwo12] from which the authors first learned semiclassical analysis. Our approach to semiclassical analysis was informed by long interactions with Maciej Zworski, Semyon Dyatlov, and Andras Vasy, and our perspective on the analysis of eigenfunctions has been guided by Dima Jakobson, Peter Sarnak, John Toth, and Steve Zelditch. This text would not have been possible without their support. Y. Canzani was supported by the Alfred P. Sloan Foundation, NSF CAREER Grant DMS-2045494, and NSF Grant DMS-1900519. J. Galkowski acknowledges support from the EPSRC through grants EP/V001760/1 and EP/V051636/1.