Quantum fluids spatial distribution evaluation and its characterization

• Ano de defesa: 2019
• Autor(a) principal: Smaira, André de Freitas
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Atomic physics; Bose-Einstein condensate; Condensado de Bose-Einstein; Condensed matter physics; Física atômica; Física da matéria condensada; Óptica; Optics
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/76/76132/tde-18052020-145912/

Bose-Einstein Condensates (BEC) are excellent macroscopic samples quantum behavior of matter study. Since it was experimentally observed in dilute atomic gases, there are important properties related to these systems that were intensely explored, as thermodynamic properties and BEC dynamics both in stationary and turbulent regime. In this work we present and discuss the results and properties determined from Bose-Einstein Condensed time-of-flight images processed using algorithms capable of extract relevant information from atomic density fluctuations observed in this kind of sample. Based on advanced data process we can go beyond the standard results, already presented, and obtain linear momentum maps, density fluctuations and correlations, vortices properties, as well as develop other analysis types to characterization and understanding improve results obtained in turbulent regime, besides other related properties. In this way, we developed specialized data processing library to be used in the general analyses and also to the determination of non-trivial properties, which are essential for the future study of quantum degenerate turbulent gases, with a three-dimensional atomic cloud reconstruction method from three non-orthogonal absorption images obtained from experimentally acquired time-of-flight images. In order to accomplish this we have used \"three-dimensional inverse Radon Transform\", analogous to tomography in the two-dimensional case, however, unlike the indetermination theorem requirement, that the number of images be large enough, we have just a few of them. This issue is lessened, but not solved, using an iterative correction method.