Angle-resolved Photoemission Spectroscopy Data Processing Method And Study Of The Electronic Structure Of Copper-based Superconductor Bi2223

As the earliest discovered macroscopic quantum phenomenon,superconductivity has always been a focal point in scientific research.It not only reveals the quantum behavior of electrons in materials,promoting the development of strongly correlated electron systems and quantum many-body theory,but also provides an important foundation for many practical applications.The discovery of cuprate high-temperature superconductors has continuously pushed the boundaries of the highest critical transition temperatures under atmospheric pressure,stimulating the enthusiasm of numerous physicists for research.Although significant progress has been made in the study of high-temperature superconductors,there is currently no comprehensive microscopic theory that can provide a unified and satisfactory explanation for most experimental phenomena,and many questions in this field remain to be explored.Angle-resolved photoemission spectroscopy(ARPES)is one of the most effective tools for studying electronic structures,as it can directly probe the electronic structure of materials and obtain the single-particle response of all occupied states,playing a significant role in the research of high-temperature superconductors.This paper focuses on the work on ARPES instrumentation and research on the cuprate high-temperature superconductor Bi2223,as well as the exploration of neural network-based ARPES data processing.1.The development history and progress of superconductors are introduced,with a focus on the crystal structure,phase diagram,and electronic structure of cuprate high-temperature superconductors.2.The basic principles of angle-resolved photoemission spectroscopy are introduced,including experimental principles and photoelectron emission theory.The vacuum ultraviolet angle-resolved photoemission spectroscopy system in the laboratory is also discussed,with details on the light source,analyzer,ultra-high vacuum system,and low-temperature sample transfer system.The maintenance work on the spin-resolved ARPES system is highlighted.3.The research on the cuprate high-temperature superconductor Bi2223 is presented.In this work,we observed the layer-split Fermi surface in Bi2223 for the first time,as well as the selective Bogoliubov hybridization between the three bands in the superconducting state and the unusual band structures.Based on these experimental data,we improved the tight-binding model of the three-layer interaction and fitted the parameters and functional forms characterizing various microscopic processes,discovering unusual electronic hopping and pairing between copper-oxygen planes.These results suggest that the high critical transition temperature may be realized in an array of coupled planes with different doping levels such that a high pairing strength is derived from the underdoped planes and a large phase stiffness from the optimally or overdoped ones.This reveals the electronic origin of the highest critical transition temperature in Bi2223 among Bi-based cuprate high-temperature superconductors and the microscopic reasons for the insensitivity of the critical transition temperature to doping in the overdoped region.These findings offer new insights for finding materials with higher critical transition temperatures and explaining their microscopic mechanisms.4.The exploration of using neural networks for ARPES data processing is introduced.An overview of the research progress of neural networks in ARPES data processing is provided.The basic principles of neural networks,the characteristics of ARPES data and noise are discussed in detail.The neural network structure and dataset construction method adopted for ARPES data denoising are described,and the practical effects of this method in ARPES data denoising processing are demonstrated.Finally,some unresolved issues and further attempts of neural networks in ARPES data denoising are discussed.