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工信部人工智能赋能中小企业典型应用场景案例(科研领域)
  • 张华伟
  • 所属院校: 北京大学科维理天文与天体物理研究所
  • 所属院系:
  • 职称: 副教授
  • 导师类型:
  • 招生专业:
  • 研究领域: StellarChemicalAbundance,NLTEAbundanceAnalysis,GalacticArchaeology,GalacticStructureandChemicalEvolution,StarFormation恒星元素丰度,NLTE元素丰度分析,银河系考古学,银河系结构和演化,恒星形成
个人简介

个人简介

Stellar chemical abundances analysis is the basic means of exploring the evolution of stars, the Milky Way, and even the universe. By obtaining high-resolution spectra of stars, we can not only determine their chemical abundances and spatial distribution, but also know the chemical distribution of different stellar populations of the Milky Way. And constraints to the galactic formation, structure, and evolution model can be provided. The vast majority of the current analysis of abundance are based on LTE(Local Thermodynamic Equilibrium) assumption. While this assumption is not suitable for each of the elements, and in some astrophysical environment which may result in serious deviations from the real situation, thus leading to wrong conclusions. In other words, we need NLTE effects of the amendments to further improve the accuracy of the abundance analysis. NLTE effects are particularly notable for metal-poor dwarfs and giants. The LAMOST (the Large Sky Area Multi-Object Fiber Spectroscopic Telescope) Experiment for Galactic Understanding and Exploration (LEGUE) survey aims to provide a larger spectroscopic sample than ever before to investigate kinematics and chemical abundances of the Galaxy.

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