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Integrated Models of Magnetic Fields and Gas Kinematics in Molecular Clouds

Integrated Models of Magnetic Fields and Gas Kinematics in Molecular Clouds
分子云中磁场和气体运动学的集成模型
批准号:
RGPIN-2017-05930
负责人:
Fiege, Jason
金额:
$1.53万
依托单位:
依托单位国家:
加拿大
项目类别:
Discovery Grants Program - Individual
财政年份:
2019
资助国家:
加拿大
项目状态:
已结题
起止时间:
2019-01-01 至 2020-12-31

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中文摘要
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英文摘要
Molecular clouds are magnetized, turbulent, self-gravitating structures, and the seat of star formation in our Galaxy. Star formation occurs on small scales (~0.1 pc), in dense cores, where gravity overcomes turbulence and outward magnetic forces. Magneto-centrifugal forces drive a pair of jets, which sweep up surrounding gas into a pair of bipolar molecular outflows. Self-gravity, outflows, and magnetic fields are strongly coupled in star formation. An integrated data-driven modelling approach, which takes into account these components simultaneously, will provide a clear picture of star forming regions. My HQP and I have developed a software package called PolCat, which uses a novel method to search an enormous space of potential density/magnetic field configurations, to automatically find those that best fit sub-millimetre polarization and intensity maps. Our models are often degenerate, but we easily rule out field geometries that cannot explain the data, and find geometries that work. PolCat models are triaxial and do not assume detailed equilibrium. However, we will use the tensor virial theorem to reject core models that are far from equilibrium along any principal axis, thus providing tighter model constraints. We will also develop a complementary method, based on the self-consistent field method of MHD, to rapidly calculate axisymmetric equilibrium models of rotating, magnetized, self-gravitating cores. The most important extension to PolCat will be a new component to model molecular outflows and their CO (carbon monoxide) line emission. My HPQ and I will develop integrated three-dimensional modelling methods, built upon techniques developed by my group, to generate and constrain models of magnetized star-forming cores and their outflows using observational data. We will explore the relationship between a core's magnetic field and the structure and orientation of outflows by applying our models to data from the BISTRO survey, which uses the new POL-2 polarimeter at the James Clerk Maxwell Telescope.******A related line of inquiry will investigate the recently discovered phenomenon of molecular tornadoes, which are pressure-bound, rotating, helically-wound molecular filaments near the Galactic Centre. It is believed that their striking helical structure arises from the magnetohydrodynamic instability of a twisted magnetic tube, which is wrapped up by a torsional Alfven wave propagating along the filament. My HQP and I will develop a comprehensive, observationally constrained theory of molecular tornadoes and their stability, which will explain their unusual morphology. We will predict submillimetre polarization maps due to these objects, for comparison with future observations.******The overarching goal of this research program is to develop new models and computational tools leading to an improved understanding of magnetic fields in molecular clouds and star formation.**
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Integrated Models of Magnetic Fields and Gas Kinematics in Molecular Clouds
  • 批准号:
    RGPIN-2017-05930
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $3.06万
  • 财政年份:
    2021
  • 负责人:
    Fiege, Jason
  • 依托单位:
Integrated Models of Magnetic Fields and Gas Kinematics in Molecular Clouds
  • 批准号:
    RGPIN-2017-05930
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $1.53万
  • 财政年份:
    2020
  • 负责人:
    Fiege, Jason
  • 依托单位:
Integrated Models of Magnetic Fields and Gas Kinematics in Molecular Clouds
  • 批准号:
    RGPIN-2017-05930
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $1.53万
  • 财政年份:
    2018
  • 负责人:
    Fiege, Jason
  • 依托单位:
Integrated Models of Magnetic Fields and Gas Kinematics in Molecular Clouds
  • 批准号:
    RGPIN-2017-05930
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $1.53万
  • 财政年份:
    2017
  • 负责人:
    Fiege, Jason
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
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