Calibrating the Davis-Chandrasekhar Fermi Method With Molecular Cloud simulations

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Park, Jinsoo
Star Formation , Magnetic Field
Star formation has long been a process that many astronomers and astrophysicists have sought to understand. Magnetic fields are believed to be one of the key driving phenomena behind this process. Modern research relies on studying computationally rigorous simulations where the magnetic field strength is known throughout the entire simulation process. One common method, coined the Davis-Chandrasekhar Fermi (DCF) method was proposed in 1953, which suggested that dust polarization could be used to measure the dispersion in the orientation of the magnetic field, which could be used to estimate the magnetic field strength of molecular clouds. In this thesis, we use a pair of Colliding Flow and Collapsing Cloud simulations to estimate the magnetic field strengths using three variations of different DCF methods: the Classical, HH09, and Skalidis DCF methods. We define a correction factor, α, which represents the ratio of the true magnetic field strength divided by estimate obtained from the DCF methods. For the entire simulation map, both the Classical and Skalidis DCF methods tend to under-predict the magnetic field strength (α < 1) for the Colliding Flow simulations but over-predict the magnetic field strength for the Collapsing Cloud simulations (α > 1). For the entire plane-of- sky maps of both the Colliding Flow and Collapsing Cloud simulations, the HH09 DCF method seems to returns consistent results, that is, 0.478 < α < 0.641 but more severely under predict the field strength in maps where the magnetic field is inclined with respect to the plane-of-the-sky. We also investigated the correlations between the polarization dispersion (σφ), the velocity dispersion (σv), and the number density n with the α values obtained from the DCF methods. We find that the strongest correlation is between σv and α which indicates that the DCF methods tend to estimate higher magnetic field strength estimates, relative to the true values, for simulations with higher σv values. Finally, we found that the of resolution of the simulations influenced the accuracy of the three DCF methods and that lower resolution data leads to higher estimations from the Classical and Skalidis DCF methods.
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