Resolving CME Characteristics with Polarized White Light Data
Matthew
West
Southwest Research Institute
Poster
The line of sight propagation of coronal mass ejections (CMEs) throughout the heliosphere can be determined directly from total and polarized brightness measurements with instruments such as the LASCO, COR, and future PUNCH imagers. By using the physics of Thomson scattering applied to synoptic polarized images, and a symmetric three-polarizer measurement and representation system, the Stokes parameters can be derived, which in turn can be used to derive distances of structures. To accurately determine the position of structures measurements require relative photometric accuracy at a few percent precision, which is undermined by instrument and photometric noise, which redistributes measured polarization angles.
In this presentation the ability to accurately measure the 3D structure of imaged objects in the heliosphere, using polarized brightness measurements is assessed. "Clean" synthetic data produced with the Gamera model, forward modeled to look like white light coronagraph data using the HAO FORWARD algorithms, is used with a polarization resolver to determine the line of sight distance of synthetic CMEs. Additionally, realistic photometric (poisson) and instrument noise is applied to the data to assess the impact on the estimated positions of structures. The noisy data is subsequently "noise-gated" to reduce noise and mitigate its impact. The impacts of noise reduction, and how it can improve estimates of 3D position are assessed.
In this presentation the ability to accurately measure the 3D structure of imaged objects in the heliosphere, using polarized brightness measurements is assessed. "Clean" synthetic data produced with the Gamera model, forward modeled to look like white light coronagraph data using the HAO FORWARD algorithms, is used with a polarization resolver to determine the line of sight distance of synthetic CMEs. Additionally, realistic photometric (poisson) and instrument noise is applied to the data to assess the impact on the estimated positions of structures. The noisy data is subsequently "noise-gated" to reduce noise and mitigate its impact. The impacts of noise reduction, and how it can improve estimates of 3D position are assessed.
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