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Coronal White Light 3D Reconstruction

Coronal White Light 3D Reconstruction. Newmark, Cook, Socker, Reiser, Crane Naval Research Lab Liewer, De Jong Jet Propulsion Lab. Goal. Develop, Test, Apply 3D reconstruction techniques to solar features from low corona through heliosphere to 1 AU.

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Coronal White Light 3D Reconstruction

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  1. Coronal White Light 3D Reconstruction Newmark, Cook, Socker, Reiser, Crane Naval Research Lab Liewer, De Jong Jet Propulsion Lab

  2. Goal • Develop, Test, Apply 3D reconstruction techniques to solar features from low corona through heliosphere to 1 AU. • Utilize B, pB, temporal, 2D white light coronagraph images and synthetic models from 2 vantage points, construct (time dependent) 3D electron density distribution

  3. Learn to Walk before Running...

  4. Science • Polar Plumes - hydrostatic equilibrium sol’n of density vs. height, tube expansion, statistics • Equatorial Streamers - projection of sheets, effect of AR’s, compare to 3D recon tie points (Liewer 2000), density enhancements vs. folds • CME’s - models - prepare for SECCHI, effect of observing angle, speed, etc.

  5. Key Aspects • Renderer - Physics (Thomson scattering), geometry, optically thin plasma • Reconstruction Algorithm - PIXON, underdetermined system, speed (large # pts) • Visualization - 3D electron density distribution, time dependent • Data - LASCO polar plumes, streamers include 3D densities rendered from tie points, synthetic CME models

  6. PIXON - What • Pina, Puetter, Yahil (1993, 1995) - high performance, non-linear, non-parametric, locally adaptive, iterative image reconstruction • Commercial package - used in radio, HXT, remote sensing, etc; develop specific code jointly - tomography from limited (2) views - mostly developed from SBIRS; data sampling fcn - renderer/transpose; visualization • Full 3D reconstruction of Ne

  7. PIXON - Why • Standard tomography-not applicable, parametric least squares - too slow; maximum entropy methods do not work well on local variations; minimum complexity solution - works locally fewer artifacts • Speed of 3D reconstruction - scales as N, estimates <10 iterations - intelligence stop when declining complexity per iteration drops 512x512=2 min,256x256x256 ~2hrs,

  8. PIXON - Details • Simple Problem, D=observation, I=reconstructed image, H=PSF, K=pixon kernel, Ф=pseudoimage, N=noise D(x) = dyH(x,y)I(y) + N(x) I(y) = dzK(y,z) Ф(z) • 2 Step soln a) minimize 2 by Ф, b) minimize # pixons and maximize size locally - each part is iterative and iterate steps • PIXON shapes - spherical, can change

  9. Conclusions • 3D reconstructions are possible • Direct application to SECCHI will require substantial effort and collaboration

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