5 th SECCHI Consortium Meeting, May Tomographic Reconstruction of CMEs from White Light Coronagraph Data Tomographic Reconstruction of CMEs from White Light Coronograph Data FITS ingest, Visualization, and PIXON Current State Alex Antunes, J.W. Cook, J. Newmark (NRL) A. Yahil (Stony Brook University)

5 th SECCHI Consortium Meeting, May Tomographic Reconstruction of CMEs from White Light Coronagraph Data Abstract We discuss our 3D tomographic reconstruction approach with PIXON for SECCHI and LASCO data: setting up the geometry using datafile FITS headers, calculation of statistical noise, and input/output for the PIXON tool. In previous meetings we have discussed our PIXON 3D reconstruction tool and shown early results from synthetic modeled coronal structures. 3D tomographic reconstruction works best with multiple overlapping yet distinct viewpoints, while early in the STEREO mission, we are still at small angles of separation. We illustrate reconstruction geometry and set-up for SECCHI Cor2 data. We also discuss issues in incorporating HI data in reconstructions.

5 th SECCHI Consortium Meeting, May Tomographic Reconstruction of CMEs from White Light Coronagraph Data Intent: Determine underlying n e density of a CME Means: ● FITS ingest ● Solver (Pixon or conjugate gradient or FM) ● Visualizing Datacubes Sample: Chen flux rope with noise Discussion and 'To Do'. 3D Reconstruction Update

5 th SECCHI Consortium Meeting, May Tomographic Reconstruction of CMEs from White Light Coronagraph Data FITS Ingest

5 th SECCHI Consortium Meeting, May Tomographic Reconstruction of CMEs from White Light Coronagraph Data Fit to the noise, then stop DN to Photons: data photons = dn2photons * (data DN – biasmean) sigma photons = √data photons sigma DN = sigma photons / dn2photons Fractional Method: Noise = data L1.0 * sigma DN /data L0.5 in DN secchi_prep Method: Noise = secchi_prep[sigma DN ] Result= the same

5 th SECCHI Consortium Meeting, May Tomographic Reconstruction of CMEs from White Light Coronagraph Data Solving (and LOS) Problems Original Data, 45 º apart Rendered Solution 3-axis projection of solution datacube Inverse modeling

5 th SECCHI Consortium Meeting, May Tomographic Reconstruction of CMEs from White Light Coronagraph Data What is Pixon? (We interrupt for some definitions) ● "Data": 2-dimensional images, from a spacecraft or created via a rendering of a model. ● "Image": a 3-dimensional data cube containing electron density measurements. An image is rendered to produce data. ● Pixon: software using the PIXON algorithm for reconstruction. "Classic" uses a cartesian grid, "Tetrahedral" uses an arbitrary grid. ● Raytrace WhiteLight, a renderer with front-end GUI.

5 th SECCHI Consortium Meeting, May Tomographic Reconstruction of CMEs from White Light Coronagraph Data And Visualize! (a busy IDL desktop) ● Row of images tv_multi,array_of_data ● Datacube axes threeview, imgcube ● Interactive 3-D GUI render_rot_gui,imgcube

5 th SECCHI Consortium Meeting, May Tomographic Reconstruction of CMEs from White Light Coronagraph Data Sample 1: Chen Fluxrope Model Model at 0 º, 45 º, 90 º Same, with noise added

5 th SECCHI Consortium Meeting, May Tomographic Reconstruction of CMEs from White Light Coronagraph Data Fluxrope Reconstruction Noisy model at 0 º, 45 º, 90 º Reconstruction at 0 º, 45 º, 90 º

5 th SECCHI Consortium Meeting, May Tomographic Reconstruction of CMEs from White Light Coronagraph Data Fluxrope Solution Densitycube view down 'x' view down 'y' view down 'z' Linear plot Log plot (Fit for 408 minutes, not to completion)

5 th SECCHI Consortium Meeting, May Tomographic Reconstruction of CMEs from White Light Coronagraph Data Computational Limits Primary limit is memory, second is run-time. Theory: ● Typical 32-bit architecture can address 2GB of memory, for 8 byte N 3 arrays → N ≤8 12. Practice: ● 32-bit IDL can rarely allocate multiple large-N arrays: with 2GB RAM, IDL managed only (4) float arrays ● Pixon uses N 3 * 1.2 × GB. N=512 → 16GB swap ● RAM max space must be unfragmented, contiguous memory Today: ● Pixon N=256 runs (barely) with 2GB of physical RAM ● Plan is to test N=512 on a 64-bit RAM=16MB system

5 th SECCHI Consortium Meeting, May Tomographic Reconstruction of CMEs from White Light Coronagraph Data To Do 1) Complete testing, GUI 2) Reconstruction of A/B Cor2 + LASCO C2 CMEs 3) Reconstruction of A/B/LASCO events, multiple instruments 4) Reconstruction of an A/B HI CME (or comet?) 5) Higher resolution reconstructions (current limit, N=256) 6) Mix of inverse and forward methods 7) Commit software to SolarSoft archive

5 th SECCHI Consortium Meeting, May Tomographic Reconstruction of CMEs from White Light Coronagraph Data Contact Info Alex (Sandy) Antunes /~antunes

5 th SECCHI Consortium Meeting, May Tomographic Reconstruction of CMEs from White Light Coronagraph Data