1. 2/14/06AIA/HMI Science Meeting C3. Thermal Studies: Techniques Happy Valentine’s Day!

2. 2/14/06AIA/HMI Science Meeting Science Context: DEM Observed pixel value p in the i th channel at pixel coordinate θ: where DEM(T, θ) describes conditions in the corona With AIA, we’ll have the best chance yet to obtain meaningful solutions for the DEM –6+ narrow EUV bandpasses → broader T coverage –high spatial resolution → study the thermal properties of elemental structures –high cadence → study evolution of coronal structures at the shortest relevant timescale –simultaneous observations from Solar-B EIS & XRT, GOES SXI, STEREO SECCHI, … –Improving spectral codes and inversion techniques

3. 2/14/06AIA/HMI Science Meeting Agenda 1.Modeling Assumptions and Uncertainties 2.Inversion Techniques 3.Data Products 4.DEM Science 5.AIA DEM recovery challenge – status update Wanted: practical ideas and questions, not necessarily solutions We’ll try to limit the first three topics to ~20 minutes each Please try to hold your comments for the appropriate topic!

4. 2/14/06AIA/HMI Science Meeting C3.1: Modeling Assumptions and Uncertainties What are the pros and cons of current spectral codes (CHIANTI, APEC, MEKAL, etc.)? What’s the best way to handle abundances and ionization equilibrium calculations? How significant are opacity effects due to optical depth of emitting plasma along the lines of sight? How do we factor in uncertainties in the instrument calibration? How do all these uncertainties propogate into DEM analysis? How can we design our DEM tools to take into account all of these questions?

5. 2/14/06AIA/HMI Science Meeting C3.3: Data Products What sort of data products should we provide, and at what level? –Nominal DEM solutions How are they calculated (algorithm, assumed abundance, etc.)? What resolution/cadence? How are uncertainties calculated and provided? –Temperature maps Derived from DEM solutions, but how, exactly? How are they displayed? To answer this question, we need to determine: –computational power required for different data products and solution methods; –useful/reasonable levels of modularity and flexibility for DEM tools provided by the instrument teams –graphical methods for communicating the information of DEM solution/uncertainty sets across images with millions of pixels –value of instrument-team provided DEM products (near real-time cadence) versus end-user tools (greater flexibility, processing)

6. 2/14/06AIA/HMI Science Meeting AIA DEM Recovery Challenge Basic Procedure: –We provided instrument response functions and 24 simulated sets of multispectral AIA observations; participants found the DEM functions that best reproduced those observations, and compared them to the targets –www.lmsal.com/~boerner/demtest/www.lmsal.com/~boerner/demtest/ So far, we have responses from: –J. Kaastra, SRON –S. Gburek –M. Siarkowski –V. Kashyap (SAO) –more may be coming; more are welcome! –full results will be posted on Friday

7. 2/14/06AIA/HMI Science Meeting Simulated Observations 6 DEM variants –plotted at left –one-parameter tuning –most are fairly smooth 4 sets of parameterizations –Case 1-6: used provided CHIANTI T responses (Feldman abund, Mazzotta ion eq.); no systematic errors –Case 7-12: used CHIANTI T responses with different abundance (Meyer) & ionization (Arnaud & Rothenflug); no errors –Case 13-18: used CHIANTI T responses; added pseudorandom calibration and atomic physics errors –Case 19-24: no errors, but used APEC T responses

8. 2/14/06AIA/HMI Science Meeting Non-uniqueness 4 perfect solutions from Marek Siarkowski –black = target –red = Withbroe-Sylwester solution –blue/green/orange = genetic algorithms

9. 2/14/06AIA/HMI Science Meeting Preliminary Conclusions DEM reconstruction is hard –Solutions are extremely sensitive to error –Solutions are extremely non-unique This sort of test is potentially valuable –A lot of people have simulated observations, then recovered their own observations –This is not as powerful a test as it could be We will continue this experiment, and are interested in suggestions for how to do it better –Focus on algorithms vs. spectral codes vs. systematic errors –Develop an understanding of the differences in the spectral codes You can help!