1. Predicting Coronal Emissions with Multiple Heating Rates Loraine Lundquist George Fisher Tom Metcalf K.D. Leka Jim McTiernan AGU 2005

2. Outline  Goal: predict emissions for different coronal heating theories.  Method  Results from 3 active regions  Possible sources of discrepancy Does any theory predict emission correctly?

3. Method Overview

4. Non-constant alpha FFF model of McTiernan, using method of Wheatland et al. 2000 Photospheric magnetogram Fieldline Extrapolation

5. Associate fieldlines with loops Fieldline Extrapolation

6. Solve steady-state energy/momentum equations along each fieldline Energy Balance

7.  Gravity  Cross-sectional area variations (varies with B to conserve flux)  Radiative losses from Chianti atomic database  Allow arbitrary heat distribution along loop (currently uniform)  Steady-state flows, driven by heating asymmetries Physics Included EHEH Energy Balance

8. Heating scaling laws from Mandrini et al. 2000 Proportionality constant: Match total active region emission to observed emission Heating rates are volumetric Energy Balance Heating scaling relationships

9. Interpolate to a 3-D data cube Interpolation

10. Image Reconstruction

11. Satellite line of sight Image Reconstruction

12. Satellite line of sight Convolve with instrument response Image Reconstruction

13. Synthetic image Image Reconstruction

14. Ready to compare with observations! Image Reconstruction

15. Results: AR 8210 Observed Emission

16. Results: AR 8651 Observed Emission

17. Results: AR 9017 Observed Emission

18. Sources of Discrepancy  Fieldline representation

19. Sources of Discrepancy  Fieldline representation Fieldlines reaching outside of box are ignored Fieldlines reaching outside of box are ignored

20. Sources of Discrepancy  Fieldline representation Fieldlines reaching outside of box are ignored Fieldlines reaching outside of box are ignored

21. Sources of Discrepancy  Fieldline representation Fieldlines reaching outside of box are ignored Fieldlines reaching outside of box are ignored Sensitivity to choice of fieldlines? Sensitivity to choice of fieldlines? Insensitive to doubling of # of fieldlines. Insensitive to doubling of # of fieldlines. May require more testing of different techniques. May require more testing of different techniques.

22. Sources of Discrepancy  Fieldline representation  Magnetic field extrapolation discrepancies

23. Sources of Discrepancy  Fieldline representation  Magnetic field extrapolation discrepancies Side and top boundaries from potential field Side and top boundaries from potential field No information from nearby active regions No information from nearby active regions May not be force free May not be force free

24. Sources of Discrepancy  Fieldline representation  Magnetic field extrapolation discrepancies  Steady-state loop discrepancies

25. Sources of Discrepancy  Fieldline representation  Magnetic field extrapolation discrepancies  Steady-state loop discrepancies Compare temperature and EM from filter ratio: Compare temperature and EM from filter ratio: Temperature too low Temperature too low EM too high EM too high (observations under-dense vs. steady loops) (observations under-dense vs. steady loops)  (Perhaps abundance differences?)

26. Sources of Discrepancy  Fieldline representation  Magnetic field extrapolation discrepancies  Steady-state loop discrepancies  Simplified heating parametrizations

27. Sources of Discrepancy  Fieldline representation  Magnetic field extrapolation discrepancies  Steady-state loop discrepancies  Simplified heating parametrizations Variables left out (velocity, density, etc.) Variables left out (velocity, density, etc.) Heat distribution along loop (now uniform) Heat distribution along loop (now uniform)

28. Conclusions  Forward modeling is possible and useful -- don’t assume we’ll get it right  Scaling relationships dramatically affect the distribution of synthetic emission  Many sources of discrepancy These show which physics is most significant These show which physics is most significant A promising observational constraint