1. On the spectroscopic detection of magnetic reconnection evidence with Solar B – I. Emission line selection and atomic physics issues P. F. Chen 1,2, H. Isobe 1, K. Shibata 1, A.C. Lanzafame 3 2. Nanjing University, China 3. Catania University, Italy 相模原 David H. Brooks 京都大学花山天文台 1. Kwasan Observatory, Kyoto University 2月4日2月4日

2. 1.Reconnection evidence from images Tsuneta et al. （ 1992) Masuda et al. (1994) Expansion and cusp shape of Soft X-ray post-flare loops Above the loop top hard X-ray source

3. 2.Reconnection evidence from images Plasmoid ejection Reconnection related inflow? Ohyama & Shibata (1998)Yokoyama et al. (2001)

4. Spectroscopic Observations will: A.Remove ambiguity between ‘ real ’ and ‘ apparent ’ motions B. Allow accurate measurement of plasma flow velocities C. Study reconnection physics, reconnection rate etc.

5. Objectives of this work 1.Study the signatures in EUV line profiles of plasma flows associated with magnetic reconnection 2.Determine which lines are best for detecting different flows e.g. reconnection inflow, jet etc. 3.Determine whether Solar-B can really detect the signatures and what are the best observation targets 95 spectral lines studied for SERTS DEM analysis (Lanzafame, Brooks, Lang, Summers, Thomas 2002) within Solar-B/EIS wavelength range. ADAS (Summers 1994) collisional-radiative models including density dependence of ionisation balance * 2.5D MHD simulations (Chen & Shibata 2000) * Extra. Consider effect of improved atomic data

6. MHD Simulation CME-flare relation Chen & Shibata (2000) 2.5D resistive MHD simulation No heat conduction Flows associated with magnetic reconnection: Inflow: about 1 MK Jet: about 10-30 MK Flux rope: 5000 K-1 MK Coronal Moreton waves: 1-3 MK

7. Example: Reconnection Inflow Select a line within the expected temperature range of the inflow (from simulations) e.g. Fe XII 195.119A formed at 1.6-2MK, and compute line profile along a chosen line of sight Difficult to distinguish inflow emission from expanding flux rope emission Observer Intensity (x 0.9) – Velocity

8. Ex: Inflow Emission Intensity at different velocities Optimise line of sight for detection Inflow (approx.) < +/- 40 km s -1 Red shifted component mainly inflow (30% approx.) Observer I (x 0.9) - v Intensity map

9. Ex: Reconnection jet Fe XXIV 192.04A formed at 13-16MK Intensity map Observer I (x 400) - v

10. Ex: Slow shock pair attached to CME Ca XVII 192.82A formed at 4-5MK Intensity map Observer I (x 15) - v

11. Result: Classification of 95 lines Classification codes: I - Inflow, S - Shock, J - Jet, M - coronal Moreton wave

12. Crude approx. of effect of simulation Te Fe XII 195 logT=6.15 Fe XV 284 logT=6.3 S XIII 256 logT=6.4 Heat conduction will change simulation temperatures and affect choice of lines

13. Preliminary selection of emission lines – dependent on this model lines Log (T) Fe XIV 2656.25 Fe XV 2846.3 S XII 288 6.35 lines Log (T) S XIII 2576.4 Ca XVII 1936.7 Fe XV 2846.3 slow shocks coronal Moreton wave lines Log (T) Fe IX 1715.8 Fe XV 1956.15 S X 258 6.15 Inflow lines Log (T) Fe XXIII 2647.05 Fe XXIV 1927.15 jet

14. Density dependence of Fe IX 171.073A Solid line – 10 4 cm -3 Dashed Line – 10 11 cm -3 G(Te,Ne) function Line Profile Inaccurate treatment of density sensitivity of G (Te, Ne) function leads to incorrect prediction of strong inflow for this line! Log scale factor 3

15. Summary Using Chen & Shibata (2000) MHD simulations and ADAS data we have simulated the profiles of 95 spectral lines which will be observed by EIS Examined signatures of reconnection inflow, jet, slow shock attached to expanding CME, coronal Moreton wave Classified 95 lines: guide for planning observations & line selection. Preliminary recommendation: Fe XV 195A (inflow), Fe XXIV 192A (jet), etc. Some line profile shapes may be altered as a result of including density sensitivity of ionisation balance: could lead to criticism of reconnection model if strong flows are not detected

16. Future work Results based on these specific simulations so: 1. Parameter survey 2. Larger range of electron densities 3. Effect on classifications 4. Include heat conduction Lines of sight etc. in the ideal case so: 1. Consider whether EIS can really observe the inflows given the instrumental characteristics 2. Consider best targets to detect evidence (Isobe)