On the spectroscopic detection of magnetic reconnection evidence with Solar B – I. Emission line

selection and atomic physics issues

P. F. Chen1,2, H. Isobe1, K. Shibata1, A.C. Lanzafame3

2. Nanjing University, China3. 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

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

2.Reconnection evidence from images

Plasmoid ejection Reconnection related inflow?

Ohyama & Shibata (1998) Yokoyama et al. (2001)

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.

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

MHD Simulation

CME-flare relationChen & Shibata (2000)

2.5D resistive MHDsimulation No heat conduction

Flows associated with magnetic reconnection:

Inflow: about 1 MKJet: about 10-30 MKFlux rope: 5000 K-1 MKCoronal Moreton waves: 1-3 MK

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 sightDifficult to distinguish inflow emission from expanding flux rope emission

Observer

Intensity (x 0.9) – Velocity

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

Ex: Reconnection jet Fe XXIV 192.04A formed at 13-16MK

Intensity map

Observer

I (x 400) - v

Ex: Slow shock pair attached to CME

Ca XVII 192.82A formed at 4-5MK

Intensity map

Observer

I (x 15) - v

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

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

Preliminary selection of emission lines – dependent on this model

lines Log (T)Fe XIV 265 6.25

Fe XV 284 6.3

S XII　 288 6.35

lines Log (T)S XIII 257 6.4

Ca XVII 193 6.7

Fe XV 284 6.3

slow shocks

coronal Moreton wave

lines Log (T)Fe IX 171 5.8

Fe XV 195 6.15

S X　 258 6.15

Inflow

lines Log (T)Fe XXIII 264 7.05

Fe XXIV 192 7.15

jet

Density dependence of Fe IX 171.073A

Solid line – 104 cm-3 Dashed Line –1011 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!

Logscale

factor 3

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

Future work Results based on these specific simulations so:

1. Parameter survey2. Larger range of electron densities3. Effect on classifications4. 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)