Solar wind diagnostic using both insitu and spectroscopic measurements

Transcription

1 Solar wind diagnostic using both insitu and spectroscopic measurements Enrico Landi University of Michigan In collaboration with: Jacob Gruesbeck (University of Michigan) Susan Lepri (University of Michigan) Len Fisk (University of Michigan) Thomas Zurbuchen (University of Michigan) Mari Paz Miralles (Smithsonian Astrophisical Observatory)

2 The Solar Wind Problem 1 How the fast and slow solar wind are generated in the Sun not yet known A How is the wind heated B How is the wind accelerated C Which structures in the Sun are the source of the different wind types 2 Two main competing categories of models A - Wave/turbulence driven models B Reconnection driven models Are there ways to discriminate between these two scenarios using observations?

3 Studying the Solar Wind 1 - Solar wind properties are measured using two very different techniques: a - In-situ measurements of local wind properties b - Remote observations of wind source regions Images Spectra 2 - Can these two techniques be combined together? a - Capitalize on the strengths of both b - Study wind evolution end-to-end

4 Linking the two ends of solar wind trajectory Wind charge composition evolution is key 1 As the wind leaves the Sun, - Temperature increases - Density decreases - Velocity increases less time spent in densest regions 2 As a consequence, ionization and recombination decrease effectiveness - The plasma disconnects from local plasma properties - Charge state composition evolves slower and eventually freezes in - In-situ plasma maintains memory of initial stages of wind life

5 The evolution of plasma ionization status can be predicted with the Michigan Ionization Code Input: - Plasma parameters as a function of distance:»»» Density Temperature Velocity - Boundary conditions for source region - Database of ionization and recombination coefficients (CHIANTI) Output: - Charge state evolution as a function of distance

6 Comparison with observations 1 Predicted frozen-in charge states: Direct comparison with in-situ measurements ACE measurements of fast wind

7 2 Spectral line intensities Contribution function Level population (from CHIANTI) Line Contribution Function Number of absorbing ions Ion abundance (from the Michigan Ionization Code) Element abundance (from measurements)

8 This requires one additional piece of information: The distribution of the plasma along the line of sight Coronal hole Line of sight Streamer Three possible assumptions: 1 Simple assumptions (spherical symmetry) 2 Educated assumptions (using SOHO, STEREO and SDO images) 3 3D tomographic reconstructions (Frazin et al. 2009)

9 Applications of the technique Two possible applications: A Feed the Michigan Ionization Code with Ne, T, v from a theoretical model» Benchmark the model» B - Empirically find Ne, T, v from trial and error procedures What I did: A - Calculated the charge state evolution using the Cranmer et al model Wind starts from lower chromosphere Powered by waves B - Assumed spherical symmetry» Calculated for both CH and EQ» EQ used for comparison purposes C - Compared with coronal hole/fast wind observations from SUMER and Ulysses

10 SUMER observations Taken on 1996 November 3 - Full spectrum - largest variety of ions - Include light and heavy elements - Temperature, density diagnostics available He II 40,000 K Fe XII 1,400,000 K Fe IX 900,000 K Fe XV 2,000,000 K

11 Is Cranmer et al reasonable? Compare measurements to model values Electron temperature Electron density» Excellent agreement! Log Ne Log T

12 Comparison with in-situ Predicted charge states are all too low

13 Comparison with spectra I Magnesium and Sodium Avoid spicules 1 - Equilibrium conditions closer to observations 2 - Delay effect is visible in the rate of decrease of model-predicted intensities

14 Comparison with spectra II - Neon Wi n d c h ar g e s t at es / E q u i l i b r i u m Extreme case of Delay Effect Model intensities far too large Equilibrium results higher than observations Model underestimates temperature

15 Comparison with spectra III - Oxygen Wi n d c h ar g e s t at es / E q u i l i b r i u m - Ions are in fake equilibrium - Cold effect is visible within 1.05 Rsun - Equilibrium results higher than observations Model underestimates temperature

16 Possible causes Two possible causes for the disagreement: A Small wind filling factor Non-moving coronal plasma in equilibrium dominates the line of sight Needs to be checked with flux mass estimates B Solar wind originates in the corona Already starts with high charge states to begin with Delay effect greatly mitigated Cold effect eliminated Spectra are less sensitive to wind induced departures from equilibrium I repeated the comparison with an empirical model

17 Empirical model Red: Cranmer et al Blue: Enrico's empirical model A Source region: 1.02 Rsun Log T=5.95 B Temperature: slightly higher than observations Peaks at 1.5 Rsun C Density from measurements where available same decrease as Cranmer D Velocity Almost the as Cranmer

18 Comparison with in-situ data Predicted charge states are improved

19 Cranmer Empirical Na and Mg No more Delay Effect: Spectra are much better reproduced with the empirical model Spectra are still sensitive to wind-induced departures from equilibrium Still agreement is not ideal Gross empirical model

20 Cranmer Empirical Neon Empirical model is much closer to observations at low heights No more Delay Effect No more Cold Effect: the temperature of the wind must be coronal from the very beginning

21 Cranmer Empirical Oxygen No more Cold Effect Empirical model is in better agreement with observed intensities at low heights The temperature of the wind must be coronal from the very beginning

22 Some conclusions A Non-equilibrium effects in an accelerating wind are significant Plasma emission Charge states B Models with chromospheric wind source have three problems The Delay Effect The Cold Effect Underestimate in-situ charge states C Coronal source models can solve all three problems

23 Future work A Apply to slow wind sources B Use more extended observations to extend wind testing with Fe ions Eclipse observations (Habbal & co-workers) C Test more theoretical models D Improve line-of-sight integration Use tomographic technique available at the University of Michigan (Frazin et al. 2009) E - Improve empirical model Trial and error approach