1 / 20

Thermal Diagnostics of Elementary and Composite Coronal Loops with AIA

Thermal Diagnostics of Elementary and Composite Coronal Loops with AIA. Markus J. Aschwanden & Richard W. Nightingale (LMSAL). AIA/HMI Science Teams Meeting, Monterey, Feb 13-17, 2006 Session C9: Coronal Heating and Irradiance (Warren/Martens).

EllenMixel
Download Presentation

Thermal Diagnostics of Elementary and Composite Coronal Loops with AIA

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Thermal Diagnostics of Elementary and Composite Coronal Loops with AIA Markus J. Aschwanden & Richard W. Nightingale (LMSAL) AIA/HMI Science Teams Meeting, Monterey, Feb 13-17, 2006 Session C9: Coronal Heating and Irradiance (Warren/Martens)

  2. A Forward-Fitting Technique to conduct Thermal Studies with AIA Using the Composite and Elementary Loop Strands in a Thermally Inhomogeneous Corona (CELTIC) • Parameterize the distribution of physical parameters of coronal loops • (i.e. elementary loop strands): • -Distribution of electron temperatures N(T) • Distribution of electron density N(n_e,T) • Distribution of loop widths N(w,T) • Assume general scaling laws: • -Scaling law of density with temperature: n_e(T) ~ T^a • -Scaling law of width with temperature: w(T) ~ T^b • Simulate cross-sectional loop profiles F_f(x) in different filters • by superimposing N_L loop strands • Self-consistent simulation of coronal background and detected loops Forward-fitting of CELTIC model to observed flux profiles F_i(x) in 3-6 AIA filters F_i yields inversion of physical loop parameters T, n_e, w as well as the composition of the background corona [N(T), N(n_e,T), N(w,T)] in a self-consistent way.

  3. TRACE Response functions 171, 195, 284 A T=0.7-2.8 MK

  4. Model: Forward- Fitting to 3 filters varying T

  5. 171AonJune 12 199812:05:20Loop #3A T=1.39 MK w=2.84 Mm

  6. Loop_19980612_A

  7. Observational constraints: Distribution of -loop width N(w), <w^loop> -loop temperature N(T), <T^loop> -loop density N(n_e), <n_e^loop> -goodness-of-fit, N(chi^2), <ch^2> -total flux 171 A, N(F1), <F1^cor> -total flux 195 A, N(F2), <F2^cor> -total flux 284 A, N(F3), <F3^cor> -ratio of good fits q_fit =N(chi^2<1.5)/N_det Observables obtained from Fitting Gaussian cross-sectional profiles F_f(x) plus linear slope to observed flux profiles in TRACE triple-filter data (171 A, 195, A, 284 A) N_det=17,908 (positions) (Aschwanden & Nightingale 2005, ApJ 633, 499)

  8. Forward-fitting of CELTIC Model: Distribution of -loop width N(w), <w^loop> -loop temperature N(T), <T^loop> -loop density N(n_e), <n_e^loop> -goodness-of-fit, N(chi^2), <ch^2> -total flux 171 A, N(F1), <F1^cor> -total flux 195 A, N(F2), <F2^cor> -total flux 284 A, N(F3), <F3^cor> -ratio of good fits q_fit =N(chi^2<1.5)/N_det With the CELTIC model we Perform a Monte-Carlo simulation of flux profiles F_i(x) in 3 Filters (with TRACE response function and point-spread function) by superimposing N_L structures with Gaussian cross-section and reproduce detection of loops to Measure T, n and w of loop and Total (background) fluxes F1,F2,F3

  9. (Aschwanden, Nightingale, & Boerner 2006, in preparation)

  10. Loop cross-section profile In CELTIC model: -Gaussian density distribution with width w_i n_e(x-x_i) -EM profile with width w_i/sqrt(2) EM(x)=Int[n^e^2(x,z)dz] /cos(theta) -loop inclination angle theta -point-spread function w^obs=w^i * q_PSF EM^obs=EM_i / q_PSF q_PSF=sqrt[ 1 + (w_PSF/w_i)^2]

  11. Parameter distributions of CELTIC model: N(T), N(n,T), N(w,T) Scaling laws in CELTIC model: n(T)~T^a, w(T)~T^b a=0 b=0 a=1 b=2

  12. Concept of CELTIC model: -Coronal flux profile F_i(x) measured in a filter i is constructed by superimposing the fluxes of N_L loops, each one characterized with 4 independent parameters: T_i,N_i,W_i,x_i drawn from random distributions N(T),N(n),N(w),N(x) The emission measure profile EM_i(x) of each loop strand is convolved with point-spread function and temperature filter response function R(T)

  13. Superposition of flux profiles f(x) of individual strands  Total flux F_f(x) The flux contrast of a detected (dominant) loop decreases with the number N_L of superimposed loop structures  makes chi^2-fit sensitive to N_L

  14. AIA Inversion of DEM • AIA covers temperature • range of log(T)=5.4-7.0 • Inversion of DEM with • TRACE triple-filter data • and CELTIC model • constrained in range of • log(T)=5.9-6.4 •  2 Gaussian DEM peaks • and scaling law (a=1,b=2) • Inversion of DEM with • AIA data and CELTIC • model will extend DEM • to larger temperature • range • 3-4 Gaussian DEM peaks and scaling laws: n_e(T) ~ T^a w(T) ~ T^b

  15. Constraints from CELTIC model • for coronal heating theory • (1) The distribution of loop widths N(w), • [corrected for point-spread function] • in the CELTIC model is consistent • with a semi-Gaussian distribution • with a Gaussian width of • w_g=0.50 Mm • which corresponds to an average FWHM • <FWHM>=w_g * 2.35/sqrt(2)=830 km • which points to heating process of • fluxtubes separated by a granulation size. • There is no physical scaling law known for • the intrinsic loop width with temperature • The CELTIC model yields • w(T) ~ T^2.0 • which could be explained by cross-sectional • expansion by overpressure in regions where • thermal pressure is larger than magnetic • pressure  plasma-beta > 1, which points • again to heating below transition region.

  16. Scaling law of width with temperature in elementary loop strands Observational result from TRACE Triple-filter data analysis of elementary loop strands (with isothermal cross-sections): • Loop widths cannot adjust to temperature in • corona because plasma- << 1, and thus • cross-section w is formed in TR at >1 • Thermal conduction across loop widths In TR predicts scaling law:

  17. CONCLUSIONS • The Composite and Elementary Loop Strands in a Thermally Inhomogeneous • Corona (CELTIC) model provides a self-consistent statistical model to quantify • the physical parameters (temperature, density, widths) of detected elementary • loop strands and the background corona, observed with a multi-filter instrument. • (2) Inversion of the CELTIC model from triple-filter measurements of 18,000 • loop structures with TRACE quantifies the temperature N(T), density N(n_e), • and width distribution N(w) of all elementary loops that make up the corona • and establish scaling laws for the density, n_e(T)~T^1.0, and loop widths • w(T) ~ T^2. (e.g., hotter loops seen in 284 and Yohkoh are “fatter” than in 171) • (3) The CELTIC model attempts an instrument-independent description of the • physical parameters of the solar corona and can predict the fluxes and • parameters of detected loops with any other instrument in a limited temperature • range (e.g., 0.7 < T < 2.7 MK for TRACE). This range can be extended to • 0.3 < T < 30 MK with AIA/SDO. • (4) The CELTIC model constrains the cross-sectional area (~1 granulation size) • and the plasma-beta (>1), both pointing to the transition region and upper • chromosphere as the location of the heating process, rather than the corona!

More Related