174. Toward Improved Understanding of Magnetic Fields Participating in Solar Flares: Statistical Analysis of Magnetic Fields within Flare Ribbons

Contributed by Maria Kazachenko. Posted on February 24, 2022

Maria D. Kazachenko1,2, Benjamin J. Lynch3, Antonia Savcheva4,5, Xudong Sun6, and Brian T. Welsch7
1 Dept. of Astrophysical and Planetary Sciences, University of Colorado, Boulder, CO 80305, USA
2 National Solar Observatory, Boulder, CO 80303, USA
3 Space Sciences Laboratory, University of California, Berkeley, CA 94720, USA
4 Harvard-Smithsonian Center for Astrophysics, Cambridge, MA 02138, USA
5 Institute of Astronomy and National Astronomical Observatory, Bulgarian Academy of Sciences, Sofia, Bulgaria
6 Institute for Astronomy, University of Hawaii at Manoa, Pukalani, HI 96768, USA
7 Dept. of Natural & Applied Sciences, University of Wisconsin–Green Bay, Green Bay, WI 54311, USA

Summary: We analyzed observations in 40 active regions with the goal of figuring out how photospheric vector magnetic fields participating in the flare differ from the rest of the active region in confined and eruptive flares. For that we have looked at only flaring parts of the sunspot, called flare ribbons. We find that 1) while flares are guided by extensive properties that scale with sunspot size, like the total amount of magnetic field that participates and the total current circulating in the sunspot, CMEs are guided by intensive mean properties, like the fraction of the sunspot that participates in the flare; 2) AR non-neutralized currents are proportional to the amount of shear at the PIL, providing direct evidence that net vertical currents are formed as a result of any mechanism that could generate magnetic shear along the PIL; 3) eruptive events tend to have smaller PIL fluxes and larger magnetic shears than confined events. Our database is available online and provides a reference for more realistic solar and stellar flare models. See more details in Ref. [1].

Motivation: Solar flares and coronal mass ejections (CMEs) arise from significant reconfiguration of magnetic fields in the solar corona. However, only a fraction of the AR magnetic field participates in the flare. Hence to understand why and how flares occur, we need to understand the properties of the magnetic fields that participate in the flare and how they differ from the active region as a whole. Flare ribbons together with vector magnetic field observations could provide us with this information. With the launch of NASA Solar Dynamics Observatory (SDO), which observes flare ribbons and vector magnetic fields on the same platform, it has become possible to perform statistical study of magnetic field properties within flare ribbons.

Figure 1| Variables of the FlareMagDB database: vertical and horizontal components of the magnetic field, magnetic shear, and the vertical current density before an X1.5 flare in active region 12158. Orange and blue contours outline ribbon and PIL areas, respectively. Black and blue arrows show observed and potential components of the horizontal magnetic field, respectively. Numbers show the total magnetic flux, mean shear and total current, respectively, within AR, ribbon and PIL areas.

Data and methodology: In our study we have analyzed magnetic fields within ribbons in 40 flares. Specifically, we created a new dataset, FlareMagDB, with the following vector magnetic field properties: magnetic flux, mean magnetic shear, total vertical current, and net current within the flare ribbons, AR and polarity inversion line areas (see example of this analysis in Figure 1).

Figure 2| Results: magnetic field properties for 40 events of the FlareMagDB database: vertical magnetic flux, reconnection flux fraction, mean magnetic shear, and total unsigned vertical current vs. the peak X-ray flux within the AR, ribbon, and PIL areas.


1. Magnetic fluxes (Figure 2a). We find strong correlations between the flare peak X-ray flux and the flare ribbon and PIL fluxes. The correlation between the peak X-ray flux and the corresponding AR quantities is weaker.
2. Reconnection flux fractions (Figure 2b). We find a moderate correlation between the flare peak X-ray flux and the fraction of AR magnetic flux participating in the flare.
3. Mean magnetic shears (Figure 2c). We find that the mean magnetic shear is strongest within the PIL areas, decreasing gradually within ribbon and AR areas. The peak X-ray flux is moderately correlated with the mean magnetic shear within the AR and weakly correlated with the mean magnetic shear within the ribbon and PIL areas.

Figure 3| Results: net currents, DC/RC, vs. peak X-ray flux (a) and shear at PIL (b); right panel (c): explanation of the scaling law in panel (b) as a consequence of Ampere’s law.

4. Current density morphology. Qualitatively, current density maps consist of threadlike and patchy structures that do not exhibit any regular shape or correlate with ribbon locations, flares, and/or CME occurrence.
5. Total vertical currents (Figure 2d). We find that the total unsigned vertical current is largest within the AR areas, decreasing gradually within the ribbon and PIL areas.
6. Net currents (Figure 3). Over the entire AR, currents are neutralized. Over separate polarities, currents are non-neutralized. The central part of the AR around the PIL has the highest net current, decreasing within the ribbon and AR areas. This net current strongly correlates with the mean magnetic shear within the PIL, implying that current non-neutralization is a manifestation of the shear accumulation along the PIL[2,3].
7. Confined versus eruptive flares. For a given peak X-ray flux, confined events have larger PIL fluxes and lower mean PIL shears and ribbon net currents than eruptive events[4,5]. CME speeds strongly correlate with the fraction of the AR that participates in the flare.


[1] Kazachenko, M. D., Lynch, B. J., Savcheva, A., Sun, X., & Welsch, B. T. 2022, ApJ, 926, 56
[2] Torok, T., & Kliem, B. 2003, A&A, 406, 1043.
[3] Dalmasse, K., Aulanier, G., Demoulin, P., et al. 2015, ApJ, 810, 17.
[4] Avallone, E. A., & Sun, X. 2020, ApJ, 893, 123.
[5] Li, T., Sun, X., Hou, Y., Chen, A., Yang, S.,& Zhang, J., 2022, ApJL, 926, L14.

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