Supra-Arcade Downflows: Results from Observational AnalysisDavid E. McKenzie (Montana State University)

Sabrina Savage (Montana State University)James E. Tolan (University of California-Berkeley)

Supra-Arcade Downflows: Results from Observational AnalysisDavid E. McKenzie (Montana State University)

Sabrina Savage (Montana State University)James E. Tolan (University of California-Berkeley)

1. Abstract

Downward motions above post-CME flare arcades are an exciting discovery of the Yohkoh mission, and have subsequently been detected with TRACE, SOHO/LASCO, SOHO/SUMER, and GOES SXI. These “supra-arcade downflows” have been interpreted as outflows from flux tube reconnection, consistent with a 3D generalization of the standard reconnection model of solar flares. We will present results from our observational analyses of downflows, including some automated schemes for detection, distributions of measured speeds, and other aspects related to 3D patchy reconnection. We will also indicate the limitations of present observations, and motivations for utilizing the Solar-B instruments for measurements of these reconnection signatures.

2. Introduction

Supra-arcade downflows (SADs), as the name implies, are downward-moving features observed in the hot, low-density region above post-eruption flare arcades. Initially detected with Yohkoh SXT as X-ray-dark, blob-shaped features (McKenzie & Hudson, 1999), downflows have since been observed with TRACE (e.g., Innes et al., 2003; Asai et al., 2004), SOHO/SUMER (Innes et al., 2003), and SOHO/LASCO (Sheeley & Wang, 2002). The darkness in X-ray and EUV images has been explained as due to very low plasma densities, i.e., plasma voids (Innes et al., 2003). [As an aside, we note that some X-ray-emitting SADs are also known. McKenzie (2000) reports faint X-ray-emitting shrinking features in several flares; in our catalog of 40 SAD flares observed by SXT and TRACE, approximately half display such bright shrinking features alongside dark SADs.]

The SADs are traced by discrete X-ray features with a characteristic size. The present interpretation states that the downflows represent the outflow of magnetic flux from a reconnection site, in keeping with the standard reconnection model of eruptive flares (see Figure 2, from McKenzie, 2002; see also Sheeley et al., 2004). If they are reconnection outflows, then these tracers strongly suggest that the reconnection takes place between discrete collections of magnetic flux, e.g., flux tubes. Such a conclusion indicates “patchy” 3D reconnection. While the observed speeds of tens-to-hundreds of km/s were slower than initially expected, recent models of 3D patchy reconnection may suggest that these low speeds are not insurmountably problematic (Linton & Longcope, 2005; see especially Linton’s talk at this meeting).

There is much to be learned from quantitative measurements of downflows. For example, measurements of the characteristic size may help to estimate the magnetic flux in individual flux tubes, and therefore the characteristic amount of flux that participates in a magnetic reconnection episode. Also, determining the path of the downward motion helps to estimate the energy released by the shrinkage (see below).

Nearly 40 observations of downflows have been catalogued with SXT, dating back to 1991. Since the end of the Yohkoh mission, TRACE has detected SADs in several flares, LASCO sees very similar flows much higher in the corona, and SXI frequently detects motions which resemble downflows, although the sharpness and contrast of the SXI images are not optimal for analyzing these features. It is certain that Solar-B XRT will also observe downflows in eruptive flares. To the extent that these flow patterns can help us to understand the circumstances of reconnection, the capabilities of XRT will provide an avenue to information about conditions in the magnetic diffusion region.

3. Autodetection S/W Development

At Montana State University, we are developing automated software for objective detection and measurement of SADs. The automated detection routine finds downflows by searching for depressed signals (“troughs”) in an image and then attempting to match trajectories between frames. The routine has been refined through repeated application to synthetic flare data which mimic the appearance of downflows via moving regions of depressed signal within a background of white noise. The noise level and parameters of the artificial voids (e.g., size, speed, and darkness) have been varied for the refinement of the analysis software. An example frame from one synthetic data sequence, designed to mimic the very low signal-to-noise ratio of SXT downflow movies, is shown in Figure 3.

1.) For a synthetic data series with trough darknesses set at 50% below the background level, with various sizes and initial coordinates, velocity, and acceleration conditions, the routine was 100% effective at detecting the troughs. One “false positive” was detected in this data set; however, the routine successfully rejected it from the final output, while maintaining a log of its detection for cross-correlating with other detection programs. X- and Y-components of the paths are automatically fitted with polynomials to obtain velocity and acceleration estimates.

2.) For a data series with much lower signal-to-noise ratio, to mimic noisy SXT data, along with various sizes and initial conditions, the routine was able to detect all of the troughs that existed, but was not able to follow all of them throughout their entire track or segmented the track due to random noise fluctuations and path crossings. Twice as many false positives as “real” detections were identified; however, the routine successfully rejected all but one of these false positives while maintaining a log of their detections. In principle, velocity and/or acceleration restrictions could be imposed to reduce the number of false positive detections even further. The routine was even able to distinguish between two troughs that followed very similar paths. The velocity and acceleration estimates are also in close agreement with the actual input values. Figure 4 demonstrates the trajectories of voids detected by the automated routine.

4. Analysis of Solar Flare Data

The autodetection routine has been applied to image sequences from two of the best-known SAD flares, 21-Apr-2002 and 20-Jan-1999. The 21-Apr flare was observed by TRACE (Figure 5), RHESSI, SOHO, and numerous other observatories (e.g., Wang et al., 2002; Innes et al., 2003). The 20-Jan flare was observed by Yohkoh SXT, and was the discovery event for downflows; excerpts from the image sequence are shown in Figure 1.

In the TRACE data from 21-Apr-2002, the automated detection routine was able to track 15 downflowing voids, though a few more are visible in the raw data. Adjustment of the detection thresholds may allow tracking of the few voids that were missed, but at the expense of additional false positives. The trajectories of the detected downflows are plotted in Figure 6, for two regions above the northern and southern ends of the arcade. No downflows were detected above the middle part of the arcade. Speeds of the downward motions, inferred from polynomial fits to the X- and Y-components of the trajectories, range from 12 to 470 km/sec (Figure 7).

5. Discussion

The supra-arcade downflows are important signatures of reconnection in flares (McKenzie, 2002; Asai et al. 2004). As tracers of reconnection outflow, they carry information about the circumstances of reconnection. In particular, their characteristics are indicative of the parameters of 3D patchy reconnection, including the size of participating flux tubes, and, by extension, the characteristic size of the localized diffusion region. The application of automated software to real flares, as shown here, demonstrates that it is possible to derive quantitative data from images of these velocity fields.

For example, consider that as a flux tube undergoes shrinkage by an amount ∆L, the energy lost is given by ∆W=B2 A ∆L / 8, where B is the field strength, and A is the cross-sectional area of the flux tube. From the measurements made possible with the automated routine, we can estimate some typical values as ∆L ~ 1--5 x 109 cm; A ~ 2 x 1017 cm2. With B on the order of 10 G, the energy liberated is on the order of 1027 ergs per shrinkage event. In the two flares analyzed here, these shrinkages would correspond to energies of 1028 ergs. If the field strength were closer to 50 G, then the total energy corresponding to the shrinkages in each of these flares would be on the order of 1029--30 ergs.

To our knowledge, the SADs have been observed with SXT more often than in the TRACE data. This is no doubt due to the full-Sun field of view of SXT as well as its sensitivity to hotter plasmas--the supra-arcade region is typically very hot, and the dark SADs are easier to see against a bright background. However, most of the SADs were observed with SXT’s half-resolution (5 arcseconds per pixel), so that the smaller features could never be detected. With only the SXT data, it is impossible to probe the full distribution of SAD sizes, which means that we have poor knowledge of the common sizes of reconnection “patches”. While TRACE offers much higher angular resolution, the cadence of TRACE images is slow enough to allow some fraction of flows--particularly the faster ones--to go undetected. In some cases, the cadence of SXT images was even too slow, so that motions faster than 700 km/s would have been unobservable (McKenzie, 2000). Moreover, TRACE’s smaller field of view means that some flares are not observed: in a study of 12 SAD flares observed by SXT, McKenzie (2000) found TRACE data for only two events. The sharper angular resolution of Solar-B XRT (compared to SXT), the range of XRT’s temperature sensitivity, and the large field of view, will almost certainly extend our knowledge of the characteristics of SADs beyond the capabilities of SXT and TRACE. Sensitivity to the faint, hot plasmas in the supra-arcade region will allow more precise determination of the height of the reconnection region above the Sun’s limb, thus enabling better estimates of ∆L. Although it is difficult to predict observing cadences during flares, it is likely that XRT’s cadence and sensitivity will make it possible to more reliably observe faster motions, not possible with previous instruments.

6. Acknowledgements

• D. McKenzie and S. Savage are supported by NASA Grant NNG04GB76G.

• J. Tolan contributed to this research as a participant in MSU’s NSF-sponsored Research Experiences for Undergraduates (REU) program, NSF Award 0243923, during the summer of 2005.

7. References

• “Downflow Motions Associated with Impulsive Nonthermal Emissions Observed in the 2002 July 23 Solar Flare”, by Asai, Ayumi; Yokoyama, Takaaki; Shimojo, Masumi; Shibata, Kazunari 2004, ApJ, 605, L77.

7. “SUMER Spectral Observations of Postflare Supra-Arcade Inflows”, by D.E. Innes, D.E. McKenzie, & T. Wang 2003, Solar Physics, 217, 247.

8. “A Model for Patchy Reconnection in Three Dimensions”, by Linton, M., and Longcope, D. 2005 (in press).

9. “X-Ray Observations of Motions and Structure Above a Solar Flare Arcade”, by D. E. McKenzie and H. S. Hudson 1999, ApJ, 519, L93.

10. “Supra-arcade Downflows in Long-Duration Solar Flare Events”, by D. E. McKenzie 2000, Solar Physics, 195, 381.

11. “Signatures of Reconnection in Eruptive Flares”, Invited Review, by McKenzie, D.E., in Multi-Wavelength Observations of Coronal Structure and Dynamics, P.C.H. Martens and D.P. Cauffman, eds., COSPAR Colloquia Series, Elsevier Science Ltd. pub. (2002), 13, 155.

12. “Characteristics of Coronal Inflows”, by Sheeley, N. R., Jr.; Wang, Y.-M. 2002, ApJ, 579, 874.

13. “The Origin of Postflare Loops”, by Sheeley, N. R., Jr.; Warren, H. P.; Wang, Y.-M. 2004, ApJ, 616, 1224.

14. “Initial features of an X-class flare observed with SUMER and TRACE”, by Wang, T. J.; Solanki, S. K.; Innes, D. E.; Curdt, W. 2002, in SOLMAG 2002. Proceedings of the Magnetic Coupling of the Solar Atmosphere Euroconference and IAU Colloquium 188, 11 - 15 June 2002, Santorini, Greece. Ed. H. Sawaya-Lacoste. ESA SP-505. Noordwijk, Netherlands: ESA Publications Division, ISBN 92-9092-815-8, 2002, p. 607 - 610.

Figure 3. Single frame from SXT-mimic synthetic data set. Five artificial voids are seen in this frame. A total of sixteen voids were created in this 50-frame movie.

Figure 4. Detected “downflows” in the SXT-mimic data set. Note the one false positive indicated by the dark blue dashed trajectory.

Figure 5. The famous flare of 21-April-2002 revealed downflows in TRACE images for the first time, as well as SUMER, and represents one of the sharpest observations of downflows.

In the SXT data from 20-Jan-1999, the automated detection routine tracked 13 downflowing voids, though again a few additional downflows are visible in the raw data. In this case, the most likely explanations for non-detection of downflows are related to the faintness of the features amid the very noisy background, or to the fact that several downflows are only visible in three successive frames (the program requires 5 for reliability in excluding false positives and for fitting the trajectory polynomial). The trajectories of the detected downflows are plotted in Figure 8; speeds ranging from 19 to 180 km/s are plotted in Figure 9.

Figure 2. Cartoon depiction of supra-arcade downflows resulting from patchy reconnection. Discrete flux tubes are created, which then individually shrink, dipolarizing to form the post-eruption arcade.

Figure 1. Late stages of the 20-Jan-1999 flare in which supra-arcades were first discovered. The white arrow is fixed at the location of a dark void at 11:48:11 UT. The black arrow follows the same feature to its location at later times.

Figure 6. Downflows detected in the 21-Apr TRACE data, as tracked by the automated routine.

Figure 7. Distribution of speeds of the tracked downflows: 21-Apr-2002.

Figure 8. Downflows detected in the 20-Jan SXT data, as tracked by the automated routine.

Figure 9. Distribution of speeds of the tracked downflows: 20-Jan-1999