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Session 7 - Corona II.
Oral session, Saturday, June 28
Ballroom A, Chair: Fran Bagenal

[7.04] Search for Periodicity in Image Sequences from Yohkoh Soft X-Ray Telescope

D. E. McKenzie (Montana State University)

We have applied time-series analysis to sequences of coronal loop images obtained with the Yohkoh Soft X-Ray Telescope (SXT). Using images obtained in fast-cadence mode (\Delta t = 4 sec), we have generated X-ray light curves and power spectra for multiple subregions of a number of coronal loops. Our analysis indicates that out of 544 spectra of loops which range in length L from 40 Mm to 150 Mm, there are 16 cases where the X-ray brightness is modulated periodically with \ge99.5% confidence. The periods \tau range from 9.6 sec to 61.6 sec, and the amplitudes range from 0.4% to 1.8%. Our result is statistically significant at the 8\sigma level.

The observed periods can hardly be due to fluctuations in gas pressure: temperatures estimated from image ratios indicate sound speeds of 200 -- 280 km/sec, with loop crossing times of hundreds of seconds. On the other hand, the ratio 2 L / \tau yields ``velocities" of 2 -- 14 Mm/sec: these overlap with published estimates of Alfvén speeds v_A in coronal loops, 3.5 -- 37 Mm/sec (Schmelz et al., 1994, ApJ, 434, 786). Now, in the context of coronal loop heating by resonant absorption of MHD waves, the period \tau_g \approx 2 L / v_A plays a key role: loops which are oscillating in their global mode (with period \tau_g) are maximally efficient at absorbing energy (Steinolfson and Davila, 1993, ApJ, 415, 354). We suggest that the loops which we have found to be modulated periodically are oscillating in their global mode. With this interpetation, we find that the plasma beta in the loops lies in the range \beta _p = 0.001 -- 0.045. According to the oscillating loop model of Zaitsev and Stepanov (1989, Sov. Astron. Letters, 15, 66), loops with such \beta _p values should be modulated with amplitudes of 1.1% or less: our data are consistent with at least the upper limit of this prediction.

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