First we will discuss the results from the dataset s16834r00, during which we detected the macro-spicule. In Figs. 2 & 3, we show time slices of the observed O V 629 Å and Mg IX 368 Å respectively. In these plots, the solar north-south () direction is in the vertical axis, the horizontal axis is time. To bring out details in the intensity map a contrast enhanced map is shown in the lower panels. This technique has been explained in detail in Doyle et al. (1999). Note that the macro-spicule event occurs between 53 to 58 minutes into the observing sequence. Although the Mg IX image (Fig. 3), is noisy due to low counting statistics, one can still see a bright jet like structure.
In Fig. 4, we show the temporal evolution map of the macro-spicule intensity and velocity in O V . The contours are those of the O V intensities. Note that in the intensity map, the macro-spicule shows extremely bright emission at its base. The brightest pixel in the velocity map shows a red shift of 130 km s-1. The emission from the brightest region in the macro-spicule shows predominantly red shifted velocities, i.e., they are directed away from the observer. The left side shows much smaller velocities, and some positions on this side of the feature also show blue shifts, e.g. at positions 8-9 pixels and a time of 54 minutes. In Fig. 5 we show the line profile corresponding to pixel position 8 and time 54 minute, fitted with a Gaussian. It indicates a blue shift of 22 km s-1. Note that at a later phase of the evolution of the macro-spicule, pixel position 11 and time 55.2 min, we find an average red shift of 92 km s-1 after fitting the line profile with a single Gaussian (see Fig. 6). Bear in mind that this is not a spatial map, rather a temporal map, thus it implies that in the initial phases of the macro-spicule we had strong blue-shifts, followed by red shifts in the decaying phase. This can be interpreted as a rotating feature - a sort of solar tornado as named by Pike & Mason (1998).
For a closer inspection of the intensity and velocity at individual locations in the , we plot the data for a series of pixels in Fig. 7. This can also be viewed as horizontal slices of the `space-time' plot and provides information about the radial dependence of intensity and velocity. The solid line represents the total counts and the dashed line represents the velocity. Note that pixel 4, which marks the limb, shows blue shifts during the event and as we progressively go outside the limb we have pronounced red shifts. This might indicate the presence of a double sided jet. Furthermore, the increase in Doppler velocities with height can be due to plasma accelerating outwards. There is clear evidence of acceleration of plasma from a distance of 20 to 45 arc sec above the limb (see 4th to 9th pixels). Above that altitude it tends to a constant apparent velocity. Thus a rotating accelerating plasma could explain the observation.
Now we turn our attention to the s16834r01 dataset, which was taken 4 seconds after the s16834r00 sequence and almost in the same location as s16834r00. The observation was programmed in such a way that the next sequence automatically traces back to the same location as the previous one. Therefore, within pointing errors we can assume that it is in the same part of the coronal plume plasma. Furthermore since we bin our data across 3 original pixels 5 arc sec, any difference in pointing should be minimum. In this part of the analysis we will study the oscillations in the plume plasma and also try to find out whether there is any effect of the macro-spicule on the plume oscillations.
A rotating plasma will also tend to asymmetrically broaden the line profiles. In order to examine this more closely, we attempted to fit two Gaussians to the profiles at several locations, particularly during the event when the single profile width was generally greater than the background. As an example of this we can look again at spatial pixel 11 at time 55.2 mins which is plotted in Fig. 6. At this location the observed line profile shows an asymmetric significantly broadened profile. If a single Gaussian is fitted the maximum red shift superficially corresponds to a redshift of around 92 km s-1 (as noted above). However, fitting with a two component Gaussian results in a blue shift of 37 km s-1 for one component and red shift of 158 km s-1 for the other, relative to the background.
3.1. Fourier transform
In the top left panel of Fig. 8, we show time slices of the observed O V 629 Å line in dataset s16834r01. Fluctuations in the bright features are clearly visible and their appearance seems periodic. These structures are so bright that in a gray scale presentation it is difficult to identify weakly emitting structures. To bring out details in the intensity map we use a technique based on enhancing the contrast of these structures, thereby filtering out the bright components in the brightness evolution displays (as done for the lower panels of Figs. 2 & 3).
The contrast enhanced space time behaviour is shown in the lower left panel. It clearly shows the periodic brightening of features with a periodicity of 25 minutes. The power spectra of the observed intensities at individual pixels along the slit, obtained from the Fourier transform are shown in the right lower panel of Fig. 8. The total number of counts in a pixel (summed counts) during the observation is shown in the upper right-hand panel, and is useful in identifying the solar limb and the plume. The O V power shows strong peaks between 0.2 to 1 mHz. From the overlay of EIT images and slit and also from the intensity variation across the slit we identify the 9th pixel (in our binned scale) to be just inside the plume structure. Thus we will now concentrate on this pixel and study its power spectra in greater detail.
In Fig. 10, the O V intensity and velocity power spectrum in a typical plume structure (9th pixel in the binned scale) is shown in panels (a) and (c) respectively. The lighter and the darker line correspond to the unsmoothed and smoothed power spectra respectively. The solid and dashed horizontal lines represent the 99.9% significance level of the unsmoothed power and the smoothed power respectively. The corresponding intensity and velocity variations with time are plotted in panels (b) and (d) respectively. The velocity oscillations are shown before (the lighter continuous line) and after a low-pass filter of everything above 4 mHz has been applied (the darker line). The O V intensity power shows a strong peak between 0.7 and 0.8 mHz, whereas the velocity power shows a peak at 1 mHz and a second peak around 0.6 mHz. Though on an average outside the limb the line shift should be zero, we do find the presence of a small blue shift (see Fig. 10d). We also find an anti-correlation between velocity and peak intensity as shown in Fig. 11. The ordinate have been normalized to arbitrary units for the overlay of intensity and velocity. Note that for the first 60 minutes of the observing sequence, when there is a peak in intensity the plasma is generally blue shifted (particularly when the oscillations are more prominent). Oscillation of long periods, are clearly visible in the light curves of Fig. 9. In this plot we show the light curves for the two consecutive time series s16834r00 and s16834r01. In principle these two light curves could be combined and one could then view it as a continuous time series of 227 minutes. The data drop out and the macro-spicule that occurred in dataset s16834r00 have been taken out and a linear interpolation applied for those time intervals between 36.4-43.8 and 53.5-60.4 minutes. These interpolated regions are shown (plotted in Fig. 9) as the straight line sections between these times. The consecutive light-curves suggest that the macro-spicule has not affected the oscillations of the plume plasma. Furthermore the transit of the macro-spicule has not affected the periodicity of the plume plasma as it is still oscillating with a long period afterwards. This tends to indicate that although the macro-spicule has come across our field of view, while observing this particular north polar coronal plume, the macro-spicule is not connected with the plume structure.
3.2. Wavelet transform
The localised nature of time series analysis by wavelets makes them ideal for the data in s16834r00/r01. We should point out that wavelet transforms suffer from edge effects at both ends of the time series. The region in which these effects are important are defined by the `cone of influence' (COI). The COI is an approximate measure that indicates the locations at which the results become unreliable due to these edge effects. We use the definition of COI given by Torrence & Compo (1998) but see also Meyers et al. (1993) for an alternate discussion.
In Figs. 12 & 13 we present the wavelet analysis for the intensity and the velocity respectively, corresponding to the second time series s16834r01. The COI is marked as cross-hatched and any power over the 99.9% significance level is marked by continuous black line contours. On the right of these figures is plotted the global wavelet spectrum, which is just the average of the wavelet power spectrum over time. The dotted line in the global wavelet spectrum is again the 99.9% significance level. Torrence & Compo (1998) have pointed out that as the Fourier spectrum is smoothed, it approaches more and more closely the global wavelet spectrum. For the sake of comparison we present the results for the same 9th pixel as done previously for the Fourier analysis (see Fig. 10). The time frequency phase plot of the light-curve does indicate strong localised power between 0.7 mHz and 0.8 mHz during the major part of the time sequence. In comparison the velocity power is significant around 0.6 and 1 mHz. Note that the power is not significant during the last 20 minutes of the observing sequence, in either the intensity or the velocity.
© European Southern Observatory (ESO) 2000
Online publication: March 21, 2000