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Modern Solar Facilities – Advanced Solar Science, 265–267 F. Kneer, K. G. Puschmann, A. D. Wittmann (eds.) c  Universitätsverlag Göttingen 2007 Wavelet-based method for coronal loop oscillations analysis D. Tothova* , D. E. Innes, and S. K. Solanki Max-Planck-Institut für Sonnensystemforschung, Katlenburg-Lindau, Germany * Email: tothova@mirage.mps.mpg.de Abstract. We study the properties of Doppler shift oscillations observed in hot coronal loops by SoHO/SUMER. These oscillations have been identified as magnetoacoustic slow mode standing waves and may be an important magnetic field diagnostic for the oscillating loops. Visual inspection of the SUMER data suggests that one in three microflares trigger oscillation. So for an average active region we can expect several oscillation events per hour. To study the statistics of the oscillations, automatic identification and classification of the events is required. We have performed a Wavelet analysis on a 12 hour time series of SUMER Fe xix data and find that almost all of the oscillation events in the data set are identified by this technique, with very few false positives. The method is expected to be useful for the analysis of future Ca xvii and Fe xxiv spectra of microflares observed with HINODE/EIS. 1 Introduction SoHO/SUMER has recently revealed standing slow mode oscillations in hot (6-8 MK) coronal loops (Wang et al. 2002; Kliem et al. 2002). The search for coronal loop oscillations is motivated by the variety of information they can provide us with. Doppler shift oscillations give us information on the excitation site, the excitation mechanism, the energy dissipation and the coronal magnetic field. The magnetoacoustic waves are only seen in hot flare lines, such as Fe xix, with a formation temperature above 6 MK, without any signature in cooler lines. Analysis and conclusions on coronal loop oscillations are limited because SUMER is only able to observe at the solar limb and is not able to raster across the loop structures. The HINODE mission will give us the possibility to make high cadence imaging and spectral observations of active region loops at any position on the Sun, and thus will provide us with the loop geometry and oscillations characteristics at different loop positions in a wave period. In order to understand the properties of these oscillations we will carry out a statististical study using the wavelet-based method. We would like to determine the percentage of active region microflares that trigger oscillations, find which modes are excited, their periods, damping times as well as the range of temperatures of the lines in which we observe them. 2 Observations The spectroscopic observations of the active region AR 9176 with a hot loop system have been obtained by SUMER in the sit-and-stare-mode on September 16-17th, 2000, with an exposure time of 1.5 min (Fig. 1). Time series in five spectral lines, Fe XIX λ 1118 (6.3 MK), 266 D. Tothova et al.: Wavelet-based method for the analysis of coronal loop oscillations Figure 1. EIT 195 Å image of the observed active region showing the position of the SUMER slit. Ca XV λ 1098 and λ 1110 (3.5 MK), Ca XIII λ 1134 (2.2 MK), and Si III λ 1113 (0.06 K), were recorded and after processing the raw data line moments have been computed. The Doppler oscillations are seen in the hot Fe XIX line coinciding with the flare like brightenings typical for this line. 3 Data Analysis We use the wavelet-based algorithm À trous (”with holes”) (Fligge & Solanki 1997; Starck et al. 1998) to help identify the oscillations in the velocity time series (Fig. 3). À trous reduces the noise by local thresholding on wavelet coefficients at each scale. First we determined the noise in shift, λs, at each position and time pixel by evaluating the error in the center of gravity of the line using the error propagation law σ2 (λs) = σ2 (Iλ)(∂λs/∂Iλ)2 , and assuming a Poisson distribution of the noise for the intensity σ(Iλ) = √ Iλ. In order to find the noise wavelet coefficients at each scale, we applied the À trous algorithm to the relative error time series, using the same parameters as for the data. After identifying those wavelet coefficients that are significantly non-zero against the noisy background, we have used the Morlet wavelet transform to find the power of the wavelet coefficients and to reconstruct the time series at each pixel...

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