Analysis of High Cadence In Situ Solar Wind Ionic Composition Data Using Wavelet Power Spectra Confidence Levels

The variability inherent in solar wind composition has implications for the variability of the physical conditions in its coronal source regions, providing constraints on models of coronal heating and solar wind generation. We present a generalized prescription for constructing a wavelet power significance measure (confidence level) for the purpose of characterizing the effects of missing data in high cadence solar wind ionic composition measurements. We describe the data gaps present in the 12 minute Advanced Composition Explorer/Solar Wind Ionic Composition Spectrometer observations of O7 +/O6 + during 2008. The decomposition of the in situ observations into good measurement and no-measurement signals allows us to evaluate the performance of a filler signal, i.e., various prescriptions for filling the data gaps. We construct Monte Carlo simulations of synthetic O7 +/O6 + composition data and impose the actual data gaps that exist in the observations in order to investigate two different filler signals: one, a linear interpolation between neighboring good data points, and two, the constant mean value of the measured data. Applied to these synthetic data plus filler signal combinations, we quantify the ability of the power spectra significance level procedure to reproduce the ensemble-averaged time-integrated wavelet power per scale of an ideal case, i.e., the synthetic data without imposed data gaps. Finally, we present the wavelet power spectra for the O7 +/O6 + data using the confidence levels derived from both the mean value and linear interpolation data gap filling signals and discuss the results.