Authors: Simone Di Matteo (CUA/NASA-GSFC), Nicholeen M. Viall (NASA-GSFC), Larry Kepko (NASA-GSFC)

In the analysis of time series, one of the major diagnostic tools for the identification of periodic fluctuations is the frequency domain characterization via the power spectral density (PSD). Periodic signals manifest as enhancements relative to the continuous PSD. Here, we present the application of a recently developed procedure based on the adaptive multitaper method (an open source IDL version is available at https://zenodo.org/record/3703168). This sophisticated nonparametric spectral analysis approach, suitable for colored PSD, provides an additional independent complex-valued regression test supporting the identification of periodic signals: the harmonic F test. A priori knowledge of the statistical properties of the multitaper PSD estimates allows a robust maximum likelihood fitting of different continuous PSD background models (e. g., power law and bending power law model). Then, the best representation is selected via objective statistical criteria. Confidence thresholds are used to determine statistically significant PSD enhancements, that, when combined with the harmonic F test, provide robust estimates of the frequency of periodic oscillations occurring in the time series. After showing the performance of this method on Monte Carlo simulations of synthetic time series, we present examples of solar wind streams that contain periodic density structures.