the cross-wavelet technique used here.

Structure Function Analysis {#sfanalysissection}
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Since the global wavelet power spectrum compares the observed signal to expected levels of red noise and white noise, we created structure functions (SFs) for each of our observations to see which noise process dominates the signal at different times.

A structure function calculates the mean deviation of data points, providing an alternate method of quantifying time variations. Here we use a structure function of the first-order [@sim85]: $$SF(\delta t) = <[F(t) - F(t + \delta t)]^{2}>$$ where $F(t)$ is the flux at time $t$ and $\delta t$ is a time lag. The slope $\alpha$ of the SF curve in $log(SF)-log(\delta t)$ space depends on the noise processes underlying the signal, giving us an indication of the nature of the process of variation. If $\alpha = 1$ red noise dominates, and for flatter slopes of $\alpha = 0$ Poisson photon noise is significant. A plateau at short time lag is due to measurement noise. The transition from plateau to power-law in the structure function curve determines where the dominant underlying noise process changes in the object. The point of turnover from power-law to plateau at longer time lags corresponds to 