deletions | additions       diff --git a/subsubsection_Delta_Variance_The_Delta__.tex b/subsubsection_Delta_Variance_The_Delta__.tex  index 8f39d3e..ab75f1a 100644  --- a/subsubsection_Delta_Variance_The_Delta__.tex  +++ b/subsubsection_Delta_Variance_The_Delta__.tex 
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\subsubsection{$\Delta$-Variance}  The $\Delta$-variance is a filtered average over the Fourier power spectrum \citep{sutzki98}. It has been used to characterize the structure distribution and turbulent power spectra of molecular cloud maps. The revised method presented by Ossenkopf et al. (2008b,a) takes into account noise variation and provides a means to discriminate between small-scale map structure and noise. We adopt this method for our analysis (K16). We generate a series of Mexican hat wavelets that vary in width. We approximate the wavelets as the difference between two Gaussians with a diameter ratio of 1.5. For each output, we weight the integrated intensity map by its inverse variance, convolve it with a Mexican hat wavelet, and calculate the $\Delta$-variance in Fourier space. Figure 9 shows the $\Delta$-variance as a function of wavelet width, which is by convention  denoted as ``lag". the ``lag" \citep{sutzki98}.  Similar to thepreviously discussed  Fourier statistics, statistics discussed previously,  we note find  a common horizontal offset between the case with feedback and the purely turbulent case. The $\Delta$-variance curve of T2t0 also declines more at scales near 0.1 arcminutes than the curve for output W2T2t2. {\bf what might this mean? Need to revisit earlier literature.}