deletions | additions       diff --git a/subsubsection_Bispectrum_Bicoherence_The_Bispectrum__.tex b/subsubsection_Bispectrum_Bicoherence_The_Bispectrum__.tex  index a327e86..2ddc674 100644  --- a/subsubsection_Bispectrum_Bicoherence_The_Bispectrum__.tex  +++ b/subsubsection_Bispectrum_Bicoherence_The_Bispectrum__.tex 
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\subsubsection{Bispectrum/Bicoherence}  The Bispectrum measures the magnitude and phase correlation between Fourier signals, signals  and is obtained by computing the Fourier Transform of the three-point correlation function. In our analysis, we use the Bispectrum to calculate the Bicoherence, a real-valued, normalized equivalent. (Explain why this is more efficient? Eric discuss why in his paper). %SSRO Yes, will need more elaboration. We also need comparison to past literature on this topic - I'll add it.  Following the analysis of Koch et al. (2015), we generate sets of randomly sampled spatial frequencies that are sampled  up to half of our the  image size. size (i.e., 127 pixels). %SSRO or do you mean 2.5pc (or the equivalent half size in ''?  For each run, we compute the Bicoherence of our integrated intensity maps using the randomly samples sets. Figure 8 depicts the Bicoherence matrices for runs W1T2t0.2 and W2T2t0. Run W1T2t0.2's matrix contains multiple Bicoherences near zero, while run W2T2t0's matrix contains multiple Bicoherences grater than 0.5. Similar to PCA, we find that our run with feedback generates more random phases between signals than that of our run without feedback. {\bf what does the stronger signal in the diagonal mean in the case with feedback?}