TD, which are similar.

The bicoherence and bispectrum estimated from the synthetic data agree well with the estimates from the real data (Figure \[figCompBispecBicohDataSynth\]) with excellent agreement for the bispectrum. The bicoherence of the synthetic data is slightly lower due to the power in the synthetic data being slightly larger than the real data; see Equation , and below for further discussion of this topic.

![image](fig4){width="1\linewidth"}

\[figNLest\]

![image](fig5-1){height="0.32\linewidth"} ![image](fig5-2){height="0.32\linewidth"}

As we know the nonlinear and Gaussian components of the synthetic data for every realization (e.g., middle and right panels in the top two rows in Figure \[figsynthexample\]), we determine the average fraction of the total power (or equivalently of the total variance) stemming from the nonlinear component; see Section \[secNLpower\]. We find a value of $\fNL = 4.0\%$, see right[-hand]{} column of Table \[tabResults\].

[lccccccr]{}

Dataset & $\hatbmax$ & $\hatmudata$ & $\hatmub$ & $\hatmufiltmax$ & $\hatfNL$ & $\hatfNLmax$ & true $\fNL$\
LCT & 0.097 $\pm$ 0.004 & 0.277 $\pm$ 0.001 & 0.276 & 0.424 & 4.2 & 4.2 & unknown\
Synthetic LCT & 0.091 $\pm$ 0.003 & 0.257 $\pm$ 0.001 & 0.257 & 0.388 & 3.6 & 3.6 & 4.0\
Gaussian component (synth. LCT) & 0.028 $\pm$ 0.011 & -0.000 