The third example in the rightmost column shows a null Kubo component (i.e. Δ 2 = 0), which is a special case of boundary stalling. It is tempting to think convergence with Δ 2 = 0 should be possible, and examples of this are in fact observed (e.g. Video S5, trial 1, iteration 24), but there are several cases in which stalling occurs instead (e.g. Video S5, trial 5, iteration 26). Numerical analysis (not shown) reveals that stalling here is caused by inflated estimates of |∇C| � stemming from near unity covariance(s) with the null component τ c , which is a form of multicollinearity. This is not surprising given the uncertainty of τ c is infinite for a true null component. In any case, we did not identify a scenario in which the program converged to an inaccurate fit after accounting for degenerate pairs, which implies that local minima are extremely rare for a three-level system assuming accurately phased spectra. Figure shows the results for fitting to data with a much lower SNR of 10:1. Simulation parameters are representative of a cyanylated cysteine residue in the Calmodulin protein. The second Kubo component is treated as static on the timescale of the waiting-time measurements by holding 𝜏𝜏 2 constant at 1 ns. The transient absorption spectrum in (A) and the 2D IR spectrum in (B) at T W = 0 ps illustrate just how modest the quality of the raw data is at 10:1 SNR. Dephasing parameters in (C) from model fitting for all 100 trials show a distribution that rarely exceeds 10-25% of the true values. Any analysis based on the CLS method would be hopeless at this 10:1 SNR. An example of dephasing parameters for an individual trial in (D) shows that the 95% confidence intervals accurately reflect the variance seen in (C), which shows that the calculated uncertainties are reliable for low SNR data. Indeed, each of the individual results for the 100 trials in panel (D) of Video S6 validates the 95% confidence intervals. Panel (E) shows the average over all 100 trials with corresponding 95% confidence intervals calculated from the standard error of the mean confirming the accuracy of model fitting for this example. Fitting trajectories seen in Video S7 are similar to the case of MeSCN in H2O previously shown in Figure and Video S3 above. Some occasions of stalling do occur which the program readily resolves. As expected, there is a decrease in the precision of the modeling results due to the increase in the noise.