We now examine the efficacy of model fitting of undersampled data with the sampling masks illustrated in Figure . There are 47 waiting time spectra in the entire series, but the first two (T w = 0 and 200 fs), where pump and probe overlap, are susceptible to spurious nonresonant and timeordering signals. Hence, we omit these spectra from model fitting in each case and "fully sampled" data corresponds to the 45-point mask. We generate masks by keeping every k th point left and right of 1 ps, where k is an integer number. For example, the 15-point mask samples every third point left and right of 1 ps. We base this on the guiding principle that every mask should include a waiting time point ~2× earlier than the shortest process expected to occur in the lineshape and ~2× later than the vibrational lifetime. Knowing a priori that the homogeneous lifetime is ~2 ps and vibrational lifetime is ~75 ps, we include points around 1 ps and 150 ps in every mask. For other -SCN systems more generally, one can reasonably assume that the shortest observable process is likely no faster than ~0.8 ps and the vibrational lifetime is between 30-80 ps. Conventional wisdom says that model fitting of multidimensional spectra is a problem riddled by local minima. In the several hundred trial examples of model fitting shown here, our program encounters and resolves many algorithmic stalls, but not a single local minimum is observed. The distinction is important. It is difficult, and often impossible, to distinguish a local minimum from the global minimum, but stalling is always distinguishable using |∇C| � . Though we have only shown that local minima are exceptionally rare for a simple three-level system, it is reasonable to believe this should apply to more advanced models including coupled oscillators, assuming reasonable separation between peaks, and more complicated lineshape functions. Therefore, conventional wisdom should be updated: model fitting is far less a problem of local minima as it is of multicollinearity and boundaries, which are manageable.