Accounting for correlated noise more generally is challenging because the nondiagonal data variance-covariance matrix 𝐕𝐕 𝐃𝐃 of size N D × N D might easily occupy a terabyte of memory for multidimensional spectra. Hence, for feasibility sake, our program assumes 𝐕𝐕 𝐃𝐃 is diagonal which is typical for most other fitting programs. Consequently, the fitting algorithm is most optimal for spectra with uncorrelated noise. While conventional referencing schemes are unreliable for achieving uncorrelated noise, calibrated referencing schemes are known to achieve virtually uncorrelated noise, which can also be realized using 100 kHz Yb laser systems. Nevertheless, SI section F provides a comparison of model fitting to edge-pixel referenced and unreferenced data, which suggests that model fitting to unreferenced data is still reliable setting aside the expected gain in uncertainty from the larger noise.