very difficult to determine with existing low-resolution spectrophotometric data. However, while we provide a demonstration of this technique, we do not apply this step in our analysis because we wish to consider all possible observing modes, and we do not need to narrow them down in this way.

Another possible metric for estimating the merit of an observing program at the intermediate stage is the Bayesian information criterion (BIC) [@2014MNRAS.444.3632H], which is defined by: $$BIC = -2\ln{\hat{L}} + k\ln{n},$$ where $\hat{L}$ is the maximum of the likelihood function, $n$ is the number of data points, and $k$ is the number of model parameters. The BIC is a metric to determine goodness of fit to a model that corrects for overdetermination of the system. A larger likelihood results in a smaller BIC, and so we wish to minimize the BIC for a better fit. If the likelihood is a Gaussian, the BIC is equivalent to: $$BIC = \chi^2 + k\ln{n},$$ so it is equivalent to chi-squared minimization, except that models are penalized for overfitting. However, the BIC does not provide information on [*how*]{} good the fit is, only a fit with a specific optimization.

Ultimately, we wish to know which observations 