Weisz and I pair-coded, with some help from Fouesneau and Foreman-Mackey, an extension to Weisz's spectral fitting program that will simultaneously fit spectroscopic and photometric data on stellar clusters. The idea is that a spectrum contains far more information about the stellar population than a few photometric points, but a typically taken, calibrated, and reduced spectrum has serious systematics in the spectrophotometric calibration. How to use these data responsibly?
Our generative model (or maybe causal model?) is that the spectroscopic data are good, but multiplied by an unknown smooth function of wavelength representing spectrophotometric wrongness. We put in a flexible (cubic spline) model for this function, and fit (with emcee) the cluster spectrum and the spline function (which enters multiplicatively). We got something working and now Weisz is running it overnight. In the end, if this model has the right flexibility, the overall spectral shape information will come from the photometry, while the line information will come from the spectroscopy.
The SOP in this business is to fit and divide out a continuum, from the spectrum and the spectrum models. That's a good idea, but it isn't the Right Thing To Do (tm) if you think your models might have certain kinds of problems. It also performs badly when no part of your spectrum is clearly continuum and you might have small resolution differences between model and data.