Rix and I discussed a project to design observing strategies that optimize calibration performance of a survey, with Euclid, WFIRST, and LSST in mind. The idea is that, as with SDSS, the best calibration will be internal calibration, and that is optimized when there is non-degenerate information about sources and pixels. Rory Holmes (MPIA) will write some code here with direct impact on the Euclid planning, but the project is general.
I worked out for Tsalmantza the non-negative updates if we moved our data-driven spectral modeling to non-negatively constrained. I hope both my readers know that non-negative matrix factorization is a solved problem (and simple at that), even for a heteroscedastic (proper chi-squared) objective function. There is no reason to ever use PCA ever again! I have in mind that we could give good LRG redshift-finding templates to the BOSS.