Foreman-Mackey called with ideas for The Cannon: He thinks we might be able to do some simple things to propagate errors or uncertainties in our labels in the training set into uncertainties in our internal parameters, and from there to uncertainties in the labels we derive for new stars. His idea is based on the “uncertainties in both directions” model in my old fitting-a-line document. He also wants to play around with a fully Bayesian Cannon. We think this will be hard, but conceivably possible. He is thinking forward to some kind of sophisticated TESS input catalog in these projects.
As for Casey and my work on the compressed-sensing version of The Cannon: We turned on optimization with our L1-regularized model and nothing seems to be working right. Diagnosis progresses.