Melissa Ness arrived in NYC for a week of hacking on The Cannon, our project to transfer stellar parameter labels from well-understood stars to new stars using a data-driven model of infrared stellar spectra from APOGEE. We discussed nomenclature, notation, figures, and the paper outline. The hope is to get a submittable draft ready by Friday. I am optimistic. There are so many things we can do in this framework, the big issue is limiting scope for paper 1.
One big important point of the project is that this is not typical machine learning: We are not transforming spectra into parameter estimates, we are building a generative model of spectra that is parameterized by the stellar parameter labels. This permits us to use the noise properties of the spectra that we know well, generalize from high signal-to-noise training data to low signal-to-noise test data, and account for missing and bad data. The second point is essential: In problems like this, the training data are always much better than the test data!