data-driven chemical tagging

Rix and I spent a big chunk of the day talking about everything we have been doing since the summer. We veered off into my dream project of doing stellar chemical abundance analyses without a good physical model of the stellar atmosphere or emission. I think this is possible. And in discussion with Rix, I realized that it could be directly related (or very similar to) support vector machines with the kernel trick. The kernel can be flexible enough to adapt to star temperature and surface gravity, but not flexible enough to adapt to chemical abundance changes. I just want to figure out how to work with missing data and noise (as my loyal reader knows, this is my biggest problem with most standard machine-learning methods). We also discussed the NSF Portfolio Review, the modeling of tidal streams, age-dating stars, the three-dimensional distribution of dust in the Milky Way, and other stuff.

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