linear models for the win

Christina Eilers (MPIA) and I have been debating what photometry and colors to put into our linear model for distance estimation or distance-modulus estimation. And then we realized: It is a general linear model! So we should just put in all photometry and the code will decide what colors to create and use. We did, and the model improved for the stars behind the most dust. Just a reminder: We don't explicitly extinction-correct anything! We ask the model to figure out extinction on its own, by training on a sample that has stars at different extinctions.

In the afternoon I had a conversation with Maryam Modjaz (NYU) and Marc Williamson (NYU) about applying PCA and other simple machine-learning techniques to their library of supernova spectra across type and phase. They have some nice results, that show that the first few PCA components do a good job of separating types, and they can show that the separation quality is a function of time (relative to maximum light, or the explosion). We discussed using something like a purely linear support vector machine to do classification that would be highly interptetable. As my loyal reader knows, I am happy to sacrifice some performance for interpretability.

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