linear models for stars

My loyal reader knows that my projects with Christina Eilers (MPIA) failed during the #GaiaSprint, and we promised to re-group. Today we decided to take one last attempt, using either heteroskedastic matrix factorization (or other factor-analysis-like method) or else probabilistic principal components analysis (or a generalization that would be heteroskedastic). The problem with these models is that they are linear in the data space. The benefit is that they are simple, fast, and interpretable. We start tomorrow.

I made a plausible paper plan with Megan Bedell (Chicago) for our extreme-precision radial-velocity project, in which we assess the information loss from various methods for treating the data. We want to make very realistic experiments and give very pragmatic advice.

I also watched as Adrian Price-Whelan (Princeton) used The Joker to find some very strange binary-star systems with red-clump-star primaries: Since a RC star has gone up the giant branch and come back down, it really can't have a companion with a small periastron distance! And yet...

No comments:

Post a Comment