latent-variable model; bound-saturating EPRV

Today, Christina Eilers (MPIA) and I switched her project over to a latent variable model. In this model, stellar spectra (every pixel of every spectrum) and stellar labels (Teff, logg, and so on for every star) are treated on an equal footing as “data”. Then we fit an underlying low-dimensional model to all these data (spectra and labels together). By the end of the day, cross-validation tests were pushing us to higher and higher dimensionality for our latent space, and the quality of our predictions was improving. This seems to work, and is a fully probabilistic generalization of The Cannon. Extremely optimistic about this!

Also today, Megan Bedell (Chicago) built a realistic-data simulation for our EPRV project, and also got pipelines working that measure radial velocities precisely. We have realistic, achievable methods that saturate the Cramér–Rao bound! This is what we planned to do this week not today. However, we have a serious puzzle: We can show that a data-driven synthetic spectral template saturates the bound for radial-velocity measurement, and that a binary mask template does not. But we find that the binary mask is so bad, we can't understand how the HARPS pipeline is doing such a great job. My hypothesis: We are wrong that HARPS is using a binary mask.

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