stars in galaxies; data-driven metallicities

MPIA Milky-Way group meeting was excellent today. Ross Fadely talked about our star–galaxy separation projects and showed some very encouraging signs that a supervised regression system to predict stellarity using the object colors might work. We are training not on the labels (star or galaxy) but on the morphological inputs that are used to make those labels. Nicholas Martin (Strasbourg) showed incredible maps of the outskirts of M31 split by metallicity. He is building the maps with a proper forward model of the data. They are gorgeous. He is also experimenting with showing the results of the MCMC sampling as a noisy movie. Cool. Greg Stinson (MPIA) showed both some incredible movies of simulations, and also some interesting results on the delay time distribution for Type Ia SNe. It appears that the direct measurements of the delay time distribution from stellar populations and the inferences of the delay times from chemical abundances might be in conflict. We argued about possible resolutions.

Late in the day, Ness and I closed the loop on the data-driven APOGEE spectral modeling: We used our data-driven model to "learn" the tags (temperature, logg, and metallicity) for a new star. We cheated—we tagged a star that was used in the training—but (a) we rocked it with accurate tag recovery, and (b) we can do cross-validation as a more conservative test. It was an exciting moment.

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