I arrived in Heidelberg today, for a fast visit before a week at Ringberg Castle for a meeting on machine learning and astrophysics. I had two long conversations about science projects, one with Neige Frankel (MPIA), and one with Greg Green (MPIA). Frankel is trying to make comprehensive maps of the Milky Way with red-clump stars from SDSS-IV APOGEE and ESA Gaia data. She is using a big linear model to calibrate the distance variations with color, magnitude, and dust. But it seems to have problems at high reddening. We found that some of those problems were an artifact of sample cuts based on Gaia uncertainties. My loyal reader knows that such cuts are dangerous! But still some odd problems remain: Bugs or conceptual issues?
Green is trying to infer the dust extinctions to stars as a function of three-dimensional position in the galaxy with reduced or no dependence on models of the stellar color–magnitude diagram. He is using a neural network to model this nuisance. My loyal reader knows that this is my dream: To only use these complex methods on the parts of our problems that are nuisances!
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