Gaia-based training data, GANs, and optical interferometry

In today's Gaia DR2 working meeting, I worked with Christina Eilers (MPIA) to build the APOGEE+TGAS training set we could use to train her post-Cannon model of stellar spectra. The important idea behind the new model is that we are no longer trying to specify the latent parameters that control the spectral generation; we are using uninterpreted latents. For this reason, we don't need complete labels (or any labels!) for the training set. That means we can train on, and predict, any labels or label subset we like. We are going to use absolute magnitude, and thereby put distances onto all APOGEE giants. And thereby map the Milky Way!

In stars group meeting, Richard Galvez (NYU) started a lively discussion by showing how generative adversarial networks work and giving some impressive examples on astronomical imaging data. This led into some good discussion about uses and abuses of complex machine-learning methods in astrophysics.

Also in stars meeting, Oliver Pfuhl (MPA) described to us how the VLT four-telescope interferometric imager GRAVITY works. It is a tremendously difficult technical problem to perform interferometric imaging in the optical: You have to keep everything aligned in real time to a tiny fraction of a micron, and you have little carts with mirrors zipping down tunnels at substantial speeds! The instrument is incredibly impressive: It is performing milli-arcsecond astrometry of the Galactic Center, and it can see star S2 move on a weekly basis!.

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