#MLringberg2019, day3

Today was the third day of Machine Learning Tools for Research in Astronomy. Some random personal highlights were the following:

Prashin Jethwa (Vienna) showed first results from a great project to model 2-d imaging spectroscopy of galaxies as a linear combination of different star-formation histories on different orbits. People have modeled galaxies as superpositions of orbits. And as superpositions of star-formation histories. But the problem is linear in both, so much more can be done. My only criticism (and to be fair, Jethwa made it himself) is that they collapse the spectroscopy to kinematic maps before starting. In my opinion, the most interesting information will be in the spectral–kinematic joint domain, because different lines (which are sensitive to different star-formation histories and different element abundance ratios) will have different shapes in different parts of each galaxy. Exciting that this is happening now; it has been a dream for years.

Francois Lanusse (Berkeley) and Greg Green (MPIA) both gave talks that were aligned strongly with my interests. Lanusse is taking galaxy generators (like VAEs and GANs) and adding causal structure (like projection onto the sky plane, pixelization, and noisification) so that the generators produce something closer to the true galaxies, and something not exactly the same as the data. That's exciting and a theme I have been talking about in this forum for a while. For Lanusse, galaxies are nuisances, to be marginalized out as we infer the cosmic weak-lensing shear maps.

In a completely different domain, Green is modeling stars as coming from a compact color—magnitude diagram but then being reddened and attenuated by dust. He is interested in the dust not the stars, so for him the CMD is a nuisance, as galaxies are for Lanusse. That makes it a great object to model with something ridiculous, like a neural net. He is living the dream I have of using the heavy learning machinery only for the nuisance parts of the problem, and reserving belief-consistent physical models for the important parts. Green was showing work that is only a few days old! But it looks very very promising.