2018-09-26

data science for stars; phase space

Our weekly Stars meeting at Flatiron was a pleasure today, as it usually is. Angus (Columbia) and Contardo (Flatiron) are looking at the possibility that we might be able to deblend binary and overlapping stars in the TESS data by their light curves alone. That's crazy, but just crazy enough that I love it! We discussed different ways they might get a training set for this. Luger (Flatiron) asked whether it might be possible to figure out the ell and em (spherical-harmonic order) of the asteroseismic modes by using projections onto transits. That also led to some good discussions about possible methods; many of the crowd liked the ideas that look like lock-in amplification. Marchetti (Leiden) gave us a nice discussion of the high-velocity star results from Gaia DR2. It's too early: The really exciting results will come in data releases 3 and 4 when the magnitude limit for the RVS data gets fainter.

Matt Buckley (Rutgers) showed Adrian Price-Whelan (Princeton) and me his results on measuring phase-space volumes of bound and disrupted objects. The idea is that you might be able to reconstruct the mass of a disrupted object, and say whether it was dark-matter dominated. And get all the attendant dark-matter-theory consequences of that. He showed (unsurprisingly) that observational noise increases the phase-space volume that you naively measure. So we discussed how to approach this. If we are frequentists, maybe we can just ”greedily“ correct the measurements in the direction that lowers the phase-space volume? If we are Bayesians, we have to make more assumptions, I think!

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