In the morning I sat in on a meeting of the GALAH team, who are preparing for a data release to precede Gaia DR2. In that meeting, Jeffrey Simpson (USyd) showed me GALAH results on the Oh et al comoving pairs of stars. He finds that pairs from the Oh sample that are confirmed to have the same radial velocity (and are therefore likely to be truly comoving) have similar detailed element abundances, and the ones that aren't, don't. So awesome! But interestingly he doesn't find that the non-confirmed pairs are as different as randomly chosen stars from the sample. That's interesting, and suggests that we should make (or should have made) a carefully constructed null sample for A/B testing etc. Definitely for Gaia DR2!
In the afternoon, I joined the USyd asteroseismology group meeting. We discussed classification of seismic spectra using neural networks (I advised against) or kernel SVM (I advised in favor). We also discussed using very narrow (think: coherent) modes in red-giant stars to find binaries. This is like what my host Simon Murphy (USyd) does for delta-Scuti stars, but we would not have enough data to phase up little chunks of spectrum: We would have to do one huge simultaneous fit. I love that idea, infinitely! I asked them to give me a KIC number.
I gave two talks today, making it six talks (every one very different) in five days! I spoke about the pros and cons of machine learning (or what is portrayed as machine learning on TV) as my final Hunstead Lecture at the University of Sydney. I ended up being very negative on neural networks in comparison to Gaussian processes, at least for astrophysics applications. In my second talk, I spoke about de-noising Gaia data at Macquarie University. I got great crowds and good feedback at both places. It's been an exhausting but absolutely excellent week.
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