Today Marc Williamson (NYU) passed (beautifully, I might say) his PhD Candidacy exam. He is working on the progenitors of core-collapse supernovae, making inferences from post-peak-brightness spectroscopy. He has a number of absolutely excellent results. One is (duh!) that the supernovae types seem to form a continuum, which makes perfect sense, given that we think they come from a continuous process of envelope loss. Another is that the best time to type a supernova with spectroscopy is 10-15 days after maximum light. That's new! His work is based on the kind of machine-learning I love: Linear models and linear support vector machines. I love them because they are convex, (relatively) interpretable, and easy to visualize and check.
One amusing idea that came up is that if the stripped supernova types were not in a continuum, but really distinct types, then it might get really hard to explain. Like really hard. So I proposed that it could be a technosignature! That's a NASA neologism, but you can guess what it means. I discussed this more late in the day with Soledad Villar (NYU) and Adrian Price-Whelan (NYU), with whom we came up with ideas about wisdom signatures and foolishness signatures. See twitter for more.
Also with Villar I worked out a very simple toy problem to think about GANs: Have the data be two-d vectors drawn from a trivial distribution (like a 2-d Gaussian) and have the generator take a one-d gaussian draw and transform it into fake data. We were able to make a strong prediction about how the transform from the one-d to the two-d should look in the generator.
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