Today was the fourth day of Machine Learning Tools for Research in Astronomy. Some of my personal highlights were the following:
Tomasz Kacprzak (ETH) showed that he can improve the precision of cosmological inferences by using machine learning to develop new statistics of cosmological surveys to compare to simulations. His technology is nearly translationally invariant, but not guaranteeed to be perfect, and not guaranteed to be rotationally symmetric (or rotationally and translationally covariant, I should say). So I wondered if any of the increased precision he showed might be coming from properties of the data that are not consistent with our symmetries. That is, precision might increase even if the features being used are not appropriate, given our assumptions. I'd love to have a playground for thinking about this more. It relates to ideas of over-fitting and adversaries we discussed earlier in the week.
Luisa Lucie-Smith (UCL) showed some related things, but more along the lines of finding interpretable latent variables that bridge the connection between the cosmological initial conditions and the dark-matter halos that are produced. I love that kind of approach! Can we use machine learning to understand the systems better? Her talk led to some controversy about how autoencoders (and the like) could be structured for interpretability. As my loyal reader knows, I don't love the “halo” description of cosmology; this could either elucidate it or injure it.
Doug Finkbeiner (Harvard) showed how he can patch and find outliers in massive spectroscopy data sets using things that aren't even machine learning, according to many in the room! That was fun, and probably very useful. This all connects to a theme of the meeting so far, which is using machine learning to aid in visualization and data discovery.
In between sessions we had a great conversation about student mentoring. This is a great idea at a workshop, where there are both students and mentors, and the participants have gotten to know one another. Related to this, Brian Nord (Fermilab) gave a nice talk about relationships between what we have been thinking about in machine learning and work in the area of science, technology, and society. He's trying to build new scientific communities, but in a research-based way. And I mean social-science-research-based. That's radical, and more likely to succeed than many of the things we physicists do without looking to our colleagues.
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