stellar parameters; machine learning in cosmology

A research-filled day started with a discussion with Vakili about final changes to his paper on centroiding compact sources. We are responding to a constructive and useful referee report. The day ended with me sending a long email out to the #GaiaSprint participants with their homework assignments, some of which are pretty non-trivial!

At stars group meeting, we heard from Tim Morton (Princeton), who has been building a system to get the best possible stellar parameters (radii, densities, distances) for exoplanet (and binary-star) host stars, given all available data. His system is very flexible in what can be used to constrain the system: photometry, spectroscopy, asteroseismology, and astrometry. What I was even more impressed with is its handling of binary stars and more complex hierarchies of stellar systems: You can have some photometry that constrains the sum of the star brightnesses, and other photometry that constrains the difference. And you can fit the binaries fixing the ages and metallicities to agree. That makes his code very useful for unresolved and marginally resolved binaries, which are always a nuisance when you want to fit models.

At cosmology group meeting, Tjitske Starkenburg (CCA) and Lauren Anderson (CCA) reviewed these two papers by Kamdar, Turk, & Brunner about using machine learning to model the outputs of cosmological simulations. The matters of greatest interest to the group were relegated to a short appendix of the first paper! These papers don't directly solve anyone's current problems, but they represent a start for using machine learning in cosmology. We closed the meeting with a discussion about where we might most productively point traditional machine-learning techniques towards unsolved problems in cosmology. Our ideas were about training on simulations but applying to real data: Maybe we could infer the (unobserved, latent) dark-matter properties given the observed galaxy properties. Or maybe we could use ideas from ML to find better statistics (that is, summary statistics from a galaxy survey) for constraining cosmological parameters.