Today was group-meeting day. In stars group meeting, Matteo Cantiello (CCA) discussed recent results on star–star interactions, including a star–star merger that may have been caught by the OGLE experiment. He gave us some order-of-magnitude thinking about the common-envelope phase and how we might use these events to understand stars. He was pessimistic about being able to do full simulations of the events; there are too many things happening at too many scales. He also showed us another tight binary system which shows period changes that suggest a merger in 2022.
Dan Foreman-Mackey (UW) spoke about linear algebra and asteroseismology. With Eric Agol (UW) he has developed linear algebra techniques such that he can solve matrix equations in linear time (and also take the determinant, which is super-important), provided that the matrix is a kernel matrix of a certain (very flexible) form. This form is capable of modeling a star's light curve as a mixture of stochastically driven oscillators. This raises the hope of automatically getting asteroseismic parameters for all TESS stars! In the discussion, we arrived at the idea of using Kepler to measure the three-point function for stellar variability. David Spergel (Flatiron) predicted that it would lead to constraints on mode coupling and other aspects of stellar physics.
Cosmology group meeting was crashed by Daniel Mortlock (Imperial) and Hiranya Peiris (UCL). Mortlock told us that there are still very high-redshift quasars being discovered, but that he still has the redshift record, and that, given Eddington time-scales, his is still the most extreme high-redshift quasar. This was followed by a wide-ranging discussion (led by Elijah Visbal, Flatiron) of the possibility that we could be using generative models or better estimators than two-point functions in 21-cm surveys designed to discover the physics of reionization of the Universe. Peiris brought up dictionary methods and we spent time discussing these, and the possibility that we could learn sparse dictionaries on simulations and use them on data. It was very vague, but gives me ideas about where we at CCA need to learn more about methodologies.