Gotham AstroFest, day 2

As my loyal reader will recall, there are AstroFest events this September at Columbia (last week), Flatiron (today), and NYU (in two weeks). Todays meeting was long but excellent. I learned many things and was pleased to see all the new faces (so many new faces)! Here are a few personal highlights:

Shy Genel (Flatiron) showed that the details of star formation and feedback affecting a simulated galaxy disk or stars is very sensitive to the initial conditions or perturbations to the conditions made mid-simulation. That caused me to wonder if it is going to be very hard to infer things about galaxies from their observed properties! But Foreman-Mackey (Flatiron) pointed out that the sensitivity might be high but also highly structured, so not necessarily a problem. Good point; but it might take a lot of simulations to find out! Whatever the case, this is an excellent line of research.

Francisco Villaescusa-Navarro (Flatiron) described a project to see if, in the non-linear regime of large-scale structure evolution, the one, two, and higher-point functions, all combined, contain as much information as the one- and two-point functions in the linear regime. That is: What is the information content in the observables? This is, in some sense, the key question of cosmology at the present day! And relates to things I have been thinking about (but doing nothing about) for years.

Suvodip Mukherjee (Flatiron) delivered a beautifully simple (and yet novel) idea: He is looking at all the cosmological observables with gravitational-wave sources that we have with galaxies and the CMB. That's clever! It includes the GW LSW effect, and GW lensing. He pointed out that there might be new cosmological constraints from cross-correlating GW event properties with CMB properties, like the CMB lensing map. Clever! And possibly big, in the mid-term to long-term future.

Doyeon Avery Kim (Columbia) is building spectral-spatial models of the all-sky fields or maps that act as CMB foregrounds. She is doing this by interpolating in spatial and spectral directions the (necessarily incomplete, different sky coverage, different angular resolutions) information from many large-angular-scale surveys. This is also very much related to my (vapor-ware) latent-variable model approach here, and is looking like it is delivering exciting results.