Today was the first day of the NYC Gaia Sprint, with 50 participants from around the world. I had an absolutely great research day. The meeting began with a set of pitches, one per participant, that included an introduction, a statement of expertise (what that participant brings to the meeting), and a statement of goals (what that participant hopes to take home from the meeting). Pitches were all over the place: Milky Way disk and halo, testing stellar models, exoplanet science, calibration, target selection, future missions, you name it! This session took two hours. But that pitch session was the entirety of the formal program of the 45-hour meeting! That is, everyone is just supposed to work from here on. Of course we will have break-out sessions, and informal discussions, check-in and wrap-up sessions, and lots and lots of co-working. But that was it.
I started working with Boris Leistedt (NYU) on modeling a slice of the color-magnitude diagram of stars, to build a data-driven photometric distance indicator (that will beat the parallax for most TGAS stars). I also started working with Adrian Price-Whelan (Princeton) on his discovery (this morning!) that the TGAS Catalog contains the most precise measurement of the Milky Way disk midplane ever. That displaced some of our plans for running the clock back on disrupting binaries and associations.
We had two break-outs, one on likelihood formulations for doing inference with parallaxes, and another on data quality and data issues in the Gaia DR1 data sets. This latter talk was by Anthony Brown (Leiden), who is the chair of the entire Gaia DPAC data processing effort. I learned a huge amount in both of these break-outs about the noise model for the TGAS parallaxes, which I ought to be using in my own inferences.
One thing we have done in this meeting—which is standard practice for me at scientific meetings now—is open a shared, editable web document to record notes. By mid afternoon this document was more than 20 pages long, filled with crowd-sourced notes about pitches, projects, data sets, and software tools. We will preserve and publish these notes after the meeting in an informal form. One of the big outcomes of this meeting could be some standard tools, standard data sets, and advice about how to use these to do reliable science. Thinking about that as we continue to hack on the data.