fitting stars, CMDs, and galaxies, day 1

Today was the first day of a workshop put together by Charlie Conroy (Harvard) to get people who model stars together with people who model stellar populations and people who model galaxies. I came because I want to understand better the “customers” for any further work we do modeling stars, either with The Cannon or else if we jump in to the 1-D modeling problem.

Everyone at the workshop said something today, which was amazing (and valuable) and way too much to report here. One highlight was Phil Cargile (Harvard) showing us how he can update atomic line parameters using observations of the Sun. We discussed how this might be done to jointly improve the predictions for many stars. Another highlight was Alexa Villaume (UCSC) showing us the provenance of the stellar parameters in some of the calibrator-level star sets. It was horrifying (one thing was a weighted average of literature values, weighed by publication date).

A number of people in the room are computing spectra (of stars or star clusters or galaxies) on a grid and then interpolating the grid at likelihood-evaluation time. This started a discussion of whether you should interpolate the spectra themselves or just the log-likelihood value. I argued very strongly for the latter: The likelihood is lower dimensionality and smoother than the spectrum in its variations. Not everyone agreed. Time for a short paper on this?

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