data-driven models of images and stars

Today was a low-research day! That said, I had two phone conversations of great value. The first was with Andy Casey (Cambridge), about possibly building a fully data-driven model of stars that goes way beyond The Cannon, using the Gaia data as labels, and de-noising the Gaia data themselves. I am trying to conceptualize a project for the upcoming #GaiaSprint.

I also had a great phone conference with Dun Wang (NYU), Dan Foreman-Mackey (UW), and Bernhard Schölkopf (MPI-IS) about image differencing, or Wang's new version of it, that has been so successful in Kepler data. We talked about the regimes in which it would fail, and vowed to test these in writing the paper. In traditional image differencing, you use the past images to make a reference image, and you use the present image to determine pointing, rotation, and PSF adjustments. In Wang's version, you use the past images to determine regression coefficients, and you use the present image to predict itself, using those regression coefficients. That's odd, but not all that different if you view it from far enough away. We have writing to do!

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