I spent some of my travel time working on a suggestion for the Gaia pipeline teams: A pillar of the Gaia data analysis plan is a fully data-driven (that is, not physical) attitude model, set by the timings of the star transits. The model they plan on implementing is immense: It consists of a hundred million attitude pseudo-vectors on a grid of spline knots. I am proposing that the team set the freedom or complexity of this model objectively, using either cross-validation or a Bayesian mixture of different complexities. Either way, objective setting of the complexity should beat the team's plan of hard-set complexity; what I don't know is whether this will or can improve the results significantly enough to justify the work. I also suggest that they can tune the complexity in two different ways: They can either change the knot spacing or else tie together adjacent knots using a smoothness prior.
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