the PSF and its variations

With Hirsch (UCL & MPI-IS) and Foreman-Mackey, I spent the whole morning discussing next-generation systems to estimate the point-spread function in heterogeneous data. Hirsch has developed a beautiful model for spatially varying PSFs, using data we took (well really he and Schölkopf took) in Valchava as the test data. We discussed various possible directions to go: In some, he works towards making a model of the PSF and its variations over collections of images from the same hardware. In others, he works towards setting PSF model complexity parameters using the data themselves. In others he models departures from smooth, parametric forms for the PSF to increase accuracy and precision. We concluded that if we can make some useful software, it almost certainly would get adopted. We also discussed integration with Astrometry.net, where we want to move towards image modeling for final calibration (and anomaly discovery!).

In the afternoon, Matias Zaldarriaga (IAS) gave a talk about measuring and understanding fluctuations in the Universe better than we can at present. In one project, he showed that we can use certain kinds of distortions away from black-body in the CMB to measure the amplitude of fluctuations on extremely small scales—scales too small to observe any other way. In another, he showed that you can do fast, precise numerical simulations by simulating not the full universe, but the departure of the universe away from a linear or second-order prediction for the growth of structure. That made me say "duh" but is really a great idea. It also gave me some ideas for machine learning in support of precise simulations, which perhaps I will post on the ideas blog.

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