The day started with Dun Wang, Steven Mohammed (Columbia), David Schiminovich and I discussing the short-term plans for our work with GALEX. My top priority is to get the flat-field right, because if we can do that, I think we will be able to do everything else (pointing model, focal-plane distortion model, etc.).
Over lunch, Greengard and Jeremy Magland (SCDA) “reminded me” how the FFT works in the case of irregularly sampled data. This in the context of using Gaussian-process kernels built not in real space but in Fourier space. And then Greengard and Magland more-or-less simultaneously suggested that maybe we can turn all our Gaussian process problems into convolution problems! The basic idea is that the matrix product of a kernel matrix and a vector looks very close to a convolution, and the product with the inverse matrix looks like a deconvolution. And we know how to do this fast in Fourier space. This could be huge for asteroseismology. The log-determinant may also be simple when we think about it all in Fourier space. We will reconvene this conversation late next week.