I gave the CCPP Brown Bag talk today, about Kepler data and exoplanets. I was going to talk about calibration, flat-field, and our white paper, but I ended up talking about very flexible models, for intrinsic stellar variability and spacecraft-induced variability in the lightcurves. People were shocked that some of our models have hundreds of thousands of parameters. I didn't let them in on the secret that, in some sense, the Gaussian Processes we use have infinite numbers of parameters!
Tim Morton (Caltech) dropped by to talk about various things exoplanet. He has a very nice system for computing and propagating probabilities for various exoplanet and non-exoplanet (false-positive) scenarios, given Kepler data. He produces most of what you need in order to do efficient follow-up and population studies, given uncertain or noisy exoplanet identifications. In other news, today was Angus's last day here at NYU. She has been visiting for a month from Oxford, and started some projects with us on search, using Gaussian Processes under the hood. They told us it couldn't be done (too slow) but we are doing it.
"People were shocked that some of our models have hundreds of thousands of parameters."
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"using Gaussian Processes under the hood. They told us it couldn't be done (too slow) but we are doing it."
Do you know of any methods faster than O(N^3) for evaluating log det C and C^{-1}y?
All I can say is (a) we are exploiting sparsity as much as we can, and (b) we are making approximations that let us do many more hypothesis tests than matrix determinant / inversion operations, and (c) we have a wavelet idea that might make things even faster (employing what some call the "matrix inversion lemma"). Right now I think we are still technically n^3 but with a very tiny prefactor.
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