I am visiting JPL this week, hosted by Leonidas Moustakas. I gave a seminar today, about our work with Kepler, with a focus on the noise modeling aspects of the project. In the rest of the day I had too many great and interesting conversations to list, but highlights were the following:
With Roland de Putter I discussed parameter estimation and inference in large-scale structure projects. We talked a bit about correlation functions and Gaussian kernels. He had some nice intuitions and scaling arguments about the Gaussianity of measurements of the power spectrum that helped me understand (and corrected some of my errors in) my thinking about my cosmological inference projects. He also helped me formulate the simplest possible demonstration that what is traditionally done (in, say BAO projects) to mock up a "likelihood function" is formally (and maybe substantially) wrong and can be replaced. We also talked about inferring the initial conditions of the Universe from what we see. I pitched a "deep learning" version of this problem that I love so much I will have to write it down on the ideas blog!Francis-Yan Cyr-Racine (JPL) told me about an absolutely awesome project to understand the statistical effect of substructure on lenses without the requirement that the substructures be detected individually. That's right up my alley, and the alley of Brewer as well. He has broken the problem into parts, combining the weak parts into a Gaussian noise and doing the strong parts the hard way. Genius.
I argued for randomized (or other regular but clever) observing strategies for WFIRST and Euclid with Jason Rhodes (JPL) and the dark-matter and dark-energy group. We lamented the lack of good observing-strategy simulations for these projects, which would make such arguments quantitative, open, transparent, and efficient.