Boris Leistedt (UCL) showed up for two days of chatting in preparation for his arrival at NYU this coming Fall. We discussed a set of projects in probabilistic cosmology. In one (which I have discussed previously with Fadely), we imagine what it would look like to infer galaxy redshifts from imaging without either a training set or models of galaxy spectral energy distributions. I feel like it might be possible, with the thought experiment being: What if you were given the photometry of the SDSS Luminous Red Galaxies? Couldn't you figure out their redshifts without being told their underlying spectral energy distributions? At least up to some geometric factor? Leistedt predicts that any method that doesn't use either models or training must do worse than any method that has good training data or models. However, it might make a framework for then including both model (theory) and training (data) information in in a principled way.
In another class of subjects, we talked about inference of the density field or the initial conditions. In the first place, if we could infer the density field in patches, we could use that to inform photometric redshifts or reconstruction. In the second, we could perhaps infer the initial conditions (and I mean the phases, not just the power spectrum); this is interesting because the Baryon Acoustic Feature ought to be much stronger and sharper in the initial conditions than it is today. We discussed some conceivably tractable approaches, some based on likelihood-free inference, some based on Gaussian Processes, and some based on machine learning (using simulations as training data!).