JPL, day 3

I spent the morning with Leonidas Moustakas (JPL), a bit of Foreman-Mackey (in Pasadena for the Sagan Fellowship Symposium), and a bit of Andrew Romero-Wolf (JPL) and Curtis McCully (LCOGT) by phone, discussing projects related to strong-lensing time-delay measurements. We discussed two challenging projects. The first is to determine (from as many as we can construct) the best model for quasar time-domain variability. There are claims in the literature that the damped random walk is the best model, but that (very sensible) model hasn't really been competed against all that much. We know how to do this, lets do this! The reason they want good probabilistic generative models is that they want to determine time delays as precisely as possible, using a probabilistic approach.

The second project is to perform high-quality photometry on the (overlapping from the ground) images of a strongly lensed quasar. In this case—when the point-spread functions overlap—you have to do your photometry by simultaneous fitting, but with the variable (and badly known) PSF of ground-based astronomy, I have never seen such photometry that really looks right: There are always fitting-induced covariances of the overlapping-source light curves. I think this is caused by model mismatch (under-fitting), but I don't really know. Romero-Wolf and McCully pointed out that image differencing methods work well in crowded fields, so I formulated an image-modeling approach to the photometry that is as close to image differencing as possible. I promised to write it up into a document. I am kind-of excited about it; it is still just image modeling, but it makes use of the power of image differencing technologies to get flexibility to fit the real PSF as it is.

Late in the day, Adam Miller (JPL) showed me the JPL Mission Control center and a few high bays, filled with awesome stuff (including some fake Mars!).

The Climate Corporation

I spent a bit of today at The Climate Corporation, hosted by former astronomer Todd Small. He told me about things they work on, which include field-level predictions for farmers about rainfall and sunlight, precise prediction and instructions for irrigation and fertilization, and advice about crop density (distances between seeds). He said they get data feeds from Earth observing but also from soil testing and even the farm machinery itself (who knew: combine harvesters produce a data feed!). A modern piece of large-scale farm equipment is precisely located on its path by GPS, makes real-time decisions about planting or whatever it is doing, and produces valuable data. They even have a tractor simulator in the house for testing systems.

We talked for a bit about the challenging task of communicating with clients (farmers, in this case) about probabilistic information, such as measurement uncertainties and distributions over predictions. This is another motivation for developing an undergraduate data-science educational program: It doesn't matter what industry you are in, you will need to be able to understand and communicate about likelihoods and posterior probabilities. I gave an informal seminar to a part of the climate modeling team about our uses of Gaussian Processes in exoplanet discovery.

The Thresher

We had a big push on our post-lucky imaging pipeline. As part of this, we renamed it The Thresher and Foreman-Mackey made a sweet github-hosted web page for it. We are very close to release, and over lunch we discussed last things to do before writing paper zero. Paper zero will compare The Thresher to traditional lucky imaging, showing that we win in signal-to-noise at fixed resolution, and win in resolution at fixed signal-to-noise. No smoothing applied! We fit the best band-limited scene to all the data. At Camp Hogg, We Don't Throw Away Data (tm).

[By the way, in case you think a thresher throws away husks, you might be confusing a thresher with a winnower. Indeed, if I ruled the world, I would rename traditional lucky imaging pipelines winnowers.]