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!).


  1. Does testing the DRW models mean testing things like CARMA models? (or other ARMA/ARIMA models?) Or something else altogether?

    1. Yes! In principle. I don't *love* the ARMA and CARMA type things because I think they implicitly or explicitly work on uniform-spaced data with no missing values; they are less generalizable in this sense.