calibration of ZTF; interpolation

I am loving the Friday-morning parallel working sessions in my office. I am not sure that anyone else is getting anything out of them! Today Anna Ho (Caltech) and I discussed things in my work on calibration and data-driven models (two extremely closely related subjects) that might be of use to the ZTF and SEDM projects going on at Caltech.

Late in the morning, an argument broke out about using deep learning to interpolate model grids. Many projects are doing this, and it is interesting (and odd) to me that you would choose a hard-to-control deep network when you could use an easy-to-control function space (like a Gaussian Process, stationary or non-stationary). But the deep-learning toothpaste is hard to put back into the tube! That said, it does have its uses. One of my medium-term goals is to write something about what those uses are.

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