#LennartFest day 3

Today was the last day of a great meeting. Both yesterday and today there were talks about future astrometric missions, including the Gaia extension, and also GaiaNIR, SmallJasmine, and Theia. In his overview talk on the latter, Alberto Krone-Martins put a lot of emphasis on the internal monitoring systems for the design, in which there will be lots of metrology of the spacecraft structure, optics, and camera. He said the value of this was a lesson from Gaia.

This point connects strongly to things I have been working on in self-calibration. In the long run, if a survey is designed properly, it will contain enough redundancy to permit self-calibration. In this sense, the internal monitoring has no long-term value. For example, the Gaia spacecraft includes a basic-angle monitor. But in the end, the data analysis pipeline will determine the basic angle continuously, from the science data themselves. They will not use the monitor data directly in the solution. The reason is: The information about calibration latent in the science data always outweighs what's in the calibration data.

That said (and Timo Prusti emphasized this to me), the internal monitoring and calibration data are very useful for diagnosing problems as they arise. So I'm not saying you don't value such systems and data; I'm saying that you should still design your projects to you don't need them at the end of the day. This is exactly how the SDSS imaging-data story played out, and it was very, very good.

I also gave my own talk at the meeting today. My slides are here. I think I surprised some part of the audience when I said that I thought we could do photometric parallax at all magnitudes without ever using any physical or numerical model of stars!

One thing I realized, as I was giving the talk, is that there is a sense in which the data-driven models make very few assumptions indeed. They assume that Gaia's geometric parallax measurements are good, and that it's noise model is close to correct. But the rest is just very weak assumptions about functional forms. So there is a sense in which our data-driven model (or a next-generation one) is purely geometric. Photometric parallaxes with a purely geometric basis. Odd to think of that.

At the end of the meeting, Amina Helmi told me about vaex, which is a very fast visualization tool for large data sets, built on clever data structures. I love those!