target selection

On the weekend, Rix (MPIA) and I got in a call to discuss the target selection for SDSS-V, which is a future survey to measure multi-epoch spectroscopy for (potentially) millions of stars. The issue is that we have many stellar targeting categories, and Rix and my view is that targeting should be based only on the measured properties of stars in a small set of public, versioned photometric and astrometric catalogs.

This might not sound like a hard constraint, but it is: It means you can't use all the things we know about the stars to select them. That seems crazy to many of our colleagues: Aren't you wasting telescope time if you observe things that you could have known, from existing observations, was not in the desired category? That is, if you require that selection be done from a certain set of public information sources, you are ensuring an efficiency hit.

But that is compensated—way more than compensated—by the point that the target selection will be understandable, repeatable, and simulate-able. That is, the more automatic the target selection it is, from simple inputs, the easier it is to do populations analyses, statistical analyses, and simulate the survey (or what the survey would have done in a different galaxy). See, for example, cosmology: The incredibly precise measurements in cosmology have been made possible by performing simple, inefficient, but easy-to-understand-and-model selection functions. And, indeed: When the selection functions get crazy (as they do in SDSS-III quasar target selection, with which I was involved), the data become very hard to use (the clustering of those quasars on large scales can never be known extremely precisely).

Side note: This problem has been disastrous for radial-velocity surveys for planets, because in most cases, the observation planning has been done by people in a room, talking. That's extremely hard to model in a data analysis.

Rix and I also discussed a couple of subtleties. One is that not only should the selection be based on public surveys, it really should be based only on the measurements from those surveys, and not the uncertainties or error estimates. This is in part because the uncertainties are rarely known correctly, and in part because the uncertainties are a property of the survey, not the Universe! But this is a subtlety. Another subtlety is that we might not just want target lists, we might want priorities. Can we easily model a survey built on target priorities rather than target selection? I think so, but I haven't faced that yet in my statistical work.