It's been a low-research week! Today I got in a brief conversation with Lang, about priorities (for writing up our results) and also source detection and measurement. As my loyal reader knows, Lang has understood in a deep way how we ought to detect stars in images, which, even in the limit of isolated sources, sky-limited noise, and well-known point-spread function, is not absolutely trivial. I committed to writing some text for the paper, which is nearly done.
In the afternoon I had a valuable conversation with Jennifer Hill (NYU), who works on (among other things) inferring causality in social science contexts. I asked her about whether it would be interesting or useful to re-cast any of the work going on in galaxy evolution in causal-inference language. The reason I think "galaxy evolution" is that this is an area where a lot of the important ideas about mechanistic relationships come (directly or indirectly) from playing around in the data. She also encounters many social-science contexts in which (like astronomy) controlled experiments and investigator interventions are not possible, so there is definitely a connection. That said, because there are no "potential outcomes" or even really "outcomes" at all for galaxies, it is not totally clear that it will be useful to perform this translation.
All that talk of "translation" caused Hill and me to formulate a possible natural-language project: Take as a training set a collection of studies in two fields (psychology and astronomy, say) that use the same underlying technical machinery; use this to develop a translator that can translate between the fields. The idea is that even though methods are general and apply across disciplines, often we can't understand one another's papers because of historical differences in language, notation, and context. This seems like a valuable natural-language project that might not require a solution that is also AI complete.