When God provides a meeting-free gap in the day, it is incumbent on the astronomer to use that time to do research. I stole time out of time today to work on preparation for my tess.ninja projects. My plan is to look forward to algorithmic approaches to adaptive observing campaigns, so that an exoplanet follow-up campaign from the ground can be simultaneously efficient at confirming true planets, measuring planet properties, and rejecting false positives, but also be useful for long-term future statistical projects. In general statistical usability and efficiency are at odds! These ideas are related to active learning but also decision theory. One question: Would a ground-based telescope time-allocation committee accept an active-learning proposal?
And I also did some information-theory-related math for Christina Eilers (MPIA): She is building latent-variable models for APOGEE spectra, but working in a low-dimensional basis that is a linear projection of the data; she needs measurement uncertainty estimates in the low-dimensional basis.
Would a TAC buy it? There may be some ammunition in the approach taken this work by Melanie Beck et al. on accelerating galaxy classification by adding some adaptive learning to human input ("Galaxy Zoo Express". (Don't look at me that way, my advisor use the stream-of-consciousness approach to telescope time - maybe why I make such a big deal with observational classes of trying to optimize time use each night).
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