calibrating Euclid

Rory Holmes, Rix, and I spent a while chatting about the scope of our uber-calibration or self-calibration or calibration-marginalization analyses for potential Euclid or WFIRST survey strategies. The default strategies proposed for these kinds of missions are usually not ideal for photometric calibration, which the investigators imagine will be determined with calibration observations. However, the science data usually contain far more bits of information about calibration than the calibration data, and even more if the science data are taken with calibration information preservation or generation in mind. Fundamentally, it is about redundancy—meaning diversity—in modes, positions, orientations, time coverage, cadence, and so on.

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