calibration and classification

At computer-vision-meets-astronomy group meeting, Fadely showed awesome results that we can calibrate astronomical imaging (determine the dark and flat) without taking specific calibration data, or even identifying stars (let alone cross-matching stars in repeat observations). This is unreal! He also showed that the information that comes from high-signal-to-noise image patches is very different from the information that comes from low-signal-to-noise (read: blank) patches. Afterwards, Krik Muandet (MPI-IS) and I discussed how to evaluate his quasar target selection methods, which are based on support measure machines, a generalization of support vector machines to the space of probability distribution functions.

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