A deep network that jointly models magnitude, color, and position dependence improves photometric calibration of digitized photographic plates, roughly halving bright-star errors versus the separable MYX25 method.
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Towards optimal photometric calibration of digital astronomical plates with deep learning
A deep network that jointly models magnitude, color, and position dependence improves photometric calibration of digitized photographic plates, roughly halving bright-star errors versus the separable MYX25 method.