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The PAU survey: Estimating galaxy photometry with deep learning
[article]
2021
arXiv
pre-print
With the dramatic rise in high-quality galaxy data expected from Euclid and Vera C. Rubin Observatory, there will be increasing demand for fast high-precision methods for measuring galaxy fluxes. These will be essential for inferring the redshifts of the galaxies. In this paper, we introduce Lumos, a deep learning method to measure photometry from galaxy images. Lumos builds on BKGnet, an algorithm to predict the background and its associated error, and predicts the background-subtracted flux
arXiv:2104.02778v1
fatcat:ejao4viqovb5phk33ilzceda4u