Transfer learning from a broad photometric-redshift sample to a spectroscopic sample cuts bias and RMS error for galaxy redshift prediction on the spectroscopic sample, but degrades performance on the broad sample.
EAZY: A Fast, Public Photometric Redshift Code
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Using different sources of ground truths and transfer learning to improve the generalization of photometric redshift estimation
Transfer learning from a broad photometric-redshift sample to a spectroscopic sample cuts bias and RMS error for galaxy redshift prediction on the spectroscopic sample, but degrades performance on the broad sample.