Improving Photometric Redshift Estimates with Training Sample Augmentation
arxiv(2024)
摘要
Large imaging surveys will rely on photometric redshifts (photo-z's), which
are typically estimated through machine learning methods. Currently planned
spectroscopic surveys will not be deep enough to produce a representative
training sample for LSST, so we seek methods to improve the photo-z estimates
that arise from non-representative training samples. Spectroscopic training
samples for photo-z's are biased towards redder, brighter galaxies, which also
tend to be at lower redshift than the typical galaxy observed by LSST, leading
to poor photo-z estimates with outlier fractions nearly 4 times larger than for
a representative training sample. In this paper, we apply the concept of
training sample augmentation, where we augment simulated non-representative
training samples with simulated galaxies possessing otherwise unrepresented
features. When we select simulated galaxies with (g-z) color, i-band magnitude
and redshift outside the range of the original training sample, we are able to
reduce the outlier fraction of the photo-z estimates for simulated LSST data by
nearly 50
compared to a fully representative training sample, augmentation can recover
nearly 70
degradation in NMAD. Training sample augmentation is a simple and effective way
to improve training samples for photo-z's without requiring additional
spectroscopic samples.
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