Augmenting machine learning photometric redshifts with Gaussian mixture models
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Date
2020-09-11
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Publisher
Oxford University Press
Abstract
Wide-area imaging surveys are one of the key ways of advancing our understanding of cosmology, galaxy formation physics,
and the large-scale structure of the Universe in the coming years. These surveys typically require calculating redshifts for
huge numbers (hundreds of millions to billions) of galaxies – almost all of which must be derived from photometry rather
than spectroscopy. In this paper, we investigate how using statistical models to understand the populations that make up the
colour–magnitude distribution of galaxies can be combined with machine learning photometric redshift codes to improve redshift
estimates. In particular, we combine the use of Gaussian mixture models with the high-performing machine-learning photo-z
algorithm GPz and show that modelling and accounting for the different colour–magnitude distributions of training and test data
separately can give improved redshift estimates, reduce the bias on estimates by up to a half, and speed up the run-time of the
algorithm. These methods are illustrated using data from deep optical and near-infrared data in two separate deep fields, where
training and test data of different colour–magnitude distributions are constructed from the galaxies with known spectroscopic
redshifts, derived from several heterogeneous surveys.
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Keywords
techniques: photometric – surveys, galaxies: distances and redshifts.
Citation
Jarvis, M et al. 2020. Augmenting machine learning photometric redshifts with Gaussian mixture models. Monthly Notices of the Royal Astronomical Society. 498(4):5498-5510