The many flavours of photometric redshifts

By Mara Salvato, O. Ilbert, Ben Hoyle. Nature Astronomy, 2018

151 citations recorded by the source corpus.

Since more than 70 years ago, the colours of galaxies derived from flux measurements at different wavelengths have been used to estimate their cosmological distances. Such distance measurements, called photometric redshifts, are necessary for many scientific projects, ranging from investigations of the formation and evolution of galaxies and active galactic nuclei to precision cosmology. The primary benefit of photometric redshifts is that distance estimates can be obtained relatively cheaply for all sources detected in photometric images. The drawback is that these cheap estimates have low precision compared with resource-expensive spectroscopic ones. The methodology for estimating redshifts has been through several revolutions in recent decades, triggered by increasingly stringent requirements on the photometric redshift accuracy. Here, we review the various techniques for obtaining photometric redshifts, from template-fitting to machine learning and hybrid schemes. We also describe state-of-the-art results on current extragalactic samples and explain how survey strategy choices affect redshift accuracy. We close with a description of the photometric redshift efforts planned for upcoming wide-field surveys, which will collect data on billions of galaxies, aiming to investigate, among other matters, the stellar mass assembly and the nature of dark energy.A review of the various techniques to obtain photometric redshifts, from template-fitting to machine learning and hybrid schemes, and a description of the latest results on extragalactic samples and how survey strategy choices impact redshift accuracy.

Subfields: Astronomy and Astrophysics, Ecology, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/419c559bbd50c7c2d444ac287b657ed84203ec28

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Citations in the corpus

Euclid preparation (2021, methodology): “Furthermore, in cosmic shear tomography, it is common to build the required tomographic bins using photo-z (see Salvato et al. 2019, for a review), which can be measured for large samples of galaxies with observations in only a few photometric bandpasses.”
Fast likelihood-free cosmology with neural density estimators and active learning (2019, background): “…et al. 2015), intrinsic alignments (Joachimi et al. 2015), shape and photo-z measurement systematics (Massey et al. 2012; Mandelbaum 2018; Salvato et al. 2018), image blending (Mandelbaum 2018), reduced shear corrections (Krause & Hirata 2010), non-trivial non-Gaussian sampling…”