Abstract
Abstract In order for the Wide-Field Infrared Survey Telescope (WFIRST) and other stage IV dark energy experiments (e.g., Large Synoptic Survey Telescope, LSST; and Euclid) to infer cosmological parameters not limited by systematic errors, accurate redshift measurements are needed. This accuracy can be met by using spectroscopic subsamples to calibrate the photometric redshifts for the full sample. In this work, we find the minimal number of spectra required for the WFIRST weak-lensing redshift calibration by employing the Self-Organizing Map (SOM) spectroscopic sampling technique. We use galaxies from the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) to build the LSST+WFIRST lensing analog sample of ∼36,000 objects and to train the LSST+WFIRST SOM. We find that 26% of the WFIRST lensing sample consists of sources fainter than the Euclid depth in the optical, 91% of which live in color cells already occupied by brighter galaxies. We demonstrate the similarity between faint and bright galaxies as well as the feasibility of redshift measurements at different brightness levels. Our results suggest that the spectroscopic sample acquired for calibration to the Euclid depth is sufficient for calibrating the majority of the WFIRST color space. For the spectroscopic sample to fully represent the synthetic color space of WFIRST, we recommend obtaining additional spectroscopy of ∼0.2–1.2k new sources in cells occupied by mostly faint galaxies. We argue that either the small area of the CANDELS fields and the small overall sample size or the large photometric errors might be the reason for no/fewer bright galaxies mapped to these cells. Acquiring the spectra of these sources will confirm the above findings and will enable the comprehensive calibration of the WFIRST color–redshift relation.
Highlights
Revealing the nature of the dark energy driving cosmic acceleration and testing general relativity on cosmological scales are essential pieces to complete our understanding of modern cosmology and physics
As part of the High Latitude Survey (HLS; Doré et al 2018) science investigation team, we extend the previous analysis of M15 to estimate the additional spectroscopic sample required to meet the Wide-Field Infrared Survey Telescope (WFIRST) cosmological requirements
The photometric redshifts published by the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) team, which we use in this work are based on combining results from multiple teams, each using a different combination of photometric redshift code, library of template spectral energy distributions (SEDs), and priors
Summary
Revealing the nature of the dark energy driving cosmic acceleration and testing general relativity on cosmological scales are essential pieces to complete our understanding of modern cosmology and physics. Spectrum of dark and luminous matter in the universe (e.g., Blandford et al 1991; Blandford & Narayan 1992) Weaklensing cosmology requires both redshift estimates and shape measurements of statistical samples of galaxies (e.g., Hu 1999). A completely data-driven technique of selecting optimal spectroscopic samples to meet the cosmological requirements was introduced by Masters et al (2015, hereafter M15). This technique uses a machine-learning algorithm called the SelfOrganizing Map (SOM; Kohonen 1982) to reduce the multidimensional color space of galaxies defined by a photometric survey to two dimensions ( maps).
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