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Register a new userSpectroscopic Needs for Imaging Dark Energy Experiments: Photometric Redshift Training and Calibration
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Large sets of objects with spectroscopic redshift measurements will be needed for imaging dark energy experiments to achieve their full potential, serving two goals:_training_, i.e., the use of objects with known redshift to develop and optimize photometric redshift algorithms; and_calibration_, i.e., the characterization of moments of redshift (or photo-z error) distributions. Better training makes cosmological constraints from a given experiment stronger, while highly-accurate calibration is needed for photo-z systematics not to dominate errors. In this white paper, we investigate the required scope of spectroscopic datasets which can serve both these purposes for ongoing and next-generation dark energy experiments, as well as the time required to obtain such data with instruments available in the next decade. Large time allocations on kilo-object spectrographs will be necessary, ideally augmented by infrared spectroscopy from space. Alternatively, precision calibrations could be obtained by measuring cross-correlation statistics using samples of bright objects from a large baryon acoustic oscillation experiment such as DESI. We also summarize the additional work on photometric redshift methods needed to prepare for ongoing and future dark energy experiments.
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The coming decade will be an exciting period for dark energy research, during which astronomers will address the question of what drives the accelerated cosmic expansion as first revealed by type Ia supernova (SN) distances, and confirmed by later observations. The mystery of dark energy poses a challenge of such magnitude that, as stated by the Dark Energy Task Force (DETF), nothing short of a revolution in our understanding of fundamental physics will be required to achieve a full understanding of the cosmic acceleration. The lack of multiple complementary precision observations is a major obstacle in developing lines of attack for dark energy theory. This lack is precisely what next-generation surveys will address via the powerful techniques of weak lensing (WL) and baryon acoustic oscillations (BAO) -- galaxy correlations more generally -- in addition to SNe, cluster counts, and other probes of geometry and growth of structure. Because of their unprecedented statistical power, these surveys demand an accurate understanding of the observables and tight control of systematics. This white paper highlights the opportunities, approaches, prospects, and challenges relevant to dark energy studies with wide-deep multiwavelength photometric redshift surveys. Quantitative predictions are presented for a 20000 sq. deg. ground-based 6-band (ugrizy) survey with 5-sigma depth of r~27.5, i.e., a Stage 4 survey as defined by the DETF.
The combination of multiple cosmological probes can produce measurements of cosmological parameters much more stringent than those possible with any individual probe. We examine the combination of two highly correlated probes of late-time structure growth: (i) weak gravitational lensing from a survey with photometric redshifts and (ii) galaxy clustering and redshift space distortions from a survey with spectroscopic redshifts. We choose generic survey designs so that our results are applicable to a range of current and future photometric redshift (e.g. KiDS, DES, HSC, Euclid) and spectroscopic redshift (e.g. DESI, 4MOST, Sumire) surveys. Combining the surveys greatly improves their power to measure both dark energy and modified gravity. An independent, non-overlapping combination sees a dark energy figure of merit more than 4 times larger than that produced by either survey alone. The powerful synergies between the surveys are strongest for modified gravity, where their constraints are orthogonal, producing a non-overlapping joint figure of merit nearly 2 orders of magnitude larger than either alone. Our projected angular power spectrum formalism makes it easy to model the cross-correlation observable when the surveys overlap on the sky, producing a joint data vector and full covariance matrix. We calculate a same-sky improvement factor, from the inclusion of these cross-correlations, relative to non-overlapping surveys. We find nearly a factor of 4 for dark energy and more than a factor of 2 for modified gravity. The exact forecast figures of merit and same-sky benefits can be radically affected by a range of forecasts assumption, which we explore methodically in a sensitivity analysis. We show that that our fiducial assumptions produce robust results which give a good average picture of the science return from combining photometric and spectroscopic surveys.
Accurate photometric redshift calibration is central to the robustness of all cosmology constraints from cosmic shear surveys. Analyses of the KiDS re-weighted training samples from all overlapping spectroscopic surveys to provide a direct redshift calibration. Using self-organising maps (SOMs) we demonstrate that this spectroscopic compilation is sufficiently complete for KiDS, representing $99%$ of the effective 2D cosmic shear sample. We use the SOM to define a $100%$ represented `gold cosmic shear sample, per tomographic bin. Using mock simulations of KiDS and the spectroscopic training set, we estimate the uncertainty on the SOM redshift calibration, and find that photometric noise, sample variance, and spectroscopic selection effects (including redshift and magnitude incompleteness) induce a combined maximal scatter on the bias of the redshift distribution reconstruction ($Delta langle z rangle=langle z rangle_{rm est}-langle z rangle_{rm true}$) of $sigma_{Delta langle z rangle} leq 0.006$ in all tomographic bins. We show that the SOM calibration is unbiased in the cases of noiseless photometry and perfectly representative spectroscopic datasets, as expected from theory. The inclusion of both photometric noise and spectroscopic selection effects in our mock data introduces a maximal bias of $Delta langle z rangle =0.013pm0.006$, or $Delta langle z rangle leq 0.025$ at $97.5%$ confidence, once quality flags have been applied to the SOM. The method presented here represents a significant improvement over the previously adopted direct redshift calibration implementation for KiDS, owing to its diagnostic and quality assurance capabilities. The implementation of this method in future cosmic shear studies will allow better diagnosis, examination, and mitigation of systematic biases in photometric redshift calibration.
This paper describes a new catalog that supplements the existing DEEP2 Galaxy Redshift Survey photometric and spectroscopic catalogs with ugriz photometry from two other surveys; the Canada-France-Hawaii Legacy Survey (CFHTLS) and the Sloan Digital Sky Survey (SDSS). Each catalog is cross-matched by position on the sky in order to assign ugriz photometry to objects in the DEEP2 catalogs. We have recalibrated the CFHTLS photometry where it overlaps DEEP2 in order to provide a more uniform dataset. We have also used this improved photometry to predict DEEP2 BRI photometry in regions where only poorer measurements were available previously. In addition, we have included improved astrometry tied to SDSS rather than USNO-A2.0 for all DEEP2 objects. In total this catalog contains ~27,000 objects with full ugriz photometry as well as robust spectroscopic redshift measurements, 64% of which have r > 23. By combining the secure and accurate redshifts of the DEEP2 Galaxy Redshift Survey with ugriz photometry, we have created a catalog that can be used as an excellent testbed for future photo-z studies, including tests of algorithms for surveys such as LSST and DES.
Calibrating the photometric redshifts of >10^9 galaxies for upcoming weak lensing cosmology experiments is a major challenge for the astrophysics community. The path to obtaining the required spectroscopic redshifts for training and calibration is daunting, given the anticipated depths of the surveys and the difficulty in obtaining secure redshifts for some faint galaxy populations. Here we present an analysis of the problem based on the self-organizing map, a method of mapping the distribution of data in a high-dimensional space and projecting it onto a lower-dimensional representation. We apply this method to existing photometric data from the COSMOS survey selected to approximate the anticipated Euclid weak lensing sample, enabling us to robustly map the empirical distribution of galaxies in the multidimensional color space defined by the expected Euclid filters. Mapping this multicolor distribution lets us determine where - in galaxy color space - redshifts from current spectroscopic surveys exist and where they are systematically missing. Crucially, the method lets us determine whether a spectroscopic training sample is representative of the full photometric space occupied by the galaxies in a survey. We explore optimal sampling techniques and estimate the additional spectroscopy needed to map out the color-redshift relation, finding that sampling the galaxy distribution in color space in a systematic way can efficiently meet the calibration requirements. While the analysis presented here focuses on the Euclid survey, similar analysis can be applied to other surveys facing the same calibration challenge, such as DES, LSST, and WFIRST.
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