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Register a new userThe impact of spectroscopic incompleteness in direct calibration of redshift distributions for weak lensing surveys
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Obtaining accurate distributions of galaxy redshifts is a critical aspect of weak lensing cosmology experiments. One of the methods used to estimate and validate redshift distributions is apply weights to a spectroscopic sample so that their weighted photometry distribution matches the target sample. In this work we estimate the textit{selection bias} in redshift that is introduced in this procedure. We do so by simulating the process of assembling a spectroscopic sample (including observer-assigned confidence flags) and highlight the impacts of spectroscopic target selection and redshift failures. We use the first year (Y1) weak lensing analysis in DES as an example data set but the implications generalise to all similar weak lensing surveys. We find that using colour cuts that are not available to the weak lensing galaxies can introduce biases of $Delta~zsim0.015$ in the weighted mean redshift of different redshift intervals. To assess the impact of incompleteness in spectroscopic samples, we select only objects with high observer-defined confidence flags and compare the weighted mean redshift with the true mean. We find that the mean redshift of the DES Y1 weak lensing sample is typically biased at the $Delta~z=0.005-0.05$ level after the weighting is applied. The bias we uncover can have either sign, depending on the samples and redshift interval considered. For the highest redshift bin, the bias is larger than the uncertainties in the other DES Y1 redshift calibration methods, justifying the decision of not using this method for the redshift estimations. We discuss several methods to mitigate this bias.
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In order for 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 employ the Self Organizing Map (SOM) spectroscopic sampling technique, to find the minimal number of spectra required for the WFIRST weak lensing calibration. We use galaxies from the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) to build the LSST+WFIRST lensing analog sample of ~36 k objects and 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 ~0.2-1.2 k 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/less 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.
Weak lensing surveys are emerging as an important tool for the construction of mass selected clusters of galaxies. We evaluate both the efficiency and completeness of a weak lensing selection by combining a dense, complete redshift survey, the Smithsonian Hectospec Lensing Survey (SHELS), with a weak lensing map from the Deep Lens Survey (DLS). SHELS includes 11,692 redshifts for galaxies with R < 20.6 in the four square degree DLS field; the survey is a solid basis for identifying massive clusters of galaxies with redshift z < 0.55. The range of sensitivity of the redshift survey is similar to the range for the DLS convergence map. Only four the twelve convergence peaks with signal-to-noise > 3.5 correspond to clusters of galaxies with M > 1.7 x 10^14 solar masses. Four of the eight massive clusters in SHELS are detected in the weak lensing map yielding a completeness of roughly 50%. We examine the seven known extended cluster x-ray sources in the DLS field: three can be detected in the weak lensing map, three should not be detected without boosting from superposed large-scale structure, and one is mysteriously undetected even though its optical properties suggest that it should produce a detectable lensing signal. Taken together, these results underscore the need for more extensive comparisons among different methods of massive cluster identification.
We use a dense redshift survey in the foreground of the Subaru GTO2deg^2 weak lensing field (centered at $alpha_{2000}$ = 16$^h04^m44^s$;$delta_{2000}$ =43^circ11^{prime}24^{primeprime}$) to assess the completeness and comment on the purity of massive halo identification in the weak lensing map. The redshift survey (published here) includes 4541 galaxies; 4405 are new redshifts measured with the Hectospec on the MMT. Among the weak lensing peaks with a signal-to-noise greater that 4.25, 2/3 correspond to individual massive systems; this result is essentially identical to the Geller et al. (2010) test of the Deep Lens Survey field F2. The Subaru map, based on images in substantially better seeing than the DLS, enables detection of less massive halos at fixed redshift as expected. We demonstrate that the procedure adopted by Miyazaki et al. (2007) for removing some contaminated peaks from the weak lensing map improves agreement between the lensing map and the redshift survey in the identification of candidate massive systems.
The weak lensing power spectrum carries cosmological information via its dependence on the growth of structure and on geometric factors. Since much of the cosmological information comes from scales affected by nonlinear clustering, measurements of the lensing power spectrum can be degraded by non-Gaussian covariances. Recently there have been conflicting studies about the level of this degradation. We use the halo model to estimate it and include new contributions related to the finite size of lensing surveys, following Rimes and Hamiltons study of 3D simulations. We find that non-Gaussian correlations between different multipoles can degrade the cumulative signal-to-noise for the power spectrum amplitude by up to a factor of 2 (or 5 for a worst-case model that exceeds current N-body simulation predictions). However, using an eight-parameter Fisher analysis we find that the marginalized errors on individual parameters are degraded by less than 10% (or 20% for the worst-case model). The smaller degradation in parameter accuracy is primarily because: individual parameters in a high-dimensional parameter space are degraded much less than the volume of the full Fisher ellipsoid; lensing involves projections along the line of sight, which reduce the non-Gaussian effect; some of the cosmological information comes from geometric factors which are not degraded at all. We contrast our findings with those of Lee & Pen (2008) who suggested a much larger degradation in information content. Finally, our results give a useful guide for exploring survey design by giving the cosmological information returns for varying survey area, depth and the level of some systematic errors.
The robust estimation of the tiny distortions (shears) of galaxy shapes caused by weak gravitational lensing in the presence of much larger shape distortions due to the point-spread function (PSF) has been widely investigated. One major problem is that most galaxy shape measurement methods are subject to bias due to pixel noise in the images (noise bias). Noise bias is usually characterized using uncorrelated noise fields; however, real images typically have low-level noise correlations due to galaxies below the detection threshold, and some types of image processing can induce further noise correlations. We investigate the effective detection significance and its impact on noise bias in the presence of correlated noise for one method of galaxy shape estimation. For a fixed noise variance, the biases in galaxy shape estimates can differ substantially for uncorrelated versus correlated noise. However, use of an estimate of detection significance that accounts for the noise correlations can almost entirely remove these differences, leading to consistent values of noise bias as a function of detection significance for correlated and uncorrelated noise. We confirm the robustness of this finding to properties of the galaxy, the PSF, and the noise field, and quantify the impact of anisotropy in the noise correlations. Our results highlight the importance of understanding the pixel noise model and its impact on detection significances when correcting for noise bias on weak lensing.
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