The Impact of Photometric Redshift Errors on Lensing Statistics in Ray-Tracing Simulations
arXiv:1810.12312 · doi:10.1093/mnras/stz1016
Abstract
Weak lensing surveys are reaching sensitivities at which uncertainties in the galaxy redshift distributions n(z) from photo-z errors degrade cosmological constraints. We use ray-tracing simulations and a simple treatment of photo-z errors to assess cosmological parameter biases from uncertainties in n(z) in an LSST-like survey. We use the power spectrum and the abundance of lensing peaks to infer cosmological parameters, and find that the former is somewhat more resilient to photo-z errors. We place conservative lower limits on the survey size at which different types of photo-z errors degrade LCDM (wCDM) parameter constraints by 50%. A residual constant photo-z bias of |dz| < 0.003(1+z), satisfying the current LSST requirement, does not significantly degrade constraints for surveys smaller than ~1300 (~490) square degrees using lensing peaks and ~6500 (~4900) square degrees using the power spectrum. Adopting a recent prediction for LSST's full photo-z probability distribution function (PDF), we find that simply approximating n(z) with the photo-z galaxy distribution directly computed from this PDF would degrade surveys as small as ~60 (~65) square degrees using lensing peaks or the power spectrum. Assuming that the centroid bias in each tomographic redshift bin can be removed from the photo-z galaxy distribution, using lensing peaks or the power spectrum still degrades surveys larger than ~200 (~255) or ~248 (~315) square degrees. These results imply that the expected broad photo-z PDF significantly biases parameters, which needs to be further mitigated using more sophisticated photo-z treatments.
submitted to MNRAS
References in corpus (18)
- The NumPy array: a structure for efficient numerical computation
- Wide-Field InfrarRed Survey Telescope-Astrophysics Focused Telescope Assets WFIRST-AFTA 2015 Report
- Why your model parameter confidences might be too optimistic -- unbiased estimation of the inverse covariance matrix
- Dark energy constraints from cosmic shear power spectra: impact of intrinsic alignments on photometric redshift requirements
- CFHTLenS: Cosmological constraints from a combination of cosmic shear two-point and three-point correlations
- KiDS-450: Cosmological Constraints from Weak Lensing Peak Statistics - II: Inference from Shear Peaks using N-body Simulations
- Non-Gaussian information from weak lensing data via deep learning
- KiDS-450: Cosmological Constraints from Weak Lensing Peak Statistics-I: Inference from Analytical Prediction of High Signal-to-Noise Ratio Convergence Peaks
- Emulating the CFHTLenS Weak Lensing data: Cosmological Constraints from moments and Minkowski functionals
- Cosmological Constraints From Weak Lensing Peak Statistics With CFHT Stripe 82 Survey
- Scientific Synergy Between LSST and Euclid
- A new model to predict weak-lensing peak counts I. Comparison with -body Simulations
- Impact of Baryonic Processes on Weak Lensing Cosmology: Power Spectrum, Non-Local Statistics, and Parameter Bias
- Size of spectroscopic calibration samples for cosmic shear photometric redshifts
- Do dark matter halos explain lensing peaks?
- Cosmological model discrimination with Deep Learning
- Geometry and growth contributions to cosmic shear observables
- The impact of spurious shear on cosmological parameter estimates from weak lensing observables
Cited by in corpus (5)
- Cosmological constraints from HSC survey first-year data using deep learning
- Cosmological Studies from HSC-SSP Tomographic Weak Lensing Peak Abundances
- pop-cosmos: Scaleable inference of galaxy properties and redshifts with a data-driven population model
- Cosmological studies from tomographic weak lensing peak abundances and impacts of photo-z errors
- CLAP. I. Resolving miscalibration for deep learning-based galaxy photometric redshift estimation