Cosmology

Abstract:

We analyse three public cosmic shear surveys; the Kilo-Degree Survey (KiDS-450), the Dark Energy Survey (DES-SV) and the Canada France Hawaii Telescope Lensing Survey (CFHTLenS). Adopting the COSEBIs statistic to cleanly and completely separate the lensing E-modes from the non-lensing B-modes, we detect B-modes in KiDS-450 and CFHTLenS at the level of about 2.7 $\sigma$. For DES- SV we detect B-modes at the level of 2.8 $\sigma$ in a non-tomographic analysis, increasing to a 5.5 $\sigma$ B-mode detection in a tomographic analysis. In order to understand the origin of these detected B-modes we measure the B-mode signature of a range of different simulated systematics including PSF leakage, random but correlated PSF modelling errors, camera-based additive shear bias and photometric redshift selection bias. We show that any correlation between photometric-noise and the relative orientation of the galaxy to the point-spread-function leads to an ellipticity selection bias in tomographic analyses. This work therefore introduces a new systematic for future lensing surveys to consider. We find that the B-modes in DES-SV appear similar to a superposition of the B-mode signatures from all of the systematics simulated. The KiDS-450 and CFHTLenS B-mode measurements show features that are consistent with a repeating additive shear bias.

Abstract:

Exploiting the full statistical power of future cosmic shear surveys will necessitate improvements to the accuracy with which the gravitational lensing signal is measured. We present a framework for calibrating shear with image simulations that demonstrates the importance of including realistic correlations between galaxy morphology, size and more importantly, photometric redshifts. This realism is essential so that selection and shape measurement biases can be calibrated accurately for a tomographic cosmic shear analysis. We emulate Kilo-Degree Survey (KiDS) observations of the COSMOS field using morphological information from {\it Hubble} Space Telescope imaging, faithfully reproducing the measured galaxy properties from KiDS observations of the same field. We calibrate our shear measurements from lensfit, and find through a range of sensitivity tests that lensfit is robust and unbiased within the allowed 2 per cent tolerance of our study. Our results show that the calibration has to be performed by selecting the tomographic samples in the simulations, consistent with the actual cosmic shear analysis, because the joint distributions of galaxy properties are found to vary with redshift. Ignoring this redshift variation could result in misestimating the shear bias by an amount that exceeds the allowed tolerance. To improve the calibration for future cosmic shear analyses, it will be essential to also correctly account for the measurement of photometric redshifts, which requires simulating multi-band observations.

Abstract:

Using the light-cone from the cosmological hydrodynamical simulation HORIZON-AGN, we produced a photometric catalogue over 0 < z < 4 with apparent magnitudes in COSMOS, Dark Energy Survey, Large Synoptic Survey Telescope (LSST)-like, and Euclid-like filters at depths comparable to these surveys. The virtual photometry accounts for the complex star formation history (SFH) and metal enrichment of HORIZON-AGN galaxies, and consistently includes magnitude errors, dust attenuation, and absorption by intergalactic medium. The COSMOS-like photometry is fitted in the same configuration as the COSMOS2015 catalogue. We then quantify random and systematic errors of photometric redshifts, stellar masses, and star formation rates (SFR). Photometric redshifts and redshift errors capture the same dependencies on magnitude and redshift as found in COSMOS2015, excluding the impact of source extraction. COSMOS-like stellar masses are well recovered with a dispersion typically lower than 0.1 dex. The simple SFHs and metallicities of the templates induce a systematic underestimation of stellar masses at z < 1.5 by at most 0.12 dex. SFR estimates exhibit a dust-induced bimodality combined with a larger scatter (typically between 0.2 and 0.6 dex). We also use our mock catalogue to predict photometric redshifts and stellar masses in future imaging surveys. We stress that adding Euclid near-infrared photometry to the LSST-like baseline improves redshift accuracy especially at the faint end and decreases the outlier fraction by a factor ∼2. It also considerably improves stellar masses, reducing the scatter up to a factor 3. It would therefore be mutually beneficial for LSST and Euclid to work in synergy.