KiDS-450: The tomographic weak lensing power spectrum and constraints on cosmological parameters

We present measurements of the weak gravitational lensing shear power spectrum based on $450$ sq. deg. of imaging data from the Kilo Degree Survey. We employ a quadratic estimator in two and three redshift bins and extract band powers of redshift auto-correlation and cross-correlation spectra in the multipole range $76 \leq \ell \leq 1310$. The cosmological interpretation of the measured shear power spectra is performed in a Bayesian framework assuming a $\Lambda$CDM model with spatially flat geometry, while accounting for small residual uncertainties in the shear calibration and redshift distributions as well as marginalising over intrinsic alignments, baryon feedback and an excess-noise power model. Moreover, massive neutrinos are included in the modelling. The cosmological main result is expressed in terms of the parameter combination $S_8 \equiv \sigma_8 \sqrt{\Omega_{\rm m}/0.3}$ yielding $S_8 = \ 0.651 \pm 0.058$ (3 z-bins), confirming the recently reported tension in this parameter with constraints from Planck at $3.2\sigma$ (3 z-bins). We cross-check the results of the 3 z-bin analysis with the weaker constraints from the 2 z-bin analysis and find them to be consistent. The high-level data products of this analysis, such as the band power measurements, covariance matrices, redshift distributions, and likelihood evaluation chains are available at http://kids.strw.leidenuniv.nl/

[1]  E. A. Valentijn,et al.  The Astro-WISE datacentric information system , 2012, 1208.0447.

[2]  R. Nichol,et al.  Euclid Definition Study Report , 2011, 1110.3193.

[3]  S. White,et al.  A Universal Density Profile from Hierarchical Clustering , 1996, astro-ph/9611107.

[4]  F. Feroz,et al.  Multimodal nested sampling: an efficient and robust alternative to Markov Chain Monte Carlo methods for astronomical data analyses , 2007, 0704.3704.

[5]  P. Schneider,et al.  KiDS-450: cosmological parameter constraints from tomographic weak gravitational lensing , 2016, 1606.05338.

[6]  H. Hoekstra,et al.  A direct measurement of tomographic lensing power spectra from CFHTLenS , 2015, 1509.04071.

[7]  F. Feroz,et al.  MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics , 2008, 0809.3437.

[8]  L. Knox Cosmic Microwave Background Anisotropy Window Functions Revisited , 1999 .

[9]  M. Takada,et al.  Super-Sample Covariance in Simulations , 2014, 1401.0385.

[10]  Yannick Mellier,et al.  CFHTLenS tomographic weak lensing cosmological parameter constraints: Mitigating the impact of intrinsic galaxy alignments , 2013, 1303.1808.

[11]  N. Gehrels,et al.  Spectroscopic Needs for Imaging Dark Energy Experiments , 2015 .

[12]  J. Lesgourgues,et al.  Non-linear matter power spectrum from Time Renormalisation Group: efficient computation and comparison with one-loop , 2011, 1106.2607.

[13]  Massimo Viola,et al.  Means of confusion: how pixel noise affects shear estimates for weak gravitational lensing , 2012, 1204.5147.

[14]  Ralf Bender,et al.  Astro-WISE: Chaining to the Universe , 2007 .

[15]  How Many Galaxies Fit in a Halo? Constraints on Galaxy Formation Efficiency from Spatial Clustering , 2000, astro-ph/0006319.

[16]  N. R. Napolitano,et al.  The third data release of the Kilo-Degree Survey and associated data products , 2017, 1703.02991.

[17]  Massimo Brescia,et al.  The first and second data releases of the Kilo-Degree Survey , 2015, 1507.00742.

[18]  P. Schneider,et al.  Analysis of two-point statistics of cosmic shear III. Covariances of shear measures made easy , 2007, 0708.0387.

[19]  J. Schaye,et al.  The physics driving the cosmic star formation history , 2009, 0909.5196.

[20]  J. Lesgourgues,et al.  The Cosmic Linear Anisotropy Solving System (CLASS) IV: efficient implementation of non-cold relics , 2011, 1104.2935.

[21]  Shahab Joudaki,et al.  An accurate halo model for fitting non-linear cosmological power spectra and baryonic feedback models , 2015, 1505.07833.

[22]  H. Hoekstra,et al.  KiDS-450: testing extensions to the standard cosmological model , 2016, 1610.04606.

[23]  S. Dye,et al.  The shear power spectrum from the COMBO-17 survey , 2003 .

[24]  C. B. D'Andrea,et al.  The DES Science Verification weak lensing shear catalogues , 2015, Monthly Notices of the Royal Astronomical Society.

[25]  H. Hoekstra,et al.  CFHTLenS: the Canada–France–Hawaii Telescope Lensing Survey , 2012, 1210.0032.

[26]  C. Heymans,et al.  Baryons, neutrinos, feedback and weak gravitational lensing , 2014, 1407.4301.

[27]  University College London,et al.  Constraints on intrinsic alignment contamination of weak lensing surveys using the MegaZ-LRG sample , 2010, 1008.3491.

[28]  Adam D. Myers,et al.  Cosmological implications of baryon acoustic oscillation measurements , 2014, 1411.1074.

[29]  Edwin Valentijn,et al.  Gravitational lensing analysis of the Kilo-Degree Survey , 2015, 1507.00738.

[30]  L. Miller,et al.  CFHTLenS: the Canada–France–Hawaii Telescope Lensing Survey – imaging data and catalogue products , 2012, 1210.0032.

[31]  C. Heymans,et al.  Accurate halo-model matter power spectra with dark energy, massive neutrinos and modified gravitational forces , 2016, 1602.02154.

[32]  M. Newman How to determine accuracy of the output of a matrix inversion program , 1974 .

[33]  Martin Kilbinger,et al.  Cosmology with cosmic shear observations: a review , 2014, Reports on progress in physics. Physical Society.

[34]  Michael S. Warren,et al.  Toward a Halo Mass Function for Precision Cosmology: The Limits of Universality , 2008, 0803.2706.

[35]  Yen-Ting Lin,et al.  Second data release of the Hyper Suprime-Cam Subaru Strategic Program , 2019, Publications of the Astronomical Society of Japan.

[36]  K. Gorski,et al.  HEALPix: A Framework for High-Resolution Discretization and Fast Analysis of Data Distributed on the Sphere , 2004, astro-ph/0409513.

[37]  Bonn,et al.  Analysis of two-point statistics of cosmic shear - I. Estimators and covariances , 2002, astro-ph/0206182.

[38]  Eduardo Serrano,et al.  LSST: From Science Drivers to Reference Design and Anticipated Data Products , 2008, The Astrophysical Journal.

[39]  C. A. Oxborrow,et al.  Planck2015 results , 2015, Astronomy & Astrophysics.

[40]  A. Merloni,et al.  X-ray spectral modelling of the AGN obscuring region in the CDFS: Bayesian model selection and catalogue , 2014, 1402.0004.

[41]  Henk Hoekstra The effect of imperfect models of point spread function anisotropy on cosmic shear measurements , 2004 .

[42]  N. Benı́tez Bayesian Photometric Redshift Estimation , 1998, astro-ph/9811189.

[43]  Edward J. Wollack,et al.  Cosmological parameters from pre-planck cosmic microwave background measurements , 2013 .

[44]  P. Hudelot,et al.  CARS: the CFHTLS-Archive-Research Survey. I. Five-band multi-colour data from 37 sq. deg. CFHTLS-wid , 2008, 0811.2239.

[45]  C. Heymans,et al.  CFHTLenS and RCSLenS: testing photometric redshift distributions using angular cross-correlations with spectroscopic galaxy surveys , 2015, 1512.03626.

[46]  T. Kitching,et al.  RCSLenS: The Red Cluster Sequence Lensing Survey , 2016, 1603.07722.

[47]  Nick Kaiser,et al.  Weak gravitational lensing of distant galaxies , 1992 .

[48]  R. Nichol,et al.  Cosmic shear measurements with Dark Energy Survey science verification data , 2015, 1507.05598.

[49]  T. Kitching,et al.  Flat-Sky Pseudo-Cls Analysis for Weak Gravitational Lensing , 2016, Monthly Notices of the Royal Astronomical Society.

[50]  C. Heymans,et al.  Precision calculations of the cosmic shear power spectrum projection , 2017, 1702.05301.

[51]  P. Schneider,et al.  Cosmic shear tomography and efficient data compression using COSEBIs , 2012, 1201.2669.

[52]  U. Toronto,et al.  Estimating the power spectrum of the cosmic microwave background , 1997, astro-ph/9708203.

[53]  M. Bartelmann,et al.  Weak gravitational lensing , 2016, Scholarpedia.

[54]  Laura Castell'o Gomar,et al.  Gauge-invariant perturbations in hybrid quantum cosmology , 2015, 1503.03907.

[55]  H. Hoekstra,et al.  Calibration of weak-lensing shear in the Kilo-Degree Survey , 2016, 1606.05337.

[56]  M. Viel,et al.  Massive neutrinos and the non‐linear matter power spectrum , 2011, 1109.4416.

[57]  M. Schirmer,et al.  THELI: CONVENIENT REDUCTION OF OPTICAL, NEAR-INFRARED, AND MID-INFRARED IMAGING DATA , 2013, 1308.4989.

[58]  Sarah Bridle,et al.  Dark energy constraints from cosmic shear power spectra: impact of intrinsic alignments on photometric redshift requirements , 2007, 0705.0166.

[59]  Christopher M. Hirata,et al.  Intrinsic alignment-lensing interference as a contaminant of cosmic shear , 2004, astro-ph/0406275.

[60]  Joop Schaye,et al.  Effect of baryonic feedback on two- and three-point shear statistics: prospects for detection and improved modelling , 2012, 1210.7303.

[61]  L. Miller,et al.  PROPERTIES OF WEAK LENSING CLUSTERS DETECTED ON HYPER SUPRIME-CAM's 2.3 deg2 FIELD , 2015 .

[62]  B. Fields,et al.  Big bang nucleosynthesis , 2006 .

[63]  N. Afshordi,et al.  Extended Limber Approximation , 2008, 0809.5112.

[64]  Nathalie Palanque-Delabrouille,et al.  Constraint on neutrino masses from SDSS-III/BOSS Lyα forest and other cosmological probes , 2014, 1410.7244.

[65]  Jeffrey M. Kubo,et al.  THE SDSS CO-ADD: COSMIC SHEAR MEASUREMENT , 2011, 1111.6622.

[66]  H. Hoekstra,et al.  CFHTLenS: Improving the quality of photometric redshifts with precision photometry , 2011, 1111.4434.

[67]  Wayne Hu,et al.  Baryonic Features in the Matter Transfer Function , 1997, astro-ph/9709112.

[68]  Alan D. Martin,et al.  Review of Particle Physics , 2014 .

[69]  M. P. Hobson,et al.  Importance Nested Sampling and the MultiNest Algorithm , 2013, The Open Journal of Astrophysics.

[70]  P. Schneider,et al.  COSEBIs: Extracting the full E-/B-mode information from cosmic shear correlation functions , 2010, 1002.2136.

[71]  U. Seljak Weak Lensing Reconstruction and Power Spectrum Estimation: Minimum Variance Methods , 1997, astro-ph/9711124.

[72]  C. B. D'Andrea,et al.  Cosmology from cosmic shear with Dark Energy Survey science verification data , 2015, 1507.05552.

[73]  K. Benabed,et al.  Conservative constraints on early cosmology with MONTE PYTHON , 2013 .

[74]  David Spergel,et al.  Shear power spectrum reconstruction using the pseudo-spectrum method , 2010, 1004.3542.

[75]  Huan Lin,et al.  Estimating the redshift distribution of photometric galaxy samples , 2008 .

[76]  Edward J. Wollack,et al.  NINE-YEAR WILKINSON MICROWAVE ANISOTROPY PROBE (WMAP) OBSERVATIONS: COSMOLOGICAL PARAMETER RESULTS , 2012, 1212.5226.

[77]  S. Bridle,et al.  Cosmic Discordance: Are Planck CMB and CFHTLenS weak lensing measurements out of tune? , 2014, 1408.4742.

[78]  H. Hoekstra,et al.  The Shear Testing Programme – I. Weak lensing analysis of simulated ground-based observations , 2005, astro-ph/0506112.

[79]  S. Kay,et al.  Dark matter halo concentrations in the Wilkinson Microwave Anisotropy Probe year 5 cosmology , 2008, 0804.2486.

[80]  Martin White,et al.  Power Spectra Estimation for Weak Lensing , 2000 .

[81]  Michael S. Warren,et al.  THE LARGE-SCALE BIAS OF DARK MATTER HALOS: NUMERICAL CALIBRATION AND MODEL TESTS , 2010, 1001.3162.

[82]  M. Takada,et al.  Power spectrum super-sample covariance , 2013, 1302.6994.

[83]  J. Lesgourgues,et al.  The Cosmic Linear Anisotropy Solving System (CLASS). Part II: Approximation schemes , 2011, 1104.2933.

[84]  H. Hoekstra,et al.  Quantifying the effect of baryon physics on weak lensing tomography , 2011, 1105.1075.

[85]  E. Deul,et al.  GaBoDS: The Garching-Bonn Deep Survey; IV. Methods for the Image reduction of multi-chip Cameras , 2005 .

[86]  H. Rix,et al.  Cosmological weak lensing with the HST GEMS survey , 2004, astro-ph/0411324.

[87]  Yannick Mellier,et al.  CFHTLenS tomographic weak lensing: quantifying accurate redshift distributions , 2012, 1212.3327.

[88]  Brad E. Tucker,et al.  A 2.4% DETERMINATION OF THE LOCAL VALUE OF THE HUBBLE CONSTANT , 2016, 1604.01424.

[89]  H. Hoekstra,et al.  3D cosmic shear: cosmology from CFHTLenS , 2014, 1401.6842.

[90]  Edwin A. Valentijn,et al.  The Kilo-Degree Survey , 2012, Experimental Astronomy.

[91]  Adam Amara,et al.  Noise bias in weak lensing shape measurements , 2012, 1203.5050.

[92]  C. Heymans,et al.  Revisiting CFHTLenS cosmic shear: Optimal E/B mode decomposition using COSEBIs and compressed COSEBIs , 2016, 1601.00115.

[93]  Joop Schaye,et al.  The effects of galaxy formation on the matter power spectrum: a challenge for precision cosmology , 2011, 1104.1174.

[94]  R. Hložek,et al.  Planck data reconsidered , 2013, 1312.3313.

[95]  Shahab Joudaki,et al.  CFHTLenS revisited: assessing concordance with Planck including astrophysical systematics , 2016, 1601.05786.

[96]  Robert A. Shaw,et al.  Astronomical data analysis software and systems IV : meeting held at Baltimore, Maryland, 25-28 September 1994 , 1995 .

[97]  P. Schneider,et al.  Systematic tests for position-dependent additive shear bias , 2016, 1605.01056.

[98]  H. Hoekstra,et al.  Bayesian galaxy shape measurement for weak lensing surveys – III. Application to the Canada–France–Hawaii Telescope Lensing Survey , 2012, 1210.8201.

[99]  Takahiro Nishimichi,et al.  REVISING THE HALOFIT MODEL FOR THE NONLINEAR MATTER POWER SPECTRUM , 2012, 1208.2701.