Weak Lensing Tomography Research Articles

Unleashing cosmic shear information with the tomographic weak lensing PDF

In this work, we demonstrate the constraining power of the tomographic weak lensing convergence PDF for StageIV-like source galaxy redshift bins and shape noise. We focus on scales of 10 to 20 arcmin in the mildly nonlinear regime, where the convergence PDF and its changes with cosmological parameters can be predicted theoretically. We model the impact of reconstructing the convergence from the shear field using the well-known Kaiser-Squires formalism. We cross-validate the predicted and the measured convergence PDF derived from convergence maps reconstructed using simulated shear catalogues. Employing a Fisher forecast, we determine the constraining power for (Ωm,S8,w0). We find that adding a 5-bin tomography improves the κ−PDF constraints by a factor of {3.8,1.3,1.6} for (Ωm,S8,w0) respectively. Additionally, we perform a joint analysis with the shear two-point correlation functions, finding an enhancement of around a factor of 1.5 on all parameters with respect to the two-point statistics alone. These improved constraints come from disentangling from w0 by extracting non-Gaussian information, in particular, including the PDF skewness at different redshift bins. We also study the effect of varying the number of parameters to forecast, in particular we add h, finding that the convergence PDF maintains its constraining power while the precision from two-point correlations degrades by a factor of {1.7,1.4,1.8} for , respectively.

Almanac: Weak Lensing power spectra and map inference on the masked sphere

We present a field-based signal extraction of weak lensing from noisy observations on the curved and masked sky. We test the analysis on a simulated Euclid-like survey, using a Euclid-like mask and noise level. To make optimal use of the information available in such a galaxy survey, we present a Bayesian method for inferring the angular power spectra of the weak lensing fields, together with an inference of the noise-cleaned tomographic weak lensing shear and convergence (projected mass) maps. The latter can be used for field-level inference with the aim of extracting cosmological parameter information including non-gaussianity of cosmic fields. We jointly infer all-sky $E$-mode and $B$-mode tomographic auto- and cross-power spectra from the masked sky, and potentially parity-violating $EB$-mode power spectra, up to a maximum multipole of $\ell_{\rm max}=2048$. We use Hamiltonian Monte Carlo sampling, inferring simultaneously the power spectra and denoised maps with a total of $\sim 16.8$ million free parameters. The main output and natural outcome is the set of samples of the posterior, which does not suffer from leakage of power from $E$ to $B$ unless reduced to point estimates. However, such point estimates of the power spectra, the mean and most likely maps, and their variances and covariances, can be computed if desired.

KiDS and Euclid: Cosmological implications of a pseudo angular power spectrum analysis of KiDS-1000 cosmic shear tomography

We present a tomographic weak lensing analysis of the Kilo Degree Survey Data Release 4 (KiDS-1000), using a new pseudo angular power spectrum estimator (pseudo-Cℓ) under development for the ESA Euclid mission. Over 21 million galaxies with shape information are divided into five tomographic redshift bins, ranging from 0.1 to 1.2 in photometric redshift. We measured pseudo-Cℓ using eight bands in the multipole range 76 < ℓ < 1500 for auto- and cross-power spectra between the tomographic bins. A series of tests were carried out to check for systematic contamination from a variety of observational sources including stellar number density, variations in survey depth, and point spread function properties. While some marginal correlations with these systematic tracers were observed, there is no evidence of bias in the cosmological inference. B-mode power spectra are consistent with zero signal, with no significant residual contamination from E/B-mode leakage. We performed a Bayesian analysis of the pseudo-Cℓ estimates by forward modelling the effects of the mask. Assuming a spatially flat ΛCDM cosmology, we constrained the structure growth parameter S8 = σ8(Ωm/0.3)1/2 = 0.754−0.029+0.027. When combining cosmic shear from KiDS-1000 with baryon acoustic oscillation and redshift space distortion data from recent Sloan Digital Sky Survey (SDSS) measurements of luminous red galaxies, as well as the Lyman-α forest and its cross-correlation with quasars, we tightened these constraints to S8 = 0.771−0.032+0.006. These results are in very good agreement with previous KiDS-1000 and SDSS analyses and confirm a ∼3σ tension with early-Universe constraints from cosmic microwave background experiments.

General relativistic effects in weak lensing angular power spectra

Advances in upcoming weak lensing surveys pose new challenges for an accurate modeling of the lensing observables. The wide sky coverage of Euclid makes angular scales down to $l_\mathrm{min}=10$ accessible. At such large angular scales, general relativistic effects manifest themselves, and the lensing magnification cannot be correctly described by the standard lensing convergence only. The impact of line-of-sight velocities on the magnification angular power spectrum, referred to as the Doppler magnification, is already well recognized in literature. In particular, it was suggested that the Doppler magnification could be extracted by measurements of both cosmic shear and magnification. In this work, we point out two previously neglected aspects with respect to this method. Firstly, the impact of the Doppler magnification is reduced through non-vanishing cross terms with the standard lensing convergence. This is particularly relevant when the sources are averaged over a bin of width $\Delta z\approx 0.1$, such as in Euclid's tomographic weak lensing survey. Secondly, general relativistic potential terms slightly enhance the signal. We present numerical calculations of all relativistic effects in the weak lensing angular power spectra on large scales.

Optimizing tomography for weak gravitational lensing surveys

ABSTRACT The subject of this paper is optimization of weak lensing tomography: we carry out numerical minimization of a measure of total statistical error as a function of the redshifts of the tomographic bin edges by means of a Nelder–Mead algorithm in order to optimize the sensitivity of weak lensing with respect to different optimization targets. Working under the assumption of a Gaussian likelihood for the parameters of a w0wa CDM (cold dark matter) model and using euclid’s conservative survey specifications, we compare an equipopulated, equidistant, and optimized bin setting and find that in general the equipopulated setting is very close to the optimal one, while an equidistant setting is far from optimal and also suffers from the ad hoc choice of a maximum redshift. More importantly, we find that nearly saturated information content can be gained using already few tomographic bins. This is crucial for photometric redshift surveys with large redshift errors. We consider a large range of targets for the optimization process that can be computed from the parameter covariance (or equivalently, from the Fisher matrix), extend these studies to information entropy measures such as the Kullback–Leibler divergence and conclude that in many cases equipopulated binning yields results close to the optimum, which we support by analytical arguments.

Parameter inference for weak lensing using Gaussian Processes and MOPED

ABSTRACT In this paper, we propose a Gaussian Process (GP) emulator for the calculation both of tomographic weak lensing band powers, and of coefficients of summary data massively compressed with the MOPED algorithm. In the former case cosmological parameter inference is accelerated by a factor of ∼10–30 compared with Boltzmann solver class applied to KiDS-450 weak lensing data. Much larger gains of order 103 will come with future data, and MOPED with GPs will be fast enough to permit the Limber approximation to be dropped, with acceleration in this case of ∼105. A potential advantage of GPs is that an error on the emulated function can be computed and this uncertainty incorporated into the likelihood. However, it is known that the GP error can be unreliable when applied to deterministic functions, and we find, using the Kullback–Leibler divergence between the emulator and class likelihoods, and from the uncertainties on the parameters, that agreement is better when the GP uncertainty is not used. In future, weak lensing surveys such as Euclid, and the Legacy Survey of Space and Time, will have up to ∼104 summary statistics, and inference will be correspondingly more challenging. However, since the speed of MOPED is determined not the number of summary data, but by the number of parameters, MOPED analysis scales almost perfectly, provided that a fast way to compute the theoretical MOPED coefficients is available. The GP provides such a fast mechanism.

KiDS+VIKING-450 and DES-Y1 combined: Cosmology with cosmic shear

We present a combined tomographic weak gravitational lensing analysis of the Kilo Degree Survey (KV450) and the Dark Energy Survey (DES-Y1). We homogenize the analysis of these two public cosmic shear datasets by adopting consistent priors and modeling of nonlinear scales, and determine new redshift distributions for DES-Y1 based on deep public spectroscopic surveys. Adopting these revised redshifts results in a 0.8σreduction in the DES-inferred value forS​8, which decreases to a 0.5σreduction when including a systematic redshift calibration error model from mock DES data based on the MICE2 simulation. The combined KV450+DES-Y1 constraint onS8= 0.762−0.024+0.025is in tension with thePlanck2018 constraint from the cosmic microwave background at the level of 2.5σ. This result highlights the importance of developing methods to provide accurate redshift calibration for current and future weak-lensing surveys.

Cosmological Studies from Tomographic Weak Lensing Peak Abundances and Impacts of Photo-z Errors

Abstract Weak lensing peak abundance analyses have been applied in different surveys and demonstrated to be a powerful statistic in extracting cosmological information complementary to cosmic shear two-point correlation studies. Future large surveys with high number densities of galaxies will enable tomographic peak analyses. Focusing on high peaks, we investigate quantitatively how the tomographic redshift binning can enhance the cosmological gains. We also perform detailed studies about the degradation of cosmological information due to photometric redshift (photo-z) errors. We show that for surveys with a number density of galaxies of ∼40 arcmin−2, a median redshift of ∼1, and a survey area of ∼15,000 deg2, the four-bin tomographic peak analyses can reduce the error contours of (Ωm, σ 8) by a factor of 5 compared to 2D peak analyses in the ideal case of the photo-z error being absent. More redshift bins can hardly lead to significantly better constraints. The photo-z error model here is parameterized by z bias and σ ph and the fiducial values of z bias = 0.003 and σ ph = 0.02 are taken. We find that using tomographic peak analyses can constrain the photo-z errors simultaneously with cosmological parameters. For four-bin analyses, we can obtain σ(z bias)/z bias ∼ 10% and σ(σ ph)/σ ph ∼ 5% without assuming priors on them. Accordingly, the cosmological constraints on Ωm and σ 8 degrade by factors of ∼2.2 and ∼1.8, respectively, with respect to zero uncertainties on photo-z parameters. We find that the uncertainty of z bias plays a more significant role in degrading the cosmological constraints than that of σ ph.

Tomographic weak lensing bispectrum: a thorough analysis towards the next generation of galaxy surveys

ABSTRACT We address key points for an efficient implementation of likelihood codes for modern weak lensing large-scale structure surveys. Specifically, we focus on the joint weak lensing convergence power spectrum–bispectrum probe and we tackle the numerical challenges required by a realistic analysis. Under the assumption of (multivariate) Gaussian likelihoods, we have developed a high performance code that allows highly parallelized prediction of the binned tomographic observables and of their joint non-Gaussian covariance matrix accounting for terms up to the six-point correlation function and supersample effects. This performance allows us to qualitatively address several interesting scientific questions. We find that the bispectrum provides an improvement in terms of signal-to-noise ratio (S/N) of about 10 per cent on top of the power spectrum, making it a non-negligible source of information for future surveys. Furthermore, we are capable to test the impact of theoretical uncertainties in the halo model used to build our observables; with presently allowed variations we conclude that the impact is negligible on the S/N. Finally, we consider data compression possibilities to optimize future analyses of the weak lensing bispectrum. We find that, ignoring systematics, five equipopulated redshift bins are enough to recover the information content of a Euclid-like survey, with negligible improvement when increasing to 10 bins. We also explore principal component analysis and dependence on the triangle shapes as ways to reduce the numerical complexity of the problem.

Cosmological constraints with deep learning from KiDS-450 weak lensing maps

Convolutional Neural Networks (CNN) have recently been demonstrated on synthetic data to improve upon the precision of cosmological inference. In particular they have the potential to yield more precise cosmological constraints from weak lensing mass maps than the two-point functions. We present the cosmological results with a CNN from the KiDS-450 tomographic weak lensing dataset, constraining the total matter density $\Omega_m$, the fluctuation amplitude $\sigma_8$, and the intrinsic alignment amplitude $A_{\rm{IA}}$. We use a grid of N-body simulations to generate a training set of tomographic weak lensing maps. We test the robustness of the expected constraints to various effects, such as baryonic feedback, simulation accuracy, different value of $H_0$, or the lightcone projection technique. We train a set of ResNet-based CNNs with varying depths to analyze sets of tomographic KiDS mass maps divided into 20 flat regions, with applied Gaussian smoothing of $\sigma=2.34$ arcmin. The uncertainties on shear calibration and $n(z)$ error are marginalized in the likelihood pipeline. Following a blinding scheme, we derive constraints of $S_8 = \sigma_8 (\Omega_m/0.3)^{0.5} = 0.777^{+0.038}_{-0.036}$ with our CNN analysis, with $A_{\rm{IA}}=1.398^{+0.779}_{-0.724}$. We compare this result to the power spectrum analysis on the same maps and likelihood pipeline and find an improvement of about $30\%$ for the CNN. We discuss how our results offer excellent prospects for the use of deep learning in future cosmological data analysis.

Dark Energy Survey Year 1 results: constraints on intrinsic alignments and their colour dependence from galaxy clustering and weak lensing

Abstract We perform a joint analysis of intrinsic alignments and cosmology using tomographic weak lensing, galaxy clustering, and galaxy–galaxy lensing measurements from Year 1 (Y1) of the Dark Energy Survey. We define early- and late-type subsamples, which are found to pass a series of systematics tests, including for spurious photometric redshift error and point spread function correlations. We analyse these split data alongside the fiducial mixed Y1 sample using a range of intrinsic alignment models. In a fiducial non-linear alignment model analysis, assuming a flat Λ cold dark matter cosmology, we find a significant difference in intrinsic alignment amplitude, with early-type galaxies favouring $A_\mathrm{IA} = 2.38^{+0.32}_{-0.31}$ and late-type galaxies consistent with no intrinsic alignments at $0.05^{+0.10}_{-0.09}$. The analysis is repeated using a number of extended model spaces, including a physically motivated model that includes both tidal torquing and tidal alignment mechanisms. In multiprobe likelihood chains in which cosmology, intrinsic alignments in both galaxy samples and all other relevant systematics are varied simultaneously, we find the tidal alignment and tidal torquing parts of the intrinsic alignment signal have amplitudes $A_1 = 2.66 ^{+0.67}_{-0.66}$, $A_2=-2.94^{+1.94}_{-1.83}$, respectively, for early-type galaxies and $A_1 = 0.62 ^{+0.41}_{-0.41}$, $A_2 = -2.26^{+1.30}_{-1.16}$ for late-type galaxies. In the full (mixed) Y1 sample the best constraints are $A_1 = 0.70 ^{+0.41}_{-0.38}$, $A_2 = -1.36 ^{+1.08}_{-1.41}$. For all galaxy splits and IA models considered, we report cosmological parameter constraints consistent with the results of the main DES Y1 cosmic shear and multiprobe cosmology papers.

Constraining neutrino mass with the tomographic weak lensing bispectrum

We explore the effect of massive neutrinos on the weak lensing shear bispectrum using the Cosmological Massive Neutrino Simulations [1]. We find that the primary effect of massive neutrinos is to suppress the amplitude of the bispectrum with limited effect on the bispectrum shape. The suppression of the bispectrum amplitude is a factor of two greater than the suppression of the small scale power-spectrum. For an LSST-like weak lensing survey that observes half of the sky with five tomographic redshift bins, we explore the constraining power of the bispectrum on three cosmological parameters: the sum of the neutrino mass ∑ mν, the matter density Ωm and the amplitude of primordial fluctuations As. Bispectrum measurements alone provide similar constraints to the power spectrum measurements and combining the two probes leads to significant improvements than using the latter alone. We find that the joint constraints tighten the power spectrum 95% constraints by ∼ 32% for ∑ mν, 13% for Ωm and 57% for As.

On the degeneracy between baryon feedback and massive neutrinos as probed by matter clustering and weak lensing

Massive neutrinos, due to their free streaming, produce a suppression in the matter power spectrum at intermediate and small scales which could be probed by galaxy clustering and/or weak lensing observables. This effect happens at scales that are also influenced by baryon feedback, i.e. galactic winds or Active Galactic Nuclei (AGN) feedback, which in realistic hydrodynamic simulations has also been shown to produce a suppression of power. Leaving aside, for the moment, the complex issue of galaxy bias, we focus here on matter clustering and tomographic weak lensing, we investigate the possible degeneracy between baryon feedback and neutrinos showing that it is not likely to degrade significantly the measurement of neutrino mass in future surveys. To do so, we generate mock data sets and fit them using the Markov Chain Monte Carlo (MCMC) technique and explore degeneracies between feedback parameters and neutrino mass. We model baryon feedback through fitting functions, while massive neutrinos are accounted for, also in the non-linear regime, using HALOFIT calibrated against accurate N-body neutrino simulations. In the error budget, we include the uncertainty in the modelling of non-linearities. For both matter clustering and weak lensing, we always recover the input neutrino mass within ∼0.25σ confidence level. Finally, we also take into account the intrinsic alignment effect in the weak lensing mock data. Even in this case, we are able to recover the right parameters: in particular, we find a significant degeneracy pattern between Mν and the intrinsic alignment parameter AIA.

Exploring the distance-redshift relation with gravitational wave standard sirens and tomographic weak lensing

Gravitational waves from inspiraling compact objects provide us with information of the distance scale since we can infer the absolute luminosity of the source from analysis of the wave form, which is known as standard sirens. The first detection of the gravitational wave signal of the binary black hole merger event by Advanced LIGO has opened up the possibility of utilizing standard sirens as cosmological probe. In order to extract information of the distance-redshift relation, we cross-correlate weak lensing, which is an unbiased tracer of matter distribution in the Universe, with the projected number density of gravitational wave sources. For weak lensing, we employ tomography technique to efficiently obtain information of large-scale structures at wide ranges of redshifts. Making use of the cross-correlations along with the auto-correlations, we present forecast of constraints on four cosmological parameters, i.e., Hubble parameter, matter density, the equation of state parameter of dark energy, and the amplitude of matter fluctuation. To fully explore the ability of cross-correlations, which require large overlapping sky coverage, we consider the specific case with the upcoming surveys by \textit{Euclid} for weak lensing and Einstein Telescope for standard sirens. We show that cosmological parameters can be tightly constrained solely by these auto- and cross-correlations of standard sirens and weak lensing. For example, the $1\text{-}\sigma$ error of Hubble parameter is expected to be $\sigma (H_0) = 0.33 \, \mathrm{km} \, \mathrm{s}^{-1} \, \mathrm{Mpc}^{-1}$. Thus, the proposed statistics will be a promising probe into the distance scale.

Angular ellipticity correlations in a composite alignment model for elliptical and spiral galaxies and inference from weak lensing

We investigate a physical, composite alignment model for both spiral and elliptical galaxies and its impact on cosmological parameter estimation from weak lensing for a tomographic survey. Ellipticity correlation functions and angular ellipticity spectra for spiral and elliptical galaxies are derived on the basis of tidal interactions with the cosmic large-scale structure and compared to the tomographic weak lensing signal. We find that elliptical galaxies cause a contribution to the weak-lensing dominated ellipticity correlation on intermediate angular scales between $\ell\simeq40$ and $\ell\simeq400$ before that of spiral galaxies dominates on higher multipoles. The predominant term on intermediate scales is the negative cross-correlation between intrinsic alignments and weak gravitational lensing (GI-alignment). We simulate parameter inference from weak gravitational lensing with intrinsic alignments unaccounted; the bias induced by ignoring intrinsic alignments in a survey like Euclid is shown to be several times larger than the statistical error and can lead to faulty conclusions when comparing to other observations. The biases generally point into different directions in parameter space, such that in some cases one can observe a partial cancellation effect. Furthermore, it is shown that the biases increase with the number of tomographic bins used for the parameter estimation process. We quantify this parameter estimation bias in units of the statistical error and compute the loss of Bayesian evidence for a model due to the presence of systematic errors as well as the Kullback-Leibler divergence to quantify the distance between the true model and the wrongly inferred one.

Bayesian weak lensing tomography: Reconstructing the 3D large-scale distribution of matter with a lognormal prior

We present a Bayesian reconstruction algorithm that infers the three-dimensional large-scale matter distribution from the weak gravitational lensing effects measured in the image shapes of galaxies. The algorithm is designed to also work with non-Gaussian posterior distributions which arise, for example, from a non-Gaussian prior distribution. In this work, we use a lognormal prior and compare the reconstruction results to a Gaussian prior in a suite of increasingly realistic tests on mock data. We find that in cases of high noise levels (i.e. for low source galaxy densities and/or high shape measurement uncertainties), both normal and lognormal priors lead to reconstructions of comparable quality, but with the lognormal reconstruction being prone to mass-sheet degeneracy. In the low-noise regime and on small scales, the lognormal model produces better reconstructions than the normal model: The lognormal model 1) enforces non-negative densities, while negative densities are present when a normal prior is employed, 2) better traces the extremal values and the skewness of the true underlying distribution, and 3) yields a higher pixel-wise correlation between the reconstruction and the true density.

Precision matrix expansion – efficient use of numerical simulations in estimating errors on cosmological parameters

Computing the inverse covariance matrix (or precision matrix) of large data vectors is crucial in weak lensing (and multi-probe) analyses of the large scale structure of the universe. Analytically computed covariances are noise-free and hence straightforward to invert, however the model approximations might be insufficient for the statistical precision of future cosmological data. Estimating covariances from numerical simulations improves on these approximations, but the sample covariance estimator is inherently noisy, which introduces uncertainties in the error bars on cosmological parameters and also additional scatter in their best fit values. For future surveys, reducing both effects to an acceptable level requires an unfeasibly large number of simulations. In this paper we describe a way to expand the true precision matrix around a covariance model and show how to estimate the leading order terms of this expansion from simulations. This is especially powerful if the covariance matrix is the sum of two contributions, $\smash{\mathbf{C} = \mathbf{A}+\mathbf{B}}$, where $\smash{\mathbf{A}}$ is well understood analytically and can be turned off in simulations (e.g. shape-noise for cosmic shear) to yield a direct estimate of $\smash{\mathbf{B}}$. We test our method in mock experiments resembling tomographic weak lensing data vectors from the Dark Energy Survey (DES) and the Large Synoptic Survey Telecope (LSST). For DES we find that $400$ N-body simulations are sufficient to achive negligible statistical uncertainties on parameter constraints. For LSST this is achieved with $2400$ simulations. The standard covariance estimator would require >$10^5$ simulations to reach a similar precision. We extend our analysis to a DES multi-probe case finding a similar performance.

Cosmological searches for a noncold dark matter component

We explore an extended cosmological scenario where the dark matter is an admixture of cold and additional non-cold species. The mass and temperature of the non-cold dark matter particles are extracted from a number of cosmological measurements. Among others, we consider tomographic weak lensing data and Milky Way dwarf satellite galaxy counts. We also study the potential of these scenarios in alleviating the existing tensions between local measurements and Cosmic Microwave Background (CMB) estimates of the $S_8$ parameter, with $S_8=\sigma_8\sqrt{\Omega_m}$, and of the Hubble constant $H_0$. In principle, a sub-dominant, non-cold dark matter particle with a mass $m_X\sim$~keV, could achieve the goals above. However, the preferred ranges for its temperature and its mass are different when extracted from weak lensing observations and from Milky Way dwarf satellite galaxy counts, since these two measurements require suppressions of the matter power spectrum at different scales. Therefore, solving simultaneously the CMB-weak lensing tensions and the small scale crisis in the standard cold dark matter picture via only one non-cold dark matter component seems to be challenging.

Parameter constraints from weak-lensing tomography of galaxy shapes and cosmic microwave background fluctuations

Recently, it has been shown that cross-correlating cosmic microwave background (CMB) lensing and three-dimensional (3D) cosmic shear allows to considerably tighten cosmological parameter constraints. We investigate whether similar improvement can be achieved in a conventional tomographic setup. We present Fisher parameter forecasts for a Euclid-like galaxy survey in combination with different ongoing and forthcoming CMB experiments. In contrast to a fully 3D analysis, we find only marginal improvement. Assuming Planck-like CMB data, we show that including the full covariance of the combined CMB and cosmic shear data improves the dark energy figure of merit (FOM) by only 3 per cent. The marginalized error on the sum of neutrino masses is reduced at the same level. For a next generation CMB satellite mission such as Prism, the predicted improvement of the dark energy FOM amounts to approximately 25 per cent. Furthermore, we show that the small improvement is contrasted by an increased bias in the dark energy parameters when the intrinsic alignment of galaxies is not correctly accounted for in the full covariance matrix.

KiDS-450: testing extensions to the standard cosmological model

We test extensions to the standard cosmological model with weak gravitational lensing tomography using 450 deg$^2$ of imaging data from the Kilo Degree Survey (KiDS). In these extended cosmologies, which include massive neutrinos, nonzero curvature, evolving dark energy, modified gravity, and running of the scalar spectral index, we also examine the discordance between KiDS and cosmic microwave background measurements from Planck. The discordance between the two datasets is largely unaffected by a more conservative treatment of the lensing systematics and the removal of angular scales most sensitive to nonlinear physics. The only extended cosmology that simultaneously alleviates the discordance with Planck and is at least moderately favored by the data includes evolving dark energy with a time-dependent equation of state (in the form of the $w_0-w_a$ parameterization). In this model, the respective $S_8 = \sigma_8 \sqrt{\Omega_{\rm m}/0.3}$ constraints agree at the $1\sigma$ level, and there is `substantial concordance' between the KiDS and Planck datasets when accounting for the full parameter space. Moreover, the Planck constraint on the Hubble constant is wider than in LCDM and in agreement with the Riess et al. (2016) direct measurement of $H_0$. The dark energy model is moderately favored as compared to LCDM when combining the KiDS and Planck measurements, and remains moderately favored after including an informative prior on the Hubble constant. In both of these scenarios, marginalized constraints in the $w_0-w_a$ plane are discrepant with a cosmological constant at the $3\sigma$ level. Moreover, KiDS constrains the sum of neutrino masses to 4.0 eV (95% CL), finds no preference for time or scale dependent modifications to the metric potentials, and is consistent with flatness and no running of the spectral index. The analysis code is public at https://github.com/sjoudaki/kids450