arXiv subjects

B. Joachimi

Publications and source records attributed to B. Joachimi.

At least 145 records · Page 8Linked to original sources

arXiv2023

The PAU Survey: Narrow-band image photometry

PAUCam is an innovative optical narrow-band imager mounted at the William Herschel Telescope built for the Physics of the Accelerating Universe Survey (PAUS). Its set of 40 filters results in images that are complex to calibrate, with specific instrumental signatures that cannot be processed with traditional data reduction techniques. In this paper we present two pipelines developed by the PAUS data management team with the objective of producing science-ready catalogues from the uncalibrated raw images. The Nightly pipeline takes care of all image processing, with bespoke algorithms for photometric calibration and scatter-light correction. The Multi-Epoch and Multi-Band Analysis (MEMBA) pipeline performs forced photometry over a reference catalogue to optimize the photometric redshift performance. We verify against spectroscopic observations that the current approach delivers an inter-band photometric calibration of 0.8% across the 40 narrow-band set. The large volume of data produced every night and the rapid survey strategy feedback constraints require operating both pipelines in the Port d'Informació Cientifica data centre with intense parallelization. While alternative algorithms for further improvements in photo-z performance are under investigation, the image calibration and photometry presented in this work already enable state-of-the-art photometric redshifts down to iAB=23.0.

astro-ph.IM↗

arXiv2023

Euclid: Calibrating photometric redshifts with spectroscopic cross-correlations

Cosmological constraints from key probes of the Euclid imaging survey rely critically on the accurate determination of the true redshift distributions, $n(z)$, of tomographic redshift bins. We determine whether the mean redshift, $ $, of ten Euclid tomographic redshift bins can be calibrated to the Euclid target uncertainties of $σ( )<0.002\,(1+z)$ via cross-correlation, with spectroscopic samples akin to those from the Baryon Oscillation Spectroscopic Survey (BOSS), Dark Energy Spectroscopic Instrument (DESI), and Euclid's NISP spectroscopic survey. We construct mock Euclid and spectroscopic galaxy samples from the Flagship simulation and measure small-scale clustering redshifts up to redshift $z<1.8$ with an algorithm that performs well on current galaxy survey data. The clustering measurements are then fitted to two $n(z)$ models: one is the true $n(z)$ with a free mean; the other a Gaussian Process modified to be restricted to non-negative values. We show that $ $ is measured in each tomographic redshift bin to an accuracy of order 0.01 or better. By measuring the clustering redshifts on subsets of the full Flagship area, we construct scaling relations that allow us to extrapolate the method performance to larger sky areas than are currently available in the mock. For the full expected Euclid, BOSS, and DESI overlap region of approximately 6000 deg$^{2}$, the uncertainties attainable by clustering redshifts exceeds the Euclid requirement by at least a factor of three for both $n(z)$ models considered, although systematic biases limit the accuracy. Clustering redshifts are an extremely effective method for redshift calibration for Euclid if the sources of systematic biases can be determined and removed, or calibrated-out with sufficiently realistic simulations. We outline possible future work, in particular an extension to higher redshifts with quasar reference samples.

astro-ph.CO↗

arXiv2023

The PAU Survey & Euclid: Improving broad-band photometric redshifts with multi-task learning

Current and future imaging surveys require photometric redshifts (photo-zs) to be estimated for millions of galaxies. Improving the photo-z quality is a major challenge but is needed to advance our understanding of cosmology. In this paper we explore how the synergies between narrow-band photometric data and large imaging surveys can be exploited to improve broadband photometric redshifts. We used a multi-task learning (MTL) network to improve broadband photo-z estimates by simultaneously predicting the broadband photo-z and the narrow-band photometry from the broadband photometry. The narrow-band photometry is only required in the training field, which also enables better photo-z predictions for the galaxies without narrow-band photometry in the wide field. This technique was tested with data from the Physics of the Accelerating Universe Survey (PAUS) in the COSMOS field. We find that the method predicts photo-zs that are 13% more precise down to magnitude i_{AB} < 23; the outlier rate is also 40% lower when compared to the baseline network. Furthermore, MTL reduces the photo-z bias for high-redshift galaxies, improving the redshift distributions for tomographic bins with z>1. Applying this technique to deeper samples is crucial for future surveys such as \Euclid or LSST. For simulated data, training on a sample with i_{AB} <23, the method reduces the photo-z scatter by 16% for all galaxies with i_{AB}<25. We also studied the effects of extending the training sample with photometric galaxies using PAUS high-precision photo-zs, which reduces the photo-z scatter by 20% in the COSMOS field.

astro-ph.IM↗

arXiv2022

Consistent lensing and clustering in a low-$S_8$ Universe with BOSS, DES Year 3, HSC Year 1 and KiDS-1000

We evaluate the consistency between lensing and clustering probes of large-scale structure based on measurements of projected galaxy clustering from BOSS combined with overlapping galaxy-galaxy lensing from three surveys: DES Y3, HSC Y1, and KiDS-1000. An intra-lensing-survey study finds good agreement between these lensing data. We model the observations using the Dark Emulator and fit the data at two fixed cosmologies: Planck, with $S_8=0.83$, and a Lensing cosmology with $S_8=0.76$. For a joint analysis limited to scales with $R>5.25h^{-1}$Mpc, we find that both cosmologies provide an acceptable fit to the data. Full utilisation of the small-scale clustering and lensing measurements is hindered by uncertainty in the impact of baryon feedback and assembly bias, which we account for with a reasoned theoretical error budget. We incorporate a systematic scaling parameter for each redshift bin, $A$, that decouples the lensing and clustering to capture any inconsistency. When a wide range of scales ($0.15<R<60h^{-1}$Mpc) are incorporated, we find different results for the consistency of clustering and lensing between the two cosmologies. Limiting the analysis to the bins for which the impact of the selection of the lens sample is expected to be minimal, for the low-$S_8$ Lensing cosmology, the measurements are consistent with $A$=1; $A=0.91\pm0.04$ using DES+KiDS and $A=0.97\pm0.06$ using HSC. For the Planck cosmology case, we find a discrepancy: $A=0.79\pm0.03$ using DES+KiDS and $A=0.84\pm0.05$ using HSC. We demonstrate that a kSZ-based estimate for baryonic effects alleviates some of the discrepancy in the Planck cosmology. This analysis demonstrates the statistical power of these small-scale measurements, but also indicates that caution is still warranted given current uncertainties in modelling baryonic effects, assembly bias, and selection effects in the foreground sample.

astro-ph.CO↗

arXiv2022

KiDS & 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-$C_{\ell}$) 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-$C_{\ell}$ using eight bands in the multipole range $76<\ell<1500$ 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-$C_{\ell}$ estimates by forward modelling the effects of the mask. Assuming a spatially flat $Λ$CDM cosmology, we constrained the structure growth parameter $S_8 = σ_8(Ω_{\rm m}/0.3)^{1/2} = 0.754_{-0.029}^{+0.027}$. 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 $S_8 = 0.771^{+0.006}_{-0.032}$. These results are in very good agreement with previous KiDS-1000 and SDSS analyses and confirm a $\sim 3σ$ tension with early-Universe constraints from cosmic microwave background experiments.

astro-ph.CO↗

arXiv2022

COSMOPOWER: emulating cosmological power spectra for accelerated Bayesian inference from next-generation surveys

We present $\it{CosmoPower}$, a suite of neural cosmological power spectrum emulators providing orders-of-magnitude acceleration for parameter estimation from two-point statistics analyses of Large-Scale Structure (LSS) and Cosmic Microwave Background (CMB) surveys. The emulators replace the computation of matter and CMB power spectra from Boltzmann codes; thus, they do not need to be re-trained for different choices of astrophysical nuisance parameters or redshift distributions. The matter power spectrum emulation error is less than $0.4\%$ in the wavenumber range $k \in [10^{-5}, 10] \, \mathrm{Mpc}^{-1}$, for redshift $z \in [0, 5]$. $\it{CosmoPower}$ emulates CMB temperature, polarisation and lensing potential power spectra in the $5σ$ region of parameter space around the $\it{Planck}$ best fit values with an error $\lesssim 10\%$ of the expected shot noise for the forthcoming Simons Observatory. $\it{CosmoPower}$ is showcased on a joint cosmic shear and galaxy clustering analysis from the Kilo-Degree Survey, as well as on a Stage IV $\it{Euclid}$-like simulated cosmic shear analysis. For the CMB case, $\it{CosmoPower}$ is tested on a $\it{Planck}$ 2018 CMB temperature and polarisation analysis. The emulators always recover the fiducial cosmological constraints with differences in the posteriors smaller than sampling noise, while providing a speed-up factor up to $O(10^4)$ to the complete inference pipeline. This acceleration allows posterior distributions to be recovered in just a few seconds, as we demonstrate in the $\it{Planck}$ likelihood case. $\it{CosmoPower}$ is written entirely in Python, can be interfaced with all commonly used cosmological samplers and is publicly available at https://github.com/alessiospuriomancini/cosmopower .

astro-ph.CO↗

arXiv2021

Lensing Without Borders. I. A Blind Comparison of the Amplitude of Galaxy-Galaxy Lensing Between Independent Imaging Surveys

Lensing Without Borders is a cross-survey collaboration created to assess the consistency of galaxy-galaxy lensing signals ($ΔΣ$) across different data-sets and to carry out end-to-end tests of systematic errors. We perform a blind comparison of the amplitude of $ΔΣ$ using lens samples from BOSS and six independent lensing surveys. We find good agreement between empirically estimated and reported systematic errors which agree to better than 2.3$σ$ in four lens bins and three radial ranges. For lenses with $z_{\rm L}>0.43$ and considering statistical errors, we detect a 3-4$σ$ correlation between lensing amplitude and survey depth. This correlation could arise from the increasing impact at higher redshift of unrecognised galaxy blends on shear calibration and imperfections in photometric redshift calibration. At $z_{\rm L}>0.54$ amplitudes may additionally correlate with foreground stellar density. The amplitude of these trends is within survey-defined systematic error budgets which are designed to include known shear and redshift calibration uncertainty. Using a fully empirical and conservative method, we do not find evidence for large unknown systematics. Systematic errors greater than 15% (25%) ruled out in three lens bins at 68% (95%) confidence at $z<0.54$. Differences with respect to predictions based on clustering are observed to be at the 20-30% level. Our results therefore suggest that lensing systematics alone are unlikely to fully explain the "lensing is low" effect at $z<0.54$. This analysis demonstrates the power of cross-survey comparisons and provides a promising path for identifying and reducing systematics in future lensing analyses.

astro-ph.CO↗

arXiv2021

Accelerating Bayesian microseismic event location with deep learning

We present a series of new open source deep learning algorithms to accelerate Bayesian full waveform point source inversion of microseismic events. Inferring the joint posterior probability distribution of moment tensor components and source location is key for rigorous uncertainty quantification. However, the inference process requires forward modelling of microseismic traces for each set of parameters explored by the sampling algorithm, which makes the inference very computationally intensive. In this paper we focus on accelerating this process by training deep learning models to learn the mapping between source location and seismic traces, for a given 3D heterogeneous velocity model, and a fixed isotropic moment tensor for the sources. These trained emulators replace the expensive solution of the elastic wave equation in the inference process. We compare our results with a previous study that used emulators based on Gaussian Processes to invert microseismic events. We show that all of our models provide more accurate predictions and $\sim 100$ times faster predictions than the method based on Gaussian Processes, and a $\mathcal{O}(10^5)$ speed-up factor over a pseudo-spectral method for waveform generation. For example, a 2-s long synthetic trace can be generated in $\sim 10$ ms on a common laptop processor, instead of $\sim$ 1 hr using a pseudo-spectral method on a high-profile Graphics Processing Units card. We also show that our inference results are in excellent agreement with those obtained from traditional location methods based on travel time estimates. The speed, accuracy and scalability of our open source deep learning models pave the way for extensions of these emulators to generic source mechanisms and application to joint Bayesian inversion of moment tensor components and source location using full waveforms.

physics.geo-ph↗

arXiv2021

Self-calibration and robust propagation of photometric redshift distribution uncertainties in weak gravitational lensing

We present a method that accurately propagates residual uncertainties in photometric redshift distributions into the cosmological inference from weak lensing measurements. The redshift distributions of tomographic redshift bins are parameterised using a flexible modified Gaussian mixture model. We fit this model to pre-calibrated redshift distributions and implement an analytic marginalisation over the potentially several hundred redshift nuisance parameters in the weak lensing likelihood, which is demonstrated to accurately recover the cosmological posterior. By iteratively fitting cosmological and nuisance parameters arising from the redshift distribution model, we perform a self-calibration of the redshift distributions via the tomographic cosmic shear measurements. Our method is applied to the third data release of the Kilo-Degree Survey combined with the VISTA Kilo-Degree Infrared Galaxy Survey (KV450). We find constraints on cosmological parameters that are in very good agreement with the fiducial KV450 cosmic shear analysis and investigate the effects of the more flexible model on the self-calibrated redshift distributions. We observe posterior shifts of the medians of the five tomographic redshift distributions of up to $Δz \approx 0.02$, which are however degenerate with an observed decrease of the amplitude of intrinsic galaxy alignments by about $10\%$.

astro-ph.CO↗

arXiv2021

When tension is just a fluctuation: How noisy data affect model comparison

Summary statistics of the likelihood, such as the Bayesian evidence, offer a principled way of comparing models and assessing tension between, or within, the results of physical experiments. Noisy realisations of the data induce scatter in these model comparison statistics. For a realistic case of cosmological inference from large-scale structure we show that the logarithm of the Bayes factor attains scatter of order unity, increasing significantly with stronger tension between the models under comparison. We develop an approximate procedure that quantifies the sampling distribution of the evidence at small additional computational cost and apply it to real data to demonstrate the impact of the scatter, which acts to reduce the significance of any model discrepancies. Data compression is highlighted as a potential avenue to suppressing noise in the evidence to negligible levels, with a proof of concept on Planck cosmic microwave background data.

astro-ph.CO↗

arXiv2021

Testing KiDS cross-correlation redshifts with simulations

Measuring cosmic shear in wide-field imaging surveys requires accurate knowledge of the redshift distribution of all sources. The clustering-redshift technique exploits the angular cross-correlation of a target galaxy sample with unknown redshifts and a reference sample with known redshifts, and is an attractive alternative to colour-based methods of redshift calibration. We test the performance of such clustering redshift measurements using mock catalogues that resemble the Kilo-Degree Survey (KiDS). These mocks are created from the MICE simulation and closely mimic the properties of the KiDS source sample and the overlapping spectroscopic reference samples. We quantify the performance of the clustering redshifts by comparing the cross-correlation results with the true redshift distributions in each of the five KiDS photometric redshift bins. Such a comparison to an informative model is necessary due to the incompleteness of the reference samples at high redshifts. Clustering mean redshifts are unbiased at $|Δz|<0.006$ under these conditions. The redshift evolution of the galaxy bias can be reliably mitigated at this level of precision using auto-correlation measurements and self-consistency relations, and will not become a dominant source of systematic error until the arrival of Stage-IV cosmic shear surveys. Using redshift distributions from a direct colour-based estimate instead of the true redshift distributions as a model for comparison with the clustering redshifts increases the biases in the mean to up to $|Δz|\sim0.04$. This indicates that the interpretation of clustering redshifts in real-world applications will require more sophisticated (parameterised) models of the redshift distribution in the future. If such better models are available, the clustering-redshift technique promises to be a highly complementary alternative to other methods of redshift calibration.

astro-ph.CO↗

arXiv2021

KiDS-1000 catalogue: Redshift distributions and their calibration

We present redshift distribution estimates of galaxies selected from the fourth data release of the Kilo-Degree Survey over an area of $\sim1000$ deg$^2$ (KiDS-1000). These redshift distributions represent one of the crucial ingredients for weak gravitational lensing measurements with the KiDS-1000 data. The primary estimate is based on deep spectroscopic reference catalogues that are re-weighted with the help of a self-organising map (SOM) to closely resemble the KiDS-1000 sources, split into five tomographic redshift bins in the photometric redshift range $0.1<z_\mathrm{B}\le1.2$. Sources are selected such that they only occupy that volume of nine-dimensional magnitude-space that is also covered by the reference samples (`gold' selection). Residual biases in the mean redshifts determined from this calibration are estimated from mock catalogues to be $\lesssim0.01$ for all five bins with uncertainties of $\sim 0.01$. This primary SOM estimate of the KiDS-1000 redshift distributions is complemented with an independent clustering redshift approach. After validation of the clustering-$z$ on the same mock catalogues and a careful assessment of systematic errors, we find no significant bias of the SOM redshift distributions with respect to the clustering-$z$ measurements. The SOM redshift distributions re-calibrated by the clustering-$z$ represent an alternative calibration of the redshift distributions with only slightly larger uncertainties in the mean redshifts of $\sim 0.01-0.02$ to be used in KiDS-1000 cosmological weak lensing analyses. As this includes the SOM uncertainty, clustering-$z$ are shown to be fully competitive on KiDS-1000 data.

astro-ph.CO↗

arXiv2020

KiDS-1000 Methodology: Modelling and inference for joint weak gravitational lensing and spectroscopic galaxy clustering analysis

We present the methodology for a joint cosmological analysis of weak gravitational lensing from the fourth data release of the ESO Kilo-Degree Survey (KiDS-1000) and galaxy clustering from the partially overlapping BOSS and 2dFLenS surveys. Cross-correlations between galaxy positions and ellipticities have been incorporated into the analysis, necessitating a hybrid model of non-linear scales that blends perturbative and non-perturbative approaches, and an assessment of contributions by astrophysical effects. All weak lensing signals are measured consistently via Fourier-space statistics that are insensitive to the survey mask and display low levels of mode mixing. The calibration of photometric redshift distributions and multiplicative gravitational shear bias has been updated, and a more complete tally of residual calibration uncertainties is propagated into the likelihood. A dedicated suite of more than 20000 mocks is used to assess the performance of covariance models and to quantify the impact of survey geometry and spatial variations of survey depth on signals and their errors. The sampling distributions for the likelihood and the $χ^2$ goodness-of-fit statistic have been validated, with proposed changes to the number of degrees of freedom. Standard weak lensing point estimates on $S_8=σ_8\,(Ω_{\rm m}/0.3)^{1/2}$ derived from its marginal posterior are easily misinterpreted to be biased low, and an alternative estimator and associated credible interval have been proposed. Known systematic effects pertaining to weak lensing modelling and inference are shown to bias $S_8$ by no more than 0.1 standard deviations, with the caveat that no conclusive validation data exist for models of intrinsic galaxy alignments. Compared to the previous KiDS analyses, $S_8$ constraints are expected to improve by 20% for weak lensing alone and by 29% for the joint analysis. [abridged]

astro-ph.CO↗

arXiv2020

The PAU Survey: Photometric redshifts using transfer learning from simulations

In this paper we introduce the \textsc{Deepz} deep learning photometric redshift (photo-$z$) code. As a test case, we apply the code to the PAU survey (PAUS) data in the COSMOS field. \textsc{Deepz} reduces the $σ_{68}$ scatter statistic by 50\% at $i_{\rm AB}=22.5$ compared to existing algorithms. This improvement is achieved through various methods, including transfer learning from simulations where the training set consists of simulations as well as observations, which reduces the need for training data. The redshift probability distribution is estimated with a mixture density network (MDN), which produces accurate redshift distributions. Our code includes an autoencoder to reduce noise and extract features from the galaxy SEDs. It also benefits from combining multiple networks, which lowers the photo-$z$ scatter by 10 percent. Furthermore, training with randomly constructed coadded fluxes adds information about individual exposures, reducing the impact of photometric outliers. In addition to opening up the route for higher redshift precision with narrow bands, these machine learning techniques can also be valuable for broad-band surveys.

astro-ph.GA↗

arXiv2020

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 $0.8σ$ reduction in the DES-inferred value for $S_8$, which decreases to a $0.5σ$ reduction when including a systematic redshift calibration error model from mock DES data based on the MICE2 simulation. The combined KV450 + DES-Y1 constraint on $S_8 = 0.762^{+0.025}_{-0.024}$ is in tension with the Planck 2018 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.

astro-ph.CO↗

arXiv2019

KiDS+VIKING-450: Cosmic shear tomography with optical+infrared data

We present a tomographic cosmic shear analysis of the Kilo-Degree Survey (KiDS) combined with the VISTA Kilo-Degree Infrared Galaxy Survey (VIKING). This is the first time that a full optical to near-infrared data set has been used for a wide-field cosmological weak lensing experiment. This unprecedented data, spanning $450~$deg$^2$, allows us to improve significantly the estimation of photometric redshifts, such that we are able to include robustly higher-redshift sources for the lensing measurement, and - most importantly - solidify our knowledge of the redshift distributions of the sources. Based on a flat $Λ$CDM model we find $S_8\equivσ_8\sqrt{Ω_{\rm m}/0.3}=0.737_{-0.036}^{+0.040}$ in a blind analysis from cosmic shear alone. The tension between KiDS cosmic shear and the Planck-Legacy CMB measurements remains in this systematically more robust analysis, with $S_8$ differing by $2.3σ$. This result is insensitive to changes in the priors on nuisance parameters for intrinsic alignment, baryon feedback, and neutrino mass. KiDS shear measurements are calibrated with a new, more realistic set of image simulations and no significant B-modes are detected in the survey, indicating that systematic errors are under control. When calibrating our redshift distributions by assuming the 30-band COSMOS-2015 photometric redshifts are correct (following the Dark Energy Survey and the Hyper Suprime-Cam Survey), we find the tension with Planck is alleviated. The robust determination of source redshift distributions remains one of the most challenging aspects for future cosmic shear surveys.

astro-ph.CO↗

arXiv2019

The PAU Survey: Early demonstration of photometric redshift performance in the COSMOS field

The PAU Survey (PAUS) is an innovative photometric survey with 40 narrow bands at the William Herschel Telescope (WHT). The narrow bands are spaced at 100Å intervals covering the range 4500Å to 8500Å and, in combination with standard broad bands, enable excellent redshift precision. This paper describes the technique, galaxy templates and additional photometric calibration used to determine early photometric redshifts from PAUS. Using BCNz2, a new photometric redshift code developed for this purpose, we characterise the photometric redshift performance using PAUS data on the COSMOS field. Comparison to secure spectra from zCOSMOS DR3 shows that PAUS achieves $σ_{68} /(1+z) = 0.0037$ to $i_{\mathrm{AB}} < 22.5$ when selecting the best 50% of the sources based on a photometric redshift quality cut. Furthermore, a higher photo-z precision ($σ_{68}/(1+z) \sim 0.001$) is obtained for a bright and high quality selection, which is driven by the identification of emission lines. We conclude that PAUS meets its design goals, opening up a hitherto uncharted regime of deep, wide, and dense galaxy survey with precise redshifts that will provide unique insights into the formation, evolution and clustering of galaxies, as well as their intrinsic alignments.

astro-ph.GA↗

arXiv2018

KiDS+2dFLenS+GAMA: Testing the cosmological model with the $E_{\rm G}$ statistic

We present a new measurement of $E_{\rm G}$, which combines measurements of weak gravitational lensing, real-space galaxy clustering and redshift space distortions. This statistic was proposed as a consistency test of General Relativity (GR) that is insensitive to linear, deterministic galaxy bias and the matter clustering amplitude. We combine deep imaging data from KiDS with overlapping spectroscopy from 2dFLenS, BOSS DR12 and GAMA and find $E_{\rm G}(\overline{z}=0.267)=0.43 \pm 0.13$ (GAMA), $E_{\rm G}(\overline{z}=0.305)=0.27 \pm 0.08$ (LOWZ+2dFLOZ) and $E_{\rm G}(\overline{z}=0.554)=0.26 \pm 0.07$ (CMASS+2dFHIZ). We demonstrate that the existing tension in the value of the matter density parameter hinders the robustness of this statistic as solely a test of GR. We find that our $E_{\rm G}$ measurements, as well as existing ones in the literature, favour a lower matter density cosmology than the Cosmic Microwave Background. For a flat $Λ$CDM Universe and assuming GR, we find $Ω_{\rm m}(z=0)=0.25\pm0.03$. With this paper we publicly release the 2dFLenS dataset at: \url{http://2dflens.swin.edu.au}.

astro-ph.CO↗