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تسجيل مستخدم جديدKiDS-450: Cosmological Constraints from Weak Lensing Peak Statistics - II: Inference from Shear Peaks using N-body Simulations
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We study the statistics of peaks in a weak lensing reconstructed mass map of the first 450 square degrees of the Kilo Degree Survey. The map is computed with aperture masses directly applied to the shear field with an NFW-like compensated filter. We compare the peak statistics in the observations with that of simulations for various cosmologies to constrain the cosmological parameter $S_8 = sigma_8 sqrt{Omega_{rm m}/0.3}$, which probes the ($Omega_{rm m}, sigma_8$) plane perpendicularly to its main degeneracy. We estimate $S_8=0.750pm0.059$, using peaks in the signal-to-noise range $0 leq {rm S/N} leq 4$, and accounting for various systematics, such as multiplicative shear bias, mean redshift bias, baryon feedback, intrinsic alignment, and shear-position coupling. These constraints are $sim25%$ tighter than the constraints from the high significance peaks alone ($3 leq {rm S/N} leq 4$) which typically trace single-massive halos. This demonstrates the gain of information from low-S/N peaks. However we find that including ${rm S/N} < 0$ peaks does not add further information. Our results are in good agreement with the tomographic shear two-point correlation function measurement in KiDS-450. Combining shear peaks with non-tomographic measurements of the shear two-point correlation functions yields a $sim20%$ improvement in the uncertainty on $S_8$ compared to the shear two-point correlation functions alone, highlighting the great potential of peaks as a cosmological probe.
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This paper is the first of a series of papers constraining cosmological parameters with weak lensing peak statistics using $sim 450~rm deg^2$ of imaging data from the Kilo Degree Survey (KiDS-450). We measure high signal-to-noise ratio (SNR: $ u$) we
ak lensing convergence peaks in the range of $3< u<5$, and employ theoretical models to derive expected values. These models are validated using a suite of simulations. We take into account two major systematic effects, the boost factor and the effect of baryons on the mass-concentration relation of dark matter haloes. In addition, we investigate the impacts of other potential astrophysical systematics including the projection effects of large scale structures, intrinsic galaxy alignments, as well as residual measurement uncertainties in the shear and redshift calibration. Assuming a flat $Lambda$CDM model, we find constraints for $S_{rm 8}=sigma_{rm 8}(Omega_{rm m}/0.3)^{0.5}=0.746^{+0.046}_{-0.107}$ according to the degeneracy direction of the cosmic shear analysis and $Sigma_{rm 8}=sigma_{rm 8}(Omega_{rm m}/0.3)^{0.38}=0.696^{+0.048}_{-0.050}$ based on the derived degeneracy direction of our high-SNR peak statistics. The difference between the power index of $S_{rm 8}$ and in $Sigma_{rm 8}$ indicates that combining the two probes has the potential to break the degeneracy in $sigma_{rm 8}$ and $Omega_{rm m}$. Our results are consistent with the cosmic shear tomographic correlation analysis of the same dataset and $sim 2sigma$ lower than the Planck 2016 results.
We present cosmological parameter constraints from a tomographic weak gravitational lensing analysis of ~450deg$^2$ of imaging data from the Kilo Degree Survey (KiDS). For a flat $Lambda$CDM cosmology with a prior on $H_0$ that encompasses the most r
ecent direct measurements, we find $S_8equivsigma_8sqrt{Omega_{rm m}/0.3}=0.745pm0.039$. This result is in good agreement with other low redshift probes of large scale structure, including recent cosmic shear results, along with pre-Planck cosmic microwave background constraints. A $2.3$-$sigma$ tension in $S_8$ and `substantial discordance in the full parameter space is found with respect to the Planck 2015 results. We use shear measurements for nearly 15 million galaxies, determined with a new improved `self-calibrating version of $lens$fit validated using an extensive suite of image simulations. Four-band $ugri$ photometric redshifts are calibrated directly with deep spectroscopic surveys. The redshift calibration is confirmed using two independent techniques based on angular cross-correlations and the properties of the photometric redshift probability distributions. Our covariance matrix is determined using an analytical approach, verified numerically with large mock galaxy catalogues. We account for uncertainties in the modelling of intrinsic galaxy alignments and the impact of baryon feedback on the shape of the non-linear matter power spectrum, in addition to the small residual uncertainties in the shear and redshift calibration. The cosmology analysis was performed blind. Our high-level data products, including shear correlation functions, covariance matrices, redshift distributions, and Monte Carlo Markov Chains are available at http://kids.strw.leidenuniv.nl.
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.
We use weak lensing data from the Hubble Space Telescope COSMOS survey to measure the second- and third-moments of the cosmic shear field, estimated from about 450,000 galaxies with average redshift <z> ~ 1.3. We measure two- and three-point shear st
atistics using a tree-code, dividing the signal in E, B and mixed components. We present a detection of the third-order moment of the aperture mass statistic and verify that the measurement is robust against systematic errors caused by point spread function (PSF) residuals and by the intrinsic alignments between galaxies. The amplitude of the measured three-point cosmic shear signal is in very good agreement with the predictions for a WMAP7 best-fit model, whereas the amplitudes of potential systematics are consistent with zero. We make use of three sets of large Lambda CDM simulations to test the accuracy of the cosmological predictions and to estimate the influence of the cosmology-dependent covariance. We perform a likelihood analysis using the measurement and find that the Omega_m-sigma_8 degeneracy direction is well fitted by the relation: sigma_8 (Omega_m/0.30)^(0.49)=0.78+0.11/-0.26. We present the first measurement of a more generalised three-point shear statistic and find a very good agreement with the WMAP7 best-fit cosmology. The cosmological interpretation of this measurement gives sigma_8 (Omega_m/0.30)^(0.46)=0.69 +0.08/-0.14. Furthermore, the combined likelihood analysis of this measurement with the measurement of the second order moment of the aperture mass improves the accuracy of the cosmological constraints, showing the high potential of this combination of measurements to infer cosmological constraints.
In order to extract full cosmological information from next-generation large and high-precision weak lensing (WL) surveys (e.g. Euclid, Roman, LSST), higher-order statistics that probe the small-scale, non-linear regime of large scale structure (LSS)
need to be utilized. WL peak counts, which trace overdensities in the cosmic web, are one promising and simple statistic for constraining cosmological parameters. The physical origin of WL peaks have previously been linked to dark matter halos along the line of sight and this peak-halo connection has been used to develop various semi-analytic halo-based models for predicting peak counts. Here, we study the origin of WL peaks and the effectiveness of halo-based models for WL peak counts using a suite of ray-tracing N-body simulations. We compare WL peaks in convergence maps from the full simulations to those in maps created from only particles associated with halos -- the latter playing the role of a perfect halo model. We find that while halo-only contributions are able to replicate peak counts qualitatively well, halos do not explain all WL peaks. Halos particularly underpredict negative peaks, which are associated with local overdensities in large-scale underdense regions along the line of sight. In addition, neglecting non-halo contributions to peaks counts leads to a significant bias on the parameters ($Omega_{rm m}$, $sigma_{8}$) for surveys larger than $geq$ 100 deg$^{2}$. We conclude that other elements of the cosmic web, outside and far away from dark matter halos, need to be incorporated into models of WL peaks in order to infer unbiased cosmological constraints.
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