Context. Among the methods employed to measure the mass of galaxy clusters, the techniques based on lensing and X-ray analyses are perhaps the most widely used; however, the comparison between these mass estimates is often difficult and, in several clusters, the results apparently inconsistent. Aims. We aim at investigating potential biases in lensing and X-ray methods to measure the cluster mass profiles. Methods. We performed realistic simulations of lensing and X-ray observations that were subsequently analyzed using observational techniques. The resulting mass estimates were compared with the input models. Three clusters obtained from state-of-the-art hydrodynamical simulations, each of which projected along three independent lines-of-sight, were used for this analysis. Results. We find that strong lensing models can be trusted over a limited region around the cluster core. Extrapolating the strong lensing mass models to outside the Einstein ring can lead to significant biases in the mass estimates, if the BCG is not modeled properly, for example. Weak-lensing mass measurements can be strongly affected by substructures, depending on the method implemented to convert the shear into a mass estimate. Using nonparametric methods which combine weak and strong lensing data, the projected masses within R 200 can be constrained with a precision of ∼10%. Deprojection of lensing masses increases the scatter around the true masses by more than a factor of two because of cluster triaxiality. X-ray mass measurements have much smaller scatter (about a factor of two less than the lensing masses), but they are generally biased toward low values between 5 and 10%. This bias is entirely ascribable to bulk motions in the gas of our simulated clusters. Using the lensing and the X-ray masses as proxies for the true and the hydrostatic equilibrium masses of the simulated clusters and by averaging over the cluster sample, we are able to measure the lack of hydrostatic equilibrium in the systems we have investigated. Conclusions. Although the comparison between lensing and X-ray masses may be difficult in individual systems due to triaxiality and substructures, using a large number of clusters with both lensing and X-ray observations may lead to important information about their gas physics and allow use of lensing masses to calibrate the X-ray scaling relations.
We present the mass calibration for galaxy clusters detected with the AMICO code in KiDS DR3 data. The cluster sample comprises ∼ 7000 objects and covers the redshift range 0.1 < z < 0.6. We perform a weak lensing stacked analysis by binning the clusters according to redshift and two different mass proxies provided by AMICO, namely the amplitude A (measure of galaxy abundance through an optimal filter) and the richness λ * (sum of membership probabilities in a consistent radial and magnitude range across redshift). For each bin, we model the data as a truncated NFW profile plus a 2-halo term, taking into account uncertainties related to concentration and miscentring. From the retrieved estimates of the mean halo masses, we construct the A-M 200 and the λ * -M 200 relations. The relations extend over more than one order of magnitude in mass, down to M 200 ∼ 2 (5) × 10 13 M /h at z = 0.2 (0.5), with small evolution in redshift. The logarithmic slope is ∼ 2.0 for the A-mass relation, and ∼ 1.7 for the λ * -mass relation, consistent with previous estimations on mock catalogues and coherent with the different nature of the two observables.
Large-scale imaging surveys will increase the number of galaxy-scale strong lensing candidates by maybe three orders of magnitudes beyond the number known today. Finding these rare objects will require picking them out of at least tens of millions of images, and deriving scientific results from them will require quantifying the efficiency and bias of any search method. To achieve these objectives automated methods must be developed. Because gravitational lenses are rare objects, reducing false positives will be particularly important. We present a description and results of an open gravitational lens finding challenge. Participants were asked to classify 100,000 candidate objects as to whether they were gravitational lenses or not with the goal of developing better automated methods for finding lenses in large data sets. A variety of methods were used including visual inspection, arc and ring finders, support vector machines (SVM) and convolutional neural networks (CNN). We find that many of the methods will be easily fast enough to analyse the anticipated data flow. In test data, several methods are able to identify upwards of half the lenses after applying some thresholds on the lens characteristics such as lensed image brightness, size or contrast with the lens galaxy without making a single false-positive identification. This is significantly better than direct inspection by humans was able to do. Having multi-band, ground based data is found to be better for this purpose than single-band space based data with lower noise and higher resolution, suggesting that multi colour data is crucial. Multi-band space based data will be superior to ground based data. The most difficult challenge for a lens finder is differentiating between rare, irregular and ring-like face-on galaxies and true gravitational lenses. The degree to which the efficiency and biases of lens finders can be quantified largely depends on the realism of the simulated data on which the finders are trained.Article number, page 1 of 26
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