Fabio Bellagamba

Weighing simulated galaxy clusters using lensing and X-ray

Context. Measuring the mass of galaxy clusters is a key issue in cosmology. Among the methods employed to achieve this goal, the techniques based on lensing and X-ray analyses are perhaps the most widely used. However, the comparison between these mass estimates is often di cult and, in several clusters, the results apparently are inconsistent. Aims. We aim at investigating potential biases in lensing and X-ray methods to measure the cluster mass profiles. Methods. We do so by performing realistic simulations of lensing and X-ray observations that are subsequently analyzed using observational techniques. The resulting mass estimates are compared among them and with the input models. Three clusters obtained from state-of-the-art hydrodynamical simulations, each of which has been projected along three independent lines-of-sight, are 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 largely a ected by substructures, depending on the method implemented to convert the shear into a mass estimate. Using non-parametric methods which combine weak and strong lensing data, the projected masses within R200 can be constrained with a precision of 10%. De-projection of lensing masses increases the scatter around the true masses by more than a factor of two due to cluster triaxiality. X-ray mass measurements have much smaller scatter (about a factor of two smaller than the lensing masses) but they are generally biased low by 5 20%. 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 di cult 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 to use lensing masses to calibrate the X-ray scaling relations.

Optimal filtering of optical and weak lensing data to search for galaxy clusters: application to the COSMOS field

Galaxy clusters are usually detected in blind optical surveys via suitable filtering methods. We present an optimal matched filter which maximizes their signal-to-noise ratio by taking advantage of the knowledge we have of their intrinsic physical properties and of the data noise properties. In this paper we restrict our application to galaxy magnitudes, positions and photometric redshifts if available, and we also apply the filter separately to weak lensing data. The method is suitable to be naturally extended to a multi-band approach which could include not only additional optical bands but also observables with different nature such as X-rays. For each detection, the filter provides its significance, an estimate for the richness and for the redshift even if photo-z are not given. The provided analytical error estimate is tested against numerical simulations. We finally apply our method to the COSMOS field and compare the results with previous cluster detections obtained with different methods. Our catalogue contains 27 galaxy clusters with minimal threshold at 3σ level including both optical and weak-lensing information.

A PCA-based automated finder for galaxy-scale strong lenses

We present an algorithm using principal component analysis (PCA) to subtract galaxies from imaging data and also two algorithms to find strong, galaxy-scale gravitational lenses in the resulting residual image. The combined method is optimised to find full or partial Einstein rings. Starting from a pre-selection of potential massive galaxies, we first perform a PCA to build a set of basis vectors. The galaxy images are reconstructed using the PCA basis and subtracted from the data. We then filter the residual image with two different methods. The first uses a curvelet (curved wavelets) filter of the residual images to enhance any curved/ring feature. The resulting image is transformed in polar coordinates, centred on the lens galaxy. In these coordinates, a ring is turned into a line, allowing us to detect very faint rings by taking advantage of the integrated signal-to-noise in the ring (a line in polar coordinates). The second way of analysing the PCA-subtracted images identifies structures in the residual images and assesses whether they are lensed images according to their orientation, multiplicity, and elongation. We applied the two methods to a sample of simulated Einstein rings as they would be observed with the ESA Euclid satellite in the VIS band. The polar coordinate transform allowed us to reach a completeness of 90% for a purity of 86%, as soon as the signal-to-noise integrated in the ring was higher than 30 and almost independent of the size of the Einstein ring. Finally, we show with real data that our PCA-based galaxy subtraction scheme performs better than traditional subtraction based on model fitting to the data. Our algorithm can be developed and improved further using machine learning and dictionary learning methods, which would extend the capabilities of the method to more complex and diverse galaxy shapes.

Searching for galaxy clusters in the Kilo-Degree Survey

In this paper, we present the tools used to search for galaxy clusters in the Kilo Degree Survey (KiDS), and our first results. The cluster detection is based on an implementation of the optimal filtering technique that enables us to identify clusters as over-densities in the distribution of galaxies using their positions on the sky, magnitudes, and photometric redshifts. The contamination and completeness of the cluster catalog are derived using mock catalogs based on the data themselves. The optimal signal to noise threshold for the cluster detection is obtained by randomizing the galaxy positions and selecting the value that produces a contamination of less than 20%. Starting from a subset of clusters detected with high significance at low redshifts, we shift them to higher redshifts to estimate the completeness as a function of redshift: the average completeness is ~ 85%. An estimate of the mass of the clusters is derived using the richness as a proxy. We obtained 1858 candidate clusters with redshift 0 <z_c <0.7 and mass 13.5 <log(M500/Msun) <15 in an area of 114 sq. degrees (KiDS ESO-DR2). A comparison with publicly available Sloan Digital Sky Survey (SDSS)-based cluster catalogs shows that we match more than 50% of the clusters (77% in the case of the redMaPPer catalog). We also cross-matched our cluster catalog with the Abell clusters, and clusters found by XMM and in the Planck-SZ survey; however, only a small number of them lie inside the KiDS area currently available.

Lensed: a code for the forward reconstruction of lenses and sources from strong lensing observations

Robust modelling of strong lensing systems is fundamental to exploit the information they contain about the distribution of matter in galaxies and clusters. In this work, we present Lensed, a new code which performs forward parametric modelling of strong lenses. Lensed takes advantage of a massively parallel ray-tracing kernel to perform the necessary calculations on a modern graphics processing unit (GPU). This makes the precise rendering of the background lensed sources much faster, and allows the simultaneous optimisation of tens of parameters for the selected model. With a single run, the code is able to obtain the full posterior probability distribution for the lens light, the mass distribution and the background source at the same time. Lensed is first tested on mock images which reproduce realistic space-based observations of lensing systems. In this way, we show that it is able to recover unbiased estimates of the lens parameters, even when the sources do not follow exactly the assumed model. Then, we apply it to a subsample of the SLACS lenses, in order to demonstrate its use on real data. The results generally agree with the literature, and highlight the flexibility and robustness of the algorithm.

Accuracy of photometric redshifts for future weak lensing surveys from space

Photometric redshifts are a key tool to extract as much information as possible from planned cosmic shear experiments. In this work we aim to test the performances that can be achieved with observations in the near-infrared from space and in the optical from the ground. This is done by performing realistic simulations of multi-band observations of a patch of the sky, and submitting these mock images to software usually applied to real images to extract the photometry and then a redshift estimate for each galaxy. In this way we mimic the most relevant sources of uncertainty present in real data analysis, including blending and light pollution between galaxies. As an example we adopt the infrared setup of the ESA-proposed Euclid mission, while we simulate dierent observations in the optical, modifying lters, exposure times and seeing values. Finally, we consider directly some future ground-based experiments, such as LSST, Pan-Starrs and DES. The results highlight the importance of u-band observations, especially to discriminate between low (z . 0.5) and high (z 3) redshifts, and the need for good observing sites, with seeing FWHM < 1. arcsec. The former of these indications clearly favours the LSST experiment as a counterpart for space observations, while for the other experiments we need to exclude at least 15 % of the galaxies to reach a precision in the photo-zs equal toh z 1+z i < 0:05.

AMICO: optimized detection of galaxy clusters in photometric surveys

We present AMICO (Adaptive Matched Identifier of Clustered Objects), a new algorithm for the detection of galaxy clusters in photometric surveys. AMICO is based on the Optimal Filtering technique, which allows to maximise the signal-to-noise ratio of the clusters. In this work we focus on the new iterative approach to the extraction of cluster candidates from the map produced by the filter. In particular, we provide a definition of membership probability for the galaxies close to any cluster candidate, which allows us to remove its imprint from the map, allowing the detection of smaller structures. As demonstrated in our tests, this method allows the deblending of close-by and aligned structures in more than

Searching for Galaxy Clusters in the VST-KiDS Survey

50\%

Zooming into the Cosmic Horseshoe: New insights on the lens profile and the source shape

of the cases for objects at radial distance equal to

SEAGLE – I. A pipeline for simulating and modelling strong lenses from cosmological hydrodynamic simulations

0.5 \times R_{200}