Reconstructing Projected Matter Density from Cosmic Microwave Background

Gravitational lensing distorts the cosmic microwave background (CMB) anisotropies and imprints a characteristic pattern onto it. The distortions depend on the projected matter density between today and redshift $z \sim 1100$. In this paper we develop a method for a direct reconstruction of the projected matter density from the CMB anisotropies. This reconstruction is obtained by averaging over quadratic combinations of the derivatives of CMB field. We test the method using simulations and show that it can successfully recover projected density profile of a cluster of galaxies if there are measurable anisotropies on scales smaller than the characteristic cluster size. In the absence of sufficient small scale power the reconstructed maps have low signal to noise on individual structures, but can give a positive detection of the power spectrum or when cross correlated with other maps of large scale structure. We develop an analytic method to reconstruct the power spectrum including the effects of noise and beam smoothing. Tests with Monte Carlo simulations show that we can recover the input power spectrum both on large and small scales, provided that we use maps with sufficiently low noise and high angular resolution.Comment: 21 pages, 9 figures, submitted to PR

gamma-ray DBSCAN: a clustering algorithm applied to Fermi-LAT gamma-ray data. I. Detection performances with real and simulated data

The Density Based Spatial Clustering of Applications with Noise (DBSCAN) is a topometric algorithm used to cluster spatial data that are affected by background noise. For the first time, we propose the use of this method for the detection of sources in gamma-ray astrophysical images obtained from the Fermi-LAT data, where each point corresponds to the arrival direction of a photon. We investigate the detection performance of the gamma-ray DBSCAN in terms of detection efficiency and rejection of spurious clusters, using a parametric approach, and exploring a large volume of the gamma-ray DBSCAN parameter space. By means of simulated data we statistically characterize the gamma-ray DBSCAN, finding signatures that differentiate purely random fields, from fields with sources. We define a significance level for the detected clusters, and we successfully test this significance with our simulated data. We apply the method to real data, and we find an excellent agreement with the results obtained with simulated data. We find that the gamma-ray DBSCAN can be successfully used in the detection of clusters in gamma-ray data. The significance returned by our algorithm is strongly correlated with that provided by the Maximum Likelihood analysis with standard Fermi-LAT software, and can be used to safely remove spurious clusters. The positional accuracy of the reconstructed cluster centroid compares to that returned by standard Maximum Likelihood analysis, allowing to look for astrophysical counterparts in narrow regions, minimizing the chance probability in the counterpart association. We find that gamma-ray DBSCAN is a powerful tool in the detection of clusters in gamma-ray data, this method can be used both to look for point-like sources, and extended sources, and can be potentially applied to any astrophysical field related with detection of clusters in data.Comment: Accepted for publication in A&

Bayesian modelling of clusters of galaxies from multi-frequency pointed Sunyaev--Zel'dovich observations

We present a Bayesian approach to modelling galaxy clusters using multi-frequency pointed observations from telescopes that exploit the Sunyaev--Zel'dovich effect. We use the recently developed MultiNest technique (Feroz, Hobson & Bridges, 2008) to explore the high-dimensional parameter spaces and also to calculate the Bayesian evidence. This permits robust parameter estimation as well as model comparison. Tests on simulated Arcminute Microkelvin Imager observations of a cluster, in the presence of primary CMB signal, radio point sources (detected as well as an unresolved background) and receiver noise, show that our algorithm is able to analyse jointly the data from six frequency channels, sample the posterior space of the model and calculate the Bayesian evidence very efficiently on a single processor. We also illustrate the robustness of our detection process by applying it to a field with radio sources and primordial CMB but no cluster, and show that indeed no cluster is identified. The extension of our methodology to the detection and modelling of multiple clusters in multi-frequency SZ survey data will be described in a future work.Comment: 12 pages, 7 figures, submitted to MNRA

On the ISW-cluster cross-correlation in future surveys

We investigate the cosmological information contained in the cross-correlation between the Integrated Sachs-Wolfe (ISW) of the Cosmic Microwave Background (CMB) anisotropy pattern and galaxy clusters from future wide surveys. Future surveys will provide cluster catalogues with a number of objects comparable with galaxy catalogues currently used for the detection of the ISW signal by cross-correlation with the CMB anisotropy pattern. By computing the angular power spectra of clusters and the corresponding cross-correlation with CMB, we perform a signal-to-noise ratio (SNR) analysis for the ISW detection as expected from the eROSITA and the Euclid space missions. We discuss the dependence of the SNR of the ISW-cluster cross-correlation on the specifications of the catalogues and on the reference cosmology. We forecast that the SNRs for ISW-cluster cross-correlation are alightly smaller compared to those which can be obtained from future galaxy surveys but the signal is expected to be detected at high significance, i.e. more than $> 3\,\sigma$. We also forecast the joint constraints on parameters of model extensions of the concordance $\Lambda$CDM cosmology by combining CMB and the ISW-cluster cross-correlation.Comment: 12 pages, 10 figures. Matches version accepted in MNRA

Detecting Sunyaev-Zel'dovich clusters with PLANCK: III. Properties of the expected SZ-cluster sample

The PLANCK-mission is the most sensitive all-sky submillimetric mission currently being planned and prepared. Special emphasis is given to the observation of clusters of galaxies by their thermal Sunyaev-Zel'dovich (SZ) effect. In this work, the results of a simulation are presented that combines all-sky maps of the thermal and kinetic SZ-effect with cosmic microwave background (CMB) fluctuations, Galactic foregrounds (synchrotron emission, thermal emission from dust, free-free emission and rotational transitions of carbon monoxide molecules) and sub-millimetric emission from planets and asteroids of the Solar System. Observational issues, such as PLANCKs beam shapes, frequency response and spatially non-uniform instrumental noise have been incorporated. Matched and scale-adaptive multi-frequency filtering schemes have been extended to spherical coordinates and are now applied to the data sets in order to isolate and amplify the weak thermal SZ-signal. The properties of the resulting SZ-cluster sample are characterised in detail: Apart from the number of clusters as a function of cluster parameters such as redshift z and total mass M, the distribution n(sigma)d sigma of the detection significance sigma, the number of detectable clusters in relation to the model cluster parameters entering the filter construction, the position accuracy of an SZ-detection and the cluster number density as a function of ecliptic latitude beta is examined.Comment: 14 pages, 16 figures, 13 tables, submitted to MNRAS, 16.Feb.200

Ringing effects reduction by improved deconvolution algorithm Application to A370 CFHT image of gravitational arcs

We develop a self-consistent automatic procedure to restore informations from astronomical observations. It relies on both a new deconvolution algorithm called LBCA (Lower Bound Constraint Algorithm) and the use of the Wiener filter. In order to explore its scientific potential for strong and weak gravitational lensing, we process a CFHT image of the galaxies cluster Abell 370 which exhibits spectacular strong gravitational lensing effects. A high quality restoration is here of particular interest to map the dark matter within the cluster. We show that the LBCA turns out specially efficient to reduce ringing effects introduced by classical deconvolution algorithms in images with a high background. The method allows us to make a blind detection of the radial arc and to recover morphological properties similar to thoseobserved from HST data. We also show that the Wiener filter is suitable to stop the iterative process before noise amplification, using only the unrestored data.Comment: A&A in press 9 pages 9 figure

Point Source Confusion in SZ Cluster Surveys

We examine the effect of point source confusion on cluster detection in Sunyaev-Zel'dovich (SZ) surveys. A filter matched to the spatial and spectral characteristics of the SZ signal optimally extracts clusters from the astrophysical backgrounds. We calculate the expected confusion (point source and primary cosmic microwave background [CMB]) noise through this filter and quantify its effect on the detection threshold for both single and multiple frequency surveys. Extrapolating current radio counts, we estimate that confusion from sources below 100 microJy limits single-frequency surveys to 1-sigma detection thresholds of Y 3.10^{-6} arcmin^2 at 30 GHz and Y 10^{-5} arcmin^2 at 15 GHz (for unresolved clusters in a 2 arcmin beam); these numbers are highly uncertain, and an extrapolation with flatter counts leads to much lower confusion limits. Bolometer surveys must contend with an important population of infrared point sources. We find that a three-band matched filter with 1 arcminute resolution (in each band) efficiently reduces confusion, but does not eliminate it: residual point source and CMB fluctuations contribute significantly the total filter noise. In this light, we find that a 3-band filter with a low-frequency channel (e.g, 90+150+220 GHz) extracts clusters more effectively than one with a high frequency channel (e.g, 150+220+300 GHz).Comment: Accepted for publication in Astronomy & Astrophysics; Updated grant information in acknowledgement

Detection of Enhancement in Number Densities of Background Galaxies due to Magnification by Massive Galaxy Clusters

We present a detection of the enhancement in the number densities of background galaxies induced from lensing magnification and use it to test the Sunyaev-Zel'dovich effect (SZE) inferred masses in a sample of 19 galaxy clusters with median redshift $z\simeq0.42$ selected from the South Pole Telescope SPT-SZ survey. Two background galaxy populations are selected for this study through their photometric colours; they have median redshifts ${z}_{\mathrm{median}}\simeq0.9$ (low-$z$ background) and ${z}_{\mathrm{median}}\simeq1.8$ (high-$z$ background). Stacking these populations, we detect the magnification bias effect at $3.3\sigma$ and $1.3\sigma$ for the low- and high-$z$ backgrounds, respectively. We fit NFW models simultaneously to all observed magnification bias profiles to estimate the multiplicative factor $\eta$ that describes the ratio of the weak lensing mass to the mass inferred from the SZE observable-mass relation. We further quantify systematic uncertainties in $\eta$ resulting from the photometric noise and bias, the cluster galaxy contamination and the estimations of the background properties. The resulting $\eta$ for the combined background populations with $1\sigma$ uncertainties is $0.83\pm0.24\mathrm{(stat)}\pm0.074\mathrm{(sys)}$, indicating good consistency between the lensing and the SZE-inferred masses. We use our best-fit $\eta$ to predict the weak lensing shear profiles and compare these predictions with observations, showing agreement between the magnification and shear mass constraints. This work demonstrates the promise of using the magnification as a complementary method to estimate cluster masses in large surveys.Comment: 16 pages, 10 figures, accepted for publication in MNRA