The impact of projection effects on cluster observables: stacked lensing and projected clustering

ABSTRACT An optical cluster finder inevitably suffers from projection effects, where it misidentifies a superposition of galaxies in multiple haloes along the line of sight as a single cluster. Using mock cluster catalogues built from cosmological N-body simulations, we quantify the impact of these projection effects with a particular focus on the observables of interest for cluster cosmology, namely the cluster-lensing and the cluster-clustering signals. We find that ‘observed’ clusters, i.e. clusters identified by our cluster finder algorithm, exhibit lensing and clustering signals that deviate from expectations based on a statistically isotropic halo model – while both signals agree with halo model expectations on small scales, they show unexpected boosts on large scales by up to a factor of 1.2 or 1.4, respectively. We identify the origin of these boosts as the inherent selection bias of optical cluster finders for clusters embedded within filaments aligned with the line of sight and show that a minority ($\sim 30{{\ \rm per\ cent}}$) of such clusters within the entire sample is responsible for this observed boost. We discuss the implications of our results on previous studies of optical cluster, as well as prospects for identifying and mitigating projection effects in future cluster cosmology analyses.

Can assembly bias explain the lensing amplitude of the BOSS CMASS sample in a Planck cosmology?

ABSTRACT In this paper, we investigate whether galaxy assembly bias can reconcile the 20–40 ${{\ \rm per\ cent}}$ disagreement between the observed galaxy projected clustering signal and the galaxy–galaxy lensing signal in the Baryon Oscillation Spectroscopic Survey CMASS galaxy sample. We use the suite of abacuscosmos lambda cold dark matter simulations at Planck best-fitting cosmology and two flexible implementations of extended halo occupation distribution (HOD) models that incorporate galaxy assembly bias to build forward models and produce joint fits of the observed galaxy clustering signal and the galaxy–galaxy lensing signal. We find that our models using the standard HODs without any assembly bias generalizations continue to show a 20–40 ${{\ \rm per\ cent}}$ overprediction of the observed galaxy–galaxy lensing signal. We find that our implementations of galaxy assembly bias do not reconcile the two measurements at Planck best-fitting cosmology. In fact, despite incorporating galaxy assembly bias, the satellite distribution parameter, and the satellite velocity bias parameter into our extended HOD model, our fits still strongly suggest a $\sim \! 34{{\ \rm per\ cent}}$ discrepancy between the observed projected clustering and galaxy–galaxy lensing measurements. It remains to be seen whether a combination of other galaxy assembly bias models, alternative cosmological parameters, or baryonic effects can explain the amplitude difference between the two signals.

Network Lifetime Enhancement in WSN using Fuzzy Based Clustering Algorithm

These-days Wireless Sensor Networks (WSNs) has become integral part of many applications include tracking, monitoring and so on. Nodes are limited in battery, memory and processing capacity. Tracking and monitoring applications continue to work for longer hours; energy is the major constraint for network to transmit sensed data. State of the art specifies that by using clustering method energy-efficiency, scalability, and efficient-data-communication is achieved. Sensors deployed in the network be partitioned to clusters then one of the nodes is designated to become a Cluster Head (CH) that accumulate sensed information and sends to Sink/Base Station (BS). Normally CH is elected by considering nodes remaining energy and topological attributes related to the node in network. In this projected clustering method a centrality-metric “Cluster-Optimal-Degree-Centrality (CODC)”, is defined and also considered other parameters residual energy, distance between CHs, plus number of nodes belonging to a cluster guarantees better cluster configuration and CH selection. Fuzzy-Inference-System takes Expected-Residual-Energy (ERE) and CODC as inputs. Experiments are carried using ns-2; the proposed clustering method improves QoS, and efficiently prolongs network lifetime.

Cosmology with galaxy–galaxy lensing on non-perturbative scales: emulation method and application to BOSS LOWZ

ABSTRACT We describe our non-linear emulation (i.e. interpolation) framework that combines the halo occupation distribution (HOD) galaxy bias model with N-body simulations of non-linear structure formation, designed to accurately predict the projected clustering and galaxy–galaxy lensing signals from luminous red galaxies in the redshift range 0.16 < z < 0.36 on comoving scales 0.6 < rp < 30 $h^{-1} \, \text{Mpc}$. The interpolation accuracy is ≲ 1–2 per cent across the entire physically plausible range of parameters for all scales considered. We correctly recover the true value of the cosmological parameter S8 = (σ8/0.8228)(Ωm/0.3107)0.6 from mock measurements produced via subhalo abundance matching (SHAM)-based light-cones designed to approximately match the properties of the SDSS LOWZ galaxy sample. Applying our model to Baryon Oscillation Spectroscopic Survey (BOSS) Data Release 14 (DR14) LOWZ galaxy clustering and galaxy-shear cross-correlation measurements made with Sloan Digital Sky Survey (SDSS) Data Release 8 (DR8) imaging, we perform a prototype cosmological analysis marginalizing over wCDM cosmological parameters and galaxy HOD parameters. We obtain a 4.4 per cent measurement of S8 = 0.847 ± 0.037, in 3.5σ tension with the Planck cosmological results of 1.00 ± 0.02. We discuss the possibility of underestimated systematic uncertainties or astrophysical effects that could explain this discrepancy.

The XXL Survey

Modern cosmological simulations heavily rely on feedback from active galactic nuclei (AGN) in order to stave off overcooling in massive galaxies, and galaxy groups and clusters. Given that AGN are a key component of such simulations, an important independent test is whether or not the simulations capture the broad demographics of the observed AGN population. However, to date, comparisons between observed and simulated AGN populations have been relatively limited. Here, we have used the cosmo-OWLS suite of cosmological hydrodynamical simulations to produce realistic synthetic catalogs of X-ray AGN out to z = 3, with the aim of comparing the catalogs to the observed X-ray AGN population in the XXL survey and other recent surveys. We focused on the unabsorbed X-ray luminosity function (XLF), the Eddington ratio distribution, the black hole mass function, and the projected clustering of X-ray AGN. To compute the unabsorbed XLF of the simulated AGN, we used recent empirically-determined (luminosity-dependent) bolometric corrections, in order to convert the simulated bolometric luminosity into an observable X-ray luminosity. We show that, using these corrections, the simulated AGN sample accurately reproduces the observed XLF over 3 orders of magnitude in X-ray luminosity in all redshift bins from z = 0 out to z = 3. To compare to the observed Eddington ratio distribution and the clustering of AGN, we produced detailed “XMM-Newton-detected” catalogs of the simulated AGN. This requires the production of synthetic X-ray images extracted from light cones of the simulations, which self-consistently contain both the X-ray AGN and the emission from diffuse, hot gas within galaxies, galaxy groups, and clusters and that fold in the relevant instrumental effects of XMM-Newton. We apply a luminosity- and redshift-dependent obscuration function for the AGN and employ the same AGN detection algorithm as used for the real XXL survey. We demonstrate that the detected population of simulated AGN reproduces the observed Eddington ratio distribution and projected clustering from XXL quite well. Based on these comparisons, we conclude that the simulations have a broadly realistic population of AGN and that our synthetic X-ray AGN catalogs should be useful for interpreting additional trends (e.g. environmental dependencies) and as a helpful tool for quantifying AGN contamination in galaxy group and cluster X-ray surveys.

Mining of high dimensional data using enhanced clustering approach

Extraction of useful data from a set is known as Data mining. Clustering has top information mining process it supposed to help an individual, divide and recognize numerous data from records inside group consistent with positive similarity measure. Clustering excessive dimensional data has been a chief undertaking. Maximum present clustering algorithms have been inefficient if desired similarity is computed among statistics factors inside the complete dimensional space. Varieties of projected clustering algorithms were counseled for addressing those problems. However many of them face problems whilst clusters conceal in some space with low dimensionality. These worrying situations inspire our system to endorse a look at partitional distance primarily based projected clustering set of rules. The aimed paintings is successfully deliberate for projects clusters in excessive huge dimension space via adapting the stepped forward method in k Mediods set of pointers. The main goal for second one gadget is to take away outliers, at the same time as the 1/3 method will find clusters in numerous spaces. The (clustering) technique is based on the adequate Mediods set of guidelines, an excess distance managed to set of attributes everywhere values are dense.