Observing the clustering properties of galaxy clusters in dynamical dark-energy cosmologies

We study the clustering properties of galaxy clusters expected to be observed by various forthcoming surveys both in the X-ray and sub-mm regimes by the thermal Sunyaev-Zel'dovich effect. Several different background cosmological models are assumed, including the concordance $\Lambda$CDM and various cosmologies with dynamical evolution of the dark energy. Particular attention is paid to models with a significant contribution of dark energy at early times which affects the process of structure formation. Past light cone and selection effects in cluster catalogs are carefully modeled by realistic scaling relations between cluster mass and observables and by properly taking into account the selection functions of the different instruments. The results show that early dark-energy models are expected to produce significantly lower values of effective bias and both spatial and angular correlation amplitudes with respect to the standard $\Lambda$CDM model. Among the cluster catalogues studied in this work, it turns out that those based on \emph{eRosita}, \emph{Planck}, and South Pole Telescope observations are the most promising for distinguishing between various dark-energy models.Comment: 16 pages, 10 figures. A&A in pres

Relativistic Effect in Galaxy Clustering

The general relativistic description of galaxy clustering provides a complete and unified treatment of all the effects in galaxy clustering such as the redshift-space distortion, gravitational lensing, Sachs-Wolfe effects, and their relativistic effects. In particular, the relativistic description resolves the gauge issues in the standard Newtonian description of galaxy clustering by providing the gauge-invariant expression for the observed galaxy number density. The relativistic effect in galaxy clustering is significant on large scales, in which dark energy models or alternative theories of modified gravity deviate from general relativity. In this paper, we review the relativistic effect in galaxy clustering by providing a pedagogical derivation of the relativistic formula and by computing the observed galaxy two-point statistics. The relativistic description of galaxy clustering is an essential tool for testing general relativity and probing the early Universe on large scales in the era of precision cosmology.Comment: 21 pages, no figures. Invited review article, accepted for publication in Classical and Quantum Gravity focus issue on "Relativistic Effects in Cosmology", edited by Kazuya Koyam

A Serendipitous Galaxy Cluster Survey with XMM: Expected Catalogue Properties and Scientific Applications

This paper describes a serendipitous galaxy cluster survey that we plan to conduct with the XMM X-ray satellite. We have modeled the expected properties of such a survey for three different cosmological models, using an extended Press-Schechter (Press & Schechter 1974) formalism, combined with a detailed characterization of the expected capabilities of the EPIC camera on board XMM. We estimate that, over the ten year design lifetime of XMM, the EPIC camera will image a total of ~800 square degrees in fields suitable for the serendipitous detection of clusters of galaxies. For the presently-favored low-density model with a cosmological constant, our simulations predict that this survey area would yield a catalogue of more than 8000 clusters, ranging from poor to very rich systems, with around 750 detections above z=1. A low-density open Universe yields similar numbers, though with a different redshift distribution, while a critical-density Universe gives considerably fewer clusters. This dependence of catalogue properties on cosmology means that the proposed survey will place strong constraints on the values of Omega-Matter and Omega-Lambda. The survey would also facilitate a variety of follow-up projects, including the quantification of evolution in the cluster X-ray luminosity-temperature relation, the study of high-redshift galaxies via gravitational lensing, follow-up observations of the Sunyaev-Zel'dovich effect and foreground analyses of cosmic microwave background maps.Comment: Accepted to ApJ. Minor changes, e.g. presentation of temperature errors as a figure (rather than as a table). Latex (20 pages, 6 figures, uses emulateapj.sty

A brief history of learning classifier systems: from CS-1 to XCS and its variants

© 2015, Springer-Verlag Berlin Heidelberg. The direction set by Wilson’s XCS is that modern Learning Classifier Systems can be characterized by their use of rule accuracy as the utility metric for the search algorithm(s) discovering useful rules. Such searching typically takes place within the restricted space of co-active rules for efficiency. This paper gives an overview of the evolution of Learning Classifier Systems up to XCS, and then of some of the subsequent developments of Wilson’s algorithm to different types of learning

Massive Science with VO and Grids

There is a growing need for massive computational resources for the analysis of new astronomical datasets. To tackle this problem, we present here our first steps towards marrying two new and emerging technologies; the Virtual Observatory (e.g, AstroGrid) and the computational grid (e.g. TeraGrid, COSMOS etc.). We discuss the construction of VOTechBroker, which is a modular software tool designed to abstract the tasks of submission and management of a large number of computational jobs to a distributed computer system. The broker will also interact with the AstroGrid workflow and MySpace environments. We discuss our planned usages of the VOTechBroker in computing a huge number of n-point correlation functions from the SDSS data and massive model-fitting of millions of CMBfast models to WMAP data. We also discuss other applications including the determination of the XMM Cluster Survey selection function and the construction of new WMAP maps.Comment: Invited talk at ADASSXV conference published as ASP Conference Series, Vol. XXX, 2005 C. Gabriel, C. Arviset, D. Ponz and E. Solano, eds. 9 page

MILCS: A mutual information learning classifier system

This paper introduces a new variety of learning classifier system (LCS), called MILCS, which utilizes mutual information as fitness feedback. Unlike most LCSs, MILCS is specifically designed for supervised learning. MILCS's design draws on an analogy to the structural learning approach of cascade correlation networks. We present preliminary results, and contrast them to results from XCS. We discuss the explanatory power of the resulting rule sets, and introduce a new technique for visualizing explanatory power. Final comments include future directions for this research, including investigations in neural networks and other systems. Copyright 2007 ACM