Mapping the large-scale anisotropy in the WMAP data

¹ Instituto Nacional de Pesquisas Espaciais, Divisão de Astrofísica, Av. dos Astronautas 1758, 12227-010 São José dos Campos – SP, Brazil e-mail: bernui@das.inpe.br
² Centro Brasileiro de Pesquisas Físicas, Rua Dr. Xavier Sigaud 150, 22290-180 Rio de Janeiro – RJ, Brazil e-mail: [brunom;reboucas]@cbpf.br
³ Astronomy Unit, School of Mathematical Sciences, Queen Mary, University of London, Mile End Road, London E1 4NS, UK e-mail: r.tavakol@qmul.ac.uk

Received: 10 May 2006
Accepted: 16 November 2006

Abstract

Aims.Analyses of recent cosmic microwave background (CMB) observations have provided increasing hints that there are deviations in the universe from statistical isotropy on large scales. Given the far reaching consequences of such an anisotropy for our understanding of the universe, it is important to employ alternative indicators in order to determine whether the reported anisotropy is cosmological in origin and, if so, extract information that may be helpful for identifying its causes.

Methods.Here we propose a new directional indicator, based on separation histograms of pairs of pixels, which provides a measure of departure from statistical isotropy. The main advantage of this indicator is that it generates a sky map of large-scale anisotropies in the CMB temperature map, thus allowing a possible additional window into their causes.

Results.Using this indicator, we find statistically significant excess of large-scale anisotropy at well over the 95% confidence level. This anisotropy defines a preferred direction in the CMB data. We discuss the statistical significance of this direction compared to Monte Carlo data obtained under the statistical isotropy hypothesis, and also compare it with other such axes recently reported in the literature. In addition we show that our findings are robust with respect to the details of the method used, so long as the statistical noise is kept under control; and they remain unchanged compared to the foreground cleaning algorithms used in CMB maps. We also find that the application of our method to the first and three-year WMAP data produces similar results.