Likelihood analysis of cosmic shear on simulated and VIRMOS-DESCART data^*

¹ Institut d'Astrophysique de Paris, 98 bis boulevard Arago, 75014 Paris, France
² Canadian Institute for Theoretical Astrophysics, 60 St George Str., Toronto, M5S 3H8, Canada
³ Observatoire de Paris, LERMA, 61 avenue de l'Observatoire, 75014 Paris, France
⁴ Observatoire Midi-Pyrénées, UMR 5572, 14 avenue E. Belin, 31400 Toulouse, France
⁵ Department of Astronomy, University of Bologna, via Ranzani 1, 40127 Bologna, Italy
⁶ Observatorio Astronomico di Bologna, via Ranzani 1, 40127 Bologna, Italy
⁷ Laboratoire d'Astrophysique de Marseille, Traverse du Siphon, 13376 Marseille Cedex 12, France
⁸ Department of Physics and Astronomy, University of Pennsylvania, 209 S. 33rd Street, Philadelphia, PA 19104, USA

Corresponding author: L. Van Waerbeke, waerbeke@iap.fr

Received: 28 February 2002
Accepted: 4 June 2002

Abstract

We present a maximum likelihood analysis of cosmological parameters from measurements of the aperture mass up to 35 arcmin using simulated and real cosmic shear data. A four-dimensional parameter space is explored which examines the mean density , the mass power spectrum normalisation , the shape parameter Γ and the redshift of the sources z_s. Constraints on and (resp. Γ and z_s) are provided by marginalising over Γ and z_s ( resp. and ). For a flat ΛCDM cosmologies, using a photometric redshift prior for the sources and , we find at the confidence level (the error budget includes statistical noise, full cosmic variance and residual systematics). The estimate of Γ, marginalised over , and z_s constrained by photometric redshifts, gives at confidence. Adopting , a flat universe, and we find . Combined with CMB measurements, our results suggest a non-zero cosmological constant and provide tight constraints on and . Finally, we compare our results to the cluster abundance ones, and discuss the possible discrepancy with the latest determinations of the cluster method. In particular we point out the actual limitations of the mass power spectrum prediction in the non-linear regime, and the importance in improving this.