Measuring the galaxy power spectrum and scale-scale correlations with multiresolution-decomposed covariance -- I. method

Details Measuring the galaxy power spectrum and scale-scale correlations with multiresolution-decomposed covariance -- I. method Measuring the galaxy power spectrum and scale-scale correlations with multiresolution-decomposed covariance -- I. method

2000-03-17

arxiv.org/abs/astro-ph/0003259v1

Astronomy and Astrophysics

Astrophysics

AAS Latex file, 44 pages, accepted for publication in ApJ

Scientific paper

10.1086/309207

We present a method of measuring galaxy power spectrum based on the multiresolution analysis of the discrete wavelet transformation (DWT). Since the DWT representation has strong capability of suppressing the off-diagonal components of the covariance for selfsimilar clustering, the DWT covariance for popular models of the cold dark matter cosmogony generally is diagonal, or $j$(scale)-diagonal in the scale range, in which the second scale-scale correlations are weak. In this range, the DWT covariance gives a lossless estimation of the power spectrum, which is equal to the corresponding Fourier power spectrum banded with a logarithmical scaling. In the scale range, in which the scale-scale correlation is significant, the accuracy of a power spectrum detection depends on the scale-scale or band-band correlations. This is, for a precision measurements of the power spectrum, a measurement of the scale-scale or band-band correlations is needed. We show that the DWT covariance can be employed to measuring both the band-power spectrum and second order scale-scale correlation. We also present the DWT algorithm of the binning and Poisson sampling with real observational data. We show that the alias effect appeared in usual binning schemes can exactly be eliminated by the DWT binning. Since Poisson process possesses diagonal covariance in the DWT representation, the Poisson sampling and selection effects on the power spectrum and second order scale-scale correlation detection are suppressed into minimum. Moreover, the effect of the non-Gaussian features of the Poisson sampling can be calculated in this frame.

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