• Refereed version of Cole et al. 2005
  
 
• The power spectrum data (see header for details)
  
 
• The power spectrum covariance matri
  
 
• The power spectrum window function
 
• Recovered C code to demonstrate likelihood 
This C code demonstrates the steps necessary to compute the likelihood of a given model power spectrum.
• reading the window function and convolving the model power spectrum, that is to be tested, by the window function
• inverting the covariance matrix and scaling it according to the convolved model power spectrum
• differencing the 2dFGRS and model spectra and computing the likelihood

Data from the fourier analysis of Percival et al (2001)

The data from the earlier fourier analysis of Percival et al (2001) of the incomplete catalogue 2dFGRS catalogue is also still available here.

The power spectrum data and appropriate covariance matrix from Percival et al. (2001, MNRAS, 327, 1297) are available as text files from the links below. They are designed to be self-explanatory, but in the event of problems please send an email to Will Percival.

Because of the difficulty of performing a 3D convolution with the appropriate survey window function (given a model power spectrum), a simple C program that demonstrates how this may be achieved for any k-value is also available (note that this comes with no warranty).

Given a sufficiently smooth P(k), it is possible to speed up this process using the window function in matrix form. Such a matrix is also available from a link below for determining the convolved power spectrum at the k-values of the data. This file is organised as follows: the first line gives the k-values at which the convolved P(k) is being calculated (chosen to be the same as the data); each subsequent line gives the k-value at which the unconvolved P(k) should be determined (100 points), and the weights required to calculate the 32 convolved P(k) values. The simple program given below demonstrates how to use this matrix and compares the result with the numerically convolved P(k). If you use the matrix method, it is suggested that you check that the power spectra that you wish to convolve are sufficiently smooth that the two methods give the same answer (to the required accuracy).

If you download these data or programs please send an email to Will Percival so that you can be contacted in case of updates or corrections.

• Brief introduction
• Percival et al. (2001, MNRAS, 327, 1297) ( ADS | astro-ph )
• The power spectrum data (see header for details)
• The power spectrum covariance matrix
• The power spectrum window function in matrix form
• Simple C code to perform the 3D convolution with the window function
• Recovered power spectrum from LCDM mock catalogues (see header for details)