The Power Spectrum
from the final 2dFGRS catalogue |

The power spectrum data, window functions and covariance matrix are available as text files from the links below. We also provide two simple "C" functions designed to demonstrate the use of these data to determine the likelihood of a given cosmological model.

Refereed version of Cole et al. 2005

The power spectrum data(see header for details)

The power spectrumcovariance matri

The power spectrum window function- This C code demonstrates the steps necessary to compute the likelihood of a given model power spectrum.
Recovered C code to demonstrate likelihood

- 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

More information about the 2dFGRS is available from a number of websites including

2dFGRS 2001 Data Release
Power Spectrum |

**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 formSimple C codeto perform the 3D convolution with the window functionRecovered power spectrumfrom LCDM mock catalogues (see header for details)

Shaun Cole, shaun.cole@durham.ac.uk, Mon 18 Jul 2005