Demo 6: Getting a CFHTLens likelihood
In this example we will generate a likelihood of a cosmology using the CFHTLens tomographic data from Heymans et al.
We will use the test sampler again, so just a single cosmology. I only just added the demo6.ini file, so if you can't see demos/demo6.ini then do "git pull" in your main directory to get these new files.
You should see some output like this:
-- Setting up module camb -- -- Setting up module halofit -- -- Setting up module load_nz -- Found 6 samples and 72 bins in redshift in file cosmosis-standard- library/likelihood/cfhtlens/combined_nz.txt -- Setting up module shear_shear -- -- Setting up module 2pt -- -- Setting up module cfhtlens -- Need to check the Anderson Hartlap when cutting matrix - cut first? xi_plus only? False Cut low thetas? True Setup all pipeline modules Pipeline ran okay. Likelihood -1.694817e+02 Prior = 0 Likelihood = -169.481692685 Posterior = -169.481692685
The last number is the CFHTLens log-posterior of the parameters in demos/values6.ini.
Now let's make some plots. For some variety, let's make them PDF plots instead of PNG:
postprocess demos/demo6.ini -o plots -p demo6 -f pdf
You will get some nice new plots in plots/demo6*.pdf. Since we are now computing shear-shear functions we will have two new plots compared to demo 1: demo6_shear_power.pdf and demo6_shear_correlation.pdf. Here's the shear power plot:
The weak lensing case is an example where there is a longer sequence of different modules. The demos/demo6.ini file contains this line:
modules = consistency camb halofit load_nz shear_shear 2pt cfhtlens
We have already looked at the consistency, camb and halofit modules in demo one. The load_nz module just loads a simple text file containing n(z) in its setup, and then each time it is executed provides the same n(z).
The shear_shear module computes the Limber integral to go from P(k,z) and n(z) to C_ell for the different bins and the correlations between them.
The 2pt module integrates the C_ell with bessel functions J0 and J4 to get correlation function xi+ and x-.
The CFHTLens module interpolates into the xi+ and xi- values to get their values at the CFHTLens observed values, and then gets a likelihood.