Implementation of cluster abundance prediction using CCL backend

[payerne]

Implementation of cluster abundance cosmological prediction with CCL as a backend for:

1. cluster count in mass (respectively mass proxy) and redshift bins (for binned likelihood)
2. cluster count for individual masses (respectively mass proxies) and redshifts (for un-binned likelihood)

[payerne]

From now it is still on my personal github repository, since the we did not really discussed about the code structure on the repo.

The code is available here. It allows to compute the cluster count:

• in mass-redshift bins (for binned-likelihood),
• for individual masses and redshifts (for un-binned likelihood),
• in mass proxy-redshift bins (proxy modeling is coded here),
• for individual mass proxies and redshifts.

For MCMC’s reasons, I implemented different methods to fasten the computation of cluster count, and I compared the recovered precision to the direct CCL prediction (by using the standard use of scipy.quad and scipy.dqblquad for numerical integration). The recovered precision is tested in this notebook for the mass-redshift case, and this notebook for the mass proxy-redshift case.