We provide the fiducial output chains of the likelihood analysis from
Hildebrandt et al. (2019; arXiv:XXX).
The chains were created using the publicly available likelihood module (after acceptance; either to use directly with ‘Monte Python’ or to use as a blueprint for your own code). This module was used with the publicly available ‘Monte Python’ package (based on the Boltzmann-code ’CLASS’ for cosmological calculations). All required software packages are public and can be obtained following the links below:
- likelihood module (available after the paper has been accepted for publication):
- Monte Python: https://github.com/baudren/montepython_public
- CLASS: https://github.com/lesgourg/class_public
Optional packages for using the highly efficient MultiNest sampler within Monte Python (including e.g. efficient evidence calculations):
- PyMultiNest: https://github.com/JohannesBuchner/PyMultiNest
- MultiNest: https://github.com/JohannesBuchner/MultiNest
Note that the likelihood inference was carried out using the MultiNest sampler, hence the ‘chains’ are much shorter than typical chains from a usual Metropolis-Hastings sampler. No 'burn-in' has to be removed from the chain.
In particular, we provide the files (in FITS and tar.gz format; the latter is e.g. compatible with getdist):
< KV450_fiducial.{fits, tar.gz} >
Note that it is important to propagate the weight of each point in the calculation of posterior probability distributions!
The format of each line in these files is as follows:
Column 1: weight of the point
Column 2: -ln(Likelihood)
Column Name Parameter/meaning
3 omega_cdm \Omega_{cdm} * h^2
4 ln10^{10}A_s \ln (10^10 * A_s)
5 omega_b \Omega_{baryons} * h^2
6 n_s n_s
7 h h = H0 / (100 km / Mpc / s)
8 A_IA amplitude for intrinsic alignments
9 c_min HMCode parameter B for the baryon feedback model
10 dc Nuisance parameter for the constant c-term
11 Ac Nuisance parameter for the 2D c-term amplitude
12 D_z1 Redshift uncertainty in the 1st bin
13 D_z2 Redshift uncertainty in the 2nd bin
14 D_z3 Redshift uncertainty in the 3rd bin
15 D_z4 Redshift uncertainty in the 4th bin
16 D_z5 Redshift uncertainty in the 5th bin
17 Omega_m \Omega_m
18 sigma8 \sigma_8
19 S8 S_8 = \sigma_8 * \sqrt(\Omega_m / 0.3)