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)