We provide the final output chains of the likelihood analysis from Koehlinger & KiDS 2017 (arXiv1706.02892), corresponding to the labels ‘\Lambda CDM + A_{IA} + A_{baby} + \Sigma M_\nu + noise; {2, 3} z-bins’ in Fig. 8 of that analysis.

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):
https://bitbucket.org/fkoehlin/kids450_qe_likelihood_public 

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.

In particular, we provide the files (in FITS and tar.gz format; the latter is e.g. compatible with getdist):

< KiDS450_QE_EB_4bins_{2, 3}zbins_basez_ia_bary_nu.{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      M_tot         total mass of 3 degenerate neutrino species
9      m_corr        parameter for marginalizing over shear calibration uncertainty 
10     A_noise_z1    parameter for marginalizing over excess-noise (z-bin 1)
11     A_noise_z2    parameter for marginalizing over excess-noise (z-bin 2)
12     A_noise_z3    parameter for marginalizing over excess-noise (z-bin 3)
13     A_IA          amplitude for intrinsic alignments
14     A_bary        amplitude for the baryon feedback model
15     Omega_m       \Omega_m
16     sigma8        \sigma_8
17     S8            S_8 = \sigma_8 * \sqrt(\Omega_m / 0.3)

Note that it is important to propagate the weight of each point in the calculation of posterior probability distributions!