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!