Costless correction of chain based nested sampling parameter estimaion in gravitational wave data and beyond (supplementary material)

<p>These are the nested sampling inference products used to obtain the results for <span><a href="https://arxiv.org/abs/2404.16428">arXiv:2404.16428</a>. The chains are given as pickled dataframes as there are over 1000 runs provided here. The script for reproducing the plots in the paper is also given.&nbsp;</span></p> <p><span>Folders:</span></p> <p><span>outdir_simulated_BBH - contains 200 runs on the same simulated signal. 'samples' contains the full sets of weighted samples from each run and 'full_dfs' contains the dataframes with the weighted samples AND the phantom points from the run. The samples in 'full_dfs' with chain_no=0 are the weighted samples, and all others are phantoms.&nbsp;</span></p> <p><span>outdir_test - contains the single run on the above simulated signal which was performed with num_repeats=25*ndims=100.&nbsp;</span></p> <p><span>outdir_coverage - contains 1000 runs on different simulated signals, with parameters drawn from the prior. The drawn values are saved as '{}_params.npy' for each run.</span></p>