These files are the data behind the figure for Figure 3 (and the corresponding Figure Set) as well as other fits presented in Table 5. They are saved in npy format which can be read into python using numpy according to the code snippet below. The files are flattened and trimmed MCMC chains produced by running emcee (Foreman-Mackey et al. 2013) using 64 walkers for 10,000 steps. The first 1,000 steps were trimmed for burn in and the remaining chains were thinned by 40 steps. The files are named according to the following convention: flatSamples<malm cor><age><prior>.npy where: <malm cor> is either 'Malm' or '' (nothing) if the model population was or was not corrected for Malmquist bias (before comparing to the observed population while fitting). <age> is '0p9', '1p2', '1p5', '1p9', '2p4', or '3p1' according to that assumed field age (in Gyr). <prior> is 'U' or 'I' for uninformed or informed (incorporating the information from Blake et al. 2010 on the unresolved population). The true underlying population corresponds to the flatSamplesMalm<age>I.npy files while the others are included for context and comparison to populations fit to the observed (not Malmquist corrected) population. The uninformed prior chains are dominated by a significant population of unresolved companions which is not consistent with previous RV studies. The files can be read into python using: import numpy as np flat_samples0p9I = np.load('flatSamples0p9I.npy') which produces an array with shape 14400 x 4. The rows are the samples and the four columns are the parameters \(F, \gamma, \overline{\log(\rho)}\), and \(\sigma_{\log(\rho)}\), respectively.

{"references": ["Foreman-Mackey et al. (2013)"]}