The results for our paper on exoplanet population inference.
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README.md
completeness.h5
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README.md

Exoplanet population inference

These are the data and results from the recently submitted paper "Exoplanet population inference and the abundance of Earth analogs from noisy, incomplete catalogs" by Daniel Foreman-Mackey, David W. Hogg, and Timothy Morton.

This archive contains the catalogs of exoplanets (both simulated and real) used in the analysis and a thinned set of posterior samples generated in the MCMC analysis. A binned representation of the empirical detection efficiency function computed using the simulated results from Petigura et al. (2013) is also included for the sake of completeness ;-).

See the paper for more details.

Products

The HDF5 file completeness.h5 gives the empirical detection efficiency computed on a fine grid in (ln-)period and (ln-)radius. The bin edges are given in the datasets ln_period_bin_edges and ln_radius_bin_edges. The dataset ln_completeness gives the natural logarithm of the empirical detection efficiency for transiting planets. The dataset ln_detect_eff is the same but it takes the geometric transit probability into account. Both grids of detection efficiency are padded with a ring of -infinity because the prior detection efficiency is exactly zero outside of the searched range.

The data and results for the simulated catalogs and real catalog are given in the directories simulated/* and real respectively. Each directory has two files: catalog.txt and samples.h5. The catalog file is the dataset and it lists the given posterior measurements of period (measured in days), radius (measured in Earth radii), and radius uncertainty for each candidate. The samples file give the posterior samples for the True occurrence rate density modeled as a set of bin heights. As above, the *_bin_edges datasets give the bin definitions. The ln_occurrence_rate_samples dataset gives a list of samples from the posterior probability for the log-bin heights. This list was generated using the method described in the paper and then thinned by the shortest integrated autocorrelation time of the parameters. Finally, the hyperparameter_samples dataset gives the corresponding list of hyperparameter samples. In the notation of the paper, the columns give "mu", "lambda_0", "lambda_P", and "lambda_R", respectively.

License

These data are being publicly shared under the terms of the Creative Commons Attribution license (v4.0; http://creativecommons.org/licenses/by/4.0/). If you make use of these data, please cite the paper.

DOI