A codebase for performing multi-channel model selection using catalogs of compact binaries
Michael Zevin, Chris Pankow
https://ui.adsabs.harvard.edu/abs/2017ApJ...846...82Z/abstract https://ui.adsabs.harvard.edu/abs/2020arXiv201110057Z/abstract
Why use one channel when you can use them all? AMAZE performs hierarchical inference on branching fractions between any number of population models, where each channel can also be parameterized by physical prescriptions. The executable model_select performs the inference, and has many options for including different channels, specifying whether to use mock observations or actual gravitational-wave observations, specifying the prescription for measurement uncertainty, etc. Run python model_select --help to learn more about all these options.
Included in this codebase are a number of notebooks (in the notebooks/ directory) that were used to pre-processing the data (process_unweighted_data.ipynb, process_GWTC_data.ipynb) and generate all the figures and numbers (paper_plots.ipynb) from Zevin et al. 2020 (https://ui.adsabs.harvard.edu/abs/2020arXiv201110057Z/abstract). Data from this project, including the processed public GW data, processed population models, and inference output, are available on Zenodo (https://zenodo.org/record/4277620#.X7w28RNKjUI).