Output
The output of BayeSN will vary depending on whether the mode you are using. The output will be saved in
outputdir/name where outputdir and name correspond to the keys present in the input file as described in
Running large BayeSN Jobs.
Training output
The output of a training job will have the following structure:
bayesn.yaml: A yaml file containing the inferred global parameter values, which can then be used to fit data.fit_summary.csv: A summary of the MCMC output, showing parameter means/medians etc. as well as the Gelman-Rubin statistic and effective sample sizes to assess fit quality.initial_chains.pkl: The MCMC chains containing posterior samples, prior to any postprocessing, saved as a pickle file. This is a dictionary, with the keys corresponding to each parameter and the values the posterior samples for that parameter.chains.pkl: The same as above, except after postprocessing is applied. Postprocessing is required for a number of reasons. For example in the BayeSN model there exists a mirror degeneracy between theta and W1 whereby flipping the signs on both will lead to an identical output since they are multiplied together. As a result, sometimes different chains can move towards mirrored solutions. Postprocessing corrects for this to ensure that all chains have the same sign for elements of W1/theta values.
Fitting output
The output of a fitting job will have the following structure:
fit_summary.csv: A summary of the MCMC output, showing parameter means/medians etc. as well as the Gelman-Rubin statistic and effective sample sizes to assess fit quality.chains.pkl: The MCMC chains, as for the training output. Unlike for training, no postprocessing is required therefore only one set of chains needs to be saved.output.fitres: A FITRES file of the same structure as those returned by fits done within SNANA e.g. from SALT, summarising the properties of each fit light curve.
The plan for large SNANA jobs is that only the last of these outputs will be saved to avoid creating a very large number of output files.
As discussed in Running large BayeSN Jobs, when running BayeSN fits on a sample of SNe all objects are fit in parallel in a single job, rather than having a separate job for each SN. These output files therefore contain the outputs for all SNe in the sample.