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.

  • output.LCPLOT: An LCPLOT data table containing both the data that was fit and corresponding model fits (with or without errors depending on whether you set save_fit_errors in the input yaml. Data is stored in rows with DATA_FLAG=1, while the fit is stored in rows with DATA_FLAG=0.

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.