Analyzing a binary neutron star signal

Analyzing a binary neutron star signal#

This page in the inference guide helps you set up the jester inference to analyze the equation of state from gravitational wave (GW) data from a binary neutron star (BNS) merger.

Get GW posteriors#

jester takes posterior samples of the GW source parameters as input to then constrain the EOS. Therefore, the first step is to run your favourite GW sampler in order to obtain these posterior samples. In particular, we require the source-frame component masses and the tidal deformabilities, which are named mass_1_source, mass_2_source, lambda_1, lambda_2.

There are various ways to then store the posterior samples:

  • As bilby result HDF5 file: bilby creates the result file at the end of the inference run. We have some internal preprocessing functions to load in the HDF5 file for the jester inference.

  • As npz file: In order to handle the output from any sampler, we also support npz files, which are a lightweight way to store your data. You can create the npz files yourself with a small utilities script by calling the np.savez function, and storing the data arrays as mass_1_source, mass_2_source, lambda_1, lambda_2.

GW posterior postprocessing: training a normalizing flow#

In order to use the posterior samples in the EOS inference, we need to approximate the 4D marginal posterior on the source-frame masses and tidal deformabilities. To achieve this, jester makes use of density estimators. In practice, we train a normalizing flow (NF) on the samples to make their density tractable for inference.

There are various ways to train the normalizing flow. Currently, we have two ways to train the flow automatically when setting up inference. The config.yaml below shows how to start from the bilby result HDF5 file.

- type: gw
  enabled: true
  events:
  - name: GW170817
    from_bilby_result: path/to/bilby/result.h5

The following config.yaml shows how to, instead, start from an npz file.

- type: gw
  enabled: true
  events:
  - name: GW170817
    from_npz_file: path/to/npz/file.npz

You can choose to use the default hyperparameters and settings for training the flow, which are chosen to be robust enough and should give good results for use in jester. However, in case you suspect the settings are suboptimal and want to pass your own flow hyperparameters, you can do so by adding a flag to the config file as follows, which points to a config.yaml file.

- type: gw
  enabled: true
  events:
  - name: GW170817
    from_npz_file: path/to/npz/file.npz
    flow_config: path/to/flow_config.yaml

This flow_config.yaml file should contain the settings necessary for FlowTrainingConfig (see jester/jesterTOV/inference/flows/config.py).

After the flow has been trained, the flow weights and some metadata is saved inside the specified output directory in a subdirectory called gw_flow_cache/. NOTE: If you rerun the jester inference and no changes have been made to the flow settings, jester will load in the flow and not retrain the flow. If you wish to retrain the flow (e.g., if you changed the dataset), then you have to remove the directory containing the flow first.

Once a trained flow exists, you can easily re-use it in subsequent jester inferences by pointing to the NF model directory containing the weights and metadata to load it into jester.

- type: gw
  enabled: true
  events:
  - name: GW170817
    nf_model_dir: path/to/model_dir/

Running jester#

Once the flow training has been set up in the config.yaml file, jester is good to go to analyze the BNS data! You can learn more about running the jester inference in the following pages: