A Bayesian analysis for constraining and comparing short gamma-ray jet structures given the gravitational wave and prompt emission observations of GW170817 and GW190425, as well at the rate of detected short gamma-ray bursts by the Swift detector as described in Unpacking merger jets: a Bayesian analysis of GW170817, GW190425 and electromagnetic observations of short gamma-ray bursts. Top-hat (TH), Gaussian jet (GJ), power-law (PL) and double Gaussian (DG) jet structures are compared and the local rate of binary neutron star mergers is constrained.
An environment with both nessai and bibly are required. These can be installed via pip:
pip install nessai bilby
Or alternatively install the packages in requirements.txt via:
pip install -r requirements.txt
To run the analysis for the Gaussian jet structure given GW170817 and a random seed of 73419243, and save to a local directory './outpath' use command:
python sample_model_nessai.py --path ./outpath --struct GJ --GW170817 --randomseed 73419243
This run is represented in a tabulated form such that:
| Structure | GW170817 | GW190425 | rates | fixed LF | random seed |
|---|---|---|---|---|---|
| GJ | ✔️ | 73419243 |
If instead we wish to run for the double Gaussian jet structure with GW170817, GW190425 and the rate of detected short gamma-ray bursts by Swift, and by using the fitted luminosity function, then run:
python sample_model_nessai.py --path ./outpath --struct DG --GW170817 --GW190425 --rates --Lfixed --randomseed 78951308
This run is represented in the table as:
| Structure | GW170817 | GW190425 | rates | fixed LF | random seed |
|---|---|---|---|---|---|
| DG | ✔️ | ✔️ | ✔️ | ✔️ | 78951308 |
Use the --cores <N> arguement to specify the number of cores to dedicate to the run, where if not specified the default will resort to the number available.
The random seeds required to produce the results in the paper are given in the following table.
| Structure | GW170817 | GW190425 | rates | fixed LF | random seed |
|---|---|---|---|---|---|
| TH | ✔️ | ✔️ | 81717499 | ||
| TH | ✔️ | ✔️ | 1898263 | ||
| TH | ✔️ | ✔️ | 81294950 | ||
| TH | ✔️ | ✔️ | ✔️ | ✔️ | 38433061 |
| TH | ✔️ | ✔️ | ✔️ | 93999094 | |
| TH | ✔️ | 54520780 | |||
| TH | ✔️ | 35185720 | |||
| TH | ✔️ | 1898263 | |||
| TH | ✔️ | ✔️ | ✔️ | 8967996 | |
| TH | ✔️ | ✔️ | 94081413 | ||
| GJ | ✔️ | ✔️ | 23096079 | ||
| GJ | ✔️ | ✔️ | ✔️ | ✔️ | 55748431 |
| GJ | ✔️ | ✔️ | ✔️ | 46968036 | |
| GJ | ✔️ | ✔️ | 12767752 | ||
| GJ | ✔️ | ✔️ | 73206677 | ||
| GJ | ✔️ | 73419243 | |||
| GJ | ✔️ | ✔️ | ✔️ | 6231946 | |
| GJ | ✔️ | ✔️ | 94081413 | ||
| GJ | ✔️ | 35185720 | |||
| GJ | ✔️ | 31277933 | |||
| PL | ✔️ | ✔️ | 669246 | ||
| PL | ✔️ | ✔️ | ✔️ | 93999094 | |
| PL | ✔️ | ✔️ | 69983429 | ||
| PL | ✔️ | ✔️ | ✔️ | ✔️ | 45040263 |
| PL | ✔️ | ✔️ | 70566340 | ||
| PL | ✔️ | 1898263 | |||
| PL | ✔️ | ✔️ | 68158422 | ||
| PL | ✔️ | 48197248 | |||
| PL | ✔️ | ✔️ | ✔️ | 91198413 | |
| PL | ✔️ | 35185720 | |||
| DG | ✔️ | ✔️ | ✔️ | 23096079 | |
| DG | ✔️ | ✔️ | 73206677 | ||
| DG | ✔️ | ✔️ | ✔️ | ✔️ | 78951308 |
| DG | ✔️ | ✔️ | 78408861 | ||
| DG | ✔️ | ✔️ | 79188414 | ||
| DG | ✔️ | ✔️ | 68158422 | ||
| DG | ✔️ | 27815768 | |||
| DG | ✔️ | ✔️ | ✔️ | 29910191 | |
| DG | ✔️ | 35185720 | |||
| DG | ✔️ | 12767752 |
To produce the plots found in the paper in a directory './figures', given the runs are stored in './outpath/', simply run:
bash run_plot_all.sh './outpath/' ./figures
which uses the plotting tools provided in plotting_tools.