Parameterized EOS Inference

GWXtreme uses the emcee implementation of MCMC sampling to sample the posterior distribution of EOS parameters given gravitational wave event or pulsar mass-radius data.

The below script demonstrates usage of the ParameterizedEoSSampler class to conduct this inference for the 4-parameter spectral decomposition EOS model. Data from GW170817 and GW190425 are supplied. The 3-dimensional approximation method is chosen (gw-3d).

For these inferences, the bounds for a uniform prior distribution of the EOS parameters must be supplied. GWXtreme additionally checks during the sampling that every proposed EOS is physically valid. For more details, consult Ray et al. 2023.

import logging
from gwxtreme.eos_inference import ParameterizedEoSSampler, load_samples

# It is worth collecting logs from gwxtreme, especially when using the sampler, because
# they will help you track convergence of the MCMC chain
logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(name)s: %(message)s")

GW170817_pe_samples_file = "./GW170817_posterior_samples_with_uniform_lambda1_lambda2_prior.dat"
GW190425_pe_samples_file = "./GW190425_posterior_samples_with_uniform_lambda1_lambda2_prior.dat"

sampler = ParameterizedEoSSampler(
   posterior_files=[GW170817_pe_samples_file, GW190425_pe_samples_file],
   event_types=['gw-3d', 'gw-3d'],
   eos_prior_bounds=[(0.2, 2.0), (-1.6, 1.7), (-0.6, 0.6), (-0.02, 0.02)],
   parameterization='spectral'
)

sampler.run_sampler(nsteps=5_000, nwalkers=30, save_file='./samples_chain.hdf5')
samples = load_samples('./samples_chain.hdf5', burn_in_frac=0.5, thin=5)

To continue sampling from a previous run, simply repeat the above but pass reset=False when running the sampler. The given HDF5 file will then be used to determine where sampling should resume (i.e. the initial state for the new run will be the last sample of the existing, stored chain). New samples will be appended to the chain in the same file.

sampler.run_sampler(nsteps=1_000, nwalkers=30, save_file='./samples_chain.hdf5', reset=False)

We provide convenience functions to make plots of the 90% credible level of EOS curves in pressure-density space from the posterior samples of EOS parameters.

from gwxtreme.usage_scripts import compute_eos_constraints_from_parameter_samples, plot_parameterized_eos_constraints

samples_file = './samples_chain.hdf5'
constraints_file = './eos_constraints.txt'

samples = load_samples(samples_file, burn_in_frac=0.5, thin=5)

compute_eos_constraints_from_parameter_samples(samples, 'spectral', save_file=constraints_file)

plot_parameterized_eos_constraints(
   constraints_files=[constraints_file],  # files/labels from several runs may be provided
   labels=['GWXtreme spectral EOS constraints'],
   save_file='./constraints_plot.png',
   named_eos_list=['APR4_EPP']   # optionally provide LALSuite EOS names to include in the plot
)