jesterTOV.inference.likelihoods.gw.GWLikelihoodResampled

jesterTOV.inference.likelihoods.gw.GWLikelihoodResampled#

class GWLikelihoodResampled(
event_name,
model_dir,
penalty_value=0.0,
N_masses_evaluation=20,
N_masses_batch_size=10,
)[source]#

Bases: LikelihoodBase

Gravitational wave likelihood for a single GW event using normalizing flow posteriors

This likelihood evaluates the GW posterior by: 1. Sampling masses (m1, m2) from the trained normalizing flow 2. Interpolating tidal deformabilities (Λ1, Λ2) from the EOS 3. Evaluating the NF log probability on (m1, m2, Λ1, Λ2)

Parameters:

  • event_name (str) – Name of the GW event (e.g., “GW170817”)

  • model_dir (str) – Path to directory containing the trained normalizing flow model

  • penalty_value (float, optional) – Penalty value for samples where masses exceed Mtov (default: 0.0, i.e. no penalty)

  • N_masses_evaluation (int, optional) – Number of mass samples per likelihood evaluation (default: 20)

  • N_masses_batch_size (int, optional) – Batch size for processing mass samples (default: 10)

Variables:

  • event_name (str) – Name of the GW event

  • model_dir (str) – Path to directory containing the trained normalizing flow model

  • penalty_value (float) – Penalty value for samples where masses exceed Mtov

  • N_masses_evaluation (int) – Number of mass samples per likelihood evaluation

  • N_masses_batch_size (int) – Batch size for processing mass samples

  • flow (Flow) – Normalizing flow model for this GW event

__init__(
event_name,
model_dir,
penalty_value=0.0,
N_masses_evaluation=20,
N_masses_batch_size=10,
)[source]#

Methods

__init__(event_name, model_dir[, ...])

evaluate(params)

Evaluate log likelihood for given EOS parameters

Attributes

data

The data for the likelihood.

model

The model for the likelihood.

event_name

model_dir

penalty_value

N_masses_evaluation

N_masses_batch_size

flow

N_masses_batch_size: int#
N_masses_evaluation: int#
evaluate(params)[source]#

Evaluate log likelihood for given EOS parameters

Parameters:

params (dict[str, Float | Array]) – Must contain: - ‘_random_key’: Random seed for mass sampling (cast to int64) - ‘masses_EOS’: Array of neutron star masses from EOS - ‘Lambdas_EOS’: Array of tidal deformabilities from EOS

Return type:

Float

Returns:

Float – Log likelihood value for this GW event

event_name: str#
flow: Flow#
model_dir: str#
penalty_value: float#
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