Likelihood

List of detector objects containing data and metadata.

Waveform model to evaluate.

Dictionary of fixed parameter values. Each value may be a constant Float, a callable returning a scalar, or a callable returning a dict (e.g. transform.backward). See TransientLikelihoodFD for a detailed description and example.

Minimum frequency for likelihood evaluation.

Maximum frequency for likelihood evaluation.

GPS time of the event trigger.

Number of frequency bins for relative binning.

Population size for the CMA-ES optimizer used when finding reference parameters automatically. Defaults to 500.

Maximum number of CMA-ES generations. Defaults to 1000.

Pre-computed reference parameters (dict). If supplied, the optimizer is skipped entirely.

Optional waveform instance used to compute the reference waveform. Defaults to waveform when not provided.

Prior distribution from which the initial CMA-ES mean is drawn. Required when reference_parameters is not provided.

Transforms mapping sampling parameters to likelihood parameters (e.g. mass-ratio → symmetric mass-ratio).

If provided, marginalize over coalescence phase phase_c. Pass a PhaseMargConfig object, a plain dict {}, or True (shorthand for PhaseMargConfig()). None or False (default) disables phase marginalization.

Make a binning scheme based on the maximum phase difference between the

Compute the maximum phase difference between the frequencies in the array.

Find the maximum-likelihood parameters using CMA-ES.

Reference chirp mass for determining frequency bands.

Same value converted to seconds.

Maximum magnetic number of GW moments (fixed to 2 for 22-mode).

Parameter L controlling approximation accuracy.

Time offset for band construction.

Frequency scale for high-frequency tapering.

Durations of each band.

Starting frequencies and taper widths for each band.

Pre-computed coefficients for (d|h) inner product.

Pre-computed coefficients for (h|h) inner product.

List of detector objects.

Waveform model to evaluate.

Fixed parameters for the likelihood.

Minimum frequency for likelihood evaluation, or a dict mapping detector name to per-detector Float.

Maximum frequency for likelihood evaluation, or a dict mapping detector name to per-detector Float.

GPS time of the event trigger.

Maximum magnetic number (default 2, for 22-mode only).

Accuracy parameter L (default 5.0).

Combined prior object. Needed when reference_chirp_mass, time_offset, or delta_f_end are None so they can be inferred automatically from the prior bounds.

Reference chirp mass in solar masses. Use the minimum of your chirp-mass prior for maximum speedup. When None, the value is inferred from the M_c component of prior.

Time offset in seconds. When None, inferred from the t_c (or t_{ifo}) prior range; falls back to 2.12 s with a warning when the prior is unavailable.

End frequency taper scale in Hz. When None, inferred from the t_c prior range; falls back to 53.0 Hz.

Upper limit on band starting frequency.

Minimum duration for bands.

Evaluate the log-likelihood for given parameters.

Args:

Apply fixed_parameters overrides and evaluate the likelihood.

Names of the detectors used in this likelihood.

Duration of the data segment in seconds (taken from the first detector).

List of detector objects containing data and metadata.

Waveform model to evaluate.

Parameters held constant during sampling. Values may be constants or callables f(params) -> Float | dict; callables are applied in insertion order. See the likelihood tutorial for details and examples.

Minimum frequency for likelihood evaluation. Can be a single float or a per-detector dictionary.

Maximum frequency for likelihood evaluation. Can be a single float or a per-detector dictionary.

GPS time of the event trigger.

If provided, marginalize over coalescence time t_c. Pass a TimeMargConfig object, a plain dict (e.g. {"tc_range": (-0.1, 0.1)}), or True (shorthand for TimeMargConfig()). False or the default None disables time marginalization.

If provided, marginalize over coalescence phase phase_c. Pass a PhaseMargConfig object, a plain dict {}, or True (shorthand for PhaseMargConfig()). False or the default None disables phase marginalization.

If provided, marginalize over luminosity distance d_L. Pass a DistanceMargConfig object or a plain dict (e.g. {"distance_prior": prior, "n_dist_points": 10000}). False or the default None disables distance marginalization. True is not supported — distance_prior has no default; pass a dict or DistanceMargConfig instead.

Return zero regardless of the parameters.