pygwb.pe.SchumannModel

class pygwb.pe.SchumannModel(**kwargs)

Bases: GWBModel

The Schumann model is defined as:

\[\Omega(f) = \sum_{ij} \kappa_i \kappa_j \left(\frac{f}{f_{\text{ref}}}\right)^{-\beta_i-\beta_j} M_{ij}(f) \times 10^{-46}\]

__init__(**kwargs)

Parameters:

• fref (float) – Reference frequency for defining the model (\(f_{\text{ref}}\))

• kappa_i (float) – Amplitude of coupling function of interferometer i at 10 Hz (\(\kappa_i\))

• beta_i (float) – Spectral index of coupling function of interferometer i (\(\beta_i\))

• frequencies (array_like) – Array of frequencies at which to evaluate the model

See also

pygwb.pe.GWBModel

The parent class used for the implementation.

__call__(*args, **kwargs)

Call self as a function.

Methods

__init__(**kwargs)	param fref: Reference frequency for defining the model (\(f_{\text{ref}}\))
log_likelihood()	Function for evaluating log likelihood of detector network.
log_likelihood_IJ(baseline, freq_mask[, noise])	Function for evaluating log likelihood of IJ baseline pair.
log_likelihood_ratio()	Difference between log likelihood and noise log likelihood
model_function(bline)	Function for evaluating model.
noise_log_likelihood()	Function for evaluating noise log likelihood of detector network.

Attributes

marginalized_parameters
meta_data
parameters	Parameters to be inferred from the data.

log_likelihood()

Function for evaluating log likelihood of detector network.

log_likelihood_IJ(baseline, freq_mask, noise=False)

Function for evaluating log likelihood of IJ baseline pair.

Parameters:

• baseline (pygwb.Baseline) – Baseline for which to run parameter estimation on.

• noise (bool, optional) – Parameter to indicate whether the likelihood should be evaluated assuming the signal model, or assuming only noise is present in the data.

Returns:

logL_IJ: float

Log likelihood value for the IJ baseline pair.

log_likelihood_ratio()

Difference between log likelihood and noise log likelihood

Returns:

float

model_function(bline)

Function for evaluating model.

noise_log_likelihood()

Function for evaluating noise log likelihood of detector network.

property parameters

Parameters to be inferred from the data. Should return a dictionary.