fast_lisa_subtraction.priors.galactic_binaries module

class fast_lisa_subtraction.priors.galactic_binaries.GalacticBinaryPopulation(priors=None, device='cpu')[source]

Bases: MultivariatePrior

Multivariate prior for monochromatic Galactic binaries.

This class defines a multivariate prior over intrinsic and extrinsic parameters and provides sampling utilities, including optional copula correlation between frequency and frequency derivative.

Parameters:

  • priors (dict or list, optional) – Dictionary or list of dictionaries specifying priors for each parameter. If None, defaults are used.

  • device (str, optional) – Torch device used for sampling.

References

[1] A. Lamberts et al. (2019)

[2] F. De Santi et al. (2026)

sample(num_samples, standardize=False, copula=True, **copula_kwargs)[source]

Sample from the prior distribution.

Parameters:

  • num_samples (int) – Number of samples to draw.

  • standardize (bool, optional) – Whether to standardize the samples to zero mean and unit variance (for training purposes).

  • copula (bool, optional) – If True, draw correlated samples for frequency and frequency derivative using a copula.

  • **copula_kwargs (dict) – Additional keyword arguments for the copula function (for example, correlation coefficient).

Returns:

Samples drawn from the prior distribution.

Return type:

TensorSamples

class fast_lisa_subtraction.priors.galactic_binaries.RandomFromCatalog(catalog_path, name, minimum=None, maximum=None, device='cpu')[source]

Bases: Prior

Sample a single parameter from a catalogue.

Parameters:

  • catalogue_path (str or os.PathLike) – Path to the catalogue HDF5 file.

  • name (str) – Column name to sample.

  • minimum (float or None, optional) – Lower bound of the support.

  • maximum (float or None, optional) – Upper bound of the support.

  • device (str, optional) – Torch device used for sampling.

sample(num_samples, standardize=False)[source]

Sample from the catalogue.

Parameters:

  • num_samples (int) – Number of samples to draw.

  • standardize (bool, optional) – If True, return standardized samples.

Returns:

Samples drawn from the catalogue.

Return type:

torch.Tensor