bilby.gw.source.binary_black_hole_frequency_sequence

bilby.gw.source.binary_black_hole_frequency_sequence(frequency_array, mass_1, mass_2, luminosity_distance, a_1, tilt_1, phi_12, a_2, tilt_2, phi_jl, theta_jn, phase, **kwargs)

A Binary Black Hole waveform model using lalsimulation. This generates a waveform only on specified frequency points. This is useful for likelihood requiring waveform values at a subset of all the frequency samples. For example, this is used for MBGravitationalWaveTransient.

Parameters:

frequency_array: array_like

The input is ignored.

mass_1: float

The mass of the heavier object in solar masses

mass_2: float

The mass of the lighter object in solar masses

luminosity_distance: float

The luminosity distance in megaparsec

a_1: float

Dimensionless primary spin magnitude

tilt_1: float

Primary tilt angle

phi_12: float

Azimuthal angle between the two component spins

a_2: float

Dimensionless secondary spin magnitude

tilt_2: float

Secondary tilt angle

phi_jl: float

Azimuthal angle between the total binary angular momentum and the orbital angular momentum

theta_jn: float

Angle between the total binary angular momentum and the line of sight

phase: float

The phase at reference frequency or peak amplitude (depends on waveform)

kwargs: dict

Required keyword arguments - frequencies:

ndarray of frequencies at which waveforms are evaluated

Optional keyword arguments - waveform_approximant - reference_frequency - catch_waveform_errors - pn_spin_order - pn_tidal_order - pn_phase_order - pn_amplitude_order - mode_array:

Activate a specific mode array and evaluate the model using those modes only. e.g. waveform_arguments = dict(waveform_approximant=’IMRPhenomHM’, mode_array=[[2,2],[2,-2]) returns the 22 and 2-2 modes only of IMRPhenomHM. You can only specify modes that are included in that particular model. e.g. waveform_arguments = dict(waveform_approximant=’IMRPhenomHM’, mode_array=[[2,2],[2,-2],[5,5],[5,-5]]) is not allowed because the 55 modes are not included in this model. Be aware that some models only take positive modes and return the positive and the negative mode together, while others need to call both. e.g. waveform_arguments = dict(waveform_approximant=’IMRPhenomHM’, mode_array=[[2,2],[4,-4]]) returns the 22 and 2-2 of IMRPhenomHM. However, waveform_arguments = dict(waveform_approximant=’IMRPhenomXHM’, mode_array=[[2,2],[4,-4]]) returns the 22 and 4-4 of IMRPhenomXHM.

Returns:

dict: A dictionary with the plus and cross polarisation strain modes