xi_priors

GWCorrect.wfu.prior.xi_priors(waveform_generator,prior,psd_data,n,minimum_frequency,xi_min=0.018,
xi_max=1/np.pi,xi_0_latex_label=r'$\xi_0$',delta_xi_tilde_latex_label=r'$\delta\tilde\xi$')

Generates xi_0 and delta_xi_tilde priors from a BBH prior and adds them to the original prior.

\[2\mathcal{A}(\xi;\vartheta)\sqrt{\frac{c^3\xi}{GM}}-\sqrt{S_n(\xi)}=0\]

\[\Pi(\xi_0)=\mathrm{TFDG}(\mu_1,\mu_2,\sigma_1,\sigma_2,\xi_\mathrm{min},\xi_\mathrm{max}),\ \Pi(\xi_0)=\mathrm{EHG}(\mu,\sigma,\xi_\mathrm{min},\xi_\mathrm{max})\]

\[\Pi(\delta\tilde\xi)=\mathrm{EHG}(\mu,\sigma,0,1)\]

Parameters:

waveform_generator: bilby.gw.WaveformGenerator

bilby waveform generator object

prior: bilby.core.prior.PriorDict

bilby prior dictionary

psd_data: numpy.ndarray

array of power spectral density data; first column needs to be the frequency points and the second column needs to be the data

n: int

number of frequency nodes

minimum_frequency: float

lower bound on the frequency band (Hz)

xi_0_latex_label: string, optional, (r’$xi_0$’)

latex label for xi_0

delta_xi_tilde_latex_label: string, optional, (r’$delta tilde xi$’)

latex_label for delta_xi_tilde

xi_min: float, optional, (0.018)

lower bound on the dimensionless frequency band

xi_max: float, optional, (1/pi)

upper bound on the dimensionless frequency band

samples: int, optional, (1000)

number of draws of amplitude to take to generate the priors

Returns:

prior: bilby.core.prior.PriorDict

input prior dictionary, but with the new xi_0 and delta_xi_tilde priors added