ml4gw.waveforms.cbc.phenom_deco

Classes

IMRPhenomDECO()

class ml4gw.waveforms.cbc.phenom_deco.IMRPhenomDECO

Bases: IMRPhenomD

fmaxCalc_deco(fRD, fDM, gamma2, gamma3, c_eff)
forward(f, chirp_mass, mass_ratio, chi1, chi2, c_eff, distance, phic, inclination, f_ref, **kwargs)

IMRPhenomDECO waveform

IMRPhenomDECO is a phenomenological extension of the standard BBH IMRPhenomD waveform model. It modifies the merger morphology to mimic broad features expected from exotic compact-object binaries by introdcuing an effective compactness parameter (c_eff) in the amplitude model, as described in Ghosh and Hannam Phys. Rev. D 112, 104017 (2025).

This model is not calibrated to any specific exotic compact-object scenario. Users should refer to Ghosh et al. (arXiv:2606.31350) for guidance on the interpretation of effective compactness inferred with this model and for the scalability of this test to a population refer to Ghosh et al. (arXiv:2606.31364).

Parameters:

  • f (Float[Tensor, 'frequency']) -- Frequency series in Hz.

  • chirp_mass (Float[Tensor, 'batch']) -- Chirp mass in solar masses

  • mass_ratio (Float[Tensor, 'batch']) -- Mass ratio m1/m2

  • chi1 (Float[Tensor, 'batch']) -- Spin of m1

  • chi2 (Float[Tensor, 'batch']) -- Spin of m2

  • c_eff (Float[Tensor, 'batch']) -- effective compactness of binary at contact

  • distance (Float[Tensor, 'batch']) -- Distance to source in Mpc

  • phic (Float[Tensor, 'batch']) -- Phase at coalescence

  • inclination (Float[Tensor, 'batch']) -- Inclination of the source

  • f_ref (float) -- Reference frequency

Returns:

Tuple[torch.Tensor, torch.Tensor]

Cross and plus polarizations

Return type:

hc, hp

phenom_deco_amp(Mf, mass_1, mass_2, eta, eta2, Seta, chi1, chi2, chi12, chi22, xi, distance, c_eff, fRD, fDM)
phenom_deco_htilde(f, chirp_mass, mass_ratio, chi1, chi2, c_eff, distance, phic, f_ref)
Return type:

Float[Tensor, 'batch frequency']

Parameters:

  • f (Float[Tensor, 'frequency'])

  • chirp_mass (Float[Tensor, 'batch'])

  • mass_ratio (Float[Tensor, 'batch'])

  • chi1 (Float[Tensor, 'batch'])

  • chi2 (Float[Tensor, 'batch'])

  • c_eff (Float[Tensor, 'batch'])

  • distance (Float[Tensor, 'batch'])

  • phic (Float[Tensor, 'batch'])

  • f_ref (float)

phenom_deco_int_amp(Mf, eta, eta2, Seta, chi1, chi2, chi12, chi22, xi, c_eff, fRD, fDM)
phenom_deco_mrd_amp(Mf, eta, eta2, chi1, chi2, xi, c_eff, fRD, fDM)