A number of models implemented in Heron make use of the George Gaussian process library which implements a number of simplifications to make the inversion of the covariance matrix required for GPR predictions more tractable.
The main model produced this way is HeronHODLR, which implements a fully-spinning BBH waveform model which is trained on waveform data from the Georgia Tech waveform catalogue.
All of the george-based models are contained in the heron.models.georgebased module.
Ther HeronHODLR model implements a surrogate model for gravitational waveforms form binary black hole events with arbitrary spin parameters between a mass ratio of 1 and 8.
heron.models.georgebased.
HeronHodlr
[source]¶Produce a BBH waveform generator using the Hodlr method.
Methods
bilby (self, time, mass_1, mass_2, …) |
Return a waveform from the GPR in a format expected by the Bilby ecosystem |
build (self[, mean, white_noise, tol]) |
Construct the GP object |
distribution (self, p, times[, samples, …]) |
Return the mean waveform and the variance at a given location in the BBH parameter space. |
eval (self) |
Prepare the model to be evaluated. |
log_evidence (self, k, n) |
Evaluate the log-evidence of the model at a hyperparameter location k. |
mean (self, p, times) |
Return the mean waveform at a given location in the BBH parameter space. |
train (self) |
Prepare the model to be trained. |
This model is a 2D prototype waveform model trained on phenomenological sample waveforms. In contrast to the full HeronHODLR model, this model models only non-spinning waveforms between mass ratios of 1 and 10.
heron.models.georgebased.
Heron2dHodlrIMR
[source]¶Produce a BBH waveform generator using the Hodlr method with IMRPhenomPv2 training data.
Methods
bilby (self, time, mass_1, mass_2, …) |
Return a waveform from the GPR in a format expected by the Bilby ecosystem |
build (self[, mean, white_noise, tol]) |
Construct the GP object |
distribution (self, p, times[, samples, …]) |
Return the mean waveform and the variance at a given location in the BBH parameter space. |
eval (self) |
Prepare the model to be evaluated. |
log_evidence (self, k, n) |
Evaluate the log-evidence of the model at a hyperparameter location k. |
mean (self, p, times) |
Return the mean waveform at a given location in the BBH parameter space. |
train (self) |
Prepare the model to be trained. |