George-based models

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.

HeronHODLR: A spinning, NR-trained waveform model

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.

class heron.models.georgebased.HeronHodlr[source]

Produce a BBH waveform generator using the Hodlr method.

Methods

bilby(self, times, 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.

Heron2DHodlrIMR

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.

class heron.models.georgebased.Heron2dHodlrIMR[source]

Produce a BBH waveform generator using the Hodlr method with IMRPhenomPv2 training data.

Methods

bilby(self, times, 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.