# 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, 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.

## 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, 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.