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 fullyspinning BBH waveform model which is trained on waveform data from the Georgia Tech waveform catalogue.
All of the georgebased 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

Return a waveform from the GPR in a format expected by the Bilby ecosystem 

Construct the GP object 

Return the mean waveform and the variance at a given location in the BBH parameter space. 

Prepare the model to be evaluated. 

Evaluate the logevidence of the model at a hyperparameter location k. 

Return the mean waveform at a given location in the BBH parameter space. 

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 nonspinning 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

Return a waveform from the GPR in a format expected by the Bilby ecosystem 

Construct the GP object 

Return the mean waveform and the variance at a given location in the BBH parameter space. 

Prepare the model to be evaluated. 

Evaluate the logevidence of the model at a hyperparameter location k. 

Return the mean waveform at a given location in the BBH parameter space. 

Prepare the model to be trained. 
This model uses a reduced basis representation of BBH waveforms in an attempt to produce waveforms (much) more rapidly than conventional GPR approaches.
The basis must be precomputed as an offline stage; this can be done using the elk
package.
heron.models.georgebased.
HodlrReducedGPR
[source]¶A Gaussian process regression surrogate using a reduced basis.
Notes
This model builds a surrogate over a reduced basis constructed by the
elk
package, and is intended to be a template which can be adapted for any
timeseries, and not just a GW waveform.
For this specific example implementation the basis is built using nonspinning waveforms from the IMRPhenomPv2 model, and as a result the model is itself nonspinning.
Examples
>>> model = HodlrReducedGPR()
>>> ts = model(0.55)
>>> ts.data[:10]
array([3.0841799347631674e20, 3.0866637782847267e20,
2.959224603984205e20, 2.703825954077637e20,
2.32824648984726e20, 1.8454458490397586e20,
1.273795996408389e20, 6.358989280244452e21,
4.1729998379767277e22, 7.302559908465424e21], dtype=object)
Methods

Call self as a function. 

Return the mean timeseries and its variance 

Returns the mean timeseries 