Machine Learning for Gravitational Waves from Binary Neutron Star mergers
This package's purpose is to speed up the generation of template gravitational waveforms for binary neutron star mergers by training a machine learning model on a dataset of waveforms generated with some physically-motivated surrogate.
It is able to reconstruct them with mismatches lower than 1/10000, with as little as 1000 training waveforms; the accuracy then steadily improves as more training waveforms are used.
Currently, the only model used for training is TEOBResumS,
but it is planned to introduce the possibility to use others.
The model shipped with the package covers the $(2,2)$, $(2,1)$, $(3,1)$,
$(3,2)$, $(3,3)$, $(4,3)$ and $(4,4)$ spherical-harmonic modes.
Below are the per-mode and full-waveform mismatch distributions against
the underlying TEOBResumS waveforms: with the residual time shift and
reference phase optimized (top), and with only the surrogate's own
predicted alignment applied (bottom):
The documentation can be found here.
Installation
To install the package, use
pip install mlgw-bns
For more details see the documentation.
Changelog
Changes across versions are documented in the CHANGELOG.
Reference
The reference paper is Tissino, Carullo, Breschi, Gamba, Schmidt & Bernuzzi, "Combining effective-one-body accuracy and reduced-order-quadrature speed for binary neutron star merger parameter estimation with machine learning", published in Physical Review D 107, 084037 (2023), doi:10.1103/PhysRevD.107.084037.
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Release files for mlgw_bns 1.0.0
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Source distribution (sdist)
| File | Size | Uploaded | |
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| mlgw_bns-1.0.0.tar.gz | 50.0 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| mlgw_bns-1.0.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 99.3 MB
Release files / mlgw_bns-1.0.0.tar.gz
| Download URL | mlgw_bns-1.0.0.tar.gz |
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| Size | 50.0 MB |
| Tags | Source |
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Release files / mlgw_bns-1.0.0-py3-none-any.whl
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| Size | 49.4 MB |
| Tags | Python 3 |
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