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 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 this one, currently only on arxiv.