Skip to main content

CI Pipeline for mlgw_bns Documentation Status

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 drops 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.

dependencygraph

Installation

To install the package, use

pip install mlgw-bns

For more details see the documentation.

Inner workings

The main steps taken by mlgw_bns to train on a dataset are as follows:

  • generate the dataset
  • decompose the Fourier transforms of the waveforms into phase and amplitude
  • downsample the dataset to a few thousand points
  • apply a PCA to reduce the dimensionality to a few tens of real numbers
  • train a neural network on the relation between the waveform parameters and the PCA components

In several of these steps data-driven optimizations are performed:

  • the points at which the waveforms are downsampled are not uniformly chosen: instead, a greedy downsampling algorithm determines them
  • the PCA is trained on a separate downsampled dataset, which is then thrown out
  • the hyperparameters for the neural network are optimized according to both the time taken for the training and the estimated reconstruction error

Release files for mlgw_bns 0.7.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for mlgw_bns 0.7.1
File Size Uploaded
mlgw_bns-0.7.1.tar.gz 3.6 MB Details

Built distribution (wheel)

Table of built distributions (wheels) for mlgw_bns 0.7.1
File Interpreter ABI Platform
mlgw_bns-0.7.1-py3-none-any.whl Python 3 none any Details

Total release size: 7.1 MB

Release files / mlgw_bns-0.7.1.tar.gz

Download URL mlgw_bns-0.7.1.tar.gz
Size 3.6 MB
Tags Source
SHA-256 checksum
How to use checksums
67faca4bb3fdffa2e2cc2763e1b914f0963e88352ca9d43eaadf392bc54f4ee7
BLAKE2b-256 checksum
How to use checksums
051dfacef12c0f5a160f4067be3c4dd52385f24905744ac35cbc19f5e3654905
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via poetry/1.1.12 CPython/3.7.12 Linux/5.13.0-28-generic

Release files / mlgw_bns-0.7.1-py3-none-any.whl

Download URL mlgw_bns-0.7.1-py3-none-any.whl
Size 3.6 MB
Tags Python 3
SHA-256 checksum
How to use checksums
250dedbdda8192aca07a86f7bb4176a04255903143b7fd55ba768dc4f4973be8
BLAKE2b-256 checksum
How to use checksums
15f6190a3a7fece4a76ca0c27fb78b7ef6d1da5cebc4c6c7e14a135f948d2508
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via poetry/1.1.12 CPython/3.7.12 Linux/5.13.0-28-generic
Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page