emri_signal_detection

Detecting GW signals from extreme mass ratio inspirals using convolutional autoencoders

https://github.com/aminboumerdassi/emri_signal_detection

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

✓
CITATION.cff file
Found CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
✓
.zenodo.json file
Found .zenodo.json file
○
DOI references
○
Academic publication links
○
Academic email domains
○
Institutional organization owner
○
JOSS paper metadata
○
Scientific vocabulary similarity
Low similarity (6.1%) to scientific vocabulary

Keywords

autoencoder convolutional-autoencoder data-analysis emri gravitational-waves lisa machine-learning

Last synced: 6 months ago · JSON representation ·

Repository

Detecting GW signals from extreme mass ratio inspirals using convolutional autoencoders

Basic Info

Host: GitHub
Owner: AminBoumerdassi
Language: Jupyter Notebook
Default Branch: OzStar
Homepage:
Size: 99.9 MB

Statistics

Stars: 4
Watchers: 1
Forks: 0
Open Issues: 1
Releases: 0

Topics

autoencoder convolutional-autoencoder data-analysis emri gravitational-waves lisa machine-learning

Created almost 2 years ago · Last pushed over 1 year ago

Metadata Files

Readme Citation

EMRIsignaldetection

This project uses ML techniques - namely, the convolutional autoencoder - to perform a signal detection of extreme mass-ratio inspirals which are expected to be detected by the LISA mission. In this context, conventional signal detection techniques such as matched filtering are impractical owing to the large number of parameters required to model EMRIs.

The autoencoder learns how to represent EMRI signals in a low-dimensional latent space through non-linear transformations on the input data which is the GW strain in the time domain. Ideally, these non-linear transformations will lead to accurate reconstructions of EMRIs, and poor reconstructions of other types of signals. Hence, the EMRI signal detection problem is framed as one of anomaly detection.

Packages/citations: (May be incomplete)

Owner

Login: AminBoumerdassi
Kind: user

Repositories: 1
Profile: https://github.com/AminBoumerdassi

Citation (CITATION.CFF)

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: EMRI_signal_detection
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Amin
    family-names: Boumerdassi
    email: abou623@aucklanduni.ac.nz
    affiliation: The University of Auckland
    orcid: 'https://orcid.org/0000-0002-8260-4072'
repository-code: 'https://github.com/AminBoumerdassi/EMRI_signal_detection'
abstract: >-
  Detecting GW signals from extreme mass ratio inspirals
  using convolutional autoencoders.
keywords:
  - Gravitational waves
  - Machine Learning
  - LISA Mission
  - EMRIs
  - Signal Detection

ecosyste.ms

Data

Tools

Indexes

Applications

Experiments

Open Source Science