Detecting the early inspiral of a gravitational-wave signal with  convolutional neural networks

[en] We introduce a novel methodology for the operation of an early warning alert system for gravitational waves. It is based on short convolutional neural networks. We focus on compact binary coalescences, for light, intermediate and heavy binary-neutron-star systems. The signals are 1-dimensional time series $-$ the whitened time-strain $-$ injected in Gaussian noise built from the power-spectral density of the LIGO detectors at design sensitivity. We build short 1-dimensional convolutional neural networks to detect these types of events by training them on part of the early inspiral. We show that such networks are able to retrieve these signals from a small portion of the waveform.

Research center :

STAR - Space sciences, Technologies and Astrophysics Research - ULiège

Disciplines :

Space science, astronomy & astrophysics

Author, co-author :

Baltus, Grégory ; Université de Liège - ULiège > Département d'astrophysique, géophysique et océanographie (AGO) > Interactions fondamentales en physique et astrophysique (IFPA)

Janquart, Justin

Lopez, Melissa

Reza, Amit

Caudill, Sarah

Cudell, Jean-René ; Université de Liège - ULiège > Département d'astrophysique, géophysique et océanographie (AGO) > Interactions fondamentales en physique et astrophysique (IFPA)

Language :

English

Title :

Detecting the early inspiral of a gravitational-wave signal with convolutional neural networks

Publication date :

24 June 2021

Journal title :

IEEE Access

ISSN :

2169-3536

Publisher :

Institute of Electrical and Electronics Engineers, United States - New Jersey

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://ieeexplore.ieee.org/document/9461919

Funders :

F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE]

Data Set :

https://ieeexplore.ieee.org/document/9461919

Commentary :

6 pages, 7 figures

Available on ORBi :

since 17 January 2023

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