Convolutional neural network for gravitational-wave early alert: Going down in frequency

Grégory Baltus, Justin Janquart, Melissa Lopez, Harsh Narola, Jean René Cudell

Research output: Contribution to journalArticleAcademicpeer-review


We present here the latest development of a machine-learning pipeline for premerger alerts from gravitational waves coming from binary neutron stars (BNSs). This work starts from the convolutional neural networks introduced in [Baltus et al., Phys. Rev. D 103, 102003 (2021)PRVDAQ2470-001010.1103/PhysRevD.103.102003] that searched for the early inspirals in simulated Gaussian noise colored with the design-sensitivity power-spectral density of LIGO. Our new network is able to search for any BNS with a chirp mass between 1 and 3 M⊙, it can take into account all the detectors available, and it can see the events even earlier than the previous one. We study the performance of our method in three different scenarios: colored Gaussian noise based on the O3 sensitivity, real O3 noise, colored Gaussian noise based on the predicted O4 sensitivity. We show that our network performs almost as well in non-Gaussian noise as in Gaussian noise: our method is robust with respect to glitches and artifacts present in real noise. Although it would not have been able to trigger on the BNSs detected during O3 because their signal-to-noise ratio was too weak, we expect our network to find around 3 BNSs during O4 with a time before the merger between 3 and 88 s in advance.

Original languageEnglish
Article number042002
Pages (from-to)1-10
JournalPhysical Review D
Issue number4
Publication statusPublished - 2 Aug 2022


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