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

TL;DR

This paper introduces an advanced convolutional neural network pipeline capable of early detection of gravitational waves from binary neutron stars, improving early alert timing and robustness across different noise conditions.

Contribution

The new neural network can detect any binary neutron star type, incorporate multiple detectors, and predict events earlier than previous models, enhancing early warning capabilities.

Findings

Performs well in Gaussian and real O3 noise

Robust against glitches and artifacts

Expected to detect around 3 BNSs in O4 with early alerts

Abstract

We present here the latest development of a machine-learning pipeline for pre-merger alerts from gravitational waves coming from binary neutron stars. This work starts from the convolutional neural networks introduced in our previous paper (PhysRevD.103.102003) that searched for three classes of 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 type of binary neutron stars, 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 types of noise: Gaussian O3 noise, real O3 noise, and predicted O4 noise. We show that our network performs almost as well in non-Gaussian noise as in Gaussian noise: our method is robust w.r.t. 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.