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

Grégory Baltus, Jean-René Cudell, Justin Janquart, Melissa Lopez, Sarah Caudill, Amit Reza

Abstract: We introduce a novel methodology for the operation of an early 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.