Deep learning for gravitational wave forecasting of neutron star mergers

TL;DR

This paper presents a deep learning approach for real-time gravitational wave forecasting of neutron star mergers, enabling early detection up to 30 seconds before merger, which could improve multi-messenger astronomy.

Contribution

The paper introduces a deep learning time-series forecasting method for gravitational wave detection that predicts signals seconds before merger, enhancing early warning capabilities.

Findings

Detects neutron star mergers up to 30 seconds before merger.

Identifies GW170817 signal 10 seconds prior to merger.

Operates efficiently on a single GPU for real-time inference.

Abstract

We introduce deep learning time-series forecasting for gravitational wave detection of binary neutron star mergers. This method enables the identification of these signals in real advanced LIGO data up to 30 seconds before merger. When applied to GW170817, our deep learning forecasting method identifies the presence of this gravitational wave signal 10 seconds before merger. This novel approach requires a single GPU for inference, and may be used as part of an early warning system for time-sensitive multi-messenger searches.