Improving early detection of gravitational waves from binary neutron stars using CNNs and FPGAs

TL;DR

This paper introduces a WaveNet-based machine learning method implemented on FPGAs to improve the speed and accuracy of early detection of gravitational waves from binary neutron stars, enabling rapid multi-messenger astronomy alerts.

Contribution

The work presents a novel FPGA-embedded WaveNet model that enhances early-warning detection accuracy and reduces latency in gravitational wave searches compared to existing ML systems.

Findings

Detection accuracy improved from 66.81% to 76.22% at 1% false alarm rate

Significant reductions in detection latency and energy consumption on FPGA platforms

Demonstrated feasibility for real-time GW detection with cost-effective hardware

Abstract

The detection of gravitational waves (GWs) from binary neutron stars (BNSs) with possible telescope follow-ups opens a window to ground-breaking discoveries in the field of multi-messenger astronomy. With the improved sensitivity of current and future GW detectors, more BNS detections are expected in the future. Therefore, enhancing low-latency GW search algorithms to achieve rapid speed, high accuracy, and low computational cost is essential. One innovative solution to reduce latency is the use of machine learning (ML) methods embedded in field-programmable gate arrays (FPGAs). In this work, we present a novel \texttt{WaveNet}-based method, leveraging the state-of-the-art ML model, to produce early-warning alerts for BNS systems. Using simulated GW signals embedded in Gaussian noise from the Advanced LIGO and Advanced Virgo detectors' third observing run (O3) as a proof-of-concept dataset, we demonstrate significant performance improvements. Compared to the current leading ML-based early-warning system, our approach enhances detection accuracy from 66.81\% to 76.22\% at a 1\% false alarm probability. Furthermore, we evaluate the time, energy, and economical cost of our model across CPU, GPU, and FPGA platforms, showcasing its potential for deployment in real-time gravitational wave detection pipelines.