Deep Learning Ensemble for Real-time Gravitational Wave Detection of Spinning Binary Black Hole Mergers

TL;DR

This paper presents a deep learning ensemble approach for real-time detection of spinning binary black hole mergers in gravitational wave data, achieving high accuracy and speed, and effectively distinguishing signals from noise.

Contribution

The study introduces the first neural network ensemble trained on millions of waveforms for real-time gravitational wave detection, demonstrating superior performance over traditional methods.

Findings

Identifies binary black hole mergers with a false positive rate of 1 per 2.7 days.

Successfully detects mergers in O2 and O3 LIGO data.

Processes data faster than real-time using GPU clusters.

Abstract

We introduce the use of deep learning ensembles for real-time, gravitational wave detection of spinning binary black hole mergers. This analysis consists of training independent neural networks that simultaneously process strain data from multiple detectors. The output of these networks is then combined and processed to identify significant noise triggers. We have applied this methodology in O2 and O3 data finding that deep learning ensembles clearly identify binary black hole mergers in open source data available at the Gravitational-Wave Open Science Center. We have also benchmarked the performance of this new methodology by processing 200 hours of open source, advanced LIGO noise from August 2017. Our findings indicate that our approach identifies real gravitational wave sources in advanced LIGO data with a false positive rate of 1 misclassification for every 2.7 days of searched data. A follow up of these misclassifications identified them as glitches. Our deep learning ensemble represents the first class of neural network classifiers that are trained with millions of modeled waveforms that describe quasi-circular, spinning, non-precessing, binary black hole mergers. Once fully trained, our deep learning ensemble processes advanced LIGO strain data faster than real-time using 4 NVIDIA V100 GPUs.