Rapid search for massive black hole binary coalescences using deep learning

TL;DR

This paper introduces a deep learning approach for rapid, low-latency detection of massive black hole binary coalescences in gravitational wave data, significantly reducing computational costs and maintaining high accuracy.

Contribution

The authors develop a deep learning model capable of processing a year's worth of gravitational wave data in seconds, enabling fast detection of black hole mergers with no false alarms.

Findings

Model processes a year's data in seconds

Achieves zero false alarms in detection

Shows robustness across waveform types and configurations

Abstract

The coalescences of massive black hole binaries are one of the main targets of space-based gravitational wave observatories. Such gravitational wave sources are expected to be accompanied by electromagnetic emissions. Low latency detection of the massive black hole mergers provides a start point for a global-fit analysis to explore the large parameter space of signals simultaneously being present in the data but at great computational cost. To alleviate this issue, we present a deep learning method for rapidly searching for signals of massive black hole binaries in gravitational wave data. Our model is capable of processing a year of data, simulated from the LISA data challenge, in only several seconds, while identifying all coalescences of massive black hole binaries with no false alarms. We further demonstrate that the model shows robust resistance to a wide range of generalization cases, including various waveform families and updated instrumental configurations. This method offers an effective approach that combines advances in artificial intelligence to open a new pathway for space-based gravitational wave observations.