Parameter inference for coalescing massive black hole binaries using deep learning

TL;DR

This paper introduces a deep learning model for rapid parameter inference of coalescing massive black hole binaries in gravitational-wave data, significantly reducing computational costs and handling multiple signals effectively.

Contribution

The work presents a novel deep learning approach capable of quickly inferring parameters of MBHBs, outperforming traditional methods in speed and robustness.

Findings

Produces 50,000 posterior samples in about twenty seconds.

Reduces parameter space by over four orders of magnitude for high SNR signals.

Handles multiple MBHB signals robustly.

Abstract

In the 2030s, a new era of gravitational-wave (GW) observations will dawn as multiple space-based GW detectors, such as the Laser Interferometer Space Antenna, Taiji and TianQin, open the millihertz window for GW astronomy. These detectors are poised to detect a multitude of GW signals emitted by different sources. It is a challenging task for GW data analysis to recover the parameters of these sources at a low computational cost. Generally, the matched filtering approach entails exploring an extensive parameter space for all resolvable sources, incurring a substantial cost owing to the generation of GW waveform templates. To alleviate the challenge, we make an attempt to perform parameter inference for coalescing massive black hole binaries (MBHBs) using deep learning. The model trained in this work has the capability to produce 50,000 posterior samples for redshifted total mass, mass ratio, coalescence time and luminosity distance of a MBHB in about twenty seconds. Our model can serve as a potent data pre-processing tool, reducing the volume of parameter space by more than four orders of magnitude for MBHB signals with a signal-to-noise ratio larger than 100. Moreover, the model exhibits robustness when handling input data that contains multiple MBHB signals.