  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.

Instrumentation and Methods for Astrophysics;General Relativity and Quantum Cosmology