  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.
