As the improvement of gravitational wave detectors, gravitational
wave events become more and more popular which opens a new win-
dow of astronomy. In 2017, a binary neutron star event, GW170817,
has been detected through the gravitational wave and also the electro-
magnetic signal. After that, people start to consider an efficient way
to detect the GW and extract its dynamics parameters. In this thesis,
we construct a Bayesian inference based on deep learning machine,
CVAE, for the parameter estimation of binary black hole coalescence.
This machine can obtain the inference of 5-dimensional parameters of
the GW event within one second, where the parameters are two com-
ponent mass m1 , m2 , luminosity distance dL , and time and phase of
coalescence (tc , φ0 ). Since the noise of real detectors varies from time
to time, in contract to previous CVAE envelopments, we train our
machine not only by strain data but also the corresponding amplitude
spectrum density, which is used to characterize the noise background.
We find our machine can obtain the compatible result in comparison
to traditional PE algorithm even with the noise drift, which means
the noise background varies event by event. Finally, we apply our
machine to the LIGO/Virgo third observing run (O3) events to test
the performance of our machine against real data.

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