Conditional Noise Deep Learning for Parameter Estimation of Gravitational Wave Events

We construct a Bayesian inference deep learning machine for parameter estimation of gravitational wave events of binaries of black hole coalescence. The structure of our deep Bayseian machine adopts the conditional variational autoencoder scheme by conditioning both the gravitational wave strains and the variations of amplitude spectral density of the detector noise. We show that our deep Bayesian machine is capable of yielding the posteriors compatible with the ones from the nest sampling method, and of fighting against the noise outliers. We also apply our deep Bayesian machine to the LIGO/Virgo O3 events, and find that conditioning detector noise to fight against its drifting is relevant for the events with medium signal-to-noise ratios.