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We propose a novel signal-consistency test applicable to a broad search for gravitational waves emitted by generic binary black hole (BBH) systems. The test generalizes the time domain ξ2 signal-consistency test currently utilized by the gstlal pipeline, which quantifies the discrepancy between the expected signal-to-noise ratio time series with the measured one.
We introduce a machine learning model designed to rapidly and accurately predict the time domain gravitational wave emission of nonprecessing binary black hole coalescences, incorporating the effects of higher order modes of the multipole expansion of the waveform. Expanding on our prior work [Phys. Rev. D 103, 043020 (2021)], we decompose each mode by amplitude and phase and reduce dimensionality using principal component analysis.
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