Data Analysis

I discuss a novel machine learning model for gravitational waves from precessing binary black holes.

Deep learning algorithms have the potential to dramatically improve predictive models of chirp like gravitational waves.

I discuss how systematic errors in waveform models can affect parameter inference and how these errors can be incorporated into the waveform model construction.

I give a comparison of classical and machine learning methods for regression of gravitational wave data.

We assess how accurate models of gravitations waves should be to avoid systematic errors in the measurement of the binaries' parameters.