Data Analysis

Modeling precessing binary black hole waveforms with machine learning

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

Deep learning surrogate model of gravitational waves

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

Incorporating waveform uncertainty into modeling and inference of GWs

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

Regression methods in waveform modeling: a comparative study

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

Prospects for observing and localizing gravitational-wave transients with Advanced LIGO, Advanced Virgo and KAGRA

Aligned-spin neutron-star-black-hole waveform model based on the effective-one-body approach and numerical-relativity simulations

Multipolar effective-one-body waveforms for precessing binary black holes: Construction and validation

Frequency-domain reduced-order model of aligned-spin effective-one-body waveforms with higher-order modes

Gravitational waveform accuracy requirements for future ground-based detectors

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

Enhancing Gravitational-Wave Science with Machine Learning