Reduced-order modeling with artificial neurons for gravitational-wave inference

Understand

Gravitational-wave data analysis is rapidly absorbing techniques from deep learning, with a focus on convolutional networks and related methods that treat noisy time series as images.

• We pursue an alternative approach, in which waveforms are first represented as weighted sums over reduced bases (reduced-order modeling); we then train artificial neural networks to map gravitational-wave source parameters into basis coefficients.
• Statistical inference proceeds directly in coefficient space, where it is theoretically straightforward and computationally efficient.
• The neural networks also provide analytic waveform derivatives, which are useful for gradient-based sampling schemes.