Deep Neural Networks to Enable Real-time Multimessenger Astrophysics

Understand

Gravitational wave astronomy has set in motion a scientific revolution.

• To further enhance the science reach of this emergent field, there is a pressing need to increase the depth and speed of the gravitational wave algorithms that have enabled these groundbreaking discoveries.
• To contribute to this effort, we introduce Deep Filtering, a new highly scalable method for end-to-end time-series signal processing, based on a system of two deep convolutional neural networks, which we designed for classification and regression to rapidly detect and estimate parameters of signals in highly noisy time-series data streams.
• We demonstrate a novel training scheme with gradually increasing noise levels, and a transfer learning procedure between the two networks.