Advancing Gravitational Wave Inference and Waveform Modeling with Deep Learning

About This Grant

In recent years, scientists made a groundbreaking discovery by detecting gravitational waves—subtle ripples in the fabric of space-time—produced by the collision of black holes and neutron stars. This achievement, which fulfilled a century-old prediction by Albert Einstein, has transformed astrophysics and earned a Nobel Prize. Gravitational waves carry critical information about the massive cosmic events that generate them, offering new insights into the origins of the universe and the fundamental forces that govern it. As the sensitivity of detectors continues to improve, these cosmic signals can be observed at Earth more frequently, potentially multiple times per day, though buried in noisy data. Extracting meaningful information from such data requires precise models of the signals and efficient computational methods for estimating the properties of the merging objects, such as their masses and rotational speeds. A key scientific challenge is to make these analyses faster and more computationally efficient to keep pace with the growing data volume. This project addresses that challenge by leveraging advanced deep learning techniques to enhance the speed and accuracy of gravitational wave analysis. In doing so, it supports rapid response to cosmic events, reinforces U.S. leadership in scientific discovery and technological innovation, and promotes national interests in space science and data-intensive research. Furthermore, the project will engage the public through outreach programs and provide technical training to students in science, technology, engineering, and math (STEM) fields, preparing them for careers requiring technical and computational skills. This award aims to significantly advance gravitational wave data analysis and modeling by developing neural network-based posterior estimation tools and waveform surrogate models. The work will focus on improving the efficiency of Bayesian inference for compact binary systems, particularly neutron star–black hole and low-mass black hole binaries, by extending neural posterior estimation methods. Simultaneously, it will build and validate efficient surrogate waveform models derived from numerical relativity simulations using deep learning architectures, with a focus on quantifying and minimizing systematic uncertainties. These innovations will be implemented in open-source software tools (Dingo, GWSurrogate) and are expected to substantially reduce computational costs while improving low-latency signal processing, directly benefiting the gravitational wave research community and beyond. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.