This is a RAISE Award supported by the Office of Integrative Activities, the Signal Processing Systems program of the Division of Computing and Communications (CCF) of the Computer & Information Science & Engineering Directorate (CISE), the Office of Multidisciplinary Activities of the Mathematical and Physical Sciences Directorate (MPS) and the Gravitational Physics program of Physics Division in MPS. The recent discovery of gravitational waves from colliding black unveiled a new era of broad opportunities for studying the Cosmos. The coming years will bring about the proliferation of detections of black hole mergers as well as other sources of gravitational waves, including stellar explosions. For every discovery, there will be numerous weak gravitational wave signals buried in the detector noise that will be difficult to unearth. Gravitational-wave detectors are incredibly complex systems, where there are myriads of independent ways noise sources can interfere with the recorded data, occasionally producing curious data artifacts that are difficult to distinguish from gravitational waves. Machine learning is uniquely suited to make sense of this complexity, and disentangle data from the noise to broaden our horizon to detecting gravitational waves.
The PIs will design machine-learning techniques to make sense of LIGO's 400,000 auxiliary data channels and identify patterns in detector behavior to enable the identification of cosmic signals in the midst of highly non-linear and non-Gaussian background noise. The PIs will research and use optimal strategies, including sparse regression and robust principal component analysis, to distinguish detector or environmental artifacts from astrophysical signals to discover gravitational waves that otherwise could have remained invisible.