Improving Data Quality of Advanced LIGO Gravitational-Wave Searches

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This award focuses on a specific task which is mission critical for the success of gravitational-wave astrophysics in the next few years: the improvement of data quality collected by the LIGO Interferometer Gravitational-wave Observatory (LIGO) detectors in future observing runs. Research will focus on (1) using existing techniques to identify and remove non-astrophysical noise in the data stream, and (2) developing new methods to build predictive models for detector noise. Broader impacts on the development of gravitational-wave astrophysics will consist in improving LIGO's search pipelines and the performance of the detectors. Educational and public outreach initiatives will strengthen programs aimed at yielding knowledgeable teachers with enough physics content to effectively teach physics courses in school. New initiatives to promote science among diverse segments of the population will be developed through collaborations with educators in other disciplines.Removing non-astrophysical artifacts from gravitational-wave data is crucial for reducing instrumental noise non-stationarity, extending the detector network duty cycle, and increasing the statistical significance of gravitational-wave candidate events. Improvements in these areas, in turn, boost parameter estimation of the gravitational-wave detections and enable refined astrophysical interpretations of the signals. Personnel funded under this award will analyze data from LIGO detector output and auxiliary sensors with the goal to isolate and identify sources of noise affecting LIGO's gravitational-wave searches. Results from these investigations will be fed back to LIGO Laboratory commissioners and instrumentation researchers to assist in the mitigation of instrumental and environmental disturbances. At the same time, Mississippi students and researchers will develop new, fast, reliable and accurate methods to model instrumental noise in interferometric gravitational-wave detectors. Machine learning-based algorithms, such as genetic programming, will be used to build predictive models to uncover the origin of non-astrophysical noise in the detectors.